chore: release python sdk (#6754)

- Release cron patch and enabled fields

.github/CONTRIBUTING.md

@@ -1,6 +0,0 @@
-# Contributing to LangGraph
-
-Hi there! Thank you for even being interested in contributing to LangGraph.
-As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
-
-To learn how to contribute to LangGraph, please follow the [contribution guide here](https://docs.langchain.com/oss/python/contributing).

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,43 +1,60 @@
 name: "\U0001F41B Bug Report"
-description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the LangChain Forum at forum.langchain.com.
-labels: [pending, bug]
+description: Report a bug in LangGraph. To report a security issue, please instead use the security option (below). For questions, please use the LangChain forum (below).
+labels: ["bug"]
+type: bug
 body:
   - type: markdown
     attributes:
       value: |
-        Thank you for taking the time to file a bug report. 
+        Thank you for taking the time to file a bug report.
 
-        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
+        For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
 
-        Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
-        if there's another way to solve your problem:
+        Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:
 
-        * [LangChain Forum](https://forum.langchain.com/),
-        * [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
-        * [LangChain documentation with the integrated search](https://docs.langchain.com/),
+        * [Documentation](https://docs.langchain.com/oss/python/langgraph/overview),
+        * [API Reference Documentation](https://reference.langchain.com/python/),
+        * [LangChain ChatBot](https://chat.langchain.com/)
         * [GitHub search](https://github.com/langchain-ai/langgraph),
+        * [LangChain Forum](https://forum.langchain.com/),
   - type: checkboxes
     id: checks
     attributes:
       label: Checked other resources
-      description: Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
+      description: Please confirm and check all the following options.
       options:
-        - label: This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
+        - label: This is a bug, not a usage question.
           required: true
-        - label: I added a clear and detailed title that summarizes the issue.
+        - label: I added a clear and descriptive title that summarizes this issue.
           required: true
-        - label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
+        - label: I used the GitHub search to find a similar question and didn't find it.
           required: true
-        - label: I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.
+        - label: I am sure that this is a bug in LangGraph rather than my code.
+          required: true
+        - label: The bug is not resolved by updating to the latest stable version of LangGraph (or the specific integration package).
+          required: true
+        - label: This is not related to the langchain-community package.
+          required: true
+        - label: I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
           required: true
   - type: textarea
     id: reproduction
     validations:
       required: true
     attributes:
-      label: Example Code
+      label: Reproduction Steps / Example Code (Python)
       description: |
-        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
+        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
+
+        If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
+
+        **Important!**
+
+        * Avoid screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+        * Reduce your code to the minimum required to reproduce the issue if possible.
+
+        (This will be automatically formatted into code, so no need for backticks.)
+      render: python
       placeholder: |
         from langgraph.graph import StateGraph
 
@@ -46,17 +63,13 @@ body:
           
         chain = StateGraph(list)
         chain.invoke('Hello!')
-      render: python
   - type: textarea
-    id: error
-    validations:
-      required: false
     attributes:
       label: Error Message and Stack Trace (if applicable)
       description: |
-        If you are reporting an error, please include the full error message and stack trace.
-      placeholder: |
-        Exception + full stack trace
+        If you are reporting an error, please copy and paste the full error message and
+        stack trace.
+        (This will be automatically formatted into code, so no need for backticks.)
       render: shell
   - type: textarea
     id: description
@@ -77,7 +90,18 @@ body:
     attributes:
       label: System Info
       description: |
-        Run on your machine: `python -m langchain_core.sys_info`
+        Please share your system info with us.
+
+        Run the following command in your terminal and paste the output here:
+
+        `python -m langchain_core.sys_info`
+
+        or if you have an existing python interpreter running:
+
+        ```python
+        from langchain_core import sys_info
+        sys_info.print_sys_info()
+        ```
       placeholder: |
         python -m langchain_core.sys_info
     validations:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,9 +1,15 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: Documentation
-    url: https://github.com/langchain-ai/docs/issues/new?template=langgraph.yml
-    about: Report an issue related to the LangGraph documentation
-  - name: LangChain Forum
+  - name: 💬 LangChain Forum
     url:  https://forum.langchain.com/
     about: General community discussions and support
+  - name: 📚 LangGraph Documentation
+    url: https://docs.langchain.com/oss/python/langgraph/overview
+    about: View the official LangGraph documentation
+  - name: 📚 API Reference Documentation
+    url: https://reference.langchain.com/python/
+    about: View the official LangGraph API reference documentation
+  - name: 📚 Documentation issue
+    url: https://github.com/langchain-ai/docs/issues/new?template=02-langgraph.yml
+    about: Report an issue related to the LangGraph documentation

.github/PULL_REQUEST_TEMPLATE.md

@@ -21,7 +21,7 @@ Thank you for contributing to LangGraph! Follow these steps to mark your pull re
   1. A test for the integration, preferably unit tests that do not rely on network access,
   2. An example notebook showing its use. It lives in `docs/docs/integrations` directory.
 
-- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) for more.
+- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://docs.langchain.com/oss/python/contributing/overview) for more.
 
 Additional guidelines:
 

.github/workflows/_test_langgraph.yml

@@ -56,46 +56,3 @@ jobs:
           # grep will exit non-zero if the target message isn't found,
           # and `set -e` above will cause the step to fail.
           echo "$STATUS" | grep 'nothing to commit, working tree clean'
-
-  build-strict-msgpack:
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: libs/langgraph
-    name: "test strict msgpack #3.12"
-    steps:
-      - uses: actions/checkout@v6
-      - name: Set up Python 3.12
-        uses: astral-sh/setup-uv@v7
-        with:
-          python-version: "3.12"
-          enable-cache: true
-          cache-suffix: "test-langgraph-strict-msgpack"
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        if: ${{ !github.event.pull_request.head.repo.fork }}
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
-
-      - name: Install dependencies
-        shell: bash
-        run: uv sync --frozen --group test --no-dev
-
-      - name: Run tests (strict msgpack)
-        shell: bash
-        env:
-          LANGGRAPH_STRICT_MSGPACK: "1"
-        run: make test_parallel
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

AGENTS.md

@@ -53,3 +53,5 @@ sdk-js (standalone)
 ```
 
 Changes to a library may impact all of its dependents shown above.
+
+- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.

CLAUDE.md

@@ -53,3 +53,5 @@ sdk-js (standalone)
 ```
 
 Changes to a library may impact all of its dependents shown above.
+
+- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.

README.md

@@ -79,7 +79,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 ## Additional resources
 
-- [Guides](https://docs.langchain.com/oss/python/langgraph/guides): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [Guides](https://docs.langchain.com/oss/python/langgraph/overview): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
 - [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
 - [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph.
 - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.

examples/README.md

@@ -1,3 +1,3 @@
 # LangGraph examples
 
-This directory should NOT be used for documentation. All new documentation must be added to `docs/docs/` directory.
+This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview). Please refer to the LangChain docs for the most up-to-date examples and usage guidelines for LangGraph.

examples/async.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "23544406",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/async.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/branching.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "14f7ca50",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/branching.ipynb"
-   ]
-  }
- ],
- "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": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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

@@ -5,7 +5,15 @@
    "id": "10251c1c",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5fc63df",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

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

@@ -5,7 +5,15 @@
    "id": "a4351a24",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cc9af1e",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/chatbots/information-gather-prompting.ipynb

@@ -5,7 +5,15 @@
    "id": "a9014f94",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f47ce992",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2b789e16",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/cloud/how-tos/langgraph_to_langgraph_cloud.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/code_assistant/langgraph_code_assistant.ipynb

