comments

.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:
 

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/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/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 = [
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+    { 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 = [
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+    { 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'" },
 ]
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+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" }
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+    { 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 = [
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 ]
 
 [[package]]

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/runtime.py

@@ -28,21 +28,6 @@ class _RuntimeOverrides(TypedDict, Generic[ContextT], total=False):
 class Runtime(Generic[ContextT]):
     """Convenience class that bundles run-scoped context and other runtime utilities.
 
-    This class is injected into graph nodes and middleware. It provides access to
-    `context`, `store`, `stream_writer`, and `previous`.
-
-    !!! note "Accessing `config`"
-
-        `Runtime` does not include `config`. To access `RunnableConfig`, you can inject
-        it directly by adding a `config: RunnableConfig` parameter to your node function
-        (recommended), or use `get_config()` from `langgraph.config`.
-
-    !!! note
-        `ToolRuntime` (from `langgraph.prebuilt`) is a subclass that provides similar
-        functionality but is designed specifically for tools. It shares `context`, `store`,
-        and `stream_writer` with `Runtime`, and adds tool-specific attributes like `config`,
-        `state`, and `tool_call_id`.
-
     !!! version-added "Added in version v0.6.0"
 
     Example:

libs/langgraph/langgraph/types.py

@@ -61,22 +61,18 @@ __all__ = (
 
 Durability = Literal["sync", "async", "exit"]
 """Durability mode for the graph execution.
-
-- `'sync'`: Changes are persisted synchronously before the next step starts.
-- `'async'`: Changes are persisted asynchronously while the next step executes.
-- `'exit'`: Changes are persisted only when the graph exits.
-"""
+- `"sync"`: Changes are persisted synchronously before the next step starts.
+- `"async"`: Changes are persisted asynchronously while the next step executes.
+- `"exit"`: Changes are persisted only when the graph exits."""
 
 All = Literal["*"]
 """Special value to indicate that graph should interrupt on all nodes."""
 
 Checkpointer = None | bool | BaseCheckpointSaver
 """Type of the checkpointer to use for a subgraph.
-
-- `True` enables persistent checkpointing for this subgraph.
-- `False` disables checkpointing, even if the parent graph has a checkpointer.
-- `None` inherits checkpointer from the parent graph.
-"""
+- True enables persistent checkpointing for this subgraph.
+- False disables checkpointing, even if the parent graph has a checkpointer.
+- None inherits checkpointer from the parent graph."""
 
 
 def ensure_valid_checkpointer(checkpointer: Checkpointer) -> Checkpointer:

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "1.0.7"
+version = "1.0.6"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -27,7 +27,7 @@ dependencies = [
     "langchain-core>=0.1",
     "langgraph-checkpoint>=2.1.0,<5.0.0",
     "langgraph-sdk>=0.3.0,<0.4.0",
-    "langgraph-prebuilt>=1.0.7,<1.1.0",
+    "langgraph-prebuilt>=1.0.2,<1.1.0",
     "xxhash>=3.5.0",
     "pydantic>=2.7.4",
 ]

libs/langgraph/uv.lock

@@ -192,7 +192,7 @@ name = "blockbuster"
 version = "1.5.26"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "forbiddenfruit", marker = "python_full_version >= '3.11' and python_full_version < '3.14' and implementation_name == 'cpython'" },
+    { name = "forbiddenfruit", marker = "python_full_version >= '3.11' and implementation_name == 'cpython'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/55/e0/dcbab602790a576b0b94108c07e2c048e5897df7cc83722a89582d733987/blockbuster-1.5.26.tar.gz", hash = "sha256:cc3ce8c70fa852a97ee3411155f31e4ad2665cd1c6c7d2f8bb1851dab61dc629", size = 36085, upload-time = "2025-12-05T10:43:47.735Z" }
 wheels = [
@@ -384,7 +384,7 @@ name = "click"
 version = "8.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "python_full_version < '3.14' and sys_platform == 'win32'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
 wheels = [
@@ -527,7 +527,7 @@ name = "cryptography"
 version = "46.0.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cffi", marker = "python_full_version >= '3.11' and python_full_version < '3.14' and platform_python_implementation != 'PyPy'" },
+    { name = "cffi", marker = "python_full_version >= '3.11' and platform_python_implementation != 'PyPy'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/9f/33/c00162f49c0e2fe8064a62cb92b93e50c74a72bc370ab92f86112b33ff62/cryptography-46.0.3.tar.gz", hash = "sha256:a8b17438104fed022ce745b362294d9ce35b4c2e45c1d958ad4a4b019285f4a1", size = 749258, upload-time = "2025-10-15T23:18:31.74Z" }
 wheels = [
@@ -692,7 +692,7 @@ name = "googleapis-common-protos"
 version = "1.72.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "protobuf", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
+    { name = "protobuf", marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/e5/7b/adfd75544c415c487b33061fe7ae526165241c1ea133f9a9125a56b39fd8/googleapis_common_protos-1.72.0.tar.gz", hash = "sha256:e55a601c1b32b52d7a3e65f43563e2aa61bcd737998ee672ac9b951cd49319f5", size = 147433, upload-time = "2025-11-06T18:29:24.087Z" }
 wheels = [
@@ -704,7 +704,7 @@ name = "grpcio"
 version = "1.76.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
+    { name = "typing-extensions", marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b6/e0/318c1ce3ae5a17894d5791e87aea147587c9e702f24122cc7a5c8bbaeeb1/grpcio-1.76.0.tar.gz", hash = "sha256:7be78388d6da1a25c0d5ec506523db58b18be22d9c37d8d3a32c08be4987bd73", size = 12785182, upload-time = "2025-10-21T16:23:12.106Z" }
 wheels = [
@@ -765,8 +765,8 @@ name = "grpcio-health-checking"
 version = "1.76.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "grpcio", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
-    { name = "protobuf", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
+    { name = "grpcio", marker = "python_full_version >= '3.11'" },
+    { name = "protobuf", marker = "python_full_version >= '3.11'" },
 ]
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@@ -1518,33 +1518,33 @@ name = "langgraph-api"
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+    { name = "grpcio-health-checking", marker = "python_full_version >= '3.11'" },
+    { name = "grpcio-tools", marker = "python_full_version >= '3.11'" },
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+    { name = "jsonschema-rs", marker = "python_full_version >= '3.11'" },
+    { name = "langchain-core", marker = "python_full_version >= '3.11'" },
+    { name = "langgraph", marker = "python_full_version >= '3.11'" },
+    { name = "langgraph-checkpoint", marker = "python_full_version >= '3.11'" },
+    { name = "langgraph-runtime-inmem", marker = "python_full_version >= '3.11'" },
+    { name = "langgraph-sdk", marker = "python_full_version >= '3.11'" },
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+    { name = "opentelemetry-api", marker = "python_full_version >= '3.11'" },
+    { name = "opentelemetry-exporter-otlp-proto-http", marker = "python_full_version >= '3.11'" },
+    { name = "opentelemetry-sdk", marker = "python_full_version >= '3.11'" },
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+    { name = "protobuf", marker = "python_full_version >= '3.11'" },
+    { name = "pyjwt", marker = "python_full_version >= '3.11'" },
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+    { name = "truststore", marker = "python_full_version >= '3.11'" },
+    { name = "uuid-utils", marker = "python_full_version >= '3.11'" },
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@@ -1600,7 +1600,7 @@ test = [
 
