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:
 

.github/scripts/run_langgraph_cli_test.py

@@ -63,7 +63,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
             try:
                 sys.stderr.write("\n== docker compose ps ==\n")
                 runner.run(
-                    subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=True)
+                    subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=False)
                 )
             except Exception:
                 pass
@@ -76,7 +76,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
                         "logs",
                         "langgraph-api",
                         input=stdin,
-                        verbose=True,
+                        verbose=False,
                     )
                 )
             except Exception:

.github/workflows/_integration_test.yml

@@ -96,8 +96,8 @@ jobs:
           timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
           echo "Finished starting up langgraph-test-h"
           LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
-          if [ "$LANGGRAPH_VERSION" != "1.0.8" ]; then
-            echo "LANGGRAPH_VERSION != 1.0.8; $LANGGRAPH_VERSION"
+          if [ "$LANGGRAPH_VERSION" != "1.0.2" ]; then
+            echo "LANGGRAPH_VERSION != 1.0.2; $LANGGRAPH_VERSION"
             exit 1
           fi
           LANGCHAIN_OPENAI_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-openai'); print(v);")

AGENTS.md

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

CLAUDE.md

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

examples/README.md

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-This directory is retained purely for archival purposes and is no longer updated.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

examples/async.ipynb

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

examples/branching.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "14f7ca50",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/branching.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

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

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

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

examples/chatbots/information-gather-prompting.ipynb

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

examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb

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

examples/code_assistant/langgraph_code_assistant.ipynb

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

examples/code_assistant/langgraph_code_assistant_mistral.ipynb

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

examples/configuration.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e9a58c69",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/configuration.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/create-react-agent-hitl.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a1e6efeb",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-hitl.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/create-react-agent-memory.ipynb

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

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

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

examples/create-react-agent.ipynb

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

examples/customer-support/customer-support.ipynb

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

examples/extraction/retries.ipynb

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

examples/human_in_the_loop/wait-user-input.ipynb

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

examples/input_output_schema.ipynb

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

examples/lats/lats.ipynb

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

examples/llm-compiler/LLMCompiler.ipynb

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

examples/map-reduce.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "42abb708",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/map-reduce.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

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

examples/memory/delete-messages.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3f4370fd",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/delete-messages.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/memory/manage-conversation-history.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6ec7cb13",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/manage-conversation-history.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/multi_agent/hierarchical_agent_teams.ipynb

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

examples/multi_agent/multi-agent-collaboration.ipynb

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

examples/node-retries.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "017a01f4",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/node-retries.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/pass-config-to-tools.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "05f6ad0a",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/pass-config-to-tools.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

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

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8f38bec5",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/pass-run-time-values-to-tools.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/pass_private_state.ipynb

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

examples/persistence.ipynb

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

examples/persistence_mongodb.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "78217098",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/persistence_mongodb.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/persistence_postgres.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "18526f23",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-memory.md"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/persistence_redis.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "eee6ecdd",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/persistence_redis.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

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

examples/rag/langgraph_adaptive_rag.ipynb

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

examples/rag/langgraph_adaptive_rag_local.ipynb

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

examples/rag/langgraph_agentic_rag.ipynb

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

examples/rag/langgraph_crag.ipynb

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

examples/rag/langgraph_crag_local.ipynb

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

examples/rag/langgraph_self_rag.ipynb

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

examples/rag/langgraph_self_rag_local.ipynb

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

examples/rag/langgraph_self_rag_pinecone_movies.ipynb

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

examples/react-agent-from-scratch.ipynb

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

examples/react-agent-structured-output.ipynb

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

examples/recursion-limit.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fa3f7c50",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/recursion-limit.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/reflection/reflection.ipynb

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

examples/reflexion/reflexion.ipynb

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

examples/rewoo/rewoo.ipynb

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

examples/run-id-langsmith.ipynb

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

examples/self-discover/self-discover.ipynb

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

examples/state-model.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4149ffcc",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/state-model.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/storm/storm.ipynb

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

examples/stream-multiple.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e663f597",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/stream-multiple.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/stream-updates.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e6829c80",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/stream-updates.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/stream-values.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5ec11895",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/stream-values.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/streaming-content.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6619387c",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-content.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "57b7e303",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8e71a0c8",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-events-from-within-tools.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/streaming-from-final-node.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "756e4554",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-from-final-node.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/streaming-subgraphs.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "47164a72",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-subgraphs.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/streaming-tokens-without-langchain.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "218dfbcb",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-tokens-without-langchain.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/streaming-tokens.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "99eb887e",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-tokens.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/subgraph-transform-state.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0de7689f",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/subgraph-transform-state.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/subgraph.ipynb

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

examples/subgraphs-manage-state.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5106959e",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/subgraphs-manage-state.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/tool-calling-errors.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dc21501d",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/tool-calling-errors.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/tool-calling.ipynb

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

examples/tutorials/sql-agent.ipynb

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

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

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

examples/usaco/usaco.ipynb

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

examples/visualization.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9c9cb15a",
+   "metadata": {},
+   "source": [
+    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/visualization.ipynb"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/web-navigation/web_voyager.ipynb

