feat: topology arg for langgraph up

.github/CONTRIBUTING.md

@@ -0,0 +1,6 @@
+# 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

@@ -1,60 +1,43 @@
 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 (below).
-labels: ["bug"]
-type: 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 at forum.langchain.com.
+labels: [pending, 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. 
 
-        For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
+        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/).
 
-        Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:
+        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:
 
-        * [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/),
+        * [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
+        * [LangChain documentation with the integrated search](https://docs.langchain.com/),
+        * [GitHub search](https://github.com/langchain-ai/langgraph),
   - type: checkboxes
     id: checks
     attributes:
       label: Checked other resources
-      description: Please confirm and check all the following options.
+      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.
       options:
-        - label: This is a bug, not a usage question.
+        - label: This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
           required: true
-        - label: I added a clear and descriptive title that summarizes this issue.
+        - label: I added a clear and detailed title that summarizes the issue.
           required: true
-        - label: I used the GitHub search to find a similar question and didn't find it.
+        - label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
           required: true
-        - 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.
+        - 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.
           required: true
   - type: textarea
     id: reproduction
     validations:
       required: true
     attributes:
-      label: Reproduction Steps / Example Code (Python)
+      label: Example Code
       description: |
-        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
+        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!
       placeholder: |
         from langgraph.graph import StateGraph
 
@@ -63,13 +46,17 @@ 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 copy and paste the full error message and
-        stack trace.
-        (This will be automatically formatted into code, so no need for backticks.)
+        If you are reporting an error, please include the full error message and stack trace.
+      placeholder: |
+        Exception + full stack trace
       render: shell
   - type: textarea
     id: description
@@ -90,18 +77,7 @@ body:
     attributes:
       label: System Info
       description: |
-        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()
-        ```
+        Run on your machine: `python -m langchain_core.sys_info`
       placeholder: |
         python -m langchain_core.sys_info
     validations:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,15 +1,9 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: 💬 LangChain Forum
+  - 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
     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://docs.langchain.com/oss/python/contributing/overview) 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://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) for more.
 
 Additional guidelines:
 

AGENTS.md

@@ -53,5 +53,3 @@ 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

@@ -53,5 +53,3 @@ 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.

examples/README.md

@@ -1,3 +1,3 @@
 # LangGraph examples
 
-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.
+This directory should NOT be used for documentation. All new documentation must be added to `docs/docs/` directory.

examples/async.ipynb

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -5,15 +5,7 @@
    "id": "10251c1c",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb"
    ]
   }
  ],

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

@@ -5,15 +5,7 @@
    "id": "a4351a24",
    "metadata": {},
    "source": [
-    "[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)."
+    "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"
    ]
   }
  ],

examples/chatbots/information-gather-prompting.ipynb

@@ -5,15 +5,7 @@
    "id": "a9014f94",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb"
    ]
   }
  ],

examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb

@@ -0,0 +1,33 @@
+{
+ "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

@@ -5,15 +5,7 @@
    "id": "1f2f13ca",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb"
    ]
   }
  ],

examples/code_assistant/langgraph_code_assistant_mistral.ipynb

@@ -1,13 +1,5 @@
 {
  "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": {
@@ -41,9 +33,7 @@
    "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",
@@ -61,12 +51,7 @@
    "id": "982e4609-86e4-4934-828f-e03d89c20393",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
-    "mistral_api_key = os.getenv(\"MISTRAL_API_KEY\")  # Ensure this is set"
-   ]
+   "source": ["import os\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\nmistral_api_key = os.getenv(\"MISTRAL_API_KEY\")  # Ensure this is set"]
   },
   {
    "cell_type": "markdown",
@@ -84,12 +69,7 @@
    "id": "37b172d2-3a9d-49a8-898c-22ed0cb45c88",
    "metadata": {},
    "outputs": [],
-   "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\""
-   ]
+   "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\""]
   },
   {
    "cell_type": "markdown",
@@ -107,42 +87,7 @@
    "id": "a188c8ca-c053-4e6d-b7af-38a3b6b371c7",
    "metadata": {},
    "outputs": [],
-   "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)"
-   ]
+   "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)"]
   },
   {
    "cell_type": "code",
@@ -150,10 +95,7 @@
    "id": "9fc0290d-5a04-4514-8664-91f9dbf2da7b",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "question = \"Write a function for fibonacci.\"\n",
-    "messages = [(\"user\", question)]"
-   ]
+   "source": ["question = \"Write a function for fibonacci.\"\nmessages = [(\"user\", question)]"]
   },
   {
    "cell_type": "code",
@@ -172,11 +114,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# Test\n",
-    "result = code_gen_chain.invoke(messages)\n",
-    "result"
-   ]
+   "source": ["# Test\nresult = code_gen_chain.invoke(messages)\nresult"]
   },
   {
    "cell_type": "markdown",
@@ -192,28 +130,7 @@
    "id": "183d77b8-f180-4815-b39f-8ef507ec0534",
    "metadata": {},
    "outputs": [],
-   "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"
-   ]
+   "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"]
   },
   {
    "cell_type": "markdown",
@@ -229,163 +146,7 @@
    "id": "14bc89d1-3ca6-4847-a048-1803e0e4600e",
    "metadata": {},
    "outputs": [],
-   "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)"
-   ]
+   "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)"]
   },
   {
    "cell_type": "code",
@@ -393,31 +154,7 @@
    "id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
    "metadata": {},
    "outputs": [],
-   "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)"
-   ]
+   "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)"]
   },
   {
    "cell_type": "code",
@@ -436,15 +173,7 @@
      "output_type": "display_data"
     }
    ],
-   "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"
-   ]
+   "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"]
   },
   {
    "cell_type": "code",
@@ -452,23 +181,7 @@
    "id": "242aa2f0-2c31-462f-a958-ff9ae0cf7c62",
    "metadata": {},
    "outputs": [],
-   "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)"
-   ]
+   "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)"]
   },
   {
    "cell_type": "markdown",
@@ -486,31 +199,7 @@
    "id": "390b2768-f395-4aea-8b0e-9d36212a31ac",
    "metadata": {},
    "outputs": [],
-   "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)"
-   ]
+   "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)"]
   },
   {
    "cell_type": "markdown",
@@ -528,26 +217,7 @@
    "id": "0a3f946b-e2f2-44d9-905b-09f36980cf9f",
    "metadata": {},
    "outputs": [],
-   "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)"
-   ]
+   "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)"]
   },
   {
    "cell_type": "markdown",
@@ -565,37 +235,7 @@
    "id": "2bb883df-540b-46ab-9415-fe27db68456f",
    "metadata": {},
    "outputs": [],
-   "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)"
-   ]
+   "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)"]
   },
   {
    "cell_type": "markdown",
@@ -613,34 +253,7 @@
    "id": "ee05da1f-c272-405d-8a7b-552cfc3106e1",
    "metadata": {},
    "outputs": [],
-   "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)"
-   ]
+   "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)"]
   },
   {
    "cell_type": "markdown",
@@ -658,7 +271,7 @@
    "id": "814fc2a4-8e5b-4faa-8f52-3977226bd09a",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [""]
   }
  ],
  "metadata": {

examples/configuration.ipynb

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -5,15 +5,7 @@
    "id": "a8232bc9",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/customer-support/customer-support.ipynb"
    ]
   }
  ],

examples/extraction/retries.ipynb

@@ -5,15 +5,7 @@
    "id": "8dbdba5b",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/extraction/retries.ipynb"
    ]
   }
  ],

examples/human_in_the_loop/wait-user-input.ipynb

@@ -5,15 +5,7 @@
    "id": "3ecab357",
    "metadata": {},
    "source": [
-    "[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)."