 [[package]]
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@@ -1692,21 +1692,21 @@ test = [
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-    { name = "langgraph-sdk", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
+    { name = "click" },
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-    { name = "python-dotenv", marker = "python_full_version < '3.14'" },
+    { name = "langgraph-api", marker = "python_full_version >= '3.11'" },
+    { name = "langgraph-runtime-inmem", marker = "python_full_version >= '3.11'" },
+    { name = "python-dotenv" },
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-    { name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.8.0" },
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@@ -1740,7 +1740,7 @@ test = [
 
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-version = "1.0.7"
+version = "1.0.6"
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@@ -1794,12 +1794,12 @@ name = "langgraph-runtime-inmem"
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-    { name = "structlog", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
+    { name = "blockbuster", marker = "python_full_version >= '3.11'" },
+    { name = "langgraph", marker = "python_full_version >= '3.11'" },
+    { name = "langgraph-checkpoint", marker = "python_full_version >= '3.11'" },
+    { name = "sse-starlette", marker = "python_full_version >= '3.11'" },
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@@ -1868,9 +1868,9 @@ wheels = [
 
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-    { name = "opentelemetry-sdk", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
+    { name = "opentelemetry-api", marker = "python_full_version >= '3.11'" },
+    { name = "opentelemetry-exporter-otlp-proto-http", marker = "python_full_version >= '3.11'" },
+    { name = "opentelemetry-sdk", marker = "python_full_version >= '3.11'" },
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+    { name = "h11", marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c3/d1/8f3c683c9561a4e6689dd3b1d345c815f10f86acd044ee1fb9a4dcd0b8c5/uvicorn-0.40.0.tar.gz", hash = "sha256:839676675e87e73694518b5574fd0f24c9d97b46bea16df7b8c05ea1a51071ea", size = 81761, upload-time = "2025-12-21T14:16:22.45Z" }
 wheels = [
@@ -3823,7 +3823,7 @@ name = "watchfiles"
 version = "1.1.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "anyio", marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
+    { name = "anyio", marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c2/c9/8869df9b2a2d6c59d79220a4db37679e74f807c559ffe5265e08b227a210/watchfiles-1.1.1.tar.gz", hash = "sha256:a173cb5c16c4f40ab19cecf48a534c409f7ea983ab8fed0741304a1c0a31b3f2", size = 94440, upload-time = "2025-10-14T15:06:21.08Z" }
 wheels = [

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -287,7 +287,10 @@ def create_react_agent(
         [StateSchema, Runtime[ContextT]],
         Awaitable[Runnable[LanguageModelInput, BaseMessage]],
     ],
-    tools: Sequence[BaseTool | Callable | dict[str, Any]] | ToolNode,
+    tools: Sequence[BaseTool | Callable | dict[str, Any]]
+    | Callable[[], Sequence[BaseTool]]
+    | Callable[[], Awaitable[Sequence[BaseTool]]]
+    | ToolNode,
     *,
     prompt: Prompt | None = None,
     response_format: StructuredResponseSchema
@@ -355,8 +358,9 @@ def create_react_agent(
                 `.bind_tools()` and support required functionality. Bound tools
                 must be a subset of those specified in the `tools` parameter.
 
-        tools: A list of tools or a `ToolNode` instance.
+        tools: A list of tools, a `ToolNode` instance, or a callable that returns tools.
             If an empty list is provided, the agent will consist of a single LLM node without tool calling.
+            Callable tools providers (both sync and async) enable dynamic tool selection at runtime.
         prompt: An optional prompt for the LLM. Can take a few different forms:
 
             - `str`: This is converted to a `SystemMessage` and added to the beginning of the list of messages in `state["messages"]`.
@@ -546,18 +550,30 @@ def create_react_agent(
         )
 
     llm_builtin_tools: list[dict] = []
+    is_dynamic_tools = callable(tools) and not isinstance(tools, ToolNode)
     if isinstance(tools, ToolNode):
         tool_classes = list(tools.tools_by_name.values())
         tool_node = tools
+    elif is_dynamic_tools:
+        # Dynamic tools provider - pass directly to ToolNode
+        tool_node = ToolNode(
+            cast(
+                "Callable[[], Sequence[BaseTool]] | Callable[[], Awaitable[Sequence[BaseTool]]]",
+                tools,
+            )
+        )
+        # For dynamic tools, we can't know the tools at compile time
+        tool_classes = []
     else:
-        llm_builtin_tools = [t for t in tools if isinstance(t, dict)]
-        tool_node = ToolNode([t for t in tools if not isinstance(t, dict)])
+        tools_seq = cast("Sequence[BaseTool | Callable | dict[str, Any]]", tools)
+        llm_builtin_tools = [t for t in tools_seq if isinstance(t, dict)]
+        tool_node = ToolNode([t for t in tools_seq if not isinstance(t, dict)])
         tool_classes = list(tool_node.tools_by_name.values())
 
     is_dynamic_model = not isinstance(model, (str, Runnable)) and callable(model)
     is_async_dynamic_model = is_dynamic_model and inspect.iscoroutinefunction(model)
 
-    tool_calling_enabled = len(tool_classes) > 0
+    tool_calling_enabled = len(tool_classes) > 0 or is_dynamic_tools
 
     if not is_dynamic_model:
         if isinstance(model, str):