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

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

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

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.4"
+version = "3.0.3"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"

libs/checkpoint-postgres/uv.lock

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

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

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

libs/cli/examples/graph_prerelease_reqs/deps/additional_deps/pyproject.toml

@@ -5,5 +5,5 @@ description = "Test for prerelease stuff"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "langgraph==1.0.8"
+  "langgraph==1.0.2"
 ]

libs/cli/examples/graph_prerelease_reqs/pyproject.toml

@@ -7,7 +7,7 @@ requires-python = ">=3.10"
 dependencies = [
   "langchain-openai==1.0.0a2",
   "langchain-anthropic==1.0.0a5",
-  "langgraph==1.0.8"
+  "langgraph==1.0.2"
 ]
 
 [tool.uv]

libs/cli/langgraph_cli/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.13"
+__version__ = "0.4.11"

libs/cli/langgraph_cli/config.py

@@ -1,5 +1,4 @@
 import json
-import logging
 import os
 import pathlib
 import re
@@ -11,19 +10,9 @@ import click
 
 from langgraph_cli.schemas import Config, Distros
 
-logger = logging.getLogger(__name__)
-
 MIN_NODE_VERSION = "20"
 DEFAULT_NODE_VERSION = "20"
 
-CONSTRAINTS_PATH = "/api/constraints.txt"
-
-CACHE_OPTIMIZE_LEVELS = {"off", "lock", "pyproject", "all"}
-CACHE_OPTIMIZE_ENV_VAR = "LANGGRAPH_CACHE_OPTIMIZE"
-DEFAULT_CACHE_OPTIMIZE = "lock"
-
-METADATA_STAGING_DIR = "/tmp/dep_metadata"
-
 MIN_PYTHON_VERSION = "3.11"
 DEFAULT_PYTHON_VERSION = "3.11"
 
@@ -207,11 +196,6 @@ def validate_config(config: Config) -> Config:
                 f"Python version {pyversion} is not supported. "
                 f"Minimum required version is {MIN_PYTHON_VERSION}."
             )
-        if "bullseye" in pyversion:
-            raise click.UsageError(
-                "Bullseye images were deprecated in version 0.4.13. "
-                "Please use 'bookworm' or 'debian' instead."
-            )
 
         if not config["dependencies"]:
             raise click.UsageError(
@@ -226,15 +210,10 @@ def validate_config(config: Config) -> Config:
 
     # Validate image_distro config
     if image_distro := config.get("image_distro"):
-        if image_distro == "bullseye":
-            raise click.UsageError(
-                "Bullseye images were deprecated in version 0.4.13. "
-                "Please use 'bookworm' or 'debian' instead."
-            )
         if image_distro not in Distros.__args__:
             raise click.UsageError(
                 f"Invalid image_distro: '{image_distro}'. "
-                "Must be one of 'debian', 'wolfi', or 'bookworm'."
+                "Must be one of 'debian', 'bullseye', or 'bookworm'."
             )
 
     if pip_installer := config.get("pip_installer"):
@@ -329,30 +308,6 @@ def validate_config_file(config_path: pathlib.Path) -> Config:
     return validated
 
 
-class ReqGenSpec(NamedTuple):
-    """Specification for a local package that needs requirements.txt generation.
-
-    Created for every real package missing a requirements.txt, regardless of
-    cache optimization eligibility.  The eligibility decision happens later
-    in ``python_config_to_docker()`` via ``_is_optimizable()``.
-
-    Attributes:
-        host_pkg_path: Absolute host path of the package directory.
-        container_pkg_path: Target path for the package source in the container.
-        container_req_path: Full path to requirements.txt inside the container.
-        package_type: ``"pyproject"`` or ``"setup"``.
-        has_uv_lock: True if the package includes uv.lock.
-        stage_name: BuildKit context name if outside the build context, else None.
-    """
-
-    host_pkg_path: pathlib.Path
-    container_pkg_path: str
-    container_req_path: str
-    package_type: Literal["pyproject", "setup"]
-    has_uv_lock: bool
-    stage_name: str | None
-
-
 class LocalDeps(NamedTuple):
     """A container for referencing and managing local Python dependencies.
 
@@ -400,11 +355,6 @@ class LocalDeps(NamedTuple):
         additional_contexts: A list of paths to directories that contain local
             dependencies in parent directories. These directories are added to the
             Docker build context to ensure that the Dockerfile can access them.
-
-        pkgs_missing_reqs: A list of ``ReqGenSpec`` entries for real packages that
-            do not have a ``requirements.txt`` file.  Populated by
-            ``_assemble_local_deps()`` and consumed by ``python_config_to_docker()``
-            for cache-optimized Dockerfile generation.
     """
 