@@ -5,7 +5,15 @@
    "id": "1f2f13ca",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e4c9bfe",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/code_assistant/langgraph_code_assistant_mistral.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1d38cbab",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
   {
    "attachments": {
     "15d3ac32-cdf3-4800-a30c-f26d828d69c8.png": {
@@ -33,7 +41,9 @@
    "id": "e501686f-323f-4b87-8f9c-8ba89133078b",
    "metadata": {},
    "outputs": [],
-   "source": ["! pip install -U langchain_community langchain-mistralai langchain langgraph"]
+   "source": [
+    "! pip install -U langchain_community langchain-mistralai langchain langgraph"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -51,7 +61,12 @@
    "id": "982e4609-86e4-4934-828f-e03d89c20393",
    "metadata": {},
    "outputs": [],
-   "source": ["import os\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\nmistral_api_key = os.getenv(\"MISTRAL_API_KEY\")  # Ensure this is set"]
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
+    "mistral_api_key = os.getenv(\"MISTRAL_API_KEY\")  # Ensure this is set"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -69,7 +84,12 @@
    "id": "37b172d2-3a9d-49a8-898c-22ed0cb45c88",
    "metadata": {},
    "outputs": [],
-   "source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\nos.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""]
+   "source": [
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
+    "os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""
+   ]
   },
   {
    "cell_type": "markdown",
@@ -87,7 +107,42 @@
    "id": "a188c8ca-c053-4e6d-b7af-38a3b6b371c7",
    "metadata": {},
    "outputs": [],
-   "source": ["# Select LLM\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.pydantic_v1 import BaseModel, Field\nfrom langchain_mistralai import ChatMistralAI\n\nmistral_model = \"mistral-large-latest\"\nllm = ChatMistralAI(model=mistral_model, temperature=0)\n\n# Prompt\ncode_gen_prompt_claude = ChatPromptTemplate.from_messages(\n    [\n        (\n            \"system\",\n            \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n            defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n            \\n Here is the user question:\"\"\",\n        ),\n        (\"placeholder\", \"{messages}\"),\n    ]\n)\n\n\n# Data model\nclass code(BaseModel):\n    \"\"\"Code output\"\"\"\n\n    prefix: str = Field(description=\"Description of the problem and approach\")\n    imports: str = Field(description=\"Code block import statements\")\n    code: str = Field(description=\"Code block not including import statements\")\n    description = \"Schema for code solutions to questions about LCEL.\"\n\n\n# LLM\ncode_gen_chain = llm.with_structured_output(code, include_raw=False)"]
+   "source": [
+    "# Select LLM\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_mistralai import ChatMistralAI\n",
+    "\n",
+    "mistral_model = \"mistral-large-latest\"\n",
+    "llm = ChatMistralAI(model=mistral_model, temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "code_gen_prompt_claude = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n",
+    "            defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n",
+    "            \\n Here is the user question:\"\"\",\n",
+    "        ),\n",
+    "        (\"placeholder\", \"{messages}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class code(BaseModel):\n",
+    "    \"\"\"Code output\"\"\"\n",
+    "\n",
+    "    prefix: str = Field(description=\"Description of the problem and approach\")\n",
+    "    imports: str = Field(description=\"Code block import statements\")\n",
+    "    code: str = Field(description=\"Code block not including import statements\")\n",
+    "    description = \"Schema for code solutions to questions about LCEL.\"\n",
+    "\n",
+    "\n",
+    "# LLM\n",
+    "code_gen_chain = llm.with_structured_output(code, include_raw=False)"
+   ]
   },
   {
    "cell_type": "code",
@@ -95,7 +150,10 @@
    "id": "9fc0290d-5a04-4514-8664-91f9dbf2da7b",
    "metadata": {},
    "outputs": [],
-   "source": ["question = \"Write a function for fibonacci.\"\nmessages = [(\"user\", question)]"]
+   "source": [
+    "question = \"Write a function for fibonacci.\"\n",
+    "messages = [(\"user\", question)]"
+   ]
   },
   {
    "cell_type": "code",
@@ -114,7 +172,11 @@
      "output_type": "execute_result"
     }
    ],
-   "source": ["# Test\nresult = code_gen_chain.invoke(messages)\nresult"]
+   "source": [
+    "# Test\n",
+    "result = code_gen_chain.invoke(messages)\n",
+    "result"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -130,7 +192,28 @@
    "id": "183d77b8-f180-4815-b39f-8ef507ec0534",
    "metadata": {},
    "outputs": [],
-   "source": ["from typing import Annotated, TypedDict\n\nfrom langgraph.graph.message import AnyMessage, add_messages\n\n\nclass GraphState(TypedDict):\n    \"\"\"\n    Represents the state of our graph.\n\n    Attributes:\n        error : Binary flag for control flow to indicate whether test error was tripped\n        messages : With user question, error messages, reasoning\n        generation : Code solution\n        iterations : Number of tries\n    \"\"\"\n\n    error: str\n    messages: Annotated[list[AnyMessage], add_messages]\n    generation: str\n    iterations: int"]
+   "source": [
+    "from typing import Annotated, TypedDict\n",
+    "\n",
+    "from langgraph.graph.message import AnyMessage, add_messages\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        error : Binary flag for control flow to indicate whether test error was tripped\n",
+    "        messages : With user question, error messages, reasoning\n",
+    "        generation : Code solution\n",
+    "        iterations : Number of tries\n",
+    "    \"\"\"\n",
+    "\n",
+    "    error: str\n",
+    "    messages: Annotated[list[AnyMessage], add_messages]\n",
+    "    generation: str\n",
+    "    iterations: int"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -146,7 +229,163 @@
    "id": "14bc89d1-3ca6-4847-a048-1803e0e4600e",
    "metadata": {},
    "outputs": [],
-   "source": ["import uuid\n\nfrom langchain_core.pydantic_v1 import BaseModel, Field\n\n### Parameters\nmax_iterations = 3\n\n\n### Nodes\ndef generate(state: GraphState):\n    \"\"\"\n    Generate a code solution\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, generation\n    \"\"\"\n\n    print(\"---GENERATING CODE SOLUTION---\")\n\n    # State\n    messages = state[\"messages\"]\n    iterations = state[\"iterations\"]\n\n    # Solution\n    code_solution = code_gen_chain.invoke(messages)\n    messages += [\n        (\n            \"assistant\",\n            f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n        )\n    ]\n\n    # Increment\n    iterations = iterations + 1\n    return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n\n\ndef code_check(state: GraphState):\n    \"\"\"\n    Check code\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, error\n    \"\"\"\n\n    print(\"---CHECKING CODE---\")\n\n    # State\n    messages = state[\"messages\"]\n    code_solution = state[\"generation\"]\n    iterations = state[\"iterations\"]\n\n    # Get solution components\n    imports = code_solution.imports\n    code = code_solution.code\n\n    # Check imports\n    try:\n        exec(imports)\n    except Exception as e:\n        print(\"---CODE IMPORT CHECK: FAILED---\")\n        error_message = [\n            (\n                \"user\",\n                f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n            )\n        ]\n        messages += error_message\n        return {\n            \"generation\": code_solution,\n            \"messages\": messages,\n            \"iterations\": iterations,\n            \"error\": \"yes\",\n        }\n\n    # Check execution\n    try:\n        combined_code = f\"{imports}\\n{code}\"\n        print(f\"CODE TO TEST: {combined_code}\")\n        # Use a shared scope for exec\n        global_scope = {}\n        exec(combined_code, global_scope)\n    except Exception as e:\n        print(\"---CODE BLOCK CHECK: FAILED---\")\n        error_message = [\n            (\n                \"user\",\n                f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n            )\n        ]\n        messages += error_message\n        return {\n            \"generation\": code_solution,\n            \"messages\": messages,\n            \"iterations\": iterations,\n            \"error\": \"yes\",\n        }\n\n    # No errors\n    print(\"---NO CODE TEST FAILURES---\")\n    return {\n        \"generation\": code_solution,\n        \"messages\": messages,\n        \"iterations\": iterations,\n        \"error\": \"no\",\n    }\n\n\n### Conditional edges\n\n\ndef decide_to_finish(state: GraphState):\n    \"\"\"\n    Determines whether to finish.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Next node to call\n    \"\"\"\n    error = state[\"error\"]\n    iterations = state[\"iterations\"]\n\n    if error == \"no\" or iterations == max_iterations:\n        print(\"---DECISION: FINISH---\")\n        return \"end\"\n    else:\n        print(\"---DECISION: RE-TRY SOLUTION---\")\n        return \"generate\"\n\n\n### Utilities\n\n\ndef _print_event(event: dict, _printed: set, max_length=1500):\n    current_state = event.get(\"dialog_state\")\n    if current_state:\n        print(\"Currently in: \", current_state[-1])\n    message = event.get(\"messages\")\n    if message:\n        if isinstance(message, list):\n            message = message[-1]\n        if message.id not in _printed:\n            msg_repr = message.pretty_repr(html=True)\n            if len(msg_repr) > max_length:\n                msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n            print(msg_repr)\n            _printed.add(message.id)"]
+   "source": [
+    "import uuid\n",
+    "\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "\n",
+    "### Parameters\n",
+    "max_iterations = 3\n",
+    "\n",
+    "\n",
+    "### Nodes\n",
+    "def generate(state: GraphState):\n",
+    "    \"\"\"\n",
+    "    Generate a code solution\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---GENERATING CODE SOLUTION---\")\n",
+    "\n",
+    "    # State\n",
+    "    messages = state[\"messages\"]\n",
+    "    iterations = state[\"iterations\"]\n",
+    "\n",
+    "    # Solution\n",
+    "    code_solution = code_gen_chain.invoke(messages)\n",
+    "    messages += [\n",
+    "        (\n",
+    "            \"assistant\",\n",
+    "            f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n",
+    "        )\n",
+    "    ]\n",
+    "\n",
+    "    # Increment\n",
+    "    iterations = iterations + 1\n",
+    "    return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n",
+    "\n",
+    "\n",
+    "def code_check(state: GraphState):\n",
+    "    \"\"\"\n",
+    "    Check code\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, error\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECKING CODE---\")\n",
+    "\n",
+    "    # State\n",
+    "    messages = state[\"messages\"]\n",
+    "    code_solution = state[\"generation\"]\n",
+    "    iterations = state[\"iterations\"]\n",
+    "\n",
+    "    # Get solution components\n",
+    "    imports = code_solution.imports\n",
+    "    code = code_solution.code\n",
+    "\n",
+    "    # Check imports\n",
+    "    try:\n",
+    "        exec(imports)\n",
+    "    except Exception as e:\n",
+    "        print(\"---CODE IMPORT CHECK: FAILED---\")\n",
+    "        error_message = [\n",
+    "            (\n",
+    "                \"user\",\n",
+    "                f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
+    "            )\n",
+    "        ]\n",
+    "        messages += error_message\n",
+    "        return {\n",
+    "            \"generation\": code_solution,\n",
+    "            \"messages\": messages,\n",
+    "            \"iterations\": iterations,\n",
+    "            \"error\": \"yes\",\n",
+    "        }\n",
+    "\n",
+    "    # Check execution\n",
+    "    try:\n",
+    "        combined_code = f\"{imports}\\n{code}\"\n",
+    "        print(f\"CODE TO TEST: {combined_code}\")\n",
+    "        # Use a shared scope for exec\n",
+    "        global_scope = {}\n",
+    "        exec(combined_code, global_scope)\n",
+    "    except Exception as e:\n",
+    "        print(\"---CODE BLOCK CHECK: FAILED---\")\n",
+    "        error_message = [\n",
+    "            (\n",
+    "                \"user\",\n",
+    "                f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
+    "            )\n",
+    "        ]\n",
+    "        messages += error_message\n",
+    "        return {\n",
+    "            \"generation\": code_solution,\n",
+    "            \"messages\": messages,\n",
+    "            \"iterations\": iterations,\n",
+    "            \"error\": \"yes\",\n",
+    "        }\n",
+    "\n",
+    "    # No errors\n",
+    "    print(\"---NO CODE TEST FAILURES---\")\n",
+    "    return {\n",
+    "        \"generation\": code_solution,\n",
+    "        \"messages\": messages,\n",
+    "        \"iterations\": iterations,\n",
+    "        \"error\": \"no\",\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "### Conditional edges\n",
+    "\n",
+    "\n",
+    "def decide_to_finish(state: GraphState):\n",
+    "    \"\"\"\n",
+    "    Determines whether to finish.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Next node to call\n",
+    "    \"\"\"\n",
+    "    error = state[\"error\"]\n",
+    "    iterations = state[\"iterations\"]\n",
+    "\n",
+    "    if error == \"no\" or iterations == max_iterations:\n",
+    "        print(\"---DECISION: FINISH---\")\n",
+    "        return \"end\"\n",
+    "    else:\n",
+    "        print(\"---DECISION: RE-TRY SOLUTION---\")\n",
+    "        return \"generate\"\n",
+    "\n",
+    "\n",
+    "### Utilities\n",
+    "\n",
+    "\n",
+    "def _print_event(event: dict, _printed: set, max_length=1500):\n",
+    "    current_state = event.get(\"dialog_state\")\n",
+    "    if current_state:\n",
+    "        print(\"Currently in: \", current_state[-1])\n",
+    "    message = event.get(\"messages\")\n",
+    "    if message:\n",
+    "        if isinstance(message, list):\n",
+    "            message = message[-1]\n",
+    "        if message.id not in _printed:\n",
+    "            msg_repr = message.pretty_repr(html=True)\n",
+    "            if len(msg_repr) > max_length:\n",
+    "                msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n",
+    "            print(msg_repr)\n",
+    "            _printed.add(message.id)"
+   ]
   },
   {
    "cell_type": "code",
@@ -154,7 +393,31 @@
    "id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
    "metadata": {},
    "outputs": [],
-   "source": ["from langgraph.checkpoint.memory import InMemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate)  # generation solution\nbuilder.add_node(\"check_code\", code_check)  # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n    \"check_code\",\n    decide_to_finish,\n    {\n        \"end\": END,\n        \"generate\": \"generate\",\n    },\n)\n\nmemory = InMemorySaver()\ngraph = builder.compile(checkpointer=memory)"]
+   "source": [
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
+    "from langgraph.graph import END, StateGraph, START\n",
+    "\n",
+    "builder = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "builder.add_node(\"generate\", generate)  # generation solution\n",
+    "builder.add_node(\"check_code\", code_check)  # check code\n",
+    "\n",
+    "# Build graph\n",
+    "builder.add_edge(START, \"generate\")\n",
+    "builder.add_edge(\"generate\", \"check_code\")\n",
+    "builder.add_conditional_edges(\n",
+    "    \"check_code\",\n",
+    "    decide_to_finish,\n",
+    "    {\n",
+    "        \"end\": END,\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "memory = InMemorySaver()\n",
+    "graph = builder.compile(checkpointer=memory)"
+   ]
   },
   {
    "cell_type": "code",
@@ -173,7 +436,15 @@
      "output_type": "display_data"
     }
    ],
-   "source": ["from IPython.display import Image, display\n\ntry:\n    display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n    # This requires some extra dependencies and is optional\n    pass"]
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "try:\n",
+    "    display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n",
+    "except Exception:\n",
+    "    # This requires some extra dependencies and is optional\n",
+    "    pass"
+   ]
   },
   {
    "cell_type": "code",
@@ -181,7 +452,23 @@
    "id": "242aa2f0-2c31-462f-a958-ff9ae0cf7c62",
    "metadata": {},
    "outputs": [],
-   "source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n    \"configurable\": {\n        # Checkpoints are accessed by thread_id\n        \"thread_id\": thread_id,\n    }\n}\n\nquestion = \"Write a Python program that prints 'Hello, World!' to the console.\"\nevents = graph.stream(\n    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n    _print_event(event, _printed)"]
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"Write a Python program that prints 'Hello, World!' to the console.\"\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -199,7 +486,31 @@
    "id": "390b2768-f395-4aea-8b0e-9d36212a31ac",
    "metadata": {},
    "outputs": [],
-   "source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n    \"configurable\": {\n        # Checkpoints are accessed by thread_id\n        \"thread_id\": thread_id,\n    }\n}\n\nquestion = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n\nRequirements:\nThe program should define a function is_palindrome(s) that takes a string s as input.\nThe function should return True if the string is a palindrome and False otherwise.\nIgnore spaces, punctuation, and case differences when checking for palindromes.\n\nGive an example of it working on an example input word.\"\"\"\n\nevents = graph.stream(\n    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n    _print_event(event, _printed)"]
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n",
+    "\n",
+    "Requirements:\n",
+    "The program should define a function is_palindrome(s) that takes a string s as input.\n",
+    "The function should return True if the string is a palindrome and False otherwise.\n",
+    "Ignore spaces, punctuation, and case differences when checking for palindromes.\n",
+    "\n",
+    "Give an example of it working on an example input word.\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -217,7 +528,26 @@
    "id": "0a3f946b-e2f2-44d9-905b-09f36980cf9f",
    "metadata": {},
    "outputs": [],
-   "source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n    \"configurable\": {\n        # Checkpoints are accessed by thread_id\n        \"thread_id\": thread_id,\n    }\n}\n\nquestion = \"\"\"Write a program that prints the numbers from 1 to 100. \nBut for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \nFor numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n\nevents = graph.stream(\n    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n    _print_event(event, _printed)"]
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"Write a program that prints the numbers from 1 to 100. \n",
+    "But for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \n",
+    "For numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -235,7 +565,37 @@
    "id": "2bb883df-540b-46ab-9415-fe27db68456f",
    "metadata": {},
    "outputs": [],
-   "source": ["import uuid\n\n_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n    \"configurable\": {\n        # Checkpoints are accessed by thread_id\n        \"thread_id\": thread_id,\n    }\n}\n\nquestion = \"\"\"I want to vectorize a function\n\n        frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n        for i, val1 in enumerate(rows):\n            for j, val2 in enumerate(cols):\n                for j, val3 in enumerate(ch):\n                    # Assuming you want to store the pair as tuples in the matrix\n                    frame[i, j, k] = image[val1, val2, val3]\n\n        out.write(np.array(frame))\n\nwith a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n\nevents = graph.stream(\n    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n    _print_event(event, _printed)"]
+   "source": [
+    "import uuid\n",
+    "\n",
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"I want to vectorize a function\n",
+    "\n",
+    "        frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n",
+    "        for i, val1 in enumerate(rows):\n",
+    "            for j, val2 in enumerate(cols):\n",
+    "                for j, val3 in enumerate(ch):\n",
+    "                    # Assuming you want to store the pair as tuples in the matrix\n",
+    "                    frame[i, j, k] = image[val1, val2, val3]\n",
+    "\n",
+    "        out.write(np.array(frame))\n",
+    "\n",
+    "with a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -253,7 +613,34 @@
    "id": "ee05da1f-c272-405d-8a7b-552cfc3106e1",
    "metadata": {},
    "outputs": [],
-   "source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n    \"configurable\": {\n        # Checkpoints are accessed by thread_id\n        \"thread_id\": thread_id,\n    }\n}\n\nquestion = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n\n- Allow players to take turns to input their moves.\n- Check for invalid moves (e.g., placing a marker on an already occupied space).\n- Determine and announce the winner or if the game ends in a draw.\n\nRequirements:\n- Use a 2D list to represent the Tic-Tac-Toe board.\n- Use functions to modularize the code.\n- Validate player input.\n- Check for win conditions and draw conditions after each move.\"\"\"\n\nevents = graph.stream(\n    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n    _print_event(event, _printed)"]
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n",
+    "\n",
+    "- Allow players to take turns to input their moves.\n",
+    "- Check for invalid moves (e.g., placing a marker on an already occupied space).\n",
+    "- Determine and announce the winner or if the game ends in a draw.\n",
+    "\n",
+    "Requirements:\n",
+    "- Use a 2D list to represent the Tic-Tac-Toe board.\n",
+    "- Use functions to modularize the code.\n",
+    "- Validate player input.\n",
+    "- Check for win conditions and draw conditions after each move.\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -271,7 +658,7 @@
    "id": "814fc2a4-8e5b-4faa-8f52-3977226bd09a",
    "metadata": {},
    "outputs": [],
-   "source": [""]
+   "source": []
   }
  ],
  "metadata": {