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -42,7 +42,7 @@ from __future__ import annotations
 import asyncio
 import inspect
 import json
-from collections.abc import Awaitable, Callable
+from collections.abc import Awaitable, Callable, Sequence
 from copy import copy, deepcopy
 from dataclasses import dataclass, replace
 from types import UnionType
@@ -90,8 +90,6 @@ from pydantic import BaseModel, ValidationError
 from typing_extensions import TypeVar, Unpack
 
 if TYPE_CHECKING:
-    from collections.abc import Sequence
-
     from langgraph.runtime import Runtime
     from pydantic_core import ErrorDetails
 
@@ -121,7 +119,6 @@ class _ToolCallRequestOverrides(TypedDict, total=False):
     """Possible overrides for ToolCallRequest.override() method."""
 
     tool_call: ToolCall
-    tool: BaseTool
     state: Any
 
 
@@ -654,9 +651,25 @@ class ToolNode(RunnableCallable):
         - `Command` can update state, trigger navigation, or send messages
 
     Args:
-        tools: A sequence of tools that can be invoked by this node.
+        tools: Tools that can be invoked by this node. Can be either:
 
-            Supports:
+            - **A sequence of tools**: A list/tuple of tools (static)
+            - **A callable**: A function that returns a sequence of tools (dynamic).
+              The callable is invoked on every `invoke()` or `ainvoke()` call,
+              allowing the available tools to change between invocations.
+
+              **Invocation semantics for dynamic tools callables:**
+
+              - Called once at the start of each ToolNode invocation
+              - Should return a consistent set of tools for a given invocation
+                (i.e., idempotent within a single invocation)
+              - Stochastic behavior (returning different tools across invocations)
+                is supported but be aware the model may reference tools that are
+                no longer available
+              - Must return `BaseTool` instances (not plain callables) to avoid
+                expensive introspection on every invocation
+
+            Each tool in the sequence supports:
 
             - **BaseTool instances**: Tools with schemas and metadata
             - **Plain functions**: Automatically converted to tools with inferred schemas
@@ -736,7 +749,9 @@ class ToolNode(RunnableCallable):
 
     def __init__(
         self,
-        tools: Sequence[BaseTool | Callable],
+        tools: Sequence[BaseTool | Callable]
+        | Callable[[], Sequence[BaseTool]]
+        | Callable[[], Awaitable[Sequence[BaseTool]]],
         *,
         name: str = "tools",
         tags: list[str] | None = None,
@@ -752,7 +767,8 @@ class ToolNode(RunnableCallable):
         """Initialize `ToolNode` with tools and configuration.
 
         Args:
-            tools: Sequence of tools to make available for execution.
+            tools: Tools to make available for execution. Can be a sequence of tools
+                or a callable that returns a sequence of tools (for dynamic tools).
             name: Node name for graph identification.
             tags: Optional metadata tags.
             handle_tool_errors: Error handling configuration.
@@ -764,25 +780,117 @@ class ToolNode(RunnableCallable):
                 If not provided, falls back to wrap_tool_call for async execution.
         """
         super().__init__(self._func, self._afunc, name=name, tags=tags, trace=False)
-        self._tools_by_name: dict[str, BaseTool] = {}
-        self._injected_args: dict[str, _InjectedArgs] = {}
         self._handle_tool_errors = handle_tool_errors
         self._messages_key = messages_key
         self._wrap_tool_call = wrap_tool_call
         self._awrap_tool_call = awrap_tool_call
+
+        self._tools_provider: Callable[[], Sequence[BaseTool]] | None = None
+        self._async_tools_provider: (
+            Callable[[], Awaitable[Sequence[BaseTool]]] | None
+        ) = None
+        self._tools_by_name: dict[str, BaseTool] = {}
+
+        if callable(tools) and not isinstance(tools, (list, tuple)):
+            # It's a dynamic tools provider
+            if inspect.iscoroutinefunction(tools):
+                self._async_tools_provider = cast(
+                    "Callable[[], Awaitable[Sequence[BaseTool]]]", tools
+                )
+            else:
+                self._tools_provider = cast("Callable[[], Sequence[BaseTool]]", tools)
+        else:
+            # It's a sequence of tools - process them statically
+            self._tools_by_name = self._build_tools_mapping(tools)
+
+    def _build_tools_mapping(
+        self,
+        tools: Sequence[BaseTool | Callable],
+        *,
+        convert_callables: bool = True,
+    ) -> dict[str, BaseTool]:
+        """Build tools_by_name mapping from a sequence of tools.
+
+        Args:
+            tools: Sequence of tools to process.
+            convert_callables: Whether to convert plain callables to BaseTools.
+                Set to False when processing tools from a dynamic provider
+                (which should already be BaseTools).
+
+        Returns:
+            Dictionary mapping tool names to BaseTool instances.
+        """
+        tools_by_name: dict[str, BaseTool] = {}
         for tool in tools:
             if not isinstance(tool, BaseTool):
-                tool_ = create_tool(cast("type[BaseTool]", tool))
+                if convert_callables:
+                    tool_ = create_tool(cast("type[BaseTool]", tool))
+                else:
+                    # Dynamic tools providers must return BaseTool instances, not plain
+                    # callables. Converting callables to tools requires calling
+                    # create_tool() which performs introspection. Doing this on every
+                    # invocation would be expensive and could have unexpected side effects.
+                    # Users should convert their callables to tools once upfront.
+                    msg = (
+                        f"Dynamic tools provider must return BaseTool instances, "
+                        f"got {type(tool).__name__}"
+                    )
+                    raise TypeError(msg)
             else:
                 tool_ = tool
-            self._tools_by_name[tool_.name] = tool_
-            # Build injected args mapping once during initialization in a single pass
-            self._injected_args[tool_.name] = _get_all_injected_args(tool_)
+            tools_by_name[tool_.name] = tool_
+        return tools_by_name
+
+    def _get_tools(self) -> dict[str, BaseTool]:
+        """Get the current tools mapping.
+
+        If a tools provider was configured, calls it to get the current tools.
+        Otherwise, returns the statically configured tools.
+
+        Returns:
+            Dictionary mapping tool names to BaseTool instances.
+
+        Raises:
+            TypeError: If an async tools provider is used in synchronous context.
+        """
+        if self._async_tools_provider is not None:
+            msg = (
+                "Cannot use async tools provider in synchronous context. "
+                "Use ainvoke() instead of invoke()."
+            )
+            raise TypeError(msg)
+        if self._tools_provider is not None:
+            tools = self._tools_provider()
+            return self._build_tools_mapping(tools, convert_callables=False)
+        return self._tools_by_name
+
+    async def _aget_tools(self) -> dict[str, BaseTool]:
+        """Get the current tools mapping asynchronously.
+
+        If an async or sync tools provider was configured, calls it to get
+        the current tools. Otherwise, returns the statically configured tools.
+
+        Returns:
+            Dictionary mapping tool names to BaseTool instances.
+        """
+        if self._async_tools_provider is not None:
+            tools = await self._async_tools_provider()
+            return self._build_tools_mapping(tools, convert_callables=False)
+        if self._tools_provider is not None:
+            tools = self._tools_provider()
+            return self._build_tools_mapping(tools, convert_callables=False)
+        return self._tools_by_name
 