     pip_reqs: list[tuple[pathlib.Path, str]]
@@ -414,124 +364,6 @@ class LocalDeps(NamedTuple):
     working_dir: str | None = None
     # if there are local dependencies in parent directories, use additional_contexts
     additional_contexts: list[pathlib.Path] = None
-    # real packages that need requirements.txt generation for cache optimization
-    pkgs_missing_reqs: list[ReqGenSpec] | None = None
-
-
-def _get_cache_optimize_level() -> str:
-    """Read the cache optimization level from environment.
-
-    Returns one of: "off", "lock", "pyproject", "all".
-    Falls back to DEFAULT_CACHE_OPTIMIZE on invalid values (with warning).
-    """
-    raw = os.getenv(CACHE_OPTIMIZE_ENV_VAR, DEFAULT_CACHE_OPTIMIZE).strip().lower()
-    if raw in CACHE_OPTIMIZE_LEVELS:
-        return raw
-    logger.warning(
-        "Invalid %s=%r. Falling back to %r.",
-        CACHE_OPTIMIZE_ENV_VAR,
-        raw,
-        DEFAULT_CACHE_OPTIMIZE,
-    )
-    return DEFAULT_CACHE_OPTIMIZE
-
-
-def _is_optimizable(spec: ReqGenSpec, level: str) -> bool:
-    """Determine if a package is eligible for deferred source copy.
-
-    Eligibility tiers (cumulative):
-    - "off":       no packages optimized
-    - "lock":      pyproject.toml + uv.lock only (safest, default)
-    - "pyproject": all pyproject.toml packages (with or without lock)
-    - "all":       all real packages including setup.py
-    """
-    if level == "off":
-        return False
-    if level == "lock":
-        return spec.package_type == "pyproject" and spec.has_uv_lock
-    if level == "pyproject":
-        return spec.package_type == "pyproject"
-    if level == "all":
-        return spec.package_type in {"pyproject", "setup"}
-    return False  # unknown level — fail safe
-
-
-def _metadata_files(spec: ReqGenSpec) -> list[str]:
-    """Return the list of packaging metadata files to copy for requirements generation."""
-    files = ["pyproject.toml"] if spec.package_type == "pyproject" else ["setup.py"]
-    if spec.package_type == "pyproject" and spec.has_uv_lock:
-        files.append("uv.lock")
-    if spec.package_type == "setup" and (spec.host_pkg_path / "setup.cfg").exists():
-        files.append("setup.cfg")
-    return files
-
-
-def _get_reqs_gen_cmd(spec: ReqGenSpec) -> str:
-    """Return the shell command to generate requirements.txt from packaging metadata."""
-    if spec.package_type == "pyproject" and spec.has_uv_lock:
-        return "uv export --no-hashes --no-dev --no-emit-local -o 'requirements.txt'"
-    if spec.package_type == "pyproject":
-        return (
-            f"uv pip compile pyproject.toml -o 'requirements.txt'"
-            f" --constraint {CONSTRAINTS_PATH}"
-        )
-    return (
-        f"uv pip compile setup.py -o 'requirements.txt' --constraint {CONSTRAINTS_PATH}"
-    )
-
-
-def _staging_path(spec: ReqGenSpec) -> str:
-    """Return the staging directory path for a spec's metadata files.
-
-    Metadata is staged in a temporary directory separate from ``/deps/``
-    so the install loop does not encounter incomplete package directories.
-    """
-    # Use the last component of container_pkg_path as the staging subdir
-    name = spec.container_pkg_path.rstrip("/").rsplit("/", 1)[-1]
-    return f"{METADATA_STAGING_DIR}/{name}"
-
-
-def _generate_requirements_from_metadata(
-    config_path: pathlib.Path,
-    specs: list[ReqGenSpec],
-) -> str:
-    """Generate Dockerfile lines to create requirements.txt from packaging metadata.
-
-    Metadata files are staged under ``METADATA_STAGING_DIR`` (not ``/deps/``)
-    so the install loop does not find incomplete package directories.
-
-    Only processes the given ``specs``.  Returns an empty string when the
-    list is empty.
-    """
-    if not specs:
-        return ""
-
-    resolved_config_parent = config_path.resolve().parent
-
-    docker_lines = ["# -- Generate requirements.txt for packages without one --"]
-    docker_lines.append("# Copy packaging metadata files")
-
-    for spec in sorted(specs, key=lambda s: s.container_pkg_path):
-        staging = _staging_path(spec)
-        for file_name in _metadata_files(spec):
-            if spec.stage_name:
-                docker_lines.append(
-                    f"COPY --from={spec.stage_name} {file_name} {staging}/{file_name}"
-                )
-            else:
-                file_relpath = (spec.host_pkg_path / file_name).relative_to(
-                    resolved_config_parent
-                )
-                docker_lines.append(f"ADD {file_relpath} {staging}/{file_name}")
-
-    docker_lines.append("")
-    docker_lines.append("# Generate requirements.txt from packaging metadata")
-    for spec in sorted(specs, key=lambda s: s.container_pkg_path):
-        staging = _staging_path(spec)
-        docker_lines.append(f"RUN cd '{staging}' && {_get_reqs_gen_cmd(spec)}")
-
-    docker_lines.append("# -- End of requirements.txt generation --")
-    return os.linesep.join(docker_lines)
 
 
 def _assemble_local_deps(config_path: pathlib.Path, config: Config) -> LocalDeps:
@@ -567,7 +399,6 @@ def _assemble_local_deps(config_path: pathlib.Path, config: Config) -> LocalDeps
     faux_pkgs = {}
     working_dir: str | None = None
     additional_contexts: list[pathlib.Path] = []
-    pkgs_missing_reqs: list[ReqGenSpec] = []
 
     for local_dep in config["dependencies"]:
         if not local_dep.startswith("."):
@@ -594,8 +425,6 @@ def _assemble_local_deps(config_path: pathlib.Path, config: Config) -> LocalDeps
         # If found, treat as a real package, if not treat as a faux package.
         # For faux packages, we'll also check for presence of requirements.txt.
         files = os.listdir(resolved)
-        # requirement_path is set in both branches and used after them
-        requirement_path: str = ""
         if "pyproject.toml" in files or "setup.py" in files:
             # real package
 