+    "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"
    ]
   }
  ],

examples/input_output_schema.ipynb

@@ -0,0 +1,33 @@
+{
+ "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

@@ -5,15 +5,7 @@
    "id": "09038b53",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/lats/lats.ipynb"
    ]
   }
  ],

examples/llm-compiler/LLMCompiler.ipynb

@@ -5,15 +5,7 @@
    "id": "85205e97",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb"
    ]
   }
  ],

examples/map-reduce.ipynb

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -5,15 +5,7 @@
    "id": "5cc8a2ad",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb"
    ]
   }
  ],

examples/multi_agent/multi-agent-collaboration.ipynb

@@ -5,15 +5,7 @@
    "id": "d2b507b9",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb"
    ]
   }
  ],

examples/node-retries.ipynb

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -5,15 +5,7 @@
    "id": "9138f92e",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb"
    ]
   }
  ],

examples/rag/langgraph_adaptive_rag.ipynb

@@ -1,13 +1,5 @@
 {
  "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

@@ -1,13 +1,5 @@
 {
  "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

@@ -1,13 +1,5 @@
 {
  "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",
@@ -208,11 +200,11 @@
      "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

@@ -1,13 +1,5 @@
 {
  "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

@@ -1,13 +1,5 @@
 {
  "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

@@ -1,13 +1,5 @@
 {
  "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

@@ -1,13 +1,5 @@
 {
  "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

@@ -1,13 +1,5 @@
 {
  "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": {
@@ -62,11 +54,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "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>\""
+    "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>\""
    ]
   },
   {
@@ -76,9 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
+    "import os\n\nos.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
    ]
   },
   {
@@ -98,18 +84,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "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()"
+    "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()"
    ]
   },
   {
@@ -138,11 +113,7 @@
     }
    ],
    "source": [
-    "docs = retriever.invoke(\"James Cameron\")\n",
-    "for doc in docs:\n",
-    "    print(\"# \" + doc.metadata[\"title\"])\n",
-    "    print(doc.page_content)\n",
-    "    print()"
+    "docs = retriever.invoke(\"James Cameron\")\nfor doc in docs:\n    print(\"# \" + doc.metadata[\"title\"])\n    print(doc.page_content)\n    print()"
    ]
   },
   {
@@ -202,12 +173,7 @@
     }
    ],
    "source": [
-    "# 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}))"
+    "# 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}))"
    ]
   },
   {
@@ -235,23 +201,7 @@
     }
    ],
    "source": [
-    "### 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)"
+    "### 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)"
    ]
   },
   {
@@ -379,17 +329,7 @@
     }
    ],
    "source": [
-    "### 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})"
+    "### 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})"
    ]
   },
   {
@@ -411,24 +351,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "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]"
+    "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]"
    ]
   },
   {
@@ -438,95 +361,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "### 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}"
+    "### 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}"
    ]
   },
   {
@@ -536,74 +371,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "### 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\""
+    "### 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\""
    ]
   },
   {
@@ -622,42 +390,7 @@
    "id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
    "metadata": {},
    "outputs": [],
-   "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()"
-   ]
+   "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()"]
   },
   {
    "cell_type": "code",
@@ -693,18 +426,7 @@
     }
    ],
    "source": [
-    "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\"])"
+    "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\"])"
    ]
   },
   {
@@ -714,15 +436,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "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\"])"
+    "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\"])"
    ]
   },
   {
@@ -731,7 +445,9 @@
    "id": "42369ab8-322d-434a-b5dd-2266e4cb2903",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    ""
+   ]
   }
  ],
  "metadata": {

examples/react-agent-from-scratch.ipynb

@@ -5,14 +5,7 @@
    "id": "294995c4",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/react-agent-from-scratch.ipynb"
    ]
   }
  ],

examples/react-agent-structured-output.ipynb

@@ -5,14 +5,7 @@
    "id": "40f0d107",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/react-agent-structured-output.ipynb"
    ]
   }
  ],

examples/recursion-limit.ipynb

@@ -0,0 +1,33 @@
+{
+ "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

@@ -5,15 +5,7 @@
    "id": "658773a2",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflection/reflection.ipynb"
    ]
   }
  ],

examples/reflexion/reflexion.ipynb

@@ -5,15 +5,7 @@
    "id": "caf07859",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflexion/reflexion.ipynb"
    ]
   }
  ],

examples/rewoo/rewoo.ipynb

@@ -5,15 +5,7 @@
    "id": "961f43ec",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rewoo/rewoo.ipynb"
    ]
   }
  ],

examples/run-id-langsmith.ipynb

@@ -5,14 +5,7 @@
    "id": "bbd6e9b8",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/run-id-langsmith.ipynb"
    ]
   }
  ],

examples/self-discover/self-discover.ipynb

@@ -5,15 +5,7 @@
    "id": "f6db1873",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/self-discover/self-discover.ipynb"
    ]
   }
  ],

examples/state-model.ipynb

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -5,14 +5,7 @@
    "id": "f49876e1",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/subgraph.ipynb"
    ]
   }
  ],

examples/subgraphs-manage-state.ipynb

@@ -0,0 +1,33 @@
+{
+ "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

@@ -0,0 +1,33 @@
+{
+ "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

@@ -5,14 +5,7 @@
    "id": "7fd8bd65",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/tool-calling.ipynb"
    ]
   }
  ],

examples/tutorials/sql-agent.ipynb

@@ -5,15 +5,7 @@
    "id": "83c2223f",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/sql-agent.ipynb"
    ]
   }
  ],

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

@@ -5,15 +5,7 @@
    "id": "11140167",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/tnt-llm/tnt-llm.ipynb"
    ]
   }
  ],

examples/usaco/usaco.ipynb

@@ -5,15 +5,7 @@
    "id": "9dffdb54",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/usaco/usaco.ipynb"
    ]
   }
  ],

examples/visualization.ipynb

@@ -0,0 +1,33 @@
+{
+ "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

@@ -5,15 +5,7 @@
    "id": "007ea2e9",
    "metadata": {},
    "source": [
-    "[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)."