     @property
     def tools_by_name(self) -> dict[str, BaseTool]:
-        """Mapping from tool name to BaseTool instance."""
-        return self._tools_by_name
+        """Mapping from tool name to BaseTool instance.
+
+        Note: If a sync dynamic tools provider was configured, this property
+        calls the provider to get the current tools on each access.
+        If an async tools provider was configured, this property will raise
+        a TypeError - use ainvoke() instead.
+        """
+        return self._get_tools()
 
     def _func(
         self,
@@ -793,6 +901,9 @@ class ToolNode(RunnableCallable):
         tool_calls, input_type = self._parse_input(input)
         config_list = get_config_list(config, len(tool_calls))
 
+        # Get tools once at the start of invocation (supports dynamic tools)
+        tools_by_name = self._get_tools()
+
         # Construct ToolRuntime instances at the top level for each tool call
         tool_runtimes = []
         for call, cfg in zip(tool_calls, config_list, strict=False):
@@ -807,11 +918,17 @@ class ToolNode(RunnableCallable):
             )
             tool_runtimes.append(tool_runtime)
 
-        # Pass original tool calls without injection
+        def run_one_with_tools(
+            call: ToolCall,
+            input_type: Literal["list", "dict", "tool_calls"],
+            tool_runtime: ToolRuntime,
+        ) -> ToolMessage | Command:
+            return self._run_one(call, input_type, tool_runtime, tools_by_name)
+
         input_types = [input_type] * len(tool_calls)
         with get_executor_for_config(config) as executor:
             outputs = list(
-                executor.map(self._run_one, tool_calls, input_types, tool_runtimes)
+                executor.map(run_one_with_tools, tool_calls, input_types, tool_runtimes)
             )
 
         return self._combine_tool_outputs(outputs, input_type)
@@ -825,6 +942,9 @@ class ToolNode(RunnableCallable):
         tool_calls, input_type = self._parse_input(input)
         config_list = get_config_list(config, len(tool_calls))
 
+        # Get tools once at the start of invocation (supports dynamic tools)
+        tools_by_name = await self._aget_tools()
+
         # Construct ToolRuntime instances at the top level for each tool call
         tool_runtimes = []
         for call, cfg in zip(tool_calls, config_list, strict=False):
@@ -839,10 +959,10 @@ class ToolNode(RunnableCallable):
             )
             tool_runtimes.append(tool_runtime)
 
-        # Pass original tool calls without injection
-        coros = []
-        for call, tool_runtime in zip(tool_calls, tool_runtimes, strict=False):
-            coros.append(self._arun_one(call, input_type, tool_runtime))  # type: ignore[arg-type]
+        coros = [
+            self._arun_one(call, input_type, tool_runtime, tools_by_name)  # type: ignore[arg-type]
+            for call, tool_runtime in zip(tool_calls, tool_runtimes, strict=False)
+        ]
         outputs = await asyncio.gather(*coros)
 
         return self._combine_tool_outputs(outputs, input_type)
@@ -896,13 +1016,17 @@ class ToolNode(RunnableCallable):
         request: ToolCallRequest,
         input_type: Literal["list", "dict", "tool_calls"],
         config: RunnableConfig,
+        tools_by_name: dict[str, BaseTool],
     ) -> ToolMessage | Command:
         """Execute tool call with configured error handling.
 
         Args:
-            request: Tool execution request.
+            request: Tool execution request (includes the specific tool to execute).
             input_type: Input format.
             config: Runnable configuration.
+            tools_by_name: Mapping from tool name to BaseTool. Used only for
+                validation error messages when the requested tool doesn't exist,
+                to list available tools in the error response.
 
         Returns:
             ToolMessage or Command.
@@ -915,14 +1039,14 @@ class ToolNode(RunnableCallable):
 
         # Validate tool exists when we actually need to execute it
         if tool is None:
-            if invalid_tool_message := self._validate_tool_call(call):
+            if invalid_tool_message := self._validate_tool_call(call, tools_by_name):
                 return invalid_tool_message
             # This should never happen if validation works correctly
             msg = f"Tool {call['name']} is not registered with ToolNode"
             raise TypeError(msg)
 
         # Inject state, store, and runtime right before invocation
-        injected_call = self._inject_tool_args(call, request.runtime)
+        injected_call = self._inject_tool_args(call, request.runtime, tool)
         call_args = {**injected_call, "type": "tool_call"}
 
         try:
@@ -930,7 +1054,7 @@ class ToolNode(RunnableCallable):
                 response = tool.invoke(call_args, config)
             except ValidationError as exc:
                 # Filter out errors for injected arguments
-                injected = self._injected_args.get(call["name"])
+                injected = _get_all_injected_args(tool)
                 filtered_errors = _filter_validation_errors(exc, injected)
                 # Use original call["args"] without injected values for error reporting
                 raise ToolInvocationError(
@@ -994,6 +1118,7 @@ class ToolNode(RunnableCallable):
         call: ToolCall,
         input_type: Literal["list", "dict", "tool_calls"],
         tool_runtime: ToolRuntime,
+        tools_by_name: dict[str, BaseTool],
     ) -> ToolMessage | Command:
         """Execute single tool call with wrap_tool_call wrapper if configured.
 
@@ -1001,13 +1126,14 @@ class ToolNode(RunnableCallable):
             call: Tool call dict.
             input_type: Input format.
             tool_runtime: Tool runtime.
+            tools_by_name: Mapping from tool name to BaseTool.
 