@@ -609,31 +438,6 @@ def _assemble_local_deps(config_path: pathlib.Path, config: Config) -> LocalDeps
             # set working_dir
             if local_dep == ".":
                 working_dir = f"/deps/{container_name}"
-
-            requirement_path = f"/deps/{container_name}/requirements.txt"
-
-            # Track real packages that need requirements.txt generation
-            if "requirements.txt" not in files:
-                has_pyproject = "pyproject.toml" in files
-                has_uv_lock = "uv.lock" in files
-                pkg_type: Literal["pyproject", "setup"] = (
-                    "pyproject" if has_pyproject else "setup"
-                )
-
-                stage_name = None
-                if resolved in additional_contexts:
-                    stage_name = container_name
-
-                pkgs_missing_reqs.append(
-                    ReqGenSpec(
-                        host_pkg_path=resolved,
-                        container_pkg_path=f"/deps/{container_name}",
-                        container_req_path=f"{METADATA_STAGING_DIR}/{container_name}/requirements.txt",
-                        package_type=pkg_type,
-                        has_uv_lock=has_uv_lock if has_pyproject else False,
-                        stage_name=stage_name,
-                    )
-                )
         else:
             # We could not find a pyproject.toml or setup.py, so treat as a faux package
             if any(file == "__init__.py" for file in files):
@@ -666,27 +470,18 @@ def _assemble_local_deps(config_path: pathlib.Path, config: Config) -> LocalDeps
             if local_dep == ".":
                 working_dir = container_path
 
-            requirement_path = f"{container_path}/requirements.txt"
-
-        # If the package has a requirements.txt, register it for pre-installation.
-        # This applies to BOTH real and faux packages.
-        if "requirements.txt" in files:
-            rfile = resolved / "requirements.txt"
-            pip_reqs.append(
-                (
-                    rfile,
-                    requirement_path,
+            # If the faux package has a requirements.txt, we'll add
+            # the path to the list of requirements to install.
+            if "requirements.txt" in files:
+                rfile = resolved / "requirements.txt"
+                pip_reqs.append(
+                    (
+                        rfile,
+                        f"{container_path}/requirements.txt",
+                    )
                 )
-            )
 
-    return LocalDeps(
-        pip_reqs,
-        real_pkgs,
-        faux_pkgs,
-        working_dir,
-        additional_contexts,
-        pkgs_missing_reqs,
-    )
+    return LocalDeps(pip_reqs, real_pkgs, faux_pkgs, working_dir, additional_contexts)
 
 
 def _update_graph_paths(
@@ -1078,106 +873,22 @@ def python_config_to_docker(
     pip_pkgs_str = (
         f"RUN {local_reqs_pip_install} {' '.join(pypi_deps)}" if pypi_deps else ""
     )
-    # --- Compute optimized package set ---
-    optimized_pkg_paths: set[pathlib.Path] = set()
-    if pip_installer == "uv":
-        level = _get_cache_optimize_level()
-        logger.info("Docker cache optimization level: %s", level)
-
-        # Source 1: packages with generated requirements
-        for spec in local_deps.pkgs_missing_reqs or []:
-            if _is_optimizable(spec, level):
-                optimized_pkg_paths.add(spec.host_pkg_path)
-
-        # Source 2: real packages with existing requirements.txt
-        # Their deps are already pre-installed via pip_reqs, so deferred copy is safe —
-        # but still subject to the same tier eligibility as generated-requirements packages.
-        if level != "off":
-            for reqpath, _ in local_deps.pip_reqs or []:
-                for fullpath in local_deps.real_pkgs:
-                    if reqpath.parent != fullpath:
-                        continue
-                    # Build a synthetic spec to reuse the same eligibility check
-                    pkg_files = os.listdir(fullpath)
-                    has_pyproject = "pyproject.toml" in pkg_files
-                    has_setup = "setup.py" in pkg_files
-                    if not has_pyproject and not has_setup:
-                        continue
-                    synthetic = ReqGenSpec(
-                        host_pkg_path=fullpath,
-                        container_pkg_path="",  # unused by _is_optimizable
-                        container_req_path="",  # unused by _is_optimizable
-                        package_type="pyproject" if has_pyproject else "setup",
-                        has_uv_lock="uv.lock" in pkg_files,
-                        stage_name=None,  # unused by _is_optimizable
-                    )
-                    if _is_optimizable(synthetic, level):
-                        optimized_pkg_paths.add(fullpath)
-
-    # --- Generate requirements.txt from packaging metadata ---
-    # Always generate for ALL real packages missing requirements.txt (when using uv).
-    # This benefits caching even for non-deferred packages because the generated
-    # requirements.txt install layer is cached and deps are pre-installed before
-    # the install loop.
-    all_specs = list(local_deps.pkgs_missing_reqs or [])
-    generated_reqs_str = (
-        _generate_requirements_from_metadata(config_path, all_specs)
-        if pip_installer == "uv"
-        else ""
-    )
-
-    # --- Build combined requirements install block ---
-    # Gather all requirement paths: existing pip_reqs + generated specs
-    copy_lines: list[str] = []
-    all_req_paths: list[str] = []
-
-    # Existing pip_reqs (from packages that already have requirements.txt)
     if local_deps.pip_reqs:
-        for reqpath, destpath in local_deps.pip_reqs:
-            # For optimized (deferred) packages, redirect the requirements.txt
-            # copy to the staging directory so we don't create a partial /deps/
-            # directory that the install loop would trip over.
-            resolved_pkg = reqpath.parent.resolve()
-            if resolved_pkg in optimized_pkg_paths:
-                name = destpath.rstrip("/").rsplit("/", 2)[-2]
-                effective_dest = f"{METADATA_STAGING_DIR}/{name}/requirements.txt"
-            else:
-                effective_dest = destpath
-            if reqpath.parent in local_deps.additional_contexts:
-                if reqpath.parent in local_deps.real_pkgs:
-                    stage_name = local_deps.real_pkgs[reqpath.parent][1]
-                else:
-                    stage_name = f"outer-{reqpath.parent.name}"
-                copy_lines.append(
-                    f"COPY --from={stage_name} requirements.txt {effective_dest}"
-                )
-            else:
-                copy_lines.append(
-                    f"ADD {reqpath.relative_to(config_path.parent)} {effective_dest}"
-                )
-            all_req_paths.append(effective_dest)
-
-    # Generated requirements from specs (all real packages with generation, uv only)
-    if pip_installer == "uv":
-        for spec in sorted(
-            local_deps.pkgs_missing_reqs or [], key=lambda s: s.container_req_path
-        ):
-            all_req_paths.append(spec.container_req_path)
-
-    pip_reqs_str = ""
-    if all_req_paths:
-        all_req_paths = sorted(set(all_req_paths))
-        install_lines = [
-            f"RUN {local_reqs_pip_install} -r '{p}'" for p in all_req_paths
-        ]
-        pip_reqs_str = f"""# -- Installing from requirements.txt files --
-{os.linesep.join(copy_lines)}
-{os.linesep.join(install_lines)}
-# -- End of requirements.txt install --"""
-        # Clean up leading/trailing blank lines in case copy_lines is empty
-        pip_reqs_str = pip_reqs_str.replace(
-            os.linesep + os.linesep + "RUN", os.linesep + "RUN"
+        pip_reqs_str = os.linesep.join(
+            (
+                f"COPY --from=outer-{reqpath.name} requirements.txt {destpath}"
+                if reqpath.parent in local_deps.additional_contexts
+                else f"ADD {reqpath.relative_to(config_path.parent)} {destpath}"
+            )
+            for reqpath, destpath in local_deps.pip_reqs
         )
+        pip_reqs_str += f"{os.linesep}RUN {local_reqs_pip_install} {' '.join('-r ' + r for _, r in local_deps.pip_reqs)}"
+        pip_reqs_str = f"""# -- Installing local requirements --
+{pip_reqs_str}
+# -- End of local requirements install --"""
+
+    else:
+        pip_reqs_str = ""
 
     # https://setuptools.pypa.io/en/latest/userguide/datafiles.html#package-data
     # https://til.simonwillison.net/python/pyproject
@@ -1205,34 +916,18 @@ RUN set -ex && \\
         for fullpath, (relpath, destpath) in local_deps.faux_pkgs.items()
     )
 
-    # --- Split real package source copy into pre-loop and post-loop ---
-    local_pkgs_pre_parts: list[str] = []
-    local_pkgs_post_parts: list[str] = []
-
-    for fullpath, (relpath, name) in local_deps.real_pkgs.items():
-        if fullpath in local_deps.additional_contexts:
-            add_line = f"COPY --from={name} . /deps/{name}"
-        else:
-            add_line = f"ADD {relpath} /deps/{name}"
-
-        if fullpath in optimized_pkg_paths:
-            # Deferred: full source copy + no-deps editable install after the loop
-            local_pkgs_post_parts.append(
-                f"# -- Adding full source for local package {relpath} --\n"
-                f"{add_line}\n"
-                f"RUN cd /deps/{name} && {global_reqs_pip_install} --no-deps -e .\n"
-                f"# -- End of full source for local package {relpath} --"
-            )
-        else:
-            # Non-optimized: keep in pre-loop position (unchanged behavior)
-            local_pkgs_pre_parts.append(
-                f"# -- Adding local package {relpath} --\n"
-                f"{add_line}\n"
-                f"# -- End of local package {relpath} --"
-            )
-
-    local_pkgs_pre_str = os.linesep.join(local_pkgs_pre_parts)
-    local_pkgs_post_str = os.linesep.join(local_pkgs_post_parts)
+    local_pkgs_str = os.linesep.join(
+        (
+            f"""# -- Adding local package {relpath} --
+COPY --from={name} . /deps/{name}
+# -- End of local package {relpath} --"""
+            if fullpath in local_deps.additional_contexts
+            else f"""# -- Adding local package {relpath} --
+ADD {relpath} /deps/{name}
+# -- End of local package {relpath} --"""
+        )
+        for fullpath, (relpath, name) in local_deps.real_pkgs.items()
+    )
 
     install_node_str: str = (
         "RUN /storage/install-node.sh"
@@ -1247,9 +942,8 @@ RUN set -ex && \\
                 install_node_str,
                 pip_config_file_str,
                 pip_pkgs_str,
-                generated_reqs_str,
                 pip_reqs_str,
-                local_pkgs_pre_str,
+                local_pkgs_str,
                 faux_pkgs_str,
             ],
         )
@@ -1329,7 +1023,6 @@ RUN set -ex && \\
             fi; \
         done""",
             "# -- End of local dependencies install --",
-            local_pkgs_post_str,
             os.linesep.join(env_vars),
             "",
             js_inst_str,

libs/cli/langgraph_cli/schemas.py

@@ -1,6 +1,6 @@
 from typing import Any, Literal, TypedDict
 
-Distros = Literal["debian", "wolfi", "bookworm"]
+Distros = Literal["debian", "wolfi", "bullseye", "bookworm"]
 MiddlewareOrders = Literal["auth_first", "middleware_first"]
 
 
@@ -107,19 +107,17 @@ class StoreConfig(TypedDict, total=False):
 class ThreadTTLConfig(TypedDict, total=False):
     """Configure a default TTL for checkpointed data within threads."""
 