examples/configuration.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e9a58c69",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/configuration.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/create-react-agent-hitl.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a1e6efeb",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-hitl.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/create-react-agent-memory.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1ef41a89",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-memory.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/create-react-agent-system-prompt.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9e2f7902",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-system-prompt.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/create-react-agent.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "eb07372e",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/customer-support/customer-support.ipynb

@@ -5,7 +5,15 @@
    "id": "a8232bc9",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/customer-support/customer-support.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/customer-support/customer-support.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63da8671",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/extraction/retries.ipynb

@@ -5,7 +5,15 @@
    "id": "8dbdba5b",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/extraction/retries.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/extraction/retries.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d444b7f",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/human_in_the_loop/wait-user-input.ipynb

@@ -5,7 +5,15 @@
    "id": "3ecab357",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f2866bd",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/input_output_schema.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fc0793cb",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/input_output_schema.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/lats/lats.ipynb

@@ -5,7 +5,15 @@
    "id": "09038b53",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/lats/lats.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/lats/lats.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1669748",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/llm-compiler/LLMCompiler.ipynb

@@ -5,7 +5,15 @@
    "id": "85205e97",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fdab366",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/map-reduce.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "42abb708",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/map-reduce.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "298784f6",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-summary-conversation-history.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/memory/delete-messages.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3f4370fd",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/delete-messages.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/memory/manage-conversation-history.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6ec7cb13",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/manage-conversation-history.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/multi_agent/hierarchical_agent_teams.ipynb

@@ -5,7 +5,15 @@
    "id": "5cc8a2ad",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9f3508a",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/multi_agent/multi-agent-collaboration.ipynb

@@ -5,7 +5,15 @@
    "id": "d2b507b9",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41a8f10a",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/node-retries.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "017a01f4",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/node-retries.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "env",
-   "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

examples/pass-config-to-tools.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "05f6ad0a",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/pass-config-to-tools.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

examples/pass-run-time-values-to-tools.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8f38bec5",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/pass-run-time-values-to-tools.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/pass_private_state.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4da17088",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/pass_private_state.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/persistence.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d16e8b9c",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-memory.md."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/persistence_mongodb.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "78217098",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/persistence_mongodb.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/persistence_postgres.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "18526f23",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-memory.md"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/persistence_redis.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "eee6ecdd",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/persistence_redis.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/plan-and-execute/plan-and-execute.ipynb

@@ -5,7 +5,15 @@
    "id": "9138f92e",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "093678ba",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/rag/langgraph_adaptive_rag.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fedd6d23",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
   {
    "attachments": {
     "36fa621a-9d3d-4860-a17c-5d20e6987481.png": {

examples/rag/langgraph_adaptive_rag_local.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "39b26b09",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
   {
    "attachments": {
     "3755396d-c4a8-45bd-87d4-00cb56339fe5.png": {

examples/rag/langgraph_agentic_rag.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "47e3b43b",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "425fb020-e864-40ce-a31f-8da40c73d14b",
@@ -200,11 +208,11 @@
      "output_type": "stream",
      "text": [
       "********************Prompt[rlm/rag-prompt]********************\n",
-      "================================\u001B[1m Human Message \u001B[0m=================================\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
       "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n",
-      "Question: \u001B[33;1m\u001B[1;3m{question}\u001B[0m \n",
-      "Context: \u001B[33;1m\u001B[1;3m{context}\u001B[0m \n",
+      "Question: \u001b[33;1m\u001b[1;3m{question}\u001b[0m \n",
+      "Context: \u001b[33;1m\u001b[1;3m{context}\u001b[0m \n",
       "Answer:\n"
      ]
     }

examples/rag/langgraph_crag.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c71da2ea",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
   {
    "attachments": {
     "683fae34-980f-43f0-a9c2-9894bebd9157.png": {

examples/rag/langgraph_crag_local.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ac7db067",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
   {
    "attachments": {
     "b77a7d3b-b28a-4dcf-9f1a-861f2f2c5f6c.png": {

examples/rag/langgraph_self_rag.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b3d959ff",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
   {
    "attachments": {
     "15cba0ab-a549-4909-8373-fb761e384eff.png": {

examples/rag/langgraph_self_rag_local.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "345488d8",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
   {
    "attachments": {
     "5fca0a3e-d13d-4bfa-95ea-58203640cc7a.png": {