         Returns:
             ToolMessage or Command.
         """
         # Validation is deferred to _execute_tool_sync to allow interceptors
         # to short-circuit requests for unregistered tools
-        tool = self.tools_by_name.get(call["name"])
+        tool = tools_by_name.get(call["name"])
 
         # Create the tool request with state and runtime
         tool_request = ToolCallRequest(
@@ -1021,12 +1147,14 @@ class ToolNode(RunnableCallable):
 
         if self._wrap_tool_call is None:
             # No wrapper - execute directly
-            return self._execute_tool_sync(tool_request, input_type, config)
+            return self._execute_tool_sync(
+                tool_request, input_type, config, tools_by_name
+            )
 
         # Define execute callable that can be called multiple times
         def execute(req: ToolCallRequest) -> ToolMessage | Command:
             """Execute tool with given request. Can be called multiple times."""
-            return self._execute_tool_sync(req, input_type, config)
+            return self._execute_tool_sync(req, input_type, config, tools_by_name)
 
         # Call wrapper with request and execute callable
         try:
@@ -1049,13 +1177,17 @@ class ToolNode(RunnableCallable):
         request: ToolCallRequest,
         input_type: Literal["list", "dict", "tool_calls"],
         config: RunnableConfig,
+        tools_by_name: dict[str, BaseTool],
     ) -> ToolMessage | Command:
         """Execute tool call asynchronously with configured error handling.
 
         Args:
-            request: Tool execution request.
+            request: Tool execution request (includes the specific tool to execute).
             input_type: Input format.
             config: Runnable configuration.
+            tools_by_name: Mapping from tool name to BaseTool. Used only for
+                validation error messages when the requested tool doesn't exist,
+                to list available tools in the error response.
 
         Returns:
             ToolMessage or Command.
@@ -1068,14 +1200,14 @@ class ToolNode(RunnableCallable):
 
         # Validate tool exists when we actually need to execute it
         if tool is None:
-            if invalid_tool_message := self._validate_tool_call(call):
+            if invalid_tool_message := self._validate_tool_call(call, tools_by_name):
                 return invalid_tool_message
             # This should never happen if validation works correctly
             msg = f"Tool {call['name']} is not registered with ToolNode"
             raise TypeError(msg)
 
         # Inject state, store, and runtime right before invocation
-        injected_call = self._inject_tool_args(call, request.runtime)
+        injected_call = self._inject_tool_args(call, request.runtime, tool)
         call_args = {**injected_call, "type": "tool_call"}
 
         try:
@@ -1083,7 +1215,7 @@ class ToolNode(RunnableCallable):
                 response = await tool.ainvoke(call_args, config)
             except ValidationError as exc:
                 # Filter out errors for injected arguments
-                injected = self._injected_args.get(call["name"])
+                injected = _get_all_injected_args(tool)
                 filtered_errors = _filter_validation_errors(exc, injected)
                 # Use original call["args"] without injected values for error reporting
                 raise ToolInvocationError(
@@ -1147,6 +1279,7 @@ class ToolNode(RunnableCallable):
         call: ToolCall,
         input_type: Literal["list", "dict", "tool_calls"],
         tool_runtime: ToolRuntime,
+        tools_by_name: dict[str, BaseTool],
     ) -> ToolMessage | Command:
         """Execute single tool call asynchronously with awrap_tool_call wrapper if configured.
 
@@ -1154,13 +1287,14 @@ class ToolNode(RunnableCallable):
             call: Tool call dict.
             input_type: Input format.
             tool_runtime: Tool runtime.
+            tools_by_name: Mapping from tool name to BaseTool.
 
         Returns:
             ToolMessage or Command.
         """
         # Validation is deferred to _execute_tool_async to allow interceptors
         # to short-circuit requests for unregistered tools
-        tool = self.tools_by_name.get(call["name"])
+        tool = tools_by_name.get(call["name"])
 
         # Create the tool request with state and runtime
         tool_request = ToolCallRequest(
@@ -1174,16 +1308,20 @@ class ToolNode(RunnableCallable):
 
         if self._awrap_tool_call is None and self._wrap_tool_call is None:
             # No wrapper - execute directly
-            return await self._execute_tool_async(tool_request, input_type, config)
+            return await self._execute_tool_async(
+                tool_request, input_type, config, tools_by_name
+            )
 
         # Define async execute callable that can be called multiple times
         async def execute(req: ToolCallRequest) -> ToolMessage | Command:
             """Execute tool with given request. Can be called multiple times."""
-            return await self._execute_tool_async(req, input_type, config)
+            return await self._execute_tool_async(
+                req, input_type, config, tools_by_name
+            )
 
         def _sync_execute(req: ToolCallRequest) -> ToolMessage | Command:
             """Sync execute fallback for sync wrapper."""
-            return self._execute_tool_sync(req, input_type, config)
+            return self._execute_tool_sync(req, input_type, config, tools_by_name)
 
         # Call wrapper with request and execute callable
         try:
@@ -1249,10 +1387,12 @@ class ToolNode(RunnableCallable):
         tool_calls = list(latest_ai_message.tool_calls)
         return tool_calls, input_type
 
-    def _validate_tool_call(self, call: ToolCall) -> ToolMessage | None:
+    def _validate_tool_call(
+        self, call: ToolCall, tools_by_name: dict[str, BaseTool]
+    ) -> ToolMessage | None:
         requested_tool = call["name"]
-        if requested_tool not in self.tools_by_name:
-            all_tool_names = list(self.tools_by_name.keys())
+        if requested_tool not in tools_by_name:
+            all_tool_names = list(tools_by_name.keys())
             content = INVALID_TOOL_NAME_ERROR_TEMPLATE.format(
                 requested_tool=requested_tool,
                 available_tools=", ".join(all_tool_names),
@@ -1281,6 +1421,7 @@ class ToolNode(RunnableCallable):
         self,
         tool_call: ToolCall,
         tool_runtime: ToolRuntime,
+        tool: BaseTool,
     ) -> ToolCall:
         """Inject graph state, store, and runtime into tool call arguments.
 