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/web-navigation/web_voyager.ipynb"
    ]
   }
  ],

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

@@ -550,11 +550,6 @@ 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
@@ -571,21 +566,21 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
                 # thread/coroutine at a time, so we acquire a lock
                 if self.supports_pipeline:
                     async with (
-                        lock,
+                        self.lock,
                         conn.pipeline(),
                         conn.cursor(binary=True, row_factory=dict_row) as cur,
                     ):
                         yield cur
                 else:
                     async with (
-                        lock,
+                        self.lock,
                         conn.transaction(),
                         conn.cursor(binary=True, row_factory=dict_row) as cur,
                     ):
                         yield cur
             else:
                 async with (
-                    lock,
+                    self.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 = "3.0.4"
+version = "3.0.3"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"

libs/checkpoint-postgres/uv.lock

@@ -306,7 +306,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.4"
+version = "3.0.3"
 source = { editable = "." }
 dependencies = [
     { name = "langgraph-checkpoint" },

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

@@ -119,25 +119,6 @@ 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/cli/langgraph_cli/__init__.py

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

libs/cli/langgraph_cli/cli.py

@@ -158,6 +158,25 @@ OPT_API_VERSION = click.option(
     help="API server version to use for the base image. If unspecified, the latest version will be used.",
 )
 
+OPT_TOPOLOGY = click.option(
+    "--topology",
+    type=click.Choice(["single", "split"]),
+    default="split",
+    show_default=True,
+    help="""Deployment topology for the LangGraph services.
+
+    \b
+    - 'single': Run API server and queue worker in the same container (legacy behavior)
+    - 'split': Run API server and queue worker in separate containers (recommended)
+    Other topologies (e.g., 'distributed') coming soon.
+
+    \b
+    Example:
+        langgraph up --topology single
+    \b
+    """,
+)
+
 
 @click.group()
 @click.version_option(version=__version__, prog_name="LangGraph CLI")
@@ -176,6 +195,7 @@ def cli():
 @OPT_WATCH
 @OPT_POSTGRES_URI
 @OPT_API_VERSION
+@OPT_TOPOLOGY
 @click.option(
     "--image",
     type=str,
@@ -210,6 +230,7 @@ def up(
     debugger_base_url: str | None,
     postgres_uri: str | None,
     api_version: str | None,
+    topology: str,
     image: str | None,
     base_image: str | None,
 ):
@@ -233,6 +254,7 @@ For production use, requires a license key in env var LANGGRAPH_CLOUD_LICENSE_KE
             debugger_base_url=debugger_base_url,
             postgres_uri=postgres_uri,
             api_version=api_version,
+            topology=topology,
             image=image,
             base_image=base_image,
         )
@@ -557,6 +579,7 @@ def dockerfile(
                 capabilities,
                 port=8123,
                 base_image=base_image,
+                topology="split",
             )
             # Add .env file to the docker-compose.yml for the langgraph-api service
             compose_dict["services"]["langgraph-api"]["env_file"] = [".env"]
@@ -570,6 +593,17 @@ def dockerfile(
                 compose_dict["services"]["langgraph-api"]["build"]["args"] = {
                     "BASE_IMAGE": base_image
                 }
+            # Also configure the worker service with the same build context
+            if "langgraph-worker" in compose_dict["services"]:
+                compose_dict["services"]["langgraph-worker"]["env_file"] = [".env"]
+                compose_dict["services"]["langgraph-worker"]["build"] = {
+                    "context": ".",
+                    "dockerfile": save_path.name,
+                }
+                if base_image:
+                    compose_dict["services"]["langgraph-worker"]["build"]["args"] = {
+                        "BASE_IMAGE": base_image
+                    }
             f.write(langgraph_cli.docker.dict_to_yaml(compose_dict))
             secho("✅ Created: docker-compose.yml", fg="green")
 
@@ -793,6 +827,8 @@ def prepare_args_and_stdin(
     debugger_base_url: str | None = None,
     postgres_uri: str | None = None,
     api_version: str | None = None,
+    # Deployment topology: "single" (combined) or "split" (separate API/worker)
+    topology: str = "split",
     # Like "my-tag" (if you already built it locally)
     image: str | None = None,
     # Like "langchain/langgraphjs-api" or "langchain/langgraph-api
@@ -809,6 +845,7 @@ def prepare_args_and_stdin(
         image=image,  # Pass image to compose YAML generator
         base_image=base_image,
         api_version=api_version,
+        topology=topology,
     )
     args = [
         "--project-directory",
@@ -826,6 +863,8 @@ def prepare_args_and_stdin(
         base_image=langgraph_cli.config.default_base_image(config),
         api_version=api_version,
         image=image,
+        topology=topology,
+        postgres_uri=postgres_uri,
     )
     return args, stdin
 
@@ -844,6 +883,7 @@ def prepare(
     debugger_base_url: str | None = None,
     postgres_uri: str | None = None,
     api_version: str | None = None,
+    topology: str = "split",
     image: str | None = None,
     base_image: str | None = None,
 ) -> tuple[list[str], str]:
@@ -872,6 +912,7 @@ def prepare(
         debugger_base_url=debugger_base_url or f"http://127.0.0.1:{port}",
         postgres_uri=postgres_uri,
         api_version=api_version,
+        topology=topology,
         image=image,
         base_image=base_image,
     )

libs/cli/langgraph_cli/config.py

@@ -1242,6 +1242,8 @@ def config_to_compose(
     api_version: str | None = None,
     image: str | None = None,
     watch: bool = False,
+    topology: str = "split",
+    postgres_uri: str | None = None,
 ) -> str:
     base_image = base_image or default_base_image(config)
 
@@ -1268,11 +1270,20 @@ def config_to_compose(
     else:
         watch_str = ""
     if image:
-        return f"""
+        api_config = f"""
 {textwrap.indent(env_vars_str, "            ")}
         {env_file_str}
         {watch_str}
 """
+        # For split topology with pre-built image, worker also needs env vars
+        if topology == "split":
+            worker_config = f"""
+    langgraph-worker:
+{textwrap.indent(env_vars_str, "            ")}
+        {env_file_str}
+"""
+            return api_config + worker_config
+        return api_config
 
     else:
         dockerfile, additional_contexts = config_to_docker(
@@ -1292,7 +1303,7 @@ def config_to_compose(
             additional_contexts:
 {additional_contexts_str}"""
 
-        return f"""
+        api_config = f"""
 {textwrap.indent(env_vars_str, "            ")}
         {env_file_str}
         pull_policy: build
@@ -1302,3 +1313,43 @@ def config_to_compose(
 {textwrap.indent(dockerfile, "                ")}
         {watch_str}
 """
+        # For split topology, add complete worker service definition with build config
+        # (worker service is NOT added in docker.py when building from Dockerfile)
+        if topology == "split":
+            # Import here to avoid circular dependency
+            from langgraph_cli.docker import DEFAULT_POSTGRES_URI
+
+            # Determine if using internal postgres (include_db) based on postgres_uri
+            include_db = postgres_uri is None
+            effective_postgres_uri = postgres_uri or DEFAULT_POSTGRES_URI
+
+            # Build depends_on based on whether we have internal postgres
+            if include_db:
+                worker_depends_on = """depends_on:
+            langgraph-redis:
+                condition: service_healthy
+            langgraph-postgres:
+                condition: service_healthy"""
+            else:
+                worker_depends_on = """depends_on:
+            langgraph-redis:
+                condition: service_healthy"""
+
+            worker_config = f"""
+    langgraph-worker:
+        {worker_depends_on}
+        environment:
+            REDIS_URI: redis://langgraph-redis:6379
+            POSTGRES_URI: {effective_postgres_uri}
+{textwrap.indent(env_vars_str, "            ")}
+        {env_file_str}
+        entrypoint:
+            - /storage/queue_entrypoint.sh
+        pull_policy: build
+        build:
+            context: .{additional_contexts_str}
+            dockerfile_inline: |
+{textwrap.indent(dockerfile, "                ")}
+"""
+            return api_config + worker_config
+        return api_config

libs/cli/langgraph_cli/docker.py

@@ -20,6 +20,7 @@ class Version(NamedTuple):
 
 
 DockerComposeType = Literal["plugin", "standalone"]
+TopologyType = Literal["single", "split"]
 
 
 class DockerCapabilities(NamedTuple):
@@ -149,6 +150,8 @@ def compose_as_dict(
     base_image: str | None = None,
     # API version of the base image
     api_version: str | None = None,
+    # Deployment topology: "single" (combined) or "split" (separate API/worker)
+    topology: TopologyType = "split",
 ) -> dict:
     """Create a docker compose file as a dictionary in YML style."""
     if postgres_uri is None:
@@ -207,15 +210,20 @@ def compose_as_dict(
         )["langgraph-debugger"]
 
     # Add langgraph-api service
+    api_environment = {
+        "REDIS_URI": "redis://langgraph-redis:6379",
+        "POSTGRES_URI": postgres_uri,
+    }
+    # In split mode, disable queue processing in the API server
+    if topology == "split":
+        api_environment["N_JOBS_PER_WORKER"] = "0"
+
     services["langgraph-api"] = {
         "ports": [f'"{port}:8000"'],
         "depends_on": {
             "langgraph-redis": {"condition": "service_healthy"},
         },
-        "environment": {
-            "REDIS_URI": "redis://langgraph-redis:6379",
-            "POSTGRES_URI": postgres_uri,
-        },
+        "environment": api_environment,
     }
     if image:
         services["langgraph-api"]["image"] = image
@@ -235,6 +243,37 @@ def compose_as_dict(
             "start_period": "10s",
         }
 
+    # Add langgraph-worker service in split mode ONLY when using pre-built image
+    # When building from Dockerfile, the worker service is added in config_to_compose
+    # to avoid duplicate service key issues with YAML concatenation
+    if topology == "split" and image:
+        services["langgraph-worker"] = {
+            "depends_on": {
+                "langgraph-redis": {"condition": "service_healthy"},
+            },
+            "environment": {
+                "REDIS_URI": "redis://langgraph-redis:6379",
+                "POSTGRES_URI": postgres_uri,
+            },
+            "entrypoint": ["/storage/queue_entrypoint.sh"],
+            "image": image,
+        }
+
+        # If Postgres is included, add it to the dependencies of langgraph-worker
+        if include_db:
+            services["langgraph-worker"]["depends_on"]["langgraph-postgres"] = {
+                "condition": "service_healthy"
+            }
+
+        # Additional healthcheck for langgraph-worker if supported
+        if capabilities.healthcheck_start_interval:
+            services["langgraph-worker"]["healthcheck"] = {
+                "test": "python /api/healthcheck.py",
+                "interval": "60s",
+                "start_interval": "1s",
+                "start_period": "10s",
+            }
+
     # Final compose dictionary with volumes included if needed
     compose_dict = {}
     if include_db:
@@ -255,6 +294,8 @@ def compose(
     image: str | None = None,
     base_image: str | None = None,
     api_version: str | None = None,
+    # Deployment topology: "single" (combined) or "split" (separate API/worker)
+    topology: TopologyType = "split",
 ) -> str:
     """Create a docker compose file as a string."""
     compose_content = compose_as_dict(
@@ -266,6 +307,7 @@ def compose(
         image=image,
         base_image=base_image,
         api_version=api_version,
+        topology=topology,
     )
     compose_str = dict_to_yaml(compose_content)
     return compose_str

libs/cli/langgraph_cli/schemas.py

@@ -179,8 +179,6 @@ 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):

libs/cli/pyproject.toml

@@ -19,7 +19,7 @@ dependencies = [
 path = "langgraph_cli/__init__.py"
 [project.optional-dependencies]
 inmem = [
-    "langgraph-api>=0.5.35,<0.8.0 ; python_version >= '3.11'",
+    "langgraph-api>=0.5.35,<0.7.0 ; python_version >= '3.11'",
     "langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
     "python-dotenv>=0.8.0",
 ]

libs/cli/schemas/schema.json

@@ -554,17 +554,6 @@
           ],
           "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": [
             {

libs/cli/schemas/schema.v0.json

@@ -554,17 +554,6 @@
           ],
           "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": [
             {

libs/cli/tests/unit_tests/cli/test_cli.py

@@ -66,6 +66,7 @@ def test_prepare_args_and_stdin() -> None:
         debugger_port=debugger_port,
         debugger_base_url=debugger_graph_url,
         watch=True,
+        topology="single",
     )
 
     expected_args = [
@@ -189,6 +190,7 @@ def test_prepare_args_and_stdin_with_image() -> None:
         debugger_base_url=debugger_graph_url,
         watch=True,
         image="my-cool-image",
+        topology="single",
     )
 
     expected_args = [

libs/cli/tests/unit_tests/test_config.py

@@ -1147,6 +1147,7 @@ def test_config_to_compose_simple_config():
         PATH_TO_CONFIG,
         validate_config({"dependencies": ["."], "graphs": graphs}),
         "langchain/langgraph-api",
+        topology="single",
     )
     assert (
         clean_empty_lines(actual_compose_stdin).strip()
@@ -1195,6 +1196,7 @@ def test_config_to_compose_env_vars():
             }
         ),
         "langchain/langgraph-api-custom",
+        topology="single",
     )
     assert clean_empty_lines(actual_compose_stdin) == expected_compose_stdin
 
@@ -1233,6 +1235,7 @@ def test_config_to_compose_env_file():
         PATH_TO_CONFIG,
         validate_config({"dependencies": ["."], "graphs": graphs, "env": ".env"}),
         "langchain/langgraph-api",
+        topology="single",
     )
     assert clean_empty_lines(actual_compose_stdin) == expected_compose_stdin
 
@@ -1279,6 +1282,7 @@ def test_config_to_compose_watch():
         validate_config({"dependencies": ["."], "graphs": graphs}),
         "langchain/langgraph-api",
         watch=True,
+        topology="single",
     )
     assert clean_empty_lines(actual_compose_stdin) == expected_compose_stdin
 
@@ -1326,6 +1330,7 @@ def test_config_to_compose_end_to_end():
         validate_config({"dependencies": ["."], "graphs": graphs, "env": ".env"}),
         "langchain/langgraph-api",
         watch=True,
+        topology="single",
     )
     assert clean_empty_lines(actual_compose_stdin) == expected_compose_stdin
 

libs/cli/tests/unit_tests/test_docker.py

@@ -20,7 +20,10 @@ def test_compose_with_no_debugger_and_custom_db():
     port = 8123
     custom_postgres_uri = "custom_postgres_uri"
     actual_compose_str = compose(
-        DEFAULT_DOCKER_CAPABILITIES, port=port, postgres_uri=custom_postgres_uri
+        DEFAULT_DOCKER_CAPABILITIES,
+        port=port,
+        postgres_uri=custom_postgres_uri,
+        topology="single",
     )
     expected_compose_str = f"""services:
     langgraph-redis:
@@ -49,6 +52,7 @@ def test_compose_with_no_debugger_and_custom_db_with_healthcheck():
         DEFAULT_DOCKER_CAPABILITIES._replace(healthcheck_start_interval=True),
         port=port,
         postgres_uri=custom_postgres_uri,
+        topology="single",
     )
     expected_compose_str = f"""services:
     langgraph-redis:
@@ -82,6 +86,7 @@ def test_compose_with_debugger_and_custom_db():
         DEFAULT_DOCKER_CAPABILITIES,
         port=port,
         postgres_uri=custom_postgres_uri,
+        topology="single",
     )
     expected_compose_str = f"""services:
     langgraph-redis:
@@ -105,7 +110,9 @@ def test_compose_with_debugger_and_custom_db():
 
 def test_compose_with_debugger_and_default_db():
     port = 8123
-    actual_compose_str = compose(DEFAULT_DOCKER_CAPABILITIES, port=port)
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES, port=port, topology="single"
+    )
     expected_compose_str = f"""volumes:
     langgraph-data:
         driver: local
@@ -157,7 +164,10 @@ def test_compose_with_api_version():
     api_version = "0.2.74"
 
     actual_compose_str = compose(
-        DEFAULT_DOCKER_CAPABILITIES, port=port, api_version=api_version
+        DEFAULT_DOCKER_CAPABILITIES,
+        port=port,
+        api_version=api_version,
+        topology="single",
     )
 
     # The compose function should generate a compose file that doesn't directly
@@ -219,6 +229,7 @@ def test_compose_with_api_version_and_base_image():
         port=port,
         api_version=api_version,
         base_image=base_image,
+        topology="single",
     )
 
     # Similar to the previous test - the compose function doesn't directly embed
@@ -280,6 +291,7 @@ def test_compose_with_api_version_and_custom_postgres():
         port=port,
         api_version=api_version,
         postgres_uri=custom_postgres_uri,
+        topology="single",
     )
 
     expected_compose_str = f"""services:
@@ -313,6 +325,7 @@ def test_compose_with_api_version_and_debugger():
         port=port,
         api_version=api_version,
         debugger_port=debugger_port,
+        topology="single",
     )
 
     expected_compose_str = f"""volumes:
@@ -368,6 +381,101 @@ services:
     assert clean_empty_lines(actual_compose_str) == expected_compose_str
 
 
+def test_compose_with_single_topology():
+    """Test compose function with single topology (legacy behavior)."""
+    port = 8123
+    custom_postgres_uri = "custom_postgres_uri"
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES,
+        port=port,
+        postgres_uri=custom_postgres_uri,
+        topology="single",
+    )
+    # Assert langgraph-worker service is NOT present
+    assert "langgraph-worker" not in actual_compose_str
+    assert "/storage/queue_entrypoint.sh" not in actual_compose_str
+    # Assert N_JOBS_PER_WORKER is NOT set (queue runs in API container)
+    assert "N_JOBS_PER_WORKER" not in actual_compose_str
+
+
+def test_compose_with_split_topology():
+    """Test compose function with split topology when using pre-built image."""
+    port = 8123
+    custom_postgres_uri = "custom_postgres_uri"
+    image = "my-custom-image:latest"
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES,
+        port=port,
+        postgres_uri=custom_postgres_uri,
+        topology="split",
+        image=image,
+    )
+    # Assert langgraph-worker service is present when using image
+    assert "langgraph-worker" in actual_compose_str
+    assert "/storage/queue_entrypoint.sh" in actual_compose_str
+    # Assert N_JOBS_PER_WORKER is set to 0 on API service
+    assert "N_JOBS_PER_WORKER" in actual_compose_str
+
+
+def test_compose_split_topology_is_default():
+    """Test that split topology is the default (N_JOBS_PER_WORKER set on api)."""
+    port = 8123
+    custom_postgres_uri = "custom_postgres_uri"
+    # Call without specifying topology
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES,
+        port=port,
+        postgres_uri=custom_postgres_uri,
+    )
+    # In split mode without image, worker is NOT in compose (added in config_to_compose)
+    # But N_JOBS_PER_WORKER should be set on api service
+    assert "N_JOBS_PER_WORKER" in actual_compose_str
+
+
+def test_compose_split_topology_with_image():
+    """Test that worker service uses the same image as api in split mode."""
+    port = 8123
+    image = "my-custom-image:latest"
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES,
+        port=port,
+        image=image,
+        topology="split",
+    )
+    # Both services should reference the same image
+    assert actual_compose_str.count(f"image: {image}") == 2
+
+
+def test_compose_split_topology_with_postgres():
+    """Test split topology includes postgres service (worker added when image provided)."""
+    port = 8123
+    image = "my-custom-image:latest"
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES,
+        port=port,
+        topology="split",
+        image=image,
+    )
+    # Worker should be present when image is provided
+    assert "langgraph-worker" in actual_compose_str
+    # The compose should include langgraph-postgres service
+    assert "langgraph-postgres" in actual_compose_str
+
+
+def test_compose_split_topology_with_healthcheck():
+    """Test split topology with healthcheck capabilities when using image."""
+    port = 8123
+    image = "my-custom-image:latest"
+    actual_compose_str = compose(
+        DEFAULT_DOCKER_CAPABILITIES._replace(healthcheck_start_interval=True),
+        port=port,
+        topology="split",
+        image=image,
+    )
+    # Both api and worker should have healthchecks
+    assert actual_compose_str.count("python /api/healthcheck.py") == 2
+
+
 @pytest.mark.parametrize(
     "input_str,expected",
     [

libs/cli/uv.lock

@@ -650,7 +650,7 @@ wheels = [
 
 [[package]]
 name = "hatch"
-version = "1.16.3"
+version = "1.16.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "backports-zstd", marker = "python_full_version < '3.14'" },
@@ -671,9 +671,9 @@ dependencies = [
     { name = "uv" },
     { name = "virtualenv" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/41/c1/976b807478878d31d467dd17b9fe642962f292e16ed13c34b593c0453fde/hatch-1.16.3.tar.gz", hash = "sha256:2a50ecc912adfc8122cd2ccdcc15254cdef829e5d158be9014180cd7f0fb7ea9", size = 5219621, upload-time = "2026-01-21T01:36:19.822Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b9/c1/8598996a6f264d430c530799dc65fb13942fb29092e35505039a5f2fb5dc/hatch-1.16.2.tar.gz", hash = "sha256:f288938da85b4b90e47d94788e19e9976dcd6fd53b48343ea251a2a37256a980", size = 5216569, upload-time = "2025-12-06T19:18:12.596Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b8/b4/5c5fa4ca8c59e7ef0a224ff10e6336e73ca61c5e0eff09ee691441c9275f/hatch-1.16.3-py3-none-any.whl", hash = "sha256:f5169025cf1cdfe981366eb96127cab1d1bc59f5f2acb87c4cc308c25d95a4b1", size = 141305, upload-time = "2026-01-21T01:36:18.13Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/7c/bbed5611b1cd7b0b42b2dadb0721d9ccfa4fa9d03abc05e0f57c85a319c6/hatch-1.16.2-py3-none-any.whl", hash = "sha256:827eaf9813c63119f172b85975c5c27110a2306b07e5304c9d38527b0239052a", size = 140658, upload-time = "2025-12-06T19:18:10.573Z" },
 ]
 
 [[package]]
@@ -903,7 +903,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "1.0.7"
+version = "1.0.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "langchain-core", marker = "python_full_version >= '3.11'" },
@@ -913,14 +913,14 @@ dependencies = [
     { name = "pydantic", marker = "python_full_version >= '3.11'" },
     { name = "xxhash", marker = "python_full_version >= '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/72/5b/f72655717c04e33d3b62f21b166dc063d192b53980e9e3be0e2a117f1c9f/langgraph-1.0.7.tar.gz", hash = "sha256:0cfdfee51e6e8cfe503ecc7367c73933437c505b03fa10a85c710975c8182d9a", size = 497098, upload-time = "2026-01-22T16:57:47.303Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c2/9c/dac99ab1732e9fb2d3b673482ac28f02bee222c0319a3b8f8f73d90727e6/langgraph-1.0.6.tar.gz", hash = "sha256:dd8e754c76d34a07485308d7117221acf63990e7de8f46ddf5fe256b0a22e6c5", size = 495092, upload-time = "2026-01-12T20:33:30.778Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7e/0e/fe80144e3e4048e5d19ccdb91ac547c1a7dc3da8dbd1443e210048194c14/langgraph-1.0.7-py3-none-any.whl", hash = "sha256:9d68e8f8dd8f3de2fec45f9a06de05766d9b075b78fb03171779893b7a52c4d2", size = 157353, upload-time = "2026-01-22T16:57:45.997Z" },
+    { url = "https://files.pythonhosted.org/packages/10/45/9960747781416bed4e531ed0c6b2f2c739bc7b5397d8e92155463735a40e/langgraph-1.0.6-py3-none-any.whl", hash = "sha256:bcfce190974519c72e29f6e5b17f0023914fd6f936bfab8894083215b271eb89", size = 157356, upload-time = "2026-01-12T20:33:29.191Z" },
 ]
 
 [[package]]
 name = "langgraph-api"
-version = "0.7.8"
+version = "0.6.39"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cloudpickle", marker = "python_full_version >= '3.11'" },
@@ -951,9 +951,9 @@ dependencies = [
     { name = "uvicorn", marker = "python_full_version >= '3.11'" },
     { name = "watchfiles", marker = "python_full_version >= '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/42/52/649481d27ced9b982166b16dcbf57bf024712830186258bf1d876b7e9361/langgraph_api-0.7.8.tar.gz", hash = "sha256:a399474c783c399d9cc503f5cde34fe2886ba468b59c4c629cfd8e4f05926f59", size = 466146, upload-time = "2026-01-23T00:11:47.204Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b2/52/f7d25b475768e5e0748d4725c64909fc7e56a714e064976a32e7c68f6d9e/langgraph_api-0.6.39.tar.gz", hash = "sha256:052191ec9c49d76b0d5592a60fe6324eafde40e7a1afba65d1ce11a6a074674b", size = 447880, upload-time = "2026-01-16T19:54:07.475Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/23/a0/0bb4a47529f4e1a53b690897d756e3da0d3d1ce6990238f71085d1ce3a50/langgraph_api-0.7.8-py3-none-any.whl", hash = "sha256:2f0bfc13674bc3347321064268ecc951c35518de054eedcc9b426ecb269613ae", size = 369565, upload-time = "2026-01-23T00:11:45.729Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/a7/5ecccdc0e2a3900e08ac263b6e9cc6c5172c52ce3c4dda7750511e6fecc6/langgraph_api-0.6.39-py3-none-any.whl", hash = "sha256:50464c4983641c8b006a7f452583127ee2819806fc6d04844b08d5d3430df12e", size = 351764, upload-time = "2026-01-16T19:54:05.81Z" },
 ]
 
 [[package]]
@@ -1012,7 +1012,7 @@ test = [
 [package.metadata]
 requires-dist = [
     { name = "click", specifier = ">=8.1.7" },
-    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.8.0" },
+    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.7.0" },
     { name = "langgraph-runtime-inmem", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.7" },
     { name = "langgraph-sdk", marker = "python_full_version >= '3.11'", specifier = ">=0.1.0" },
     { name = "python-dotenv", marker = "extra == 'inmem'", specifier = ">=0.8.0" },
@@ -1046,20 +1046,20 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.7"
+version = "1.0.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "langchain-core", marker = "python_full_version >= '3.11'" },
     { name = "langgraph-checkpoint", marker = "python_full_version >= '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a7/59/711aecd1a50999456850dc328f3cad72b4372d8218838d8d5326f80cb76f/langgraph_prebuilt-1.0.7.tar.gz", hash = "sha256:38e097e06de810de4d0e028ffc0e432bb56d1fb417620fb1dfdc76c5e03e4bf9", size = 163692, upload-time = "2026-01-22T16:45:22.801Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3c/f5/8c75dace0d729561dce2966e630c5e312193df7e5df41a7e10cd7378c3a7/langgraph_prebuilt-1.0.6.tar.gz", hash = "sha256:c5f6cf0f5a0ac47643d2e26ae6faa38cb28885ecde67911190df9e30c4f72361", size = 162623, upload-time = "2026-01-12T20:31:28.425Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/47/49/5e37abb3f38a17a3487634abc2a5da87c208cc1d14577eb8d7184b25c886/langgraph_prebuilt-1.0.7-py3-none-any.whl", hash = "sha256:e14923516504405bb5edc3977085bc9622c35476b50c1808544490e13871fe7c", size = 35324, upload-time = "2026-01-22T16:45:21.784Z" },
+    { url = "https://files.pythonhosted.org/packages/26/6c/4045822b0630cfc0f8624c4499ceaf90644142143c063a8dc385a7424fc3/langgraph_prebuilt-1.0.6-py3-none-any.whl", hash = "sha256:9fdc35048ff4ac985a55bd2a019a86d45b8184551504aff6780d096c678b39ae", size = 35322, upload-time = "2026-01-12T20:31:27.161Z" },
 ]
 
 [[package]]
 name = "langgraph-runtime-inmem"
-version = "0.23.1"
+version = "0.22.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "blockbuster", marker = "python_full_version >= '3.11'" },
@@ -1069,9 +1069,9 @@ dependencies = [
     { name = "starlette", marker = "python_full_version >= '3.11'" },
     { name = "structlog", marker = "python_full_version >= '3.11'" },
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 ]
 
 [[package]]

libs/langgraph/langgraph/_internal/_queue.py

@@ -25,7 +25,7 @@ class AsyncQueue(asyncio.Queue):
             self._getters.append(getter)
             try:
                 await getter
-            except BaseException:
+            except:
                 getter.cancel()  # Just in case getter is not done yet.
                 try:
                     # Clean self._getters from canceled getters.

libs/langgraph/langgraph/graph/state.py

@@ -300,56 +300,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
         """Add a new node to the `StateGraph`, input schema is inferred as the state schema.
-
         Will take the name of the function/runnable as the node name.
-
-        Args:
-            node: The function or runnable this node will run.
-            defer: Whether to defer the execution of the node until the run is about to end.
-            metadata: The metadata associated with the node.
-            input_schema: The input schema for the node. (Default: the graph's state schema)
-            retry_policy: The retry policy for the node.
-
-                If a sequence is provided, the first matching policy will be applied.
-            cache_policy: The cache policy for the node.
-            destinations: Destinations that indicate where a node can route to.
-
-                Useful for edgeless graphs with nodes that return `Command` objects.
-
-                If a `dict` is provided, the keys will be used as the target node names and the values will be used as the labels for the edges.
-
-                If a `tuple` is provided, the values will be used as the target node names.
-
-                !!! warning
-
-                    This is only used for graph rendering and doesn't have any effect on the graph execution.
-
-        Example:
-            ```python
-            from typing_extensions import TypedDict
-
-            from langchain_core.runnables import RunnableConfig
-            from langgraph.graph import START, StateGraph
-
-
-            class State(TypedDict):
-                x: int
-
-
-            def my_node(state: State, config: RunnableConfig) -> State:
-                return {"x": state["x"] + 1}
-
-
-            builder = StateGraph(State)
-            builder.add_node(my_node)  # node name will be 'my_node'
-            builder.add_edge(START, "my_node")
-            graph = builder.compile()
-            graph.invoke({"x": 1})
-            # {'x': 2}
-            ```
-
-        Returns:
-            Self: The instance of the `StateGraph`, allowing for method chaining.
         """
         ...
 
@@ -366,61 +317,8 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the `StateGraph` where input schema is specified.
-
+        """Add a new node to the `StateGraph`, input schema is specified.
         Will take the name of the function/runnable as the node name.
-
-        Args:
-            node: The function or runnable this node will run.
-            defer: Whether to defer the execution of the node until the run is about to end.
-            metadata: The metadata associated with the node.
-            input_schema: The input schema for the node.
-            retry_policy: The retry policy for the node.
-
-                If a sequence is provided, the first matching policy will be applied.
-            cache_policy: The cache policy for the node.
-            destinations: Destinations that indicate where a node can route to.
-
-                Useful for edgeless graphs with nodes that return `Command` objects.
-
-                If a `dict` is provided, the keys will be used as the target node names and the values will be used as the labels for the edges.
-
-                If a `tuple` is provided, the values will be used as the target node names.
-
-                !!! warning
-
-                    This is only used for graph rendering and doesn't have any effect on the graph execution.
-
-        Example:
-            ```python
-            from typing_extensions import TypedDict
-
-            from langchain_core.runnables import RunnableConfig
-            from langgraph.graph import START, StateGraph
-
-
-            class State(TypedDict):
-                x: int
-
-
-            class NodeInput(TypedDict):
-                x: int
-
-
-            def my_node(state: NodeInput, config: RunnableConfig) -> State:
-                return {"x": state["x"] + 1}
-
-
-            builder = StateGraph(State)
-            builder.add_node(my_node, input_schema=NodeInput)  # node name will be 'my_node'
-            builder.add_edge(START, "my_node")
-            graph = builder.compile()
-            graph.invoke({"x": 1})
-            # {'x': 2}
-            ```
-
-        Returns:
-            Self: The instance of the `StateGraph`, allowing for method chaining.
         """
         ...
 
@@ -438,57 +336,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the `StateGraph`, input schema is inferred as the state schema.
-
-        Args:
-            node: The name of the node.
-            action: The function or runnable this node will run.
-            defer: Whether to defer the execution of the node until the run is about to end.
-            metadata: The metadata associated with the node.
-            input_schema: The input schema for the node. (Default: the graph's state schema)
-            retry_policy: The retry policy for the node.
-
-                If a sequence is provided, the first matching policy will be applied.
-            cache_policy: The cache policy for the node.
-            destinations: Destinations that indicate where a node can route to.
-
-                Useful for edgeless graphs with nodes that return `Command` objects.
-
-                If a `dict` is provided, the keys will be used as the target node names and the values will be used as the labels for the edges.
-
-                If a `tuple` is provided, the values will be used as the target node names.
-
-                !!! warning
-
-                    This is only used for graph rendering and doesn't have any effect on the graph execution.
-
-        Example:
-            ```python
-            from typing_extensions import TypedDict
-
-            from langchain_core.runnables import RunnableConfig
-            from langgraph.graph import START, StateGraph
-
-
-            class State(TypedDict):
-                x: int
-
-
-            def my_node(state: State, config: RunnableConfig) -> State:
-                return {"x": state["x"] + 1}
-
-
-            builder = StateGraph(State)
-            builder.add_node("my_fair_node", my_node)
-            builder.add_edge(START, "my_fair_node")
-            graph = builder.compile()
-            graph.invoke({"x": 1})
-            # {'x': 2}
-            ```
-
-        Returns:
-            Self: The instance of the `StateGraph`, allowing for method chaining.
-        """
+        """Add a new node to the `StateGraph`, input schema is inferred as the state schema."""
         ...
 
     @overload
@@ -505,65 +353,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the `StateGraph`, input schema is specified.
-
-        Args:
-            node: The function or runnable this node will run.
-
-                If a string is provided, it will be used as the node name, and action will be used as the function or runnable.
-            action: The action associated with the node.
-
-                Will be used as the node function or runnable if `node` is a string (node name).
-            defer: Whether to defer the execution of the node until the run is about to end.
-            metadata: The metadata associated with the node.
-            input_schema: The input schema for the node.
-            retry_policy: The retry policy for the node.
-
-                If a sequence is provided, the first matching policy will be applied.
-            cache_policy: The cache policy for the node.
-            destinations: Destinations that indicate where a node can route to.
-
-                Useful for edgeless graphs with nodes that return `Command` objects.
-
-                If a `dict` is provided, the keys will be used as the target node names and the values will be used as the labels for the edges.
-
-                If a `tuple` is provided, the values will be used as the target node names.
-
-                !!! warning
-
-                    This is only used for graph rendering and doesn't have any effect on the graph execution.
-
-        Example:
-            ```python
-            from typing_extensions import TypedDict
-
-            from langchain_core.runnables import RunnableConfig
-            from langgraph.graph import START, StateGraph
-
-
-            class State(TypedDict):
-                x: int
-
-
-            class NodeInput(TypedDict):
-                x: int
-
-
-            def my_node(state: NodeInput, config: RunnableConfig) -> State:
-                return {"x": state["x"] + 1}
-
-
-            builder = StateGraph(State)
-            builder.add_node("my_fair_node", my_node, input_schema=NodeInput)
-            builder.add_edge(START, "my_fair_node")
-            graph = builder.compile()
-            graph.invoke({"x": 1})
-            # {'x': 2}
-            ```
-
-        Returns:
-            Self: The instance of the `StateGraph`, allowing for method chaining.
-        """
+        """Add a new node to the `StateGraph`, input schema is specified."""
         ...
 
     def add_node(
@@ -586,7 +376,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
 
                 If a string is provided, it will be used as the node name, and action will be used as the function or runnable.
             action: The action associated with the node.
-
                 Will be used as the node function or runnable if `node` is a string (node name).
             defer: Whether to defer the execution of the node until the run is about to end.
             metadata: The metadata associated with the node.
@@ -603,7 +392,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
 
                 If a `tuple` is provided, the values will be used as the target node names.
 
-                !!! warning
+                !!! note
 
                     This is only used for graph rendering and doesn't have any effect on the graph execution.
 
@@ -1057,19 +846,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
                 If `None`, it may inherit the parent graph's checkpointer when used as a subgraph.
 
                 If `False`, it will not use or inherit any checkpointer.
-
-                **Important**: When a checkpointer is enabled, 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 key used to store and retrieve checkpoints. Use a
-                unique ID for independent runs, or reuse the same ID to accumulate state
-                across invocations (e.g., for conversation memory).
-
             interrupt_before: An optional list of node names to interrupt before.
             interrupt_after: An optional list of node names to interrupt after.
             debug: A flag indicating whether to enable debug mode.

libs/langgraph/langgraph/pregel/_messages.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 from collections.abc import AsyncIterator, Callable, Iterator, Sequence
-from dataclasses import fields, is_dataclass
 from typing import (
     Any,
     TypeVar,
@@ -12,7 +11,6 @@ from uuid import UUID, uuid4
 from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.messages import BaseMessage
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult
-from pydantic import BaseModel
 
 from langgraph._internal._constants import NS_SEP
 from langgraph.constants import TAG_HIDDEN, TAG_NOSTREAM
@@ -28,17 +26,6 @@ T = TypeVar("T")
 Meta = tuple[tuple[str, ...], dict[str, Any]]
 
 
-def _state_values(obj: Any) -> Sequence[Any]:
-    """Extract top-level field values from a state object (dict, BaseModel, or dataclass)."""
-    if isinstance(obj, dict):
-        return list(obj.values())
-    elif isinstance(obj, BaseModel):
-        return [getattr(obj, k) for k in type(obj).model_fields]
-    elif is_dataclass(obj) and not isinstance(obj, type):
-        return [getattr(obj, f.name) for f in fields(obj)]
-    return ()
-
-
 class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
     """A callback handler that implements stream_mode=messages.
 
@@ -103,14 +90,26 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
             for value in response:
                 if isinstance(value, BaseMessage):
                     self._emit(meta, value, dedupe=True)
-        else:
-            for value in _state_values(response):
+        elif isinstance(response, dict):
+            for value in response.values():
                 if isinstance(value, BaseMessage):
                     self._emit(meta, value, dedupe=True)
                 elif isinstance(value, Sequence):
                     for item in value:
                         if isinstance(item, BaseMessage):
                             self._emit(meta, item, dedupe=True)
+        elif hasattr(response, "__dir__") and callable(response.__dir__):
+            for key in dir(response):
+                try:
+                    value = getattr(response, key)
+                    if isinstance(value, BaseMessage):
+                        self._emit(meta, value, dedupe=True)
+                    elif isinstance(value, Sequence):
+                        for item in value:
+                            if isinstance(item, BaseMessage):
+                                self._emit(meta, item, dedupe=True)
+                except AttributeError:
+                    pass
 
     def tap_output_aiter(
         self, run_id: UUID, output: AsyncIterator[T]
@@ -204,15 +203,16 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
             if not self.subgraphs and len(ns) > 0:
                 return
             self.metadata[run_id] = (ns, metadata)
-            for value in _state_values(inputs):
-                if isinstance(value, BaseMessage):
-                    if value.id is not None:
-                        self.seen.add(value.id)
-                elif isinstance(value, Sequence) and not isinstance(value, str):
-                    for item in value:
-                        if isinstance(item, BaseMessage):
-                            if item.id is not None:
-                                self.seen.add(item.id)
+            if isinstance(inputs, dict):
+                for key, value in inputs.items():
+                    if isinstance(value, BaseMessage):
+                        if value.id is not None:
+                            self.seen.add(value.id)
+                    elif isinstance(value, Sequence) and not isinstance(value, str):
+                        for item in value:
+                            if isinstance(item, BaseMessage):
+                                if item.id is not None:
+                                    self.seen.add(item.id)
 
     def on_chain_end(
         self,

libs/langgraph/langgraph/pregel/_runner.py

@@ -565,7 +565,7 @@ def _call(
         if fut := next(
             (
                 f
-                for f, t in list(futures().items())  # type: ignore[union-attr]
+                for f, t in futures().items()  # type: ignore[union-attr]
                 if t is not None and t == next_task.id
             ),
             None,
@@ -708,7 +708,7 @@ async def _acall_impl(
             if fut := next(
                 (
                     f
-                    for f, t in list(futures().items())  # type: ignore[union-attr]
+                    for f, t in futures().items()  # type: ignore[union-attr]
                     if t is not None and t == next_task.id
                 ),
                 None,