examples/rag/langgraph_self_rag_pinecone_movies.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "403aeb6e",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
   {
    "attachments": {
     "15cba0ab-a549-4909-8373-fb761e384eff.png": {
@@ -54,7 +62,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
+    "import os\n",
+    "\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
+    "os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
    ]
   },
   {
@@ -64,7 +76,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n\nos.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
+    "import os\n",
+    "\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
    ]
   },
   {
@@ -84,7 +98,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_openai import OpenAIEmbeddings\nfrom langchain_pinecone import PineconeVectorStore\n\n# use pinecone movies database\n\n# Add to vectorDB\nvectorstore = PineconeVectorStore(\n    embedding=OpenAIEmbeddings(),\n    index_name=\"sample-movies\",\n    text_key=\"summary\",\n)\nretriever = vectorstore.as_retriever()"
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_pinecone import PineconeVectorStore\n",
+    "\n",
+    "# use pinecone movies database\n",
+    "\n",
+    "# Add to vectorDB\n",
+    "vectorstore = PineconeVectorStore(\n",
+    "    embedding=OpenAIEmbeddings(),\n",
+    "    index_name=\"sample-movies\",\n",
+    "    text_key=\"summary\",\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
    ]
   },
   {
@@ -113,7 +138,11 @@
     }
    ],
    "source": [
-    "docs = retriever.invoke(\"James Cameron\")\nfor doc in docs:\n    print(\"# \" + doc.metadata[\"title\"])\n    print(doc.page_content)\n    print()"
+    "docs = retriever.invoke(\"James Cameron\")\n",
+    "for doc in docs:\n",
+    "    print(\"# \" + doc.metadata[\"title\"])\n",
+    "    print(doc.page_content)\n",
+    "    print()"
    ]
   },
   {
@@ -173,7 +202,12 @@
     }
    ],
    "source": [
-    "# Test the retrieval grader\nquestion = \"movies starring jason momoa\"\ndocs = retriever.invoke(question)\ndoc_txt = docs[0].page_content\nprint(doc_txt)\nprint(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+    "# Test the retrieval grader\n",
+    "question = \"movies starring jason momoa\"\n",
+    "docs = retriever.invoke(question)\n",
+    "doc_txt = docs[0].page_content\n",
+    "print(doc_txt)\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
    ]
   },
   {
@@ -201,7 +235,23 @@
     }
    ],
    "source": [
-    "### Generate\n\nfrom langchain import hub\nfrom langchain_core.output_parsers import StrOutputParser\n\n# Prompt\nprompt = hub.pull(\"rlm/rag-prompt\")\n\n# LLM\nllm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n\n# Chain\nrag_chain = prompt | llm | StrOutputParser()\n\n# Run\ngeneration = rag_chain.invoke({\"context\": docs, \"question\": question})\nprint(generation)"
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
    ]
   },
   {
@@ -329,7 +379,17 @@
     }
    ],
    "source": [
-    "### Question Re-writer\n\n# LLM\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n\n# Prompt\nre_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n\nquestion_rewriter = re_write_prompt | llm | StrOutputParser()\nprint(question)\nquestion_rewriter.invoke({\"question\": question})"
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "re_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "print(question)\n",
+    "question_rewriter.invoke({\"question\": question})"
    ]
   },
   {
@@ -351,7 +411,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import List\n\nfrom typing_extensions import TypedDict\n\n\nclass GraphState(TypedDict):\n    \"\"\"\n    Represents the state of our graph.\n\n    Attributes:\n        question: question\n        generation: LLM generation\n        documents: list of documents\n    \"\"\"\n\n    question: str\n    generation: str\n    documents: List[str]"
+    "from typing import List\n",
+    "\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question: str\n",
+    "    generation: str\n",
+    "    documents: List[str]"
    ]
   },
   {
@@ -361,7 +438,95 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "### Nodes\n\n\ndef retrieve(state):\n    \"\"\"\n    Retrieve documents\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, documents, that contains retrieved documents\n    \"\"\"\n    print(\"---RETRIEVE---\")\n    question = state[\"question\"]\n\n    # Retrieval\n    documents = retriever.invoke(question)\n    return {\"documents\": documents, \"question\": question}\n\n\ndef generate(state):\n    \"\"\"\n    Generate answer\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, generation, that contains LLM generation\n    \"\"\"\n    print(\"---GENERATE---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # RAG generation\n    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n\n\ndef grade_documents(state):\n    \"\"\"\n    Determines whether the retrieved documents are relevant to the question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates documents key with only filtered relevant documents\n    \"\"\"\n\n    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Score each doc\n    filtered_docs = []\n    for d in documents:\n        score = retrieval_grader.invoke(\n            {\"question\": question, \"document\": d.page_content}\n        )\n        grade = score.binary_score\n        if grade == \"yes\":\n            print(\"---GRADE: DOCUMENT RELEVANT---\")\n            filtered_docs.append(d)\n        else:\n            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n            continue\n    return {\"documents\": filtered_docs, \"question\": question}\n\n\ndef transform_query(state):\n    \"\"\"\n    Transform the query to produce a better question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates question key with a re-phrased question\n    \"\"\"\n\n    print(\"---TRANSFORM QUERY---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Re-write question\n    better_question = question_rewriter.invoke({\"question\": question})\n    return {\"documents\": documents, \"question\": better_question}"
+    "### Nodes\n",
+    "\n",
+    "\n",
+    "def retrieve(state):\n",
+    "    \"\"\"\n",
+    "    Retrieve documents\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
+    "    \"\"\"\n",
+    "    print(\"---RETRIEVE---\")\n",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.invoke(question)\n",
+    "    return {\"documents\": documents, \"question\": question}\n",
+    "\n",
+    "\n",
+    "def generate(state):\n",
+    "    \"\"\"\n",
+    "    Generate answer\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation, that contains LLM generation\n",
+    "    \"\"\"\n",
+    "    print(\"---GENERATE---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
+    "\n",
+    "\n",
+    "def grade_documents(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the retrieved documents are relevant to the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke(\n",
+    "            {\"question\": question, \"document\": d.page_content}\n",
+    "        )\n",
+    "        grade = score.binary_score\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
+    "            filtered_docs.append(d)\n",
+    "        else:\n",
+    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
+    "            continue\n",
+    "    return {\"documents\": filtered_docs, \"question\": question}\n",
+    "\n",
+    "\n",
+    "def transform_query(state):\n",
+    "    \"\"\"\n",
+    "    Transform the query to produce a better question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates question key with a re-phrased question\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---TRANSFORM QUERY---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}"
    ]
   },
   {
@@ -371,7 +536,74 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "### Edges\n\n\ndef decide_to_generate(state):\n    \"\"\"\n    Determines whether to generate an answer, or re-generate a question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Binary decision for next node to call\n    \"\"\"\n\n    print(\"---ASSESS GRADED DOCUMENTS---\")\n    state[\"question\"]\n    filtered_documents = state[\"documents\"]\n\n    if not filtered_documents:\n        # All documents have been filtered check_relevance\n        # We will re-generate a new query\n        print(\n            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n        )\n        return \"transform_query\"\n    else:\n        # We have relevant documents, so generate answer\n        print(\"---DECISION: GENERATE---\")\n        return \"generate\"\n\n\ndef grade_generation_v_documents_and_question(state):\n    \"\"\"\n    Determines whether the generation is grounded in the document and answers question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Decision for next node to call\n    \"\"\"\n\n    print(\"---CHECK HALLUCINATIONS---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n    generation = state[\"generation\"]\n\n    score = hallucination_grader.invoke(\n        {\"documents\": documents, \"generation\": generation}\n    )\n    grade = score.binary_score\n\n    # Check hallucination\n    if grade == \"yes\":\n        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n        # Check question-answering\n        print(\"---GRADE GENERATION vs QUESTION---\")\n        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n        grade = score.binary_score\n        if grade == \"yes\":\n            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n            return \"useful\"\n        else:\n            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n            return \"not useful\"\n    else:\n        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n        return \"not supported\""
+    "### Edges\n",
+    "\n",
+    "\n",
+    "def decide_to_generate(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether to generate an answer, or re-generate a question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    state[\"question\"]\n",
+    "    filtered_documents = state[\"documents\"]\n",
+    "\n",
+    "    if not filtered_documents:\n",
+    "        # All documents have been filtered check_relevance\n",
+    "        # We will re-generate a new query\n",
+    "        print(\n",
+    "            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
+    "        )\n",
+    "        return \"transform_query\"\n",
+    "    else:\n",
+    "        # We have relevant documents, so generate answer\n",
+    "        print(\"---DECISION: GENERATE---\")\n",
+    "        return \"generate\"\n",
+    "\n",
+    "\n",
+    "def grade_generation_v_documents_and_question(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation is grounded in the document and answers question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK HALLUCINATIONS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = state[\"generation\"]\n",
+    "\n",
+    "    score = hallucination_grader.invoke(\n",
+    "        {\"documents\": documents, \"generation\": generation}\n",
+    "    )\n",
+    "    grade = score.binary_score\n",
+    "\n",
+    "    # Check hallucination\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
+    "        # Check question-answering\n",
+    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
+    "        grade = score.binary_score\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
+    "    else:\n",
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
    ]
   },
   {
@@ -390,7 +622,42 @@
    "id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
    "metadata": {},
    "outputs": [],
-   "source": ["from langgraph.graph import END, StateGraph, START\n\nworkflow = StateGraph(GraphState)\n\n# Define the nodes\nworkflow.add_node(\"retrieve\", retrieve)  # retrieve\nworkflow.add_node(\"grade_documents\", grade_documents)  # grade documents\nworkflow.add_node(\"generate\", generate)  # generate\nworkflow.add_node(\"transform_query\", transform_query)  # transform_query\n\n# Build graph\nworkflow.add_edge(START, \"retrieve\")\nworkflow.add_edge(\"retrieve\", \"grade_documents\")\nworkflow.add_conditional_edges(\n    \"grade_documents\",\n    decide_to_generate,\n    {\n        \"transform_query\": \"transform_query\",\n        \"generate\": \"generate\",\n    },\n)\nworkflow.add_edge(\"transform_query\", \"retrieve\")\nworkflow.add_conditional_edges(\n    \"generate\",\n    grade_generation_v_documents_and_question,\n    {\n        \"not supported\": \"generate\",\n        \"useful\": END,\n        \"not useful\": \"transform_query\",\n    },\n)\n\n# Compile\napp = workflow.compile()"]
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\n",
+    "\n",
+    "workflow = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
+    "workflow.add_node(\"generate\", generate)  # generate\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.add_edge(START, \"retrieve\")\n",
+    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"grade_documents\",\n",
+    "    decide_to_generate,\n",
+    "    {\n",
+    "        \"transform_query\": \"transform_query\",\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"transform_query\", \"retrieve\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"generate\",\n",
+    "    grade_generation_v_documents_and_question,\n",
+    "    {\n",
+    "        \"not supported\": \"generate\",\n",
+    "        \"useful\": END,\n",
+    "        \"not useful\": \"transform_query\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
   },
   {
    "cell_type": "code",
@@ -426,7 +693,18 @@
     }
    ],
    "source": [
-    "from pprint import pprint\n\n# Run\ninputs = {\"question\": \"Movies that star Daniel Craig\"}\nfor output in app.stream(inputs):\n    for key, value in output.items():\n        # Node\n        pprint(f\"Node '{key}':\")\n    pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"question\": \"Movies that star Daniel Craig\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
    ]
   },
   {
@@ -436,7 +714,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "inputs = {\"question\": \"Which movies are about aliens?\"}\nfor output in app.stream(inputs):\n    for key, value in output.items():\n        # Node\n        pprint(f\"Node '{key}':\")\n    pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
+    "inputs = {\"question\": \"Which movies are about aliens?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
    ]
   },
   {
@@ -445,9 +731,7 @@
    "id": "42369ab8-322d-434a-b5dd-2266e4cb2903",
    "metadata": {},
    "outputs": [],
-   "source": [
-    ""
-   ]
+   "source": []
   }
  ],
  "metadata": {

examples/react-agent-from-scratch.ipynb

@@ -5,7 +5,14 @@
    "id": "294995c4",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/react-agent-from-scratch.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/react-agent-from-scratch.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/react-agent-structured-output.ipynb