@@ -1299,6 +1440,7 @@ class ToolNode(RunnableCallable):
                 Must contain 'name', 'args', 'id', and 'type' fields.
             tool_runtime: The ToolRuntime instance containing all runtime context
                 (state, config, store, context, stream_writer) to inject into tools.
+            tool: The BaseTool instance to inject arguments for.
 
         Returns:
             A new ToolCall dictionary with the same structure as the input but with
@@ -1312,11 +1454,8 @@ class ToolNode(RunnableCallable):
             This method is called automatically during tool execution. It should not
             be called from outside the `ToolNode`.
         """
-        if tool_call["name"] not in self.tools_by_name:
-            return tool_call
-
-        injected = self._injected_args.get(tool_call["name"])
-        if not injected:
+        injected = _get_all_injected_args(tool)
+        if not injected.state and not injected.store and not injected.runtime:
             return tool_call
 
         tool_call_copy: ToolCall = copy(tool_call)
@@ -1531,23 +1670,16 @@ def tools_condition(
 class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
     """Runtime context automatically injected into tools.
 
-    !!! note
-
-        This is distinct from `Runtime` (from `langgraph.runtime`), which is injected
-        into graph nodes and middleware. `ToolRuntime` includes additional tool-specific
-        attributes like `config`, `state`, and `tool_call_id` that `Runtime` does not
-        have.
-
-    When a tool function has a parameter named `runtime` with type hint
+    When a tool function has a parameter named `tool_runtime` with type hint
     `ToolRuntime`, the tool execution system will automatically inject an instance
     containing:
 
     - `state`: The current graph state
     - `tool_call_id`: The ID of the current tool call
     - `config`: `RunnableConfig` for the current execution
-    - `context`: Runtime context (shared with `Runtime`)
-    - `store`: `BaseStore` instance for persistent storage (shared with `Runtime`)
-    - `stream_writer`: `StreamWriter` for streaming output (shared with `Runtime`)
+    - `context`: Runtime context (from langgraph `Runtime`)
+    - `store`: `BaseStore` instance for persistent storage (from langgraph `Runtime`)
+    - `stream_writer`: `StreamWriter` for streaming output (from langgraph `Runtime`)
 
     No `Annotated` wrapper is needed - just use `runtime: ToolRuntime`
     as a parameter.

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "1.0.7"
+version = "1.0.6"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.10"