-    strategy: Literal["delete", "keep_latest"]
-    """Action taken when a thread exceeds its TTL.
-
-    - "delete": Remove the thread and all its data entirely.
-    - "keep_latest": Prune old checkpoints but keep the thread and its latest state.
+    strategy: Literal["delete"]
+    """Strategy to use for deleting checkpointed data.
+    
+    Choices:
+      - "delete": Delete all checkpoints for a thread after TTL expires.
     """
     default_ttl: float | None
     """Default TTL (time-to-live) in minutes for checkpointed data."""
     sweep_interval_minutes: int | None
     """Interval in minutes between sweep iterations.
     If omitted, a default interval will be used (typically ~ 5 minutes)."""
-    sweep_limit: int | None
-    """Maximum number of threads to process per sweep iteration. Defaults to 1000."""
 
 
 class SerdeConfig(TypedDict, total=False):
@@ -175,7 +173,7 @@ class CheckpointerConfig(TypedDict, total=False):
     """
     serde: SerdeConfig | None
     """Optional. Defines the serde configuration.
-
+    
     If provided, the checkpointer will apply serde settings according to the configuration.
     If omitted, no serde behavior is configured.
 
@@ -559,7 +557,7 @@ class Config(TypedDict, total=False):
     image_distro: Distros | None
     """Optional. Linux distribution for the base image.
     
-    Must be one of 'wolfi', 'debian', or 'bookworm'.
+    Must be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.
     If omitted, defaults to 'debian' ('latest').
     """
 

libs/cli/pyproject.toml

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

libs/cli/schemas/schema.json

@@ -147,6 +147,7 @@
                 {
                   "enum": [
                     "bookworm",
+                    "bullseye",
                     "debian",
                     "wolfi"
                   ]
@@ -155,7 +156,7 @@
                   "type": "null"
                 }
               ],
-              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
+              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
             },
             "keep_pkg_tools": {
               "anyOf": [
@@ -369,7 +370,7 @@
                   "type": "null"
                 }
               ],
-              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
+              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
             },
             "keep_pkg_tools": {
               "anyOf": [
@@ -617,10 +618,9 @@
         },
         "strategy": {
           "enum": [
-            "delete",
-            "keep_latest"
+            "delete"
           ],
-          "description": "Action taken when a thread exceeds its TTL.\n\n"
+          "description": "Strategy to use for deleting checkpointed data.\n"
         },
         "sweep_interval_minutes": {
           "anyOf": [
@@ -632,17 +632,6 @@
             }
           ],
           "description": "Interval in minutes between sweep iterations.\nIf omitted, a default interval will be used (typically ~ 5 minutes)."
-        },
-        "sweep_limit": {
-          "anyOf": [
-            {
-              "type": "integer"
-            },
-            {
-              "type": "null"
-            }
-          ],
-          "description": "Maximum number of threads to process per sweep iteration. Defaults to 1000."
         }
       },
       "required": []

libs/cli/schemas/schema.v0.json

@@ -147,6 +147,7 @@
                 {
                   "enum": [
                     "bookworm",
+                    "bullseye",
                     "debian",
                     "wolfi"
                   ]
@@ -155,7 +156,7 @@
                   "type": "null"
                 }
               ],
-              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
+              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
             },
             "keep_pkg_tools": {
               "anyOf": [
@@ -369,7 +370,7 @@
                   "type": "null"
                 }
               ],
-              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
+              "description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', 'bullseye', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
             },
             "keep_pkg_tools": {
               "anyOf": [
@@ -617,10 +618,9 @@
         },
         "strategy": {
           "enum": [
-            "delete",
-            "keep_latest"
+            "delete"
           ],
-          "description": "Action taken when a thread exceeds its TTL.\n\n"
+          "description": "Strategy to use for deleting checkpointed data.\n"
         },
         "sweep_interval_minutes": {
           "anyOf": [
@@ -632,17 +632,6 @@
             }
           ],
           "description": "Interval in minutes between sweep iterations.\nIf omitted, a default interval will be used (typically ~ 5 minutes)."
-        },
-        "sweep_limit": {
-          "anyOf": [
-            {
-              "type": "integer"
-            },
-            {
-              "type": "null"
-            }
-          ],
-          "description": "Maximum number of threads to process per sweep iteration. Defaults to 1000."
         }
       },
       "required": []

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

@@ -1,5 +1,4 @@
 import json
-import os
 import pathlib
 import re
 import shutil
@@ -7,7 +6,6 @@ import tempfile
 import textwrap
 from contextlib import contextmanager
 from pathlib import Path
-from unittest.mock import patch
 
 from click.testing import CliRunner
 
@@ -49,7 +47,6 @@ def temporary_config_folder(config_content: dict, levels: int = 0):
         shutil.rmtree(temp_dir)
 
 
-@patch.dict(os.environ, {"LANGGRAPH_CACHE_OPTIMIZE": "lock"})
 def test_prepare_args_and_stdin() -> None:
     # this basically serves as an end-to-end test for using config and docker helpers
     config_path = pathlib.Path(__file__).parent / "langgraph.json"
@@ -146,29 +143,15 @@ services:
             dockerfile_inline: |
                 # syntax=docker/dockerfile:1.4
                 FROM langchain/langgraph-api:3.11
-                # -- Generate requirements.txt for packages without one --
-                # Copy packaging metadata files
-                ADD pyproject.toml /tmp/dep_metadata/cli/pyproject.toml
-                COPY --from=cli_1 pyproject.toml /tmp/dep_metadata/cli_1/pyproject.toml
-                COPY --from=cli_1 uv.lock /tmp/dep_metadata/cli_1/uv.lock
-                # Generate requirements.txt from packaging metadata
-                RUN cd '/tmp/dep_metadata/cli' && uv pip compile pyproject.toml -o 'requirements.txt' --constraint /api/constraints.txt
-                RUN cd '/tmp/dep_metadata/cli_1' && uv export --no-hashes --no-dev --no-emit-local -o 'requirements.txt'
-                # -- End of requirements.txt generation --
-                # -- Installing from requirements.txt files --
-                RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -r '/tmp/dep_metadata/cli/requirements.txt'
-                RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -r '/tmp/dep_metadata/cli_1/requirements.txt'
-                # -- End of requirements.txt install --
                 # -- Adding local package . --
                 ADD . /deps/cli
                 # -- End of local package . --
+                # -- Adding local package ../../.. --
+                COPY --from=cli_1 . /deps/cli_1
+                # -- End of local package ../../.. --
                 # -- Installing all local dependencies --
                 RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
-                # -- Adding full source for local package ../../.. --
-                COPY --from=cli_1 . /deps/cli_1
-                RUN cd /deps/cli_1 && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt --no-deps -e .
-                # -- End of full source for local package ../../.. --
                 ENV LANGSERVE_GRAPHS='{{"agent": "agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
                 WORKDIR /deps/cli

libs/cli/tests/unit_tests/test_cache_optimization.py

@@ -1,100 +0,0 @@
-import json
-import os
-import pathlib
-from unittest.mock import patch
-
-import pytest
-
-from langgraph_cli.config import config_to_docker, validate_config
-
-FIXTURES_ROOT = (
-    pathlib.Path(__file__).parent.parent / "local_cache_validation" / "projects"
-)
-CACHE_ENV = "LANGGRAPH_CACHE_OPTIMIZE"
-TIERS = ("off", "lock", "pyproject", "all")
-
-pytestmark = pytest.mark.skipif(
-    os.getenv("RUN_LOCAL_CACHE_VALIDATION") != "1",
-    reason="Local-only cache optimization TDD tests.",
-)
-
-
-def _dockerfile_for_fixture(fixture_name: str, tier: str) -> str:
-    config_path = FIXTURES_ROOT / fixture_name / "langgraph.json"
-    with open(config_path, encoding="utf-8") as f:
-        raw = json.load(f)
-    config = validate_config(raw)
-
-    with patch.dict(os.environ, {CACHE_ENV: tier}):
-        dockerfile, _ = config_to_docker(
-            config_path=config_path,
-            config=config,
-            base_image="langchain/langgraph-api",
-        )
-    return dockerfile
-
-
-@pytest.mark.parametrize(
-    "fixture_name,expected",
-    [
-        (
-            "real_pyproject_lock",
-            {"off": False, "lock": True, "pyproject": True, "all": True},
-        ),
-        (
-            "real_pyproject_no_lock",
-            {"off": False, "lock": False, "pyproject": True, "all": True},
-        ),
-        (
-            "real_setup_py",
-            {"off": False, "lock": False, "pyproject": False, "all": True},
-        ),
-        (
-            "faux_package",
-            {"off": False, "lock": False, "pyproject": False, "all": False},
-        ),
-        (
-            "pip_installer_fallback",
-            {"off": False, "lock": False, "pyproject": False, "all": False},
-        ),
-    ],
-)
-def test_tier_deferred_copy_matrix(
-    fixture_name: str, expected: dict[str, bool]
-) -> None:
-    for tier in TIERS:
-        dockerfile = _dockerfile_for_fixture(fixture_name, tier)
-        has_no_deps_editable = "--no-deps -e ." in dockerfile
-        assert has_no_deps_editable is expected[tier], (
-            f"{fixture_name=} {tier=} expected deferred copy {expected[tier]}, "
-            f"got {has_no_deps_editable}"
-        )
-
-
-def test_requirements_before_no_deps_for_eligible_tier() -> None:
-    dockerfile = _dockerfile_for_fixture("real_pyproject_lock", "lock")
-    reqs_idx = dockerfile.find("Installing from requirements.txt files")
-    no_deps_idx = dockerfile.find("--no-deps -e .")
-    assert reqs_idx != -1
-    assert no_deps_idx != -1
-    assert reqs_idx < no_deps_idx
-
-
-def test_generation_command_selection_by_fixture() -> None:
-    docker_lock = _dockerfile_for_fixture("real_pyproject_lock", "lock")
-    assert "uv export --no-hashes --no-dev --no-emit-local" in docker_lock
-
-    docker_pyproject = _dockerfile_for_fixture("real_pyproject_no_lock", "pyproject")
-    assert "uv pip compile pyproject.toml" in docker_pyproject
-
-    docker_setup = _dockerfile_for_fixture("real_setup_py", "all")
-    assert "uv pip compile setup.py" in docker_setup
-
-
-def test_pip_installer_fallback_ignores_tier() -> None:
-    outputs = {
-        tier: _dockerfile_for_fixture("pip_installer_fallback", tier) for tier in TIERS
-    }
-    first = outputs["off"]
-    for tier, dockerfile in outputs.items():
-        assert dockerfile == first, f"Expected identical dockerfile for {tier=}"

libs/cli/tests/unit_tests/test_config.py

@@ -4,7 +4,6 @@ import os
 import pathlib
 import tempfile
 import textwrap
-from unittest.mock import patch
 
 import click
 import pytest
@@ -121,14 +120,14 @@ def test_validate_config():
         validate_config({"python_version": "3.10"})
     assert "Minimum required version" in str(exc_info.value)
 
-    with pytest.raises(click.UsageError, match="Bullseye images were deprecated"):
-        validate_config(
-            {
-                "python_version": "3.11-bullseye",
-                "dependencies": ["."],
-                "graphs": {"agent": "./agent.py:graph"},
-            }
-        )
+    config = validate_config(
+        {
+            "python_version": "3.11-bullseye",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+        }
+    )
+    assert config["python_version"] == "3.11-bullseye"
 
     config = validate_config(
         {
@@ -182,17 +181,6 @@ def test_validate_config_image_distro():
     )
     assert config["image_distro"] == "debian"
 
-    # Bullseye should raise deprecation error
-    with pytest.raises(click.UsageError, match="Bullseye images were deprecated"):
-        validate_config(
-            {
-                "python_version": "3.11",
-                "dependencies": ["."],
-                "graphs": {"agent": "./agent.py:graph"},
-                "image_distro": "bullseye",
-            }
-        )
-
     # Invalid image_distro values should raise error
     with pytest.raises(click.UsageError) as exc_info:
         validate_config(
@@ -421,7 +409,6 @@ def test_validate_config_multiplatform():
 
 
 # config_to_docker
-@patch.dict(os.environ, {"LANGGRAPH_CACHE_OPTIMIZE": "lock"})
 def test_config_to_docker_simple():
     graphs = {"agent": "./agent.py:graph"}
     actual_docker_stdin, additional_contexts = config_to_docker(
@@ -438,17 +425,10 @@ def test_config_to_docker_simple():
     expected_docker_stdin = f"""\
 # syntax=docker/dockerfile:1.4
 FROM langchain/langgraph-api:3.11
-# -- Generate requirements.txt for packages without one --
-# Copy packaging metadata files
-COPY --from=examples pyproject.toml /tmp/dep_metadata/examples/pyproject.toml
-# Generate requirements.txt from packaging metadata
-RUN cd '/tmp/dep_metadata/examples' && uv pip compile pyproject.toml -o 'requirements.txt' --constraint /api/constraints.txt
-# -- End of requirements.txt generation --
-# -- Installing from requirements.txt files --
-COPY --from=outer-graphs_reqs_a requirements.txt /deps/outer-graphs_reqs_a/graphs_reqs_a/requirements.txt
-RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -r '/deps/outer-graphs_reqs_a/graphs_reqs_a/requirements.txt'
-RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -r '/tmp/dep_metadata/examples/requirements.txt'
-# -- End of requirements.txt install --
+# -- Installing local requirements --
+COPY --from=outer-requirements.txt requirements.txt /deps/outer-graphs_reqs_a/graphs_reqs_a/requirements.txt
+RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -r /deps/outer-graphs_reqs_a/graphs_reqs_a/requirements.txt
+# -- End of local requirements install --
 # -- Adding local package ../../examples --
 COPY --from=examples . /deps/examples
 # -- End of local package ../../examples --
@@ -656,7 +636,6 @@ ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-graphs/src/agent.py:graph"}}'
     assert additional_contexts == {}
 
 
-@patch.dict(os.environ, {"LANGGRAPH_CACHE_OPTIMIZE": "lock"})
 def test_config_to_docker_pyproject():
     pyproject_str = """[project]
 name = "custom"
@@ -680,15 +659,6 @@ dependencies = ["langchain"]"""
     os.remove(pyproject_path)
     expected_docker_stdin = (
         """FROM langchain/langgraph-api:3.11
-# -- Generate requirements.txt for packages without one --
-# Copy packaging metadata files
-ADD pyproject.toml /tmp/dep_metadata/unit_tests/pyproject.toml
-# Generate requirements.txt from packaging metadata
-RUN cd '/tmp/dep_metadata/unit_tests' && uv pip compile pyproject.toml -o 'requirements.txt' --constraint /api/constraints.txt
-# -- End of requirements.txt generation --
-# -- Installing from requirements.txt files --
-RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -r '/tmp/dep_metadata/unit_tests/requirements.txt'
-# -- End of requirements.txt install --
 # -- Adding local package . --
 ADD . /deps/unit_tests
 # -- End of local package . --

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 = [
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+    { 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" },
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 [[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'" },
 ]
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+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" }
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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'" },
 ]
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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" },
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 [[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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-version = "0.23.1"
+version = "0.22.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -1069,9 +1069,9 @@ dependencies = [
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 [[package]]

libs/langgraph/langgraph/_internal/_queue.py

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

libs/langgraph/langgraph/graph/state.py

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