@@ -5,7 +5,14 @@
    "id": "40f0d107",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/react-agent-structured-output.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/react-agent-structured-output.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/recursion-limit.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fa3f7c50",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/recursion-limit.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

examples/reflection/reflection.ipynb

@@ -5,7 +5,15 @@
    "id": "658773a2",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflection/reflection.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/reflection/reflection.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cb60657",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/reflexion/reflexion.ipynb

@@ -5,7 +5,15 @@
    "id": "caf07859",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflexion/reflexion.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/reflexion/reflexion.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd1df0e0",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/rewoo/rewoo.ipynb

@@ -5,7 +5,15 @@
    "id": "961f43ec",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rewoo/rewoo.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/rewoo/rewoo.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f00c427",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/run-id-langsmith.ipynb

@@ -5,7 +5,14 @@
    "id": "bbd6e9b8",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/run-id-langsmith.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/run-id-langsmith.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/self-discover/self-discover.ipynb

@@ -5,7 +5,15 @@
    "id": "f6db1873",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/self-discover/self-discover.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/self-discover/self-discover.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "219a78f9",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/state-model.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4149ffcc",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/state-model.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/storm/storm.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3e05d7f9",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/storm/storm.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

examples/stream-multiple.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e663f597",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/stream-multiple.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/stream-updates.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e6829c80",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/stream-updates.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/stream-values.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5ec11895",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/stream-values.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/streaming-content.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6619387c",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-content.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/streaming-events-from-within-tools-without-langchain.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "57b7e303",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/streaming-events-from-within-tools.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8e71a0c8",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-events-from-within-tools.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/streaming-from-final-node.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "756e4554",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-from-final-node.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/streaming-subgraphs.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "47164a72",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-subgraphs.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

examples/streaming-tokens-without-langchain.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "218dfbcb",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-tokens-without-langchain.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/streaming-tokens.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "99eb887e",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-tokens.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/subgraph-transform-state.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0de7689f",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/subgraph-transform-state.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

examples/subgraph.ipynb

@@ -5,7 +5,14 @@
    "id": "f49876e1",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/subgraph.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/subgraph.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/subgraphs-manage-state.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5106959e",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/subgraphs-manage-state.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

examples/tool-calling-errors.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "dc21501d",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/tool-calling-errors.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

examples/tool-calling.ipynb

@@ -5,7 +5,14 @@
    "id": "7fd8bd65",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/tool-calling.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/tool-calling.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/tutorials/sql-agent.ipynb

@@ -5,7 +5,15 @@
    "id": "83c2223f",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/sql-agent.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/sql/sql-agent.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57f924b1",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/tutorials/tnt-llm/tnt-llm.ipynb

@@ -5,7 +5,15 @@
    "id": "11140167",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/tnt-llm/tnt-llm.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/tnt-llm/tnt-llm.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a2ba3e6",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/usaco/usaco.ipynb

@@ -5,7 +5,15 @@
    "id": "9dffdb54",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/usaco/usaco.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/usaco/usaco.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "579c9959",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

examples/visualization.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9c9cb15a",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/visualization.ipynb"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/web-navigation/web_voyager.ipynb

@@ -5,7 +5,15 @@
    "id": "007ea2e9",
    "metadata": {},
    "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/web-navigation/web_voyager.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/web-navigation/web_voyager.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0d7b895",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
    ]
   }
  ],

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

@@ -550,6 +550,11 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
                 Will be applied regardless of whether the PostgresStore instance was initialized with a pipeline.
                 If pipeline mode is not supported, will fall back to using transaction context manager.
         """
+        is_pooled_conn = isinstance(self.conn, AsyncConnectionPool)
+        # With AsyncConnectionPool, each _cursor() call checks out its own connection.
+        # The pool does not hand out the same connection concurrently, so a shared lock
+        # across calls is unnecessary here.
+        lock = asyncio.Lock() if is_pooled_conn else self.lock
         async with _ainternal.get_connection(self.conn) as conn:
             if self.pipe:
                 # a connection in pipeline mode can be used concurrently
@@ -566,21 +571,21 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
                 # thread/coroutine at a time, so we acquire a lock
                 if self.supports_pipeline:
                     async with (
-                        self.lock,
+                        lock,
                         conn.pipeline(),
                         conn.cursor(binary=True, row_factory=dict_row) as cur,
                     ):
                         yield cur
                 else:
                     async with (
-                        self.lock,
+                        lock,
                         conn.transaction(),
                         conn.cursor(binary=True, row_factory=dict_row) as cur,
                     ):
                         yield cur
             else:
                 async with (
-                    self.lock,
+                    lock,
                     conn.cursor(binary=True, row_factory=dict_row) as cur,
                 ):
                     yield cur

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.3"
+version = "3.0.4"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"
@@ -20,7 +20,7 @@ dependencies = [
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-postgres"
-Twitter = "https://x.com/LangChainAI"
+Twitter = "https://x.com/LangChain"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/checkpoint-postgres/uv.lock

@@ -13,16 +13,16 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.12.0"
+version = "4.12.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "idna" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0", size = 228266, upload-time = "2025-11-28T23:37:38.911Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = "2026-01-06T11:45:21.246Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb", size = 113362, upload-time = "2025-11-28T23:36:57.897Z" },
+    { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
 ]
 
 [[package]]
@@ -36,11 +36,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2025.11.12"
+version = "2026.1.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a2/8c/58f469717fa48465e4a50c014a0400602d3c437d7c0c468e17ada824da3a/certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316", size = 160538, upload-time = "2025-11-12T02:54:51.517Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e0/2d/a891ca51311197f6ad14a7ef42e2399f36cf2f9bd44752b3dc4eab60fdc5/certifi-2026.1.4.tar.gz", hash = "sha256:ac726dd470482006e014ad384921ed6438c457018f4b3d204aea4281258b2120", size = 154268, upload-time = "2026-01-04T02:42:41.825Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b", size = 159438, upload-time = "2025-11-12T02:54:49.735Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/ad/3cc14f097111b4de0040c83a525973216457bbeeb63739ef1ed275c1c021/certifi-2026.1.4-py3-none-any.whl", hash = "sha256:9943707519e4add1115f44c2bc244f782c0249876bf51b6599fee1ffbedd685c", size = 152900, upload-time = "2026-01-04T02:42:40.15Z" },
 ]
 
 [[package]]
@@ -240,7 +240,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.1.3"
+version = "1.2.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
@@ -252,9 +252,9 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0b/6f/9e959821df556cf32d57d26aa7b86905a63a91e6e9e744994f8045fe2d70/langchain_core-1.1.3.tar.gz", hash = "sha256:ff0bc5e6e701c4d6fe00c73c4feae5c993a7a8e0b91f0a1d07015277d4634275", size = 801750, upload-time = "2025-12-09T14:55:55.741Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a2/0e/664d8d81b3493e09cbab72448d2f9d693d1fa5aa2bcc488602203a9b6da0/langchain_core-1.2.7.tar.gz", hash = "sha256:e1460639f96c352b4a41c375f25aeb8d16ffc1769499fb1c20503aad59305ced", size = 837039, upload-time = "2026-01-09T17:44:25.505Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/58/41/6db768d4b208a33b4f09d5415e617d489f68167bb5dd27f87c7a49d13caf/langchain_core-1.1.3-py3-none-any.whl", hash = "sha256:e06efbf55bf7c7e4fcffc2f5b0a39a855176df16b02077add063534d7dabb740", size = 475307, upload-time = "2025-12-09T14:55:54.516Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/6f/34a9fba14d191a67f7e2ee3dbce3e9b86d2fa7310e2c7f2c713583481bd2/langchain_core-1.2.7-py3-none-any.whl", hash = "sha256:452f4fef7a3d883357b22600788d37e3d8854ef29da345b7ac7099f33c31828b", size = 490232, upload-time = "2026-01-09T17:44:24.236Z" },
 ]
 
 [[package]]
@@ -306,7 +306,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.3"
+version = "3.0.4"
 source = { editable = "." }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -381,7 +381,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.4.57"
+version = "0.6.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -393,128 +393,128 @@ dependencies = [
     { name = "uuid-utils" },
     { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/66/18/fbd779dd73bae0389f8dde1de02299e9cda7ee59db56dcadb761a8b22cec/langsmith-0.4.57.tar.gz", hash = "sha256:c6e55266ec82c559517359cfbd82c70ea6c216b48f873ba9b268896bde381e35", size = 991971, upload-time = "2025-12-09T21:53:53.196Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
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 ]
 
 [[package]]

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

@@ -3,6 +3,7 @@ from __future__ import annotations
 import asyncio
 import json
 import random
+import threading
 from collections.abc import AsyncIterator, Callable, Iterator, Sequence
 from contextlib import asynccontextmanager
 from typing import Any, TypeVar, cast
@@ -281,8 +282,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         async with self.lock:
             if self.is_setup:
                 return
-            if not self.conn.is_alive():
-                await self.conn
+            await _ensure_connected(self.conn)
             async with self.conn.executescript(
                 """
                 PRAGMA journal_mode=WAL;
@@ -609,3 +609,23 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         next_v = current_v + 1
         next_h = random.random()
         return f"{next_v:032}.{next_h:016}"
+
+
+async def _ensure_connected(conn: aiosqlite.Connection) -> None:
+    if not _CONN_STARTED_CHECK(conn):
+        await conn
+
+
+def _build_conn_started_check() -> Callable[[aiosqlite.Connection], bool]:
+    is_alive = getattr(aiosqlite.Connection, "is_alive", None)
+    if callable(is_alive):
+        return lambda conn: conn.is_alive()  # type: ignore[attr-defined]
+
+    def _started(conn: aiosqlite.Connection) -> bool:
+        thread: threading.Thread | None = getattr(conn, "_thread", None)
+        return False if thread is None else thread.is_alive()
+
+    return _started
+
+
+_CONN_STARTED_CHECK = _build_conn_started_check()

libs/checkpoint-sqlite/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-sqlite"
-version = "3.0.2"
+version = "3.0.3"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"
@@ -19,7 +19,7 @@ dependencies = [
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-sqlite"
-Twitter = "https://x.com/LangChainAI"
+Twitter = "https://x.com/LangChain"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

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

@@ -119,6 +119,25 @@ class BaseCheckpointSaver(Generic[V]):
     Checkpointers allow LangGraph agents to persist their state
     within and across multiple interactions.
 
+    When a checkpointer is configured, you should pass a `thread_id` in the config when
+    invoking the graph:
+
+    ```python
+    config = {"configurable": {"thread_id": "my-thread"}}
+    graph.invoke(inputs, config)
+    ```
+
+    The `thread_id` is the primary key used to store and retrieve checkpoints. Without
+    it, the checkpointer cannot save state, resume from interrupts, or enable
+    time-travel debugging.
+
+    How you choose ``thread_id`` depends on your use case:
+
+    - **Single-shot workflows**: Use a unique ID (e.g., uuid4) for each run when
+        executions are independent.
+    - **Conversational memory**: Reuse the same `thread_id` across invocations
+        to accumulate state (e.g., chat history) within a conversation.
+
     Attributes:
         serde (SerializerProtocol): Serializer for encoding/decoding checkpoints.
 