libs/prebuilt/tests/test_tool_node.py

@@ -1902,3 +1902,356 @@ async def test_tool_node_tool_runtime_generic() -> None:
     assert tool_message.type == "tool"
     assert tool_message.content == "test_info"
     assert tool_message.tool_call_id == "call_1"
+
+
+async def test_tool_node_dynamic_tools() -> None:
+    """Test ToolNode with a dynamic tools provider callable."""
+
+    @dec_tool
+    def add(a: int, b: int) -> int:
+        """Add two numbers."""
+        return a + b
+
+    @dec_tool
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        return a * b
+
+    @dec_tool
+    def subtract(a: int, b: int) -> int:
+        """Subtract two numbers."""
+        return a - b
+
+    # Track which tools are available
+    available_tools: list[BaseTool] = [add, multiply]
+
+    def get_tools() -> list[BaseTool]:
+        return available_tools
+
+    # Create ToolNode with dynamic tools provider
+    tool_node = ToolNode(get_tools)
+
+    # Test that tools_by_name returns the current tools
+    assert set(tool_node.tools_by_name.keys()) == {"add", "multiply"}
+
+    # Test invoking a tool
+    result = tool_node.invoke(
+        {
+            "messages": [
+                AIMessage(
+                    "test",
+                    tool_calls=[
+                        {"name": "add", "args": {"a": 2, "b": 3}, "id": "call_1"}
+                    ],
+                )
+            ]
+        },
+        config=_create_config_with_runtime(),
+    )
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "5"
+
+    # Test invoking another tool
+    result = await tool_node.ainvoke(
+        {
+            "messages": [
+                AIMessage(
+                    "test",
+                    tool_calls=[
+                        {"name": "multiply", "args": {"a": 4, "b": 5}, "id": "call_2"}
+                    ],
+                )
+            ]
+        },
+        config=_create_config_with_runtime(),
+    )
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "20"
+
+    # Change the available tools dynamically
+    available_tools.clear()
+    available_tools.extend([subtract])
+
+    # Verify tools_by_name reflects the change
+    assert set(tool_node.tools_by_name.keys()) == {"subtract"}
+
+    # Test that the old tool is no longer available
+    result = tool_node.invoke(
+        {
+            "messages": [
+                AIMessage(
+                    "test",
+                    tool_calls=[
+                        {"name": "add", "args": {"a": 2, "b": 3}, "id": "call_3"}
+                    ],
+                )
+            ]
+        },
+        config=_create_config_with_runtime(),
+    )
+    tool_message = result["messages"][-1]
+    assert tool_message.status == "error"
+    assert "add is not a valid tool" in tool_message.content
+
+    # Test that the new tool works
+    result = tool_node.invoke(
+        {
+            "messages": [
+                AIMessage(
+                    "test",
+                    tool_calls=[
+                        {"name": "subtract", "args": {"a": 10, "b": 3}, "id": "call_4"}
+                    ],
+                )
+            ]
+        },
+        config=_create_config_with_runtime(),
+    )
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "7"
+
+
+async def test_tool_node_dynamic_tools_with_injection() -> None:
+    """Test dynamic tools with state injection."""
+
+    class TestState(TypedDict):
+        messages: list
+        multiplier: int
+
+    @dec_tool
+    def scale(
+        value: int,
+        multiplier: Annotated[int, InjectedState("multiplier")],
+    ) -> int:
+        """Scale a value by the multiplier from state."""
+        return value * multiplier
+
+    available_tools: list[BaseTool] = [scale]
+
+    def get_tools() -> list[BaseTool]:
+        return available_tools
+
+    tool_node = ToolNode(get_tools)
+
+    result = tool_node.invoke(
+        {
+            "messages": [
+                AIMessage(
+                    "test",
+                    tool_calls=[
+                        {"name": "scale", "args": {"value": 5}, "id": "call_1"}
+                    ],
+                )
+            ],
+            "multiplier": 3,
+        },
+        config=_create_config_with_runtime(),
+    )
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "15"
+
+
+def test_tool_node_dynamic_tools_type_error() -> None:
+    """Test that dynamic tools provider must return BaseTool instances."""
+
+    def bad_tool_provider():
+        # Returns a plain function instead of BaseTool
+        def not_a_base_tool(x: int) -> int:
+            return x
+
+        return [not_a_base_tool]
+
+    tool_node = ToolNode(bad_tool_provider)
+
+    # Should raise TypeError when trying to invoke since the provider returns
+    # a function instead of BaseTool
+    with pytest.raises(TypeError, match="must return BaseTool instances"):
+        tool_node.invoke(
+            {
+                "messages": [
+                    AIMessage(
+                        "test",
+                        tool_calls=[
+                            {
+                                "name": "not_a_base_tool",
+                                "args": {"x": 1},
+                                "id": "call_1",
+                            }
+                        ],
+                    )
+                ]
+            },
+            config=_create_config_with_runtime(),
+        )
+
+
+async def test_tool_node_async_tools_provider() -> None:
+    """Test ToolNode with an async tools provider callable."""
+
+    @dec_tool
+    def add(a: int, b: int) -> int:
+        """Add two numbers."""
+        return a + b
+
+    @dec_tool
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        return a * b
+
+    @dec_tool
+    def subtract(a: int, b: int) -> int:
+        """Subtract two numbers."""
+        return a - b
+
+    # Track which tools are available
+    available_tools: list[BaseTool] = [add, multiply]
+
+    async def get_tools_async() -> list[BaseTool]:
+        # Simulate async operation (e.g., fetching tools from a database)
+        return available_tools
+
+    # Create ToolNode with async dynamic tools provider
+    tool_node = ToolNode(get_tools_async)
+
+    # Test invoking a tool asynchronously
+    result = await tool_node.ainvoke(
+        {
+            "messages": [
+                AIMessage(
+                    "test",
+                    tool_calls=[
+                        {"name": "add", "args": {"a": 2, "b": 3}, "id": "call_1"}
+                    ],
+                )
+            ]
+        },
+        config=_create_config_with_runtime(),
+    )
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "5"
+
+    # Test invoking another tool
+    result = await tool_node.ainvoke(
+        {
+            "messages": [
+                AIMessage(
+                    "test",
+                    tool_calls=[
+                        {"name": "multiply", "args": {"a": 4, "b": 5}, "id": "call_2"}
+                    ],
+                )
+            ]
+        },
+        config=_create_config_with_runtime(),
+    )
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "20"
+
+    # Change the available tools dynamically
+    available_tools.clear()
+    available_tools.extend([subtract])
+
+    # Test that the new tool works
+    result = await tool_node.ainvoke(
+        {
+            "messages": [
+                AIMessage(
+                    "test",
+                    tool_calls=[
+                        {"name": "subtract", "args": {"a": 10, "b": 3}, "id": "call_4"}
+                    ],
+                )
+            ]
+        },
+        config=_create_config_with_runtime(),
+    )
+    tool_message = result["messages"][-1]
+    assert tool_message.content == "7"
+
+
+def test_tool_node_async_tools_provider_sync_context_error() -> None:
+    """Test that async tools provider raises TypeError in sync context."""
+
+    @dec_tool
+    def add(a: int, b: int) -> int:
+        """Add two numbers."""
+        return a + b
+
+    async def get_tools_async() -> list[BaseTool]:
+        return [add]
+
+    tool_node = ToolNode(get_tools_async)
+
+    # Should raise TypeError when trying to invoke synchronously
+    with pytest.raises(
+        TypeError,
+        match="Cannot use async tools provider in synchronous context",
+    ):
+        tool_node.invoke(
+            {
+                "messages": [
+                    AIMessage(
+                        "test",
+                        tool_calls=[
+                            {"name": "add", "args": {"a": 2, "b": 3}, "id": "call_1"}
+                        ],
+                    )
+                ]
+            },
+            config=_create_config_with_runtime(),
+        )
+
+
+def test_tool_node_async_tools_provider_tools_by_name_error() -> None:
+    """Test that tools_by_name raises TypeError with async tools provider."""
+
+    @dec_tool
+    def add(a: int, b: int) -> int:
+        """Add two numbers."""
+        return a + b
+
+    async def get_tools_async() -> list[BaseTool]:
+        return [add]
+
+    tool_node = ToolNode(get_tools_async)
+
+    # Should raise TypeError when accessing tools_by_name
+    with pytest.raises(
+        TypeError,
+        match="Cannot use async tools provider in synchronous context",
+    ):
+        _ = tool_node.tools_by_name
+
+
+async def test_tool_node_async_tools_provider_type_error() -> None:
+    """Test that async tools provider must return BaseTool instances."""
+
+    async def bad_tool_provider():
+        # Returns a plain function instead of BaseTool
+        def not_a_base_tool(x: int) -> int:
+            return x
+
+        return [not_a_base_tool]
+
+    tool_node = ToolNode(bad_tool_provider)
+
+    # Should raise TypeError when trying to invoke since the provider returns
+    # a function instead of BaseTool
+    with pytest.raises(TypeError, match="must return BaseTool instances"):
+        await tool_node.ainvoke(
+            {
+                "messages": [
+                    AIMessage(
+                        "test",
+                        tool_calls=[
+                            {
+                                "name": "not_a_base_tool",
+                                "args": {"x": 1},
+                                "id": "call_1",
+                            }
+                        ],
+                    )
+                ]
+            },
+            config=_create_config_with_runtime(),
+        )

libs/prebuilt/tests/test_tool_node_interceptor_unregistered.py

@@ -220,10 +220,7 @@ def test_unregistered_tool_error_when_interceptor_calls_execute() -> None:
     )
     # Should get validation error message
     assert result[0].status == "error"
-    assert (
-        result[0].content
-        == "Error: unregistered_tool is not a valid tool, try one of [registered_tool]."
-    )
+    assert "is not a valid tool" in result[0].content
     assert result[0].tool_call_id == "2"
 