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

@@ -37,48 +37,6 @@ LC_REVIVER = Reviver()
 EMPTY_BYTES = b""
 logger = logging.getLogger(__name__)
 
-SAFE_MSGPACK_TYPES: frozenset[tuple[str, ...]] = frozenset(
-    {
-        # datetime types
-        ("datetime", "datetime"),
-        ("datetime", "date"),
-        ("datetime", "time"),
-        ("datetime", "timedelta"),
-        ("datetime", "timezone"),
-        # uuid
-        ("uuid", "UUID"),
-        # numeric
-        ("decimal", "Decimal"),
-        # collections
-        ("builtins", "set"),
-        ("builtins", "frozenset"),
-        ("collections", "deque"),
-        # ip addresses
-        ("ipaddress", "IPv4Address"),
-        ("ipaddress", "IPv4Interface"),
-        ("ipaddress", "IPv4Network"),
-        ("ipaddress", "IPv6Address"),
-        ("ipaddress", "IPv6Interface"),
-        ("ipaddress", "IPv6Network"),
-        # pathlib
-        ("pathlib", "Path"),
-        ("pathlib", "PosixPath"),
-        ("pathlib", "WindowsPath"),
-        # pathlib in Python 3.13+
-        ("pathlib._local", "Path"),
-        ("pathlib._local", "PosixPath"),
-        ("pathlib._local", "WindowsPath"),
-        # regex
-        ("re", "compile"),
-        # langgraph
-        ("langgraph.types", "Send"),
-        ("langgraph.types", "Interrupt"),
-        ("langgraph.types", "Command"),
-        ("langgraph.types", "StateSnapshot"),
-        ("langgraph.types", "PregelTask"),
-    }
-)
-
 
 class JsonPlusSerializer(SerializerProtocol):
     """Serializer that uses ormsgpack, with optional fallbacks.
@@ -96,29 +54,18 @@ class JsonPlusSerializer(SerializerProtocol):
         *,
         pickle_fallback: bool = False,
         allowed_json_modules: Sequence[tuple[str, ...]] | Literal[True] | None = None,
-        # TODO: change default to None once users have had time to configure allowlists
-        allowed_msgpack_modules: Sequence[tuple[str, ...]]
-        | Literal[True]
-        | None = True,
         __unpack_ext_hook__: Callable[[int, bytes], Any] | None = None,
     ) -> None:
         self.pickle_fallback = pickle_fallback
-        # JSON allowlist
-        self._allowed_json_modules: set[tuple[str, ...]] | Literal[True] | None = (
+        self._allowed_modules = (
             {mod_and_name for mod_and_name in allowed_json_modules}
             if allowed_json_modules and allowed_json_modules is not True
             else (allowed_json_modules if allowed_json_modules is True else None)
         )
-        # Msgpack allowlist
-        self._allowed_msgpack_modules: set[tuple[str, ...]] | Literal[True] | None = (
-            {mod_and_name for mod_and_name in allowed_msgpack_modules}
-            if allowed_msgpack_modules and allowed_msgpack_modules is not True
-            else (allowed_msgpack_modules if allowed_msgpack_modules is True else None)
-        )
         self._unpack_ext_hook = (
             __unpack_ext_hook__
             if __unpack_ext_hook__ is not None
-            else _create_msgpack_ext_hook(self._allowed_msgpack_modules)
+            else _msgpack_ext_hook
         )
 
     def _encode_constructor_args(
@@ -143,7 +90,7 @@ class JsonPlusSerializer(SerializerProtocol):
         return out
 
     def _reviver(self, value: dict[str, Any]) -> Any:
-        if self._allowed_json_modules and (
+        if self._allowed_modules and (
             value.get("lc", None) == 2
             and value.get("type", None) == "constructor"
             and value.get("id", None) is not None
@@ -160,7 +107,7 @@ class JsonPlusSerializer(SerializerProtocol):
         return LC_REVIVER(value)
 
     def _revive_lc2(self, value: dict[str, Any]) -> Any:
-        self._check_allowed_json_modules(value)
+        self._check_allowed_modules(value)
 
         [*module, name] = value["id"]
         try:
@@ -192,7 +139,7 @@ class JsonPlusSerializer(SerializerProtocol):
         except Exception:
             return None
 
-    def _check_allowed_json_modules(self, value: dict[str, Any]) -> None:
+    def _check_allowed_modules(self, value: dict[str, Any]) -> None:
         needed = tuple(value["id"])
         method = value.get("method")
         if isinstance(method, list):
@@ -203,7 +150,7 @@ class JsonPlusSerializer(SerializerProtocol):
             method_display = "<init>"
 
         dotted = ".".join(needed)
-        if not self._allowed_json_modules:
+        if not self._allowed_modules:
             raise InvalidModuleError(
                 f"Refused to deserialize JSON constructor: {dotted} (method: {method_display}). "
                 "No allowed_json_modules configured.\n\n"
@@ -214,9 +161,9 @@ class JsonPlusSerializer(SerializerProtocol):
                 "or plain-JSON representations revived without import-time side effects."
             )
 
-        if self._allowed_json_modules is True:
+        if self._allowed_modules is True:
             return
-        if needed in self._allowed_json_modules:
+        if needed in self._allowed_modules:
             return
 
         raise InvalidModuleError(
@@ -501,158 +448,92 @@ def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
         raise TypeError(f"Object of type {obj.__class__.__name__} is not serializable")
 
 
-def _create_msgpack_ext_hook(
-    allowed_modules: set[tuple[str, ...]] | Literal[True] | None,
-) -> Callable[[int, bytes], Any]:
-    """Create msgpack ext hook with allowlist.
-
-    Args:
-        allowed_modules: Set of (module, name) tuples that are allowed to be
-        deserialized, or True to allow all with warnings for unregistered types, or None to only allow safe types.
-
-    Returns:
-        An ext_hook function for use with ormsgpack.unpackb.
-    """
-
-    def _check_allowed(module: str, name: str) -> bool:
-        """Check if type is allowed. Returns True if allowed, False if blocked."""
-        key = (module, name)
-
-        if key in SAFE_MSGPACK_TYPES:
-            return True
-
-        if allowed_modules is not None and allowed_modules is not True:
-            if key in allowed_modules:
-                return True
-
-        if allowed_modules is True:
-            # default is to warn but allow unregistered types
-            logger.warning(
-                "Deserializing unregistered type %s.%s from checkpoint. "
-                "This will be blocked in a future version. "
-                "Add to allowed_msgpack_modules to silence: [(%r, %r)]",
-                module,
-                name,
-                module,
-                name,
+def _msgpack_ext_hook(code: int, data: bytes) -> Any:
+    if code == EXT_CONSTRUCTOR_SINGLE_ARG:
+        try:
+            tup = ormsgpack.unpackb(
+                data, ext_hook=_msgpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
             )
-            return True
-        else:
-            # strict mode blocks unregistered types
-            logger.warning(
-                "Blocked deserialization of %s.%s - not in allowed_msgpack_modules. "
-                "Add to allowed_msgpack_modules to allow: [(%r, %r)]",
-                module,
-                name,
-                module,
-                name,
+            # module, name, arg
+            return getattr(importlib.import_module(tup[0]), tup[1])(tup[2])
+        except Exception:
+            return
+    elif code == EXT_CONSTRUCTOR_POS_ARGS:
+        try:
+            tup = ormsgpack.unpackb(
+                data, ext_hook=_msgpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
             )
-            return False
+            # module, name, args
+            return getattr(importlib.import_module(tup[0]), tup[1])(*tup[2])
+        except Exception:
+            return
+    elif code == EXT_CONSTRUCTOR_KW_ARGS:
+        try:
+            tup = ormsgpack.unpackb(
+                data, ext_hook=_msgpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
+            )
+            # module, name, args
+            return getattr(importlib.import_module(tup[0]), tup[1])(**tup[2])
+        except Exception:
+            return
+    elif code == EXT_METHOD_SINGLE_ARG:
+        try:
+            tup = ormsgpack.unpackb(
+                data, ext_hook=_msgpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
+            )
+            # module, name, arg, method
+            return getattr(getattr(importlib.import_module(tup[0]), tup[1]), tup[3])(
+                tup[2]
+            )
+        except Exception:
+            return
+    elif code == EXT_PYDANTIC_V1:
+        try:
+            tup = ormsgpack.unpackb(
+                data, ext_hook=_msgpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
+            )
+            # module, name, kwargs
+            cls = getattr(importlib.import_module(tup[0]), tup[1])
+            try:
+                return cls(**tup[2])
+            except Exception:
+                return cls.construct(**tup[2])
+        except Exception:
+            # for pydantic objects we can't find/reconstruct
+            # let's return the kwargs dict instead
+            try:
+                return tup[2]
+            except NameError:
+                return
+    elif code == EXT_PYDANTIC_V2:
+        try:
+            tup = ormsgpack.unpackb(
+                data, ext_hook=_msgpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
+            )
+            # module, name, kwargs, method
+            cls = getattr(importlib.import_module(tup[0]), tup[1])
+            try:
+                return cls(**tup[2])
+            except Exception:
+                return cls.model_construct(**tup[2])
+        except Exception:
+            # for pydantic objects we can't find/reconstruct
+            # let's return the kwargs dict instead
+            try:
+                return tup[2]
+            except NameError:
+                return
+    elif code == EXT_NUMPY_ARRAY:
+        try:
+            import numpy as _np
 
-    def ext_hook(code: int, data: bytes) -> Any:
-        if code == EXT_CONSTRUCTOR_SINGLE_ARG:
-            try:
-                tup = ormsgpack.unpackb(
-                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
-                )
-                if not _check_allowed(tup[0], tup[1]):
-                    return None
-                # module, name, arg
-                return getattr(importlib.import_module(tup[0]), tup[1])(tup[2])
-            except Exception:
-                return None
-        elif code == EXT_CONSTRUCTOR_POS_ARGS:
-            try:
-                tup = ormsgpack.unpackb(
-                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
-                )
-                if not _check_allowed(tup[0], tup[1]):
-                    return None
-                # module, name, args
-                return getattr(importlib.import_module(tup[0]), tup[1])(*tup[2])
-            except Exception:
-                return None
-        elif code == EXT_CONSTRUCTOR_KW_ARGS:
-            try:
-                tup = ormsgpack.unpackb(
-                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
-                )
-                if not _check_allowed(tup[0], tup[1]):
-                    return None
-                # module, name, kwargs
-                return getattr(importlib.import_module(tup[0]), tup[1])(**tup[2])
-            except Exception:
-                return None
-        elif code == EXT_METHOD_SINGLE_ARG:
-            try:
-                tup = ormsgpack.unpackb(
-                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
-                )
-                if not _check_allowed(tup[0], tup[1]):
-                    return None
-                # module, name, arg, method
-                return getattr(
-                    getattr(importlib.import_module(tup[0]), tup[1]), tup[3]
-                )(tup[2])
-            except Exception:
-                return None
-        elif code == EXT_PYDANTIC_V1:
-            try:
-                tup = ormsgpack.unpackb(
-                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
-                )
-                if not _check_allowed(tup[0], tup[1]):
-                    return None
-                # module, name, kwargs
-                cls = getattr(importlib.import_module(tup[0]), tup[1])
-                try:
-                    return cls(**tup[2])
-                except Exception:
-                    return cls.construct(**tup[2])
-            except Exception:
-                # for pydantic objects we can't find/reconstruct
-                # let's return the kwargs dict instead
-                try:
-                    return tup[2]
-                except NameError:
-                    return None
-        elif code == EXT_PYDANTIC_V2:
-            try:
-                tup = ormsgpack.unpackb(
-                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
-                )
-                if not _check_allowed(tup[0], tup[1]):
-                    return None
-                # module, name, kwargs, method
-                cls = getattr(importlib.import_module(tup[0]), tup[1])
-                try:
-                    return cls(**tup[2])
-                except Exception:
-                    return cls.model_construct(**tup[2])
-            except Exception:
-                # for pydantic objects we can't find/reconstruct
-                # let's return the kwargs dict instead
-                try:
-                    return tup[2]
-                except NameError:
-                    return None
-        elif code == EXT_NUMPY_ARRAY:
-            try:
-                import numpy as _np
-
-                dtype_str, shape, order, buf = ormsgpack.unpackb(
-                    data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
-                )
-                arr = _np.frombuffer(buf, dtype=_np.dtype(dtype_str))
-                return arr.reshape(shape, order=order)
-            except Exception:
-                return None
-        return None
-
-    return ext_hook
-
-
-_msgpack_ext_hook = _create_msgpack_ext_hook(allowed_modules=None)
+            dtype_str, shape, order, buf = ormsgpack.unpackb(
+                data, ext_hook=_msgpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
+            )
+            arr = _np.frombuffer(buf, dtype=_np.dtype(dtype_str))
+            return arr.reshape(shape, order=order)
+        except Exception:
+            return
 
 
 def _msgpack_ext_hook_to_json(code: int, data: bytes) -> Any:

libs/checkpoint/pyproject.toml

@@ -18,7 +18,7 @@ dependencies = [
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint"
-Twitter = "https://x.com/LangChainAI"
+Twitter = "https://x.com/LangChain"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/checkpoint/tests/test_jsonplus.py

@@ -37,10 +37,6 @@ class MyPydantic(BaseModel):
     inner: InnerPydantic
 
 
-class AnotherPydantic(BaseModel):
-    foo: str
-
-
 class InnerPydanticV1(BaseModelV1):
     hello: str
 
@@ -516,240 +512,5 @@ def test_serde_jsonplus_pandas_series(series: pd.Series) -> None:
 
     assert dumped[0] == "pickle"
     result = serde.loads_typed(dumped)
+
     assert result.equals(series)
-
-
-def test_msgpack_safe_types_no_warning(caplog: pytest.LogCaptureFixture) -> None:
-    """Test safe types deserialize without warnings."""
-
-    serde = JsonPlusSerializer()
-
-    safe_objects = [
-        datetime.now(),
-        date.today(),
-        time(12, 30),
-        timezone.utc,
-        uuid.uuid4(),
-        Decimal("123.45"),
-        {1, 2, 3},
-        frozenset([1, 2, 3]),
-        deque([1, 2, 3]),
-        IPv4Address("192.168.1.1"),
-        pathlib.Path("/tmp/test"),
-    ]
-
-    for obj in safe_objects:
-        caplog.clear()
-        dumped = serde.dumps_typed(obj)
-        result = serde.loads_typed(dumped)
-        assert "unregistered type" not in caplog.text.lower(), (
-            f"Unexpected warning for {type(obj)}"
-        )
-        assert result is not None
-
-
-def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) -> None:
-    """Pydantic models not in allowlist should log warning but still deserialize.
-
-    TODO: We'll want to change this to block unregistered types in the future."""
-
-    serde = JsonPlusSerializer()
-
-    obj = MyPydantic(foo="test", bar=42, inner=InnerPydantic(hello="world"))
-
-    caplog.clear()
-    dumped = serde.dumps_typed(obj)
-    result = serde.loads_typed(dumped)
-
-    assert "unregistered type" in caplog.text.lower()
-    assert "allowed_msgpack_modules" in caplog.text
-    assert result == obj
-
-
-def test_msgpack_allowlist_silences_warning(caplog: pytest.LogCaptureFixture) -> None:
-    """Types in allowed_msgpack_modules should deserialize without warnings."""
-
-    serde = JsonPlusSerializer(
-        allowed_msgpack_modules=[
-            ("tests.test_jsonplus", "MyPydantic"),
-            ("tests.test_jsonplus", "InnerPydantic"),
-        ]
-    )
-
-    obj = MyPydantic(foo="test", bar=42, inner=InnerPydantic(hello="world"))
-
-    caplog.clear()
-    dumped = serde.dumps_typed(obj)
-    result = serde.loads_typed(dumped)
-
-    assert "unregistered type" not in caplog.text.lower()
-    assert result == obj
-
-
-def test_msgpack_none_blocks_unregistered(caplog: pytest.LogCaptureFixture) -> None:
-    """allowed_msgpack_modules=None should block unregistered types.
-
-    TODO: This will be the default behavior in the future."""
-
-    serde = JsonPlusSerializer(allowed_msgpack_modules=None)
-
-    obj = MyPydantic(foo="test", bar=42, inner=InnerPydantic(hello="world"))
-
-    caplog.clear()
-    dumped = serde.dumps_typed(obj)
-    result = serde.loads_typed(dumped)
-
-    assert "blocked" in caplog.text.lower()
-    assert result is None
-
-
-def test_msgpack_allowlist_blocks_non_listed(
-    caplog: pytest.LogCaptureFixture,
-) -> None:
-    """Allowlists should block unregistered types even if msgpack is enabled."""
-
-    serde = JsonPlusSerializer(
-        allowed_msgpack_modules=[("tests.test_jsonplus", "MyPydantic")]
-    )
-
-    obj = AnotherPydantic(foo="nope")
-
-    caplog.clear()
-    dumped = serde.dumps_typed(obj)
-    result = serde.loads_typed(dumped)
-
-    assert "blocked" in caplog.text.lower()
-    assert result is None
-
-
-def test_msgpack_strict_allows_safe_types(
-    caplog: pytest.LogCaptureFixture,
-) -> None:
-    """Safe types should still deserialize in strict mode without warnings."""
-
-    serde = JsonPlusSerializer(allowed_msgpack_modules=None)
-    safe = uuid.uuid4()
-
-    caplog.clear()
-    dumped = serde.dumps_typed(safe)
-    result = serde.loads_typed(dumped)
-
-    assert "blocked" not in caplog.text.lower()
-    assert result == safe
-
-
-def test_msgpack_regex_safe_type(caplog: pytest.LogCaptureFixture) -> None:
-    """re.compile patterns should deserialize without warnings as a safe type."""
-
-    serde = JsonPlusSerializer(allowed_msgpack_modules=None)
-    pattern = re.compile(r"foo.*bar", re.IGNORECASE | re.DOTALL)
-
-    caplog.clear()
-    dumped = serde.dumps_typed(pattern)
-    result = serde.loads_typed(dumped)
-
-    assert "blocked" not in caplog.text.lower()
-    assert "unregistered" not in caplog.text.lower()
-    assert result.pattern == pattern.pattern
-    assert result.flags == pattern.flags
-
-
-@pytest.mark.skipif(sys.version_info >= (3, 14), reason="pydantic v1 not on 3.14+")
-def test_msgpack_pydantic_v1_allowlist(caplog: pytest.LogCaptureFixture) -> None:
-    """Pydantic v1 models in allowlist should deserialize without warnings."""
-
-    serde = JsonPlusSerializer(
-        allowed_msgpack_modules=[
-            ("tests.test_jsonplus", "MyPydanticV1"),
-            ("tests.test_jsonplus", "InnerPydanticV1"),
-        ]
-    )
-
-    obj = MyPydanticV1(foo="test", bar=42, inner=InnerPydanticV1(hello="world"))
-
-    caplog.clear()
-    dumped = serde.dumps_typed(obj)
-    result = serde.loads_typed(dumped)
-
-    assert "unregistered type" not in caplog.text.lower()
-    assert "blocked" not in caplog.text.lower()
-    assert result == obj
-
-
-def test_msgpack_dataclass_allowlist(caplog: pytest.LogCaptureFixture) -> None:
-    """Dataclasses in allowlist should deserialize without warnings."""
-
-    serde = JsonPlusSerializer(
-        allowed_msgpack_modules=[
-            ("tests.test_jsonplus", "MyDataclass"),
-            ("tests.test_jsonplus", "InnerDataclass"),
-        ]
-    )
-
-    obj = MyDataclass(foo="test", bar=42, inner=InnerDataclass(hello="world"))
-
-    caplog.clear()
-    dumped = serde.dumps_typed(obj)
-    result = serde.loads_typed(dumped)
-
-    assert "unregistered type" not in caplog.text.lower()
-    assert "blocked" not in caplog.text.lower()
-    assert result == obj
-
-
-def test_msgpack_safe_types_value_equality(caplog: pytest.LogCaptureFixture) -> None:
-    """Verify safe types are correctly restored with proper values."""
-
-    serde = JsonPlusSerializer(allowed_msgpack_modules=None)
-
-    test_cases = [
-        datetime(2024, 1, 15, 12, 30, 45, 123456),
-        date(2024, 6, 15),
-        time(14, 30, 0),
-        uuid.UUID("12345678-1234-5678-1234-567812345678"),
-        Decimal("123.456789"),
-        {1, 2, 3, 4, 5},
-        frozenset(["a", "b", "c"]),
-        deque([1, 2, 3]),
-        IPv4Address("10.0.0.1"),
-        pathlib.Path("/some/test/path"),
-        re.compile(r"\d+", re.MULTILINE),
-    ]
-
-    for obj in test_cases:
-        caplog.clear()
-        dumped = serde.dumps_typed(obj)
-        result = serde.loads_typed(dumped)
-
-        assert "blocked" not in caplog.text.lower(), f"Blocked for {type(obj)}"
-        # For regex patterns, compare pattern and flags
-        if isinstance(obj, re.Pattern):
-            assert result.pattern == obj.pattern
-            assert result.flags == obj.flags
-        else:
-            assert result == obj, f"Value mismatch for {type(obj)}: {result} != {obj}"
-
-
-def test_msgpack_nested_pydantic_serializes_as_dict(
-    caplog: pytest.LogCaptureFixture,
-) -> None:
-    """Nested Pydantic models are serialized via model_dump() as dicts.
-
-    This means nested models don't go through the ext hook and don't need
-    to be in the allowlist - only the outer type does.
-    """
-
-    # Only allow outer type - inner is serialized as dict via model_dump()
-    serde = JsonPlusSerializer(
-        allowed_msgpack_modules=[("tests.test_jsonplus", "MyPydantic")]
-    )
-
-    obj = MyPydantic(foo="test", bar=42, inner=InnerPydantic(hello="world"))
-
-    caplog.clear()
-    dumped = serde.dumps_typed(obj)
-    result = serde.loads_typed(dumped)
-
-    # No blocking should occur - inner is serialized as dict, not ext
-    assert "blocked" not in caplog.text.lower()
-    assert result == obj

libs/checkpoint/tests/test_memory.py

@@ -1,9 +1,7 @@
-import logging
 from typing import Any
 
 import pytest
 from langchain_core.runnables import RunnableConfig
-from pydantic import BaseModel
 
 from langgraph.checkpoint.base import (
     Checkpoint,
@@ -12,11 +10,6 @@ from langgraph.checkpoint.base import (
     empty_checkpoint,
 )
 from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
-
-
-class MemoryPydantic(BaseModel):
-    foo: str
 
 
 class TestMemorySaver:
@@ -206,77 +199,3 @@ async def test_memory_saver() -> None:
 
     with memory_saver as sync_memory_saver:
         assert sync_memory_saver is memory_saver
-
-
-def test_memory_saver_warns_on_unregistered_msgpack(
-    caplog: pytest.LogCaptureFixture,
-) -> None:
-    serde = JsonPlusSerializer()
-    memory_saver = InMemorySaver(serde=serde)
-    obj = MemoryPydantic(foo="bar")
-
-    checkpoint = empty_checkpoint()
-    checkpoint["channel_values"] = {"foo": obj}
-    checkpoint["channel_versions"] = {"foo": 1}
-
-    config: RunnableConfig = {
-        "configurable": {"thread_id": "thread-1", "checkpoint_ns": ""}
-    }
-
-    caplog.set_level(logging.WARNING, logger="langgraph.checkpoint.serde.jsonplus")
-    new_config = memory_saver.put(config, checkpoint, {}, {"foo": 1})
-    result = memory_saver.get_tuple(new_config)
-
-    assert result is not None
-    assert "unregistered type" in caplog.text.lower()
-    assert result.checkpoint["channel_values"]["foo"] == obj
-
-
-def test_memory_saver_allowlist_silences_warning(
-    caplog: pytest.LogCaptureFixture,
-) -> None:
-    serde = JsonPlusSerializer(
-        allowed_msgpack_modules=[("tests.test_memory", "MemoryPydantic")]
-    )
-    memory_saver = InMemorySaver(serde=serde)
-    obj = MemoryPydantic(foo="bar")
-
-    checkpoint = empty_checkpoint()
-    checkpoint["channel_values"] = {"foo": obj}
-    checkpoint["channel_versions"] = {"foo": 1}
-
-    config: RunnableConfig = {
-        "configurable": {"thread_id": "thread-1", "checkpoint_ns": ""}
-    }
-
-    caplog.set_level(logging.WARNING, logger="langgraph.checkpoint.serde.jsonplus")
-    new_config = memory_saver.put(config, checkpoint, {}, {"foo": 1})
-    result = memory_saver.get_tuple(new_config)
-
-    assert result is not None
-    assert "unregistered type" not in caplog.text.lower()
-    assert result.checkpoint["channel_values"]["foo"] == obj
-
-
-def test_memory_saver_strict_blocks_unregistered(
-    caplog: pytest.LogCaptureFixture,
-) -> None:
-    serde = JsonPlusSerializer(allowed_msgpack_modules=None)
-    memory_saver = InMemorySaver(serde=serde)
-    obj = MemoryPydantic(foo="bar")
-
-    checkpoint = empty_checkpoint()
-    checkpoint["channel_values"] = {"foo": obj}
-    checkpoint["channel_versions"] = {"foo": 1}
-
-    config: RunnableConfig = {
-        "configurable": {"thread_id": "thread-1", "checkpoint_ns": ""}
-    }
-
-    caplog.set_level(logging.WARNING, logger="langgraph.checkpoint.serde.jsonplus")
-    new_config = memory_saver.put(config, checkpoint, {}, {"foo": 1})
-    result = memory_saver.get_tuple(new_config)
-
-    assert result is not None
-    assert "blocked" in caplog.text.lower()
-    assert result.checkpoint["channel_values"]["foo"] is None

libs/cli/langgraph_cli/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.11"
+__version__ = "0.4.12"

libs/cli/langgraph_cli/schemas.py

@@ -126,7 +126,7 @@ class SerdeConfig(TypedDict, total=False):
     If omitted, no serde is set up (the object store will still be present, however)."""
 
     allowed_json_modules: list[list[str]] | bool | None
-    """Optional. List of allowed python modules to de-serialize custom objects from JSON.
+    """Optional. List of allowed python modules to de-serialize custom objects from.
     
     If provided, only the specified modules will be allowed to be deserialized.
     If omitted, no modules are allowed, and the object returned will simply be a json object OR
@@ -146,34 +146,7 @@ class SerdeConfig(TypedDict, total=False):
     Example:
     {...
         "serde": {
-            "allowed_json_modules": True
-        }
-    }
-
-    """
-    allowed_msgpack_modules: list[list[str]] | bool | None
-    """Optional. List of allowed python modules to de-serialize custom objects from msgpack.
-
-    Known safe types (langgraph.checkpoint.serde.jsonplus.SAFE_MSGPACK_TYPES) are always
-    allowed regardless of this setting. Use this to allowlist your custom Pydantic models,
-    dataclasses, and other user-defined types.
-
-    If True (default), unregistered types will log a warning but still be deserialized.
-    If None, only known safe types will be deserialized; unregistered types will be blocked.
-
-    Example - allowlist specific types (no warnings for these):
-    {...
-        "serde": {
-            "allowed_msgpack_modules": [
-                ["my_agent.models", "MyState"],
-            ]
-        }
-    }
-
-    Example - strict mode (only safe types allowed):
-    {...
-        "serde": {
-            "allowed_msgpack_modules": null
+            "allowed_json_modules": true
         }
     }
     
@@ -206,6 +179,8 @@ class CheckpointerConfig(TypedDict, total=False):
 
     This configuration requires server version 0.5 or later to take effect.
     """
+    sweep_limit: int | None
+    """Maximum number of threads to process per sweep iteration. Defaults to 1000."""
 
 
 class SecurityConfig(TypedDict, total=False):
@@ -333,7 +308,8 @@ class EncryptionConfig(TypedDict, total=False):
     """Configuration for custom at-rest encryption logic.
 
     Allows you to implement custom encryption for sensitive data stored in the database,
-    including metadata fields and checkpoint blobs."""
+    including metadata fields and checkpoint blobs.
+    """
 
     path: str
     """Required. Path to an instance of the Encryption() class that implements custom encryption handlers.

libs/cli/pyproject.toml

@@ -19,14 +19,14 @@ dependencies = [
 path = "langgraph_cli/__init__.py"
 [project.optional-dependencies]
 inmem = [
-    "langgraph-api>=0.5.35,<0.7.0 ; python_version >= '3.11'",
+    "langgraph-api>=0.5.35,<0.8.0 ; python_version >= '3.11'",
     "langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
     "python-dotenv>=0.8.0",
 ]
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/cli"
-Twitter = "https://x.com/LangChainAI"
+Twitter = "https://x.com/LangChain"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"
 

libs/cli/schemas/schema.json

@@ -554,6 +554,17 @@
           ],
           "description": "Optional. Defines the serde configuration.\n\nIf provided, the checkpointer will apply serde settings according to the configuration.\nIf omitted, no serde behavior is configured.\n\nThis configuration requires server version 0.5 or later to take effect.\n"
         },
+        "sweep_limit": {
+          "anyOf": [
+            {
+              "type": "integer"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Maximum number of threads to process per sweep iteration. Defaults to 1000."
+        },
         "ttl": {
           "anyOf": [
             {
@@ -591,27 +602,7 @@
               "type": "null"
             }
           ],
-          "description": "Optional. List of allowed python modules to de-serialize custom objects from JSON.\n\nIf provided, only the specified modules will be allowed to be deserialized.\nIf omitted, no modules are allowed, and the object returned will simply be a json object OR\na deserialized langchain object.\n"
-        },
-        "allowed_msgpack_modules": {
-          "anyOf": [
-            {
-              "type": "array",
-              "items": {
-                "type": "array",
-                "items": {
-                  "type": "string"
-                }
-              }
-            },
-            {
-              "type": "boolean"
-            },
-            {
-              "type": "null"
-            }
-          ],
-          "description": "Optional. List of allowed python modules to de-serialize custom objects from msgpack.\n\nKnown safe types (langgraph.checkpoint.serde.jsonplus.SAFE_MSGPACK_TYPES) are always\nallowed regardless of this setting. Use this to allowlist your custom Pydantic models,\ndataclasses, and other user-defined types.\n\nIf True (default), unregistered types will log a warning but still be deserialized.\nIf None, only known safe types will be deserialized; unregistered types will be blocked.\n\n{...\n[\"my_agent.models\", \"MyState\"],\n]\n}\n}\n\n{...\n}\n}\n\n"
+          "description": "Optional. List of allowed python modules to de-serialize custom objects from.\n\nIf provided, only the specified modules will be allowed to be deserialized.\nIf omitted, no modules are allowed, and the object returned will simply be a json object OR\na deserialized langchain object.\n"
         },
         "pickle_fallback": {
           "type": "boolean",

libs/cli/schemas/schema.v0.json

@@ -554,6 +554,17 @@
           ],
           "description": "Optional. Defines the serde configuration.\n\nIf provided, the checkpointer will apply serde settings according to the configuration.\nIf omitted, no serde behavior is configured.\n\nThis configuration requires server version 0.5 or later to take effect.\n"
         },
+        "sweep_limit": {
+          "anyOf": [
+            {
+              "type": "integer"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Maximum number of threads to process per sweep iteration. Defaults to 1000."
+        },
         "ttl": {
           "anyOf": [
             {
@@ -591,27 +602,7 @@
               "type": "null"
             }
           ],
-          "description": "Optional. List of allowed python modules to de-serialize custom objects from JSON.\n\nIf provided, only the specified modules will be allowed to be deserialized.\nIf omitted, no modules are allowed, and the object returned will simply be a json object OR\na deserialized langchain object.\n"
-        },
-        "allowed_msgpack_modules": {
-          "anyOf": [
-            {
-              "type": "array",
-              "items": {
-                "type": "array",
-                "items": {
-                  "type": "string"
-                }
-              }
-            },
-            {
-              "type": "boolean"
-            },
-            {
-              "type": "null"
-            }
-          ],
-          "description": "Optional. List of allowed python modules to de-serialize custom objects from msgpack.\n\nKnown safe types (langgraph.checkpoint.serde.jsonplus.SAFE_MSGPACK_TYPES) are always\nallowed regardless of this setting. Use this to allowlist your custom Pydantic models,\ndataclasses, and other user-defined types.\n\nIf True (default), unregistered types will log a warning but still be deserialized.\nIf None, only known safe types will be deserialized; unregistered types will be blocked.\n\n{...\n[\"my_agent.models\", \"MyState\"],\n]\n}\n}\n\n{...\n}\n}\n\n"
+          "description": "Optional. List of allowed python modules to de-serialize custom objects from.\n\nIf provided, only the specified modules will be allowed to be deserialized.\nIf omitted, no modules are allowed, and the object returned will simply be a json object OR\na deserialized langchain object.\n"
         },
         "pickle_fallback": {
           "type": "boolean",