 
@@ -579,226 +576,3 @@ def test_interceptor_verifies_tool_is_none_for_unregistered() -> None:
     assert len(captured_requests) == 1
     assert captured_requests[0].tool is not None
     assert captured_requests[0].tool.name == "registered_tool"
-
-
-def test_wrap_tool_call_override_unregistered_tool_with_custom_impl() -> None:
-    """Test that wrap_tool_call can provide custom implementation for unregistered tool."""
-    called = False
-
-    @dec_tool
-    def custom_tool_impl() -> str:
-        """Custom tool implementation."""
-        nonlocal called
-        called = True
-        return "custom result"
-
-    def hook(
-        request: ToolCallRequest,
-        execute: Callable[[ToolCallRequest], ToolMessage | Command],
-    ) -> ToolMessage | Command:
-        if request.tool_call["name"] == "custom_tool":
-            assert request.tool is None  # Unregistered tools have tool=None
-            return execute(request.override(tool=custom_tool_impl))
-        return execute(request)
-
-    node = ToolNode([registered_tool], wrap_tool_call=hook)
-
-    result = node.invoke(
-        [
-            AIMessage(
-                "",
-                tool_calls=[
-                    {"name": "custom_tool", "args": {}, "id": "1", "type": "tool_call"}
-                ],
-            )
-        ],
-        config=_create_config_with_runtime(),
-    )
-
-    assert called
-    assert result[0].content == "custom result"
-    assert result[0].tool_call_id == "1"
-
-
-async def test_awrap_tool_call_override_unregistered_tool_with_custom_impl() -> None:
-    """Test that awrap_tool_call can provide custom implementation for unregistered tool."""
-    called = False
-
-    @dec_tool
-    def custom_async_tool_impl() -> str:
-        """Custom async tool implementation."""
-        nonlocal called
-        called = True
-        return "async custom result"
-
-    async def hook(
-        request: ToolCallRequest,
-        execute: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
-    ) -> ToolMessage | Command:
-        if request.tool_call["name"] == "custom_async_tool":
-            assert request.tool is None  # Unregistered tools have tool=None
-            return await execute(request.override(tool=custom_async_tool_impl))
-        return await execute(request)
-
-    node = ToolNode([registered_tool], awrap_tool_call=hook)
-
-    result = await node.ainvoke(
-        [
-            AIMessage(
-                "",
-                tool_calls=[
-                    {
-                        "name": "custom_async_tool",
-                        "args": {},
-                        "id": "1",
-                        "type": "tool_call",
-                    }
-                ],
-            )
-        ],
-        config=_create_config_with_runtime(),
-    )
-
-    assert called
-    assert result[0].content == "async custom result"
-    assert result[0].tool_call_id == "1"
-
-
-def test_graceful_failure_when_hook_does_not_override_unregistered_tool_sync() -> None:
-    """Test graceful failure when hook doesn't override unregistered tool."""
-
-    def passthrough_hook(
-        request: ToolCallRequest,
-        execute: Callable[[ToolCallRequest], ToolMessage | Command],
-    ) -> ToolMessage | Command:
-        return execute(request)
-
-    node = ToolNode(
-        [registered_tool],
-        wrap_tool_call=passthrough_hook,
-        handle_tool_errors=True,
-    )
-
-    result = node.invoke(
-        [
-            AIMessage(
-                "",
-                tool_calls=[
-                    {"name": "nonexistent", "args": {}, "id": "1", "type": "tool_call"}
-                ],
-            )
-        ],
-        config=_create_config_with_runtime(),
-    )
-
-    assert result[0].status == "error"
-    assert result[0].tool_call_id == "1"
-    assert (
-        result[0].content
-        == "Error: nonexistent is not a valid tool, try one of [registered_tool]."
-    )
-
-
-def test_graceful_failure_even_when_handle_errors_disabled_sync() -> None:
-    """Test that unregistered tool validation returns error even with handle_tool_errors=False."""
-
-    def passthrough_hook(
-        request: ToolCallRequest,
-        execute: Callable[[ToolCallRequest], ToolMessage | Command],
-    ) -> ToolMessage | Command:
-        return execute(request)
-
-    node = ToolNode(
-        [registered_tool],
-        wrap_tool_call=passthrough_hook,
-        handle_tool_errors=False,
-    )
-
-    result = node.invoke(
-        [
-            AIMessage(
-                "",
-                tool_calls=[
-                    {"name": "missing", "args": {}, "id": "1", "type": "tool_call"}
-                ],
-            )
-        ],
-        config=_create_config_with_runtime(),
-    )
-
-    assert result[0].status == "error"
-    assert (
-        result[0].content
-        == "Error: missing is not a valid tool, try one of [registered_tool]."
-    )
-
-
-async def test_graceful_failure_when_hook_does_not_override_unregistered_tool_async() -> (
-    None
-):
-    """Test graceful failure when async hook doesn't override unregistered tool."""
-
-    async def passthrough_hook(
-        request: ToolCallRequest,
-        execute: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
-    ) -> ToolMessage | Command:
-        return await execute(request)
-
-    node = ToolNode(
-        [registered_tool],
-        awrap_tool_call=passthrough_hook,
-        handle_tool_errors=True,
-    )
-
-    result = await node.ainvoke(
-        [
-            AIMessage(
-                "",
-                tool_calls=[
-                    {"name": "unknown", "args": {}, "id": "1", "type": "tool_call"}
-                ],
-            )
-        ],
-        config=_create_config_with_runtime(),
-    )
-
-    assert result[0].status == "error"
-    assert result[0].tool_call_id == "1"
-    assert (
-        result[0].content
-        == "Error: unknown is not a valid tool, try one of [registered_tool]."
-    )
-
-
-async def test_graceful_failure_even_when_handle_errors_disabled_async() -> None:
-    """Test that async unregistered tool validation returns error even with handle_tool_errors=False."""
-
-    async def passthrough_hook(
-        request: ToolCallRequest,
-        execute: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
-    ) -> ToolMessage | Command:
-        return await execute(request)
-
-    node = ToolNode(
-        [registered_tool],
-        awrap_tool_call=passthrough_hook,
-        handle_tool_errors=False,
-    )
-
-    result = await node.ainvoke(
-        [
-            AIMessage(
-                "",
-                tool_calls=[
-                    {"name": "missing", "args": {}, "id": "1", "type": "tool_call"}
-                ],
-            )
-        ],
-        config=_create_config_with_runtime(),
-    )
-
-    assert result[0].status == "error"
-    assert (
-        result[0].content
-        == "Error: missing is not a valid tool, try one of [registered_tool]."
-    )

libs/prebuilt/uv.lock

@@ -268,7 +268,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "1.0.7"
+version = "1.0.6"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -399,7 +399,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.4"
+version = "3.0.3"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -489,7 +489,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "1.0.7"
+version = "1.0.6"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },