chore: server runtime type (#6774)

Main jtbd here:
a) clarify who/how a graph is being accessed and make the factory aware
of the `context` where relevant (and make it obvious when it is
available)
b) make it more clear when you can bypass / defer resources with
expensive lifespans (like MCp connections)
c) make auth access more type-safe.

Gives us room to add other information, like:
- langsmith distributed tracing information



---- old
Can start doing things like this:

```
def my_graph(runtime: ServerRuntime):
     if runtime.ensure_user().permissions not in ("foo"):
         raise ValueError("bar")
```
etc.

Points of expected confusion:
- You won't have a stream_writer in this context.
- This won't be an accessible object within the graph, only the graph
factory.


For maintainers, related draft PR int he server
https://github.com/langchain-ai/langgraph/pull/6774

.github/CONTRIBUTING.md

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

.github/ISSUE_TEMPLATE/bug-report.yml

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

.github/ISSUE_TEMPLATE/config.yml

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

.github/PULL_REQUEST_TEMPLATE.md

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

AGENTS.md

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

CLAUDE.md

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

examples/README.md

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

examples/async.ipynb

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

examples/branching.ipynb

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

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

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

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

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

examples/chatbots/information-gather-prompting.ipynb

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

examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb

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

examples/code_assistant/langgraph_code_assistant.ipynb

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

examples/code_assistant/langgraph_code_assistant_mistral.ipynb

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

examples/configuration.ipynb

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

examples/create-react-agent-hitl.ipynb

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

examples/create-react-agent-memory.ipynb

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

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

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

examples/create-react-agent.ipynb

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

examples/customer-support/customer-support.ipynb

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

examples/extraction/retries.ipynb

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

examples/human_in_the_loop/wait-user-input.ipynb

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

examples/input_output_schema.ipynb

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

examples/lats/lats.ipynb

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

examples/llm-compiler/LLMCompiler.ipynb

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

examples/map-reduce.ipynb

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

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

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

examples/memory/delete-messages.ipynb

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

examples/memory/manage-conversation-history.ipynb

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

examples/multi_agent/hierarchical_agent_teams.ipynb

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

examples/multi_agent/multi-agent-collaboration.ipynb

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

examples/node-retries.ipynb

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

examples/pass-config-to-tools.ipynb

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

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

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

examples/pass_private_state.ipynb

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

examples/persistence.ipynb

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

examples/persistence_mongodb.ipynb

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

examples/persistence_postgres.ipynb

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

examples/persistence_redis.ipynb

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

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

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

examples/rag/langgraph_adaptive_rag.ipynb

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

examples/rag/langgraph_adaptive_rag_local.ipynb

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

examples/rag/langgraph_agentic_rag.ipynb

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

examples/rag/langgraph_crag.ipynb

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

examples/rag/langgraph_crag_local.ipynb

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

examples/rag/langgraph_self_rag.ipynb

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

examples/rag/langgraph_self_rag_local.ipynb

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

examples/rag/langgraph_self_rag_pinecone_movies.ipynb

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

examples/react-agent-from-scratch.ipynb

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

examples/react-agent-structured-output.ipynb

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

examples/recursion-limit.ipynb

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

examples/reflection/reflection.ipynb

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

examples/reflexion/reflexion.ipynb

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

examples/rewoo/rewoo.ipynb

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

examples/run-id-langsmith.ipynb

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

examples/self-discover/self-discover.ipynb

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

examples/state-model.ipynb

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

examples/storm/storm.ipynb

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

examples/stream-multiple.ipynb

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

examples/stream-updates.ipynb

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

examples/stream-values.ipynb

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

examples/streaming-content.ipynb

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

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

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

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

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

examples/streaming-from-final-node.ipynb

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

examples/streaming-subgraphs.ipynb

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

examples/streaming-tokens-without-langchain.ipynb

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

examples/streaming-tokens.ipynb

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

examples/subgraph-transform-state.ipynb

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

examples/subgraph.ipynb

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

examples/subgraphs-manage-state.ipynb

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

examples/tool-calling-errors.ipynb

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

examples/tool-calling.ipynb

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

examples/tutorials/sql-agent.ipynb

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

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

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

examples/usaco/usaco.ipynb

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

examples/visualization.ipynb

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

examples/web-navigation/web_voyager.ipynb

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

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

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

libs/checkpoint-postgres/pyproject.toml

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

libs/checkpoint-postgres/uv.lock

@@ -13,16 +13,16 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.12.0"
+version = "4.12.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "idna" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = "2026-01-06T11:45:21.246Z" }
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+    { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
 ]
 
 [[package]]
@@ -36,11 +36,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2025.11.12"
+version = "2026.1.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a2/8c/58f469717fa48465e4a50c014a0400602d3c437d7c0c468e17ada824da3a/certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316", size = 160538, upload-time = "2025-11-12T02:54:51.517Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e0/2d/a891ca51311197f6ad14a7ef42e2399f36cf2f9bd44752b3dc4eab60fdc5/certifi-2026.1.4.tar.gz", hash = "sha256:ac726dd470482006e014ad384921ed6438c457018f4b3d204aea4281258b2120", size = 154268, upload-time = "2026-01-04T02:42:41.825Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/e6/ad/3cc14f097111b4de0040c83a525973216457bbeeb63739ef1ed275c1c021/certifi-2026.1.4-py3-none-any.whl", hash = "sha256:9943707519e4add1115f44c2bc244f782c0249876bf51b6599fee1ffbedd685c", size = 152900, upload-time = "2026-01-04T02:42:40.15Z" },
 ]
 
 [[package]]
@@ -240,7 +240,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.1.3"
+version = "1.2.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
@@ -252,9 +252,9 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uuid-utils" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0b/6f/9e959821df556cf32d57d26aa7b86905a63a91e6e9e744994f8045fe2d70/langchain_core-1.1.3.tar.gz", hash = "sha256:ff0bc5e6e701c4d6fe00c73c4feae5c993a7a8e0b91f0a1d07015277d4634275", size = 801750, upload-time = "2025-12-09T14:55:55.741Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a2/0e/664d8d81b3493e09cbab72448d2f9d693d1fa5aa2bcc488602203a9b6da0/langchain_core-1.2.7.tar.gz", hash = "sha256:e1460639f96c352b4a41c375f25aeb8d16ffc1769499fb1c20503aad59305ced", size = 837039, upload-time = "2026-01-09T17:44:25.505Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/6e/6f/34a9fba14d191a67f7e2ee3dbce3e9b86d2fa7310e2c7f2c713583481bd2/langchain_core-1.2.7-py3-none-any.whl", hash = "sha256:452f4fef7a3d883357b22600788d37e3d8854ef29da345b7ac7099f33c31828b", size = 490232, upload-time = "2026-01-09T17:44:24.236Z" },
 ]
 
 [[package]]
@@ -306,7 +306,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.3"
+version = "3.0.4"
 source = { editable = "." }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -381,7 +381,7 @@ test = [
 
 [[package]]
 name = "langsmith"
-version = "0.4.57"
+version = "0.6.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -393,128 +393,128 @@ dependencies = [
     { name = "uuid-utils" },
     { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/66/18/fbd779dd73bae0389f8dde1de02299e9cda7ee59db56dcadb761a8b22cec/langsmith-0.4.57.tar.gz", hash = "sha256:c6e55266ec82c559517359cfbd82c70ea6c216b48f873ba9b268896bde381e35", size = 991971, upload-time = "2025-12-09T21:53:53.196Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
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+    { url = "https://files.pythonhosted.org/packages/66/0f/09a6637a7ba777eb307b7c80852d9ee26438e2bdafbad6fcc849ff9d9192/langsmith-0.6.4-py3-none-any.whl", hash = "sha256:ac4835860160be371042c7adbba3cb267bcf8d96a5ea976c33a8a4acad6c5486", size = 283503, upload-time = "2026-01-15T20:02:26.662Z" },
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 [[package]]
 name = "librt"
-version = "0.7.3"
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 ]
 
 [[package]]

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

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

libs/checkpoint-sqlite/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-sqlite"
-version = "3.0.2"
+version = "3.0.3"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"

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

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

libs/cli/langgraph_cli/__init__.py

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

libs/cli/langgraph_cli/archive.py

@@ -1,133 +0,0 @@
-"""Create a tarball of project source for remote builds."""
-
-import os
-import pathlib
-import tarfile
-import tempfile
-
-import click
-
-
-_WARN_SIZE = 50 * 1024 * 1024  # 50 MB
-_MAX_SIZE = 200 * 1024 * 1024  # 200 MB
-
-
-def _tar_filter(tarinfo: tarfile.TarInfo) -> tarfile.TarInfo | None:
-    """Strip symlinks, hardlinks, and traversal paths from archive."""
-    if tarinfo.issym() or tarinfo.islnk():
-        return None
-    if ".." in tarinfo.name.split("/"):
-        return None
-    return tarinfo
-
-
-def _read_ignore_patterns(context_dir: pathlib.Path) -> list[str]:
-    """Read .dockerignore patterns if present."""
-    dockerignore = context_dir / ".dockerignore"
-    if dockerignore.is_file():
-        patterns = []
-        for line in dockerignore.read_text().splitlines():
-            line = line.strip()
-            if line and not line.startswith("#"):
-                patterns.append(line)
-        return patterns
-    return []
-
-
-def _should_ignore(rel_path: str, patterns: list[str]) -> bool:
-    """Check if a relative path matches any dockerignore pattern."""
-    import fnmatch
-
-    rel_path = rel_path.replace(os.sep, "/")
-
-    for pattern in patterns:
-        negate = pattern.startswith("!")
-        if negate:
-            pattern = pattern[1:]
-
-        if fnmatch.fnmatch(rel_path, pattern) or fnmatch.fnmatch(
-            rel_path, f"**/{pattern}"
-        ):
-            if negate:
-                return False
-            return True
-
-        parts = rel_path.split("/")
-        for i in range(len(parts)):
-            partial = "/".join(parts[: i + 1])
-            if fnmatch.fnmatch(partial, pattern):
-                if negate:
-                    return False
-                return True
-
-    return False
-
-
-def create_archive(
-    config_path: pathlib.Path,
-) -> tuple[str, int]:
-    """Create a .tar.gz archive of the project source.
-
-    Returns (archive_path, file_size).
-    The archive root is config.parent (the directory containing langgraph.json).
-    """
-    context_dir = config_path.parent.resolve()
-    config_filename = config_path.name
-    ignore_patterns = _read_ignore_patterns(context_dir)
-
-    tmp_dir = tempfile.mkdtemp(prefix="langgraph-deploy-")
-    archive_path = os.path.join(tmp_dir, "source.tar.gz")
-
-    with tarfile.open(archive_path, "w:gz") as tar:
-        for root, dirs, files in os.walk(context_dir):
-            rel_root = os.path.relpath(root, context_dir)
-            if rel_root == ".":
-                rel_root = ""
-
-            dirs[:] = [
-                d
-                for d in dirs
-                if not _should_ignore(
-                    os.path.join(rel_root, d) if rel_root else d, ignore_patterns
-                )
-            ]
-
-            for f in files:
-                rel_path = os.path.join(rel_root, f) if rel_root else f
-                if _should_ignore(rel_path, ignore_patterns):
-                    continue
-                full_path = os.path.join(root, f)
-                arcname = rel_path.replace(os.sep, "/")
-                info = tar.gettarinfo(full_path, arcname=arcname)
-                filtered = _tar_filter(info)
-                if filtered is None:
-                    continue
-                with open(full_path, "rb") as fobj:
-                    tar.addfile(filtered, fobj)
-
-    file_size = os.path.getsize(archive_path)
-
-    # Validate config file is at archive root
-    with tarfile.open(archive_path, "r:gz") as tar:
-        names = tar.getnames()
-        if config_filename not in names:
-            os.unlink(archive_path)
-            raise click.ClickException(
-                f"Archive validation failed: {config_filename} not found at archive root"
-            )
-
-    if file_size > _MAX_SIZE:
-        os.unlink(archive_path)
-        raise click.ClickException(
-            f"Source archive is {file_size / 1_048_576:.1f} MB, which exceeds the 200 MB limit. "
-            "Check your .dockerignore for large files (model weights, data, node_modules, .venv)."
-        )
-
-    if file_size > _WARN_SIZE:
-        click.secho(
-            f"   Warning: source archive is {file_size / 1_048_576:.1f} MB. "
-            "Consider adding large files to .dockerignore.",
-            fg="yellow",
-        )
-
-    return archive_path, file_size

libs/cli/langgraph_cli/cli.py

@@ -1,18 +1,10 @@
 """CLI entrypoint for LangGraph API server."""
 
-import base64
-import copy
-import json as json_mod
 import os
 import pathlib
-import platform
-import re
 import shutil
 import sys
-import tempfile
-import time
 from collections.abc import Callable, Sequence
-from contextlib import contextmanager
 
 import click
 import click.exceptions
@@ -25,147 +17,11 @@ from langgraph_cli.config import Config
 from langgraph_cli.constants import DEFAULT_CONFIG, DEFAULT_PORT
 from langgraph_cli.docker import DockerCapabilities
 from langgraph_cli.exec import Runner, subp_exec
-from langgraph_cli.host_backend import HostBackendClient, HostBackendError
 from langgraph_cli.progress import Progress
 from langgraph_cli.templates import TEMPLATE_HELP_STRING, create_new
 from langgraph_cli.util import warn_non_wolfi_distro
 from langgraph_cli.version import __version__
 
-RESERVED_ENV_VARS = frozenset(
-    [
-        # LANGCHAIN_RESERVED_ENV_VARS from host-backend
-        "LANGCHAIN_TRACING_V2",
-        "LANGSMITH_TRACING_V2",
-        "LANGCHAIN_ENDPOINT",
-        "LANGCHAIN_PROJECT",
-        "LANGSMITH_PROJECT",
-        "LANGSMITH_LANGGRAPH_GIT_REPO",
-        "LANGGRAPH_GIT_REPO_PATH",
-        "LANGCHAIN_API_KEY",
-        "LANGSMITH_CONTROL_PLANE_API_KEY",
-        "POSTGRES_URI",
-        "POSTGRES_PASSWORD",
-        "DATABASE_URI",
-        "LANGSMITH_LANGGRAPH_GIT_REF",
-        "LANGSMITH_LANGGRAPH_GIT_REF_SHA",
-        "LANGGRAPH_AUTH_TYPE",
-        "LANGSMITH_AUTH_ENDPOINT",
-        "LANGSMITH_TENANT_ID",
-        "LANGSMITH_AUTH_VERIFY_TENANT_ID",
-        "LANGSMITH_HOST_PROJECT_ID",
-        "LANGSMITH_HOST_PROJECT_NAME",
-        "LANGSMITH_HOST_REVISION_ID",
-        "LOG_JSON",
-        "LOG_DICT_TRACEBACKS",
-        "REDIS_URI",
-        "LANGCHAIN_CALLBACKS_BACKGROUND",
-        "DD_TRACE_PSYCOPG_ENABLED",
-        "DD_TRACE_REDIS_ENABLED",
-        "LANGGRAPH_CLOUD_LICENSE_KEY",
-        # ALLOWED_SELF_HOSTED_ENV_VARS (rejected for non-self-hosted)
-        "LANGSMITH_API_KEY",
-        "LANGSMITH_ENDPOINT",
-        "POSTGRES_URI_CUSTOM",
-        "REDIS_URI_CUSTOM",
-        "PATH",
-        "PORT",
-        "MOUNT_PREFIX",
-        "LSD_ENV",
-        "LSD_DD_API_KEY",
-        "LSD_DD_ENDPOINT",
-        "LSD_DEPLOYMENT_TYPE",
-    ]
-)
-
-_API_KEY_ENV_NAMES = (
-    "LANGGRAPH_HOST_API_KEY",
-    "LANGSMITH_API_KEY",
-    "LANGCHAIN_API_KEY",
-)
-
-
-def _parse_dotenv_file(path: pathlib.Path) -> dict[str, str]:
-    """Parse a .env file into a dict, skipping comments and blank lines."""
-    result: dict[str, str] = {}
-    if not path.is_file():
-        return result
-    for line in path.read_text(encoding="utf-8").splitlines():
-        line = line.strip()
-        if not line or line.startswith("#"):
-            continue
-        if "=" not in line:
-            continue
-        key, _, value = line.partition("=")
-        key = key.strip()
-        value = value.strip()
-        if (
-            value
-            and len(value) >= 2
-            and value[0] == value[-1]
-            and value[0] in ("'", '"')
-        ):
-            value = value[1:-1]
-        if key:
-            result[key] = value
-    return result
-
-
-def _parse_env_from_config(
-    config_json: dict, config_path: pathlib.Path
-) -> dict[str, str]:
-    """Resolve env vars from langgraph.json 'env' field or a .env fallback."""
-    env_field = config_json.get("env")
-    if isinstance(env_field, dict):
-        return {str(k): str(v) for k, v in env_field.items()}
-    if isinstance(env_field, str):
-        env_path = (config_path.parent / env_field).resolve()
-        return _parse_dotenv_file(env_path)
-    fallback = pathlib.Path.cwd() / ".env"
-    return _parse_dotenv_file(fallback)
-
-
-def _secrets_from_env(
-    env_vars: dict[str, str],
-) -> list[dict[str, str]]:
-    """Convert env dict to secrets list, filtering reserved vars with warnings."""
-    secrets: list[dict[str, str]] = []
-    for name, value in env_vars.items():
-        if name in RESERVED_ENV_VARS:
-            click.secho(f"   Skipping reserved env var: {name}", fg="yellow")
-            continue
-        if not value:
-            continue
-        secrets.append({"name": name, "value": value})
-    return secrets
-
-
-_TERMINAL_STATUSES = frozenset(
-    [
-        "DEPLOYED",
-        "CREATE_FAILED",
-        "BUILD_FAILED",
-        "DEPLOY_FAILED",
-        "SKIPPED",
-    ]
-)
-
-
-@contextmanager
-def _docker_config_for_token(registry_host: str, token: str):
-    """Create a temporary Docker config with only the push token.
-
-    Yields the path to a temporary config directory that can be passed
-    to ``docker --config <path>`` so that system credential helpers
-    (e.g. gcloud) don't interfere with the push token.
-    """
-    auth_b64 = base64.b64encode(f"oauth2accesstoken:{token}".encode()).decode()
-    config_data = {"auths": {registry_host: {"auth": auth_b64}}}
-    with tempfile.TemporaryDirectory() as tmpdir:
-        with open(os.path.join(tmpdir, "config.json"), "w") as f:
-            json_mod.dump(config_data, f)
-        yield tmpdir
-
-
 OPT_DOCKER_COMPOSE = click.option(
     "--docker-compose",
     "-d",
@@ -448,37 +304,37 @@ def _build(
     passthrough: Sequence[str] = (),
     install_command: str | None = None,
     build_command: str | None = None,
-    docker_command: Sequence[str] | None = None,
-    extra_flags: Sequence[str] = (),
-    verbose: bool = True,
 ):
+    # pull latest images
     if pull:
         runner.run(
             subp_exec(
                 "docker",
                 "pull",
                 langgraph_cli.config.docker_tag(config_json, base_image, api_version),
-                verbose=verbose,
+                verbose=True,
             )
         )
     set("Building...")
+    # apply options
     args = [
         "-f",
         "-",  # stdin
         "-t",
         tag,
     ]
+    # determine build context: use current directory for JS projects, config parent for Python
     is_js_project = config_json.get("node_version") and not config_json.get(
         "python_version"
     )
+    # build/install commands only apply to JS projects for now
+    # without install/build command, JS projects will follow the old behavior
     if is_js_project and (build_command or install_command):
         build_context = str(pathlib.Path.cwd())
     else:
         build_context = str(config.parent)
 
-    # Deep copy to avoid mutating the caller's config (config_to_docker
-    # rewrites graph paths to container-internal paths in place).
-    config_json = copy.deepcopy(config_json)
+    # apply config
     stdin, additional_contexts = langgraph_cli.config.config_to_docker(
         config_path=config,
         config=config_json,
@@ -488,19 +344,19 @@ def _build(
         build_command=build_command,
         build_context=build_context,
     )
+    # add additional_contexts
     if additional_contexts:
         for k, v in additional_contexts.items():
             args.extend(["--build-context", f"{k}={v}"])
-    cmd = tuple(docker_command) if docker_command else ("docker", "build")
     runner.run(
         subp_exec(
-            *cmd,
+            "docker",
+            "build",
             *args,
-            *extra_flags,
             *passthrough,
             build_context,
             input=stdin,
-            verbose=verbose,
+            verbose=True,
         )
     )
 
@@ -573,662 +429,6 @@ def build(
         )
 
 
-@click.option(
-    "--api-key",
-    envvar="LANGGRAPH_HOST_API_KEY",
-    help="API key. If omitted, resolved from .env or prompted.",
-)
-@click.option(
-    "--name",
-    help=(
-        "Deployment name. Defaults to current directory name "
-        "if --deployment-id is not provided."
-    ),
-)
-@click.option(
-    "--deployment-id",
-    help=(
-        "ID of an existing deployment to update. If omitted, "
-        "--name is used to find or create the deployment."
-    ),
-)
-@click.option(
-    "--deployment-type",
-    type=click.Choice(["dev", "prod"]),
-    default="dev",
-    show_default=True,
-    help="Deployment type (used when creating a new deployment).",
-)
-@click.option(
-    "--no-wait",
-    is_flag=True,
-    default=False,
-    help="Skip waiting for deployment status.",
-)
-@OPT_VERBOSE
-@click.option(
-    "--host-url",
-    envvar="LANGGRAPH_HOST_URL",
-    default="https://api.host.langchain.com",
-    hidden=True,
-)
-@click.option("--image-name", hidden=True)
-@click.option("--image-tag", default="latest", hidden=True)
-@click.option(
-    "--config",
-    "-c",
-    default=DEFAULT_CONFIG,
-    hidden=True,
-    type=click.Path(
-        exists=True,
-        file_okay=True,
-        dir_okay=False,
-        resolve_path=True,
-        path_type=pathlib.Path,
-    ),
-)
-@click.option("--pull/--no-pull", default=True, hidden=True)
-@click.option("--base-image", hidden=True)
-@click.option("--install-command", hidden=True)
-@click.option("--build-command", hidden=True)
-@click.option("--api-version", type=str, hidden=True)
-@click.option(
-    "--remote/--no-remote",
-    default=None,
-    help=(
-        "Force or disable remote build. Default: auto-detect "
-        "(use remote build when Docker is unavailable)."
-    ),
-)
-@click.argument("docker_build_args", nargs=-1, type=click.UNPROCESSED)
-@cli.command(
-    help=(
-        "Build and deploy a LangGraph image to LangSmith Deployments.\n\n"
-        "Run from the root of your LangGraph project (where langgraph.json "
-        "is located). This command also accepts build flags (--base-image, "
-        "--pull, etc.). See 'langgraph build --help' for details."
-    ),
-    context_settings=dict(ignore_unknown_options=True),
-)
-@log_command
-def deploy(
-    config: pathlib.Path,
-    pull: bool,
-    verbose: bool,
-    api_version: str | None,
-    host_url: str | None,
-    api_key: str | None,
-    deployment_id: str | None,
-    deployment_type: str,
-    name: str | None,
-    image_name: str | None,
-    image_tag: str,
-    base_image: str | None,
-    install_command: str | None,
-    build_command: str | None,
-    no_wait: bool,
-    remote: bool | None,
-    docker_build_args: Sequence[str],
-):
-    config_json = langgraph_cli.config.validate_config_file(config)
-    warn_non_wolfi_distro(config_json)
-
-    env_vars = _parse_env_from_config(config_json, config)
-
-    if not api_key:
-        for key_name in _API_KEY_ENV_NAMES:
-            if key_name in env_vars:
-                api_key = env_vars[key_name]
-                break
-    if not api_key:
-        api_key = click.prompt("Host API key", hide_input=True)
-
-    if not deployment_id and not name:
-        default_name = _normalize_image_name(pathlib.Path.cwd().name)
-        name = click.prompt("Deployment name", default=default_name)
-
-    secrets = _secrets_from_env(env_vars)
-
-    # Determine whether to use remote build
-    use_remote = remote
-    if use_remote is None:
-        docker_available = langgraph_cli.docker.is_docker_available()
-        use_remote = not docker_available
-    elif use_remote is False:
-        pass  # --no-remote: fail if Docker is missing (existing behavior)
-
-    if use_remote:
-        _deploy_remote(
-            config=config,
-            config_json=config_json,
-            verbose=verbose,
-            api_version=api_version,
-            host_url=host_url,
-            api_key=api_key,
-            deployment_id=deployment_id,
-            deployment_type=deployment_type,
-            name=name,
-            base_image=base_image,
-            install_command=install_command,
-            build_command=build_command,
-            no_wait=no_wait,
-            secrets=secrets,
-        )
-    else:
-        _deploy_local(
-            config=config,
-            config_json=config_json,
-            verbose=verbose,
-            api_version=api_version,
-            host_url=host_url,
-            api_key=api_key,
-            deployment_id=deployment_id,
-            deployment_type=deployment_type,
-            name=name,
-            image_name=image_name,
-            image_tag=image_tag,
-            base_image=base_image,
-            install_command=install_command,
-            build_command=build_command,
-            no_wait=no_wait,
-            pull=pull,
-            docker_build_args=docker_build_args,
-            secrets=secrets,
-        )
-
-
-def _deploy_local(
-    *,
-    config: pathlib.Path,
-    config_json: dict,
-    verbose: bool,
-    api_version: str | None,
-    host_url: str | None,
-    api_key: str,
-    deployment_id: str | None,
-    deployment_type: str,
-    name: str | None,
-    image_name: str | None,
-    image_tag: str,
-    base_image: str | None,
-    install_command: str | None,
-    build_command: str | None,
-    no_wait: bool,
-    pull: bool,
-    docker_build_args: Sequence[str],
-    secrets: list[dict[str, str]],
-):
-    """Local Docker build + push deploy path."""
-    needs_buildx = platform.machine() != "x86_64"
-    local_tag = f"langgraph-deploy-tmp:{int(time.time())}"
-
-    with Runner() as runner:
-        langgraph_cli.docker.check_capabilities(
-            runner,
-            require_compose=False,
-            require_buildx=needs_buildx,
-        )
-
-        def log_step(message: str) -> None:
-            click.secho(message, fg="cyan")
-
-        step = 1
-
-        log_step(f"{step}. Building image")
-        if needs_buildx:
-            build_flags: list[str] = [
-                "--platform",
-                "linux/amd64",
-                "--load",
-            ]
-            if not verbose:
-                build_flags.append("--progress=quiet")
-            with Progress(message="Building...", elapsed=not verbose):
-                _build(
-                    runner,
-                    lambda _msg: None,
-                    config,
-                    config_json,
-                    base_image,
-                    api_version,
-                    pull,
-                    local_tag,
-                    docker_build_args,
-                    install_command,
-                    build_command,
-                    docker_command=("docker", "buildx", "build"),
-                    extra_flags=build_flags,
-                    verbose=verbose,
-                )
-        else:
-            with Progress(message="Building...", elapsed=not verbose):
-                _build(
-                    runner,
-                    lambda _msg: None,
-                    config,
-                    config_json,
-                    base_image,
-                    api_version,
-                    pull,
-                    local_tag,
-                    docker_build_args,
-                    install_command,
-                    build_command,
-                    verbose=verbose,
-                )
-        step += 1
-
-        client = HostBackendClient(host_url, api_key)
-
-        deployment_id = _find_or_create_deployment(
-            client, deployment_id, name, deployment_type, secrets, "internal_docker",
-            step, log_step,
-        )
-        step += 1
-
-        log_step(f"{step}. Requesting push token")
-        push_data = client.request_push_token(deployment_id)
-        deployment_token = push_data.get("token")
-        registry_url = push_data.get("registry_url")
-        if not deployment_token or not registry_url:
-            raise click.ClickException(
-                "Push token response missing token or registry_url"
-            )
-        step += 1
-
-        normalized_registry = registry_url.rstrip("/")
-        if "://" in normalized_registry:
-            normalized_registry = normalized_registry.split("//", 1)[1]
-        repo_seed = image_name or name or config.parent.name
-        repo_name = _normalize_image_name(repo_seed)
-        tag_value = _normalize_image_tag(image_tag)
-        remote_image = f"{normalized_registry}/{repo_name}:{tag_value}"
-
-        registry_host = normalized_registry.split("/")[0]
-
-        with _docker_config_for_token(registry_host, deployment_token) as cfg:
-            log_step(f"{step}. Logging into {registry_host}")
-            token_input = (
-                deployment_token
-                if deployment_token.endswith("\n")
-                else f"{deployment_token}\n"
-            )
-            runner.run(
-                subp_exec(
-                    "docker",
-                    "--config",
-                    cfg,
-                    "login",
-                    "-u",
-                    "oauth2accesstoken",
-                    "--password-stdin",
-                    registry_host,
-                    input=token_input,
-                    verbose=verbose,
-                )
-            )
-            step += 1
-
-            log_step(f"{step}. Pushing image {remote_image}")
-            runner.run(
-                subp_exec(
-                    "docker",
-                    "tag",
-                    local_tag,
-                    remote_image,
-                    verbose=verbose,
-                )
-            )
-            with Progress(message="Pushing...", elapsed=not verbose):
-                runner.run(
-                    subp_exec(
-                        "docker",
-                        "--config",
-                        cfg,
-                        "push",
-                        remote_image,
-                        verbose=verbose,
-                    )
-                )
-        step += 1
-
-        log_step(f"{step}. Updating deployment {deployment_id}")
-        client.update_deployment(deployment_id, remote_image, secrets=secrets)
-
-        if no_wait:
-            click.secho("   Deployment updated", fg="green")
-            return
-
-        _poll_revision_status(client, deployment_id, verbose=verbose)
-
-
-def _deploy_remote(
-    *,
-    config: pathlib.Path,
-    config_json: dict,
-    verbose: bool,
-    api_version: str | None,
-    host_url: str | None,
-    api_key: str,
-    deployment_id: str | None,
-    deployment_type: str,
-    name: str | None,
-    base_image: str | None,
-    install_command: str | None,
-    build_command: str | None,
-    no_wait: bool,
-    secrets: list[dict[str, str]],
-):
-    """Remote build deploy path (no local Docker required)."""
-    import urllib.request
-
-    from langgraph_cli.archive import create_archive
-
-    def log_step(message: str) -> None:
-        click.secho(message, fg="cyan")
-
-    step = 1
-    click.secho("Docker not available. Using remote build.", fg="yellow")
-
-    # -- Step: Create tarball --
-    log_step(f"{step}. Creating source archive")
-    try:
-        archive_path, file_size = create_archive(config)
-    except KeyboardInterrupt:
-        click.echo("\nCancelled.")
-        raise click.exceptions.Exit(1)
-    click.secho(
-        f"   Archive created ({file_size / 1_048_576:.1f} MB)", fg="green"
-    )
-    step += 1
-
-    client = HostBackendClient(host_url, api_key)
-
-    # -- Step: Find or create deployment --
-    deployment_id = _find_or_create_deployment(
-        client, deployment_id, name, deployment_type, secrets, "internal_source",
-        step, log_step,
-    )
-    step += 1
-
-    # -- Step: Request upload URL --
-    log_step(f"{step}. Requesting upload URL")
-    upload_data = client.request_upload_url(deployment_id)
-    signed_url = upload_data.get("upload_url")
-    object_path = upload_data.get("object_path")
-    if not signed_url or not object_path:
-        raise click.ClickException("Upload URL response missing required fields")
-    step += 1
-
-    # -- Step: Upload tarball --
-    log_step(f"{step}. Uploading source")
-    try:
-        _upload_to_gcs(signed_url, archive_path, file_size)
-    except KeyboardInterrupt:
-        click.echo("\nUpload cancelled.")
-        raise click.exceptions.Exit(1)
-    finally:
-        try:
-            os.unlink(archive_path)
-        except OSError:
-            pass
-    step += 1
-
-    # -- Step: Update deployment --
-    log_step(f"{step}. Triggering remote build")
-    client.update_deployment_internal_source(
-        deployment_id,
-        source_tarball_path=object_path,
-        secrets=secrets,
-        config_path=config.name,
-        install_command=install_command,
-        build_command=build_command,
-    )
-    step += 1
-
-    if no_wait:
-        click.secho("   Build triggered", fg="green")
-        return
-
-    # -- Poll revision status with optional log streaming --
-    _poll_revision_status(client, deployment_id, verbose=verbose, is_remote_build=True)
-
-
-def _upload_to_gcs(signed_url: str, file_path: str, file_size: int) -> None:
-    """Upload tarball to GCS via signed PUT URL with progress display."""
-    import urllib.request
-
-    uploaded = 0
-
-    with open(file_path, "rb") as f:
-        original_read = f.read
-
-        def tracked_read(size=-1):
-            nonlocal uploaded
-            data = original_read(size)
-            if data:
-                uploaded += len(data)
-                pct = int(uploaded * 100 / file_size) if file_size else 100
-                click.echo(
-                    f"\r   Uploading ({file_size / 1_048_576:.1f} MB)... {pct}%",
-                    nl=False,
-                )
-            return data
-
-        f.read = tracked_read
-
-        req = urllib.request.Request(
-            signed_url,
-            data=f,
-            method="PUT",
-            headers={
-                "Content-Type": "application/gzip",
-                "Content-Length": str(file_size),
-                "X-Goog-Content-Length-Range": "0,209715200",
-            },
-        )
-        try:
-            urllib.request.urlopen(req)
-        except urllib.error.HTTPError as err:
-            detail = err.read().decode("utf-8", errors="ignore")
-            raise click.ClickException(
-                f"Upload failed with status {err.code}: {detail}"
-            ) from None
-    click.echo()
-
-
-def _find_or_create_deployment(
-    client: HostBackendClient,
-    deployment_id: str | None,
-    name: str | None,
-    deployment_type: str,
-    secrets: list[dict[str, str]],
-    source: str,
-    step: int,
-    log_step: Callable[[str], None],
-) -> str:
-    """Find an existing deployment or create a new one. Returns deployment_id."""
-    if deployment_id:
-        log_step(f"{step}. Using deployment {deployment_id}")
-        return deployment_id
-
-    log_step(f"{step}. Looking up deployment '{name}'")
-    existing = client.list_deployments(name_contains=name)
-    found_id = None
-    if isinstance(existing, dict):
-        for dep in existing.get("resources", []):
-            if isinstance(dep, dict) and dep.get("name") == name:
-                found_id = dep.get("id")
-                break
-    if found_id:
-        deployment_id = str(found_id)
-        click.secho(
-            f"   Found existing deployment (ID: {deployment_id})",
-            fg="green",
-        )
-        return deployment_id
-
-    log_step(f"   Creating deployment '{name}'")
-    payload = {
-        "name": name,
-        "source": source,
-        "source_config": {"deployment_type": deployment_type},
-        "source_revision_config": {},
-        "secrets": secrets,
-    }
-    created = client.create_deployment(payload)
-    created_id = created.get("id") if isinstance(created, dict) else None
-    if not isinstance(created_id, str) or not created_id:
-        raise HostBackendError(
-            "POST /v2/deployments succeeded but response missing a valid 'id'"
-        )
-    deployment_id = created_id
-    click.secho(f"   Deployment ID: {deployment_id}", fg="green")
-    return deployment_id
-
-
-def _poll_revision_status(
-    client: HostBackendClient,
-    deployment_id: str,
-    *,
-    verbose: bool = False,
-    is_remote_build: bool = False,
-) -> None:
-    """Poll revision status until terminal, optionally streaming build logs."""
-    revisions_resp = client.list_revisions(deployment_id, limit=1)
-    resources = (
-        revisions_resp.get("resources", [])
-        if isinstance(revisions_resp, dict)
-        else []
-    )
-    if not resources:
-        click.secho("   Deployment updated", fg="green")
-        return
-
-    revision_id = str(resources[0]["id"])
-    last_status = ""
-    log_offset: str | None = None
-
-    deadline = time.time() + 900 if is_remote_build else time.time() + 300
-    with Progress(message="Deploying...", elapsed=True) as set_progress:
-        while time.time() < deadline:
-            try:
-                rev = client.get_revision(deployment_id, revision_id)
-            except KeyboardInterrupt:
-                set_progress("")
-                click.secho(
-                    f"\n   Interrupted. Deployment ID: {deployment_id}, "
-                    f"Revision ID: {revision_id}",
-                    fg="yellow",
-                )
-                click.secho(
-                    "   The build will continue remotely.",
-                    fg="yellow",
-                )
-                raise click.exceptions.Exit(1)
-
-            status = (
-                rev.get("status", "UNKNOWN") if isinstance(rev, dict) else "UNKNOWN"
-            )
-            if status != last_status:
-                last_status = status
-                set_progress("")
-                click.secho(f"   Status: {status}", fg="cyan")
-                if status in _TERMINAL_STATUSES:
-                    break
-                set_progress(f"{status}...")
-
-            # Stream build logs when verbose and building
-            if (
-                is_remote_build
-                and verbose
-                and status in ("AWAITING_BUILD", "BUILDING")
-            ):
-                try:
-                    logs_resp = client.list_build_logs(
-                        deployment_id, revision_id, offset=log_offset
-                    )
-                    if isinstance(logs_resp, dict):
-                        for entry in logs_resp.get("logs", []):
-                            msg = entry.get("message", "")
-                            if msg:
-                                click.echo(f"   | {msg}")
-                        log_offset = logs_resp.get("next_offset") or log_offset
-                except Exception:
-                    pass
-
-            time.sleep(3)
-        else:
-            set_progress("")
-
-    # On BUILD_FAILED, tail the last few build log lines
-    if is_remote_build and last_status == "BUILD_FAILED" and not verbose:
-        click.secho("   Last build log lines:", fg="red")
-        try:
-            logs_resp = client.list_build_logs(
-                deployment_id, revision_id, order="desc", limit=30
-            )
-            if isinstance(logs_resp, dict):
-                entries = list(reversed(logs_resp.get("logs", [])))
-                for entry in entries:
-                    msg = entry.get("message", "")
-                    if msg:
-                        click.echo(f"   | {msg}")
-        except Exception:
-            click.secho("   (failed to fetch build logs)", fg="red")
-        click.secho(
-            "   Re-run with --verbose to see full build output.",
-            fg="yellow",
-        )
-
-    dep_info = client.get_deployment(deployment_id)
-    custom_url = None
-    if isinstance(dep_info, dict):
-        sc = dep_info.get("source_config")
-        if isinstance(sc, dict):
-            custom_url = sc.get("custom_url")
-
-    if last_status == "DEPLOYED":
-        click.secho("   Deployment successful!", fg="green")
-        if custom_url:
-            click.secho(f"   URL: {custom_url}", fg="green")
-    elif last_status in ("BUILD_FAILED", "DEPLOY_FAILED", "CREATE_FAILED"):
-        click.secho(f"   Deployment failed: {last_status}", fg="red")
-        raise click.exceptions.Exit(1)
-    else:
-        click.secho(
-            f"   Timed out waiting for deployment (last status: {last_status}).",
-            fg="yellow",
-        )
-        if custom_url:
-            click.secho(
-                f"   Check status at: {custom_url}",
-                fg="yellow",
-            )
-        else:
-            click.secho(
-                "   Check status in the LangSmith Deployments dashboard.",
-                fg="yellow",
-            )
-
-
-def _normalize_image_name(value: str | None) -> str:
-    if not value:
-        return "app"
-    slug = re.sub(r"[^a-z0-9._-]+", "-", value.lower()).strip("-.")
-    return slug or "app"
-
-
-def _normalize_image_tag(value: str) -> str:
-    if not value:
-        value = "latest"
-    if not re.fullmatch(r"[A-Za-z0-9_.-]+", value):
-        raise click.UsageError(
-            "Image tag may only contain characters A-Z, a-z, 0-9, '_', '-', '.'"
-        )
-    return value
-
-
 def _get_docker_ignore_content() -> str:
     """Return the content of a .dockerignore file.
 

libs/cli/langgraph_cli/docker.py

@@ -24,9 +24,9 @@ DockerComposeType = Literal["plugin", "standalone"]
 
 class DockerCapabilities(NamedTuple):
     version_docker: Version
-    version_compose: Version | None
+    version_compose: Version
     healthcheck_start_interval: bool
-    compose_type: DockerComposeType | None = None
+    compose_type: DockerComposeType = "plugin"
 
 
 def _parse_version(version: str) -> Version:
@@ -45,26 +45,7 @@ def _parse_version(version: str) -> Version:
     )
 
 
-def is_docker_available() -> bool:
-    """Check if Docker is installed and running without raising."""
-    if shutil.which("docker") is None:
-        return False
-    try:
-        import subprocess
-
-        result = subprocess.run(
-            ["docker", "info"],
-            capture_output=True,
-            timeout=10,
-        )
-        return result.returncode == 0
-    except Exception:
-        return False
-
-
-def check_capabilities(
-    runner, *, require_compose: bool = True, require_buildx: bool = False
-) -> DockerCapabilities:
+def check_capabilities(runner) -> DockerCapabilities:
     # check docker available
     if shutil.which("docker") is None:
         raise click.UsageError("Docker not installed") from None
@@ -80,33 +61,25 @@ def check_capabilities(
     if not info["ServerVersion"]:
         raise click.UsageError("Docker not running") from None
 
-    compose_type: DockerComposeType | None = None
-    compose_version: Version | None = None
-    if require_compose:
-        try:
-            compose = next(
-                p for p in info["ClientInfo"]["Plugins"] if p["Name"] == "compose"
-            )
-            compose_version_str = compose["Version"]
-            compose_type = "plugin"
-        except (KeyError, StopIteration):
-            if shutil.which("docker-compose") is None:
-                raise click.UsageError("Docker Compose not installed") from None
+    compose_type: DockerComposeType
+    try:
+        compose = next(
+            p for p in info["ClientInfo"]["Plugins"] if p["Name"] == "compose"
+        )
+        compose_version_str = compose["Version"]
+        compose_type = "plugin"
+    except (KeyError, StopIteration):
+        if shutil.which("docker-compose") is None:
+            raise click.UsageError("Docker Compose not installed") from None
 
-            compose_version_str, _ = runner.run(
-                subp_exec("docker-compose", "--version", "--short", collect=True)
-            )
-            compose_type = "standalone"
-        compose_version = _parse_version(compose_version_str)
-
-    if require_buildx:
-        try:
-            runner.run(subp_exec("docker", "buildx", "version", collect=True))
-        except click.exceptions.Exit:
-            raise click.UsageError("Docker Buildx not installed") from None
+        compose_version_str, _ = runner.run(
+            subp_exec("docker-compose", "--version", "--short", collect=True)
+        )
+        compose_type = "standalone"
 
     # parse versions
     docker_version = _parse_version(info["ServerVersion"])
+    compose_version = _parse_version(compose_version_str)
 
     # check capabilities
     return DockerCapabilities(

libs/cli/langgraph_cli/host_backend.py

@@ -1,163 +0,0 @@
-"""HTTP client for LangGraph host backend deployments."""
-
-from __future__ import annotations
-
-import json
-import urllib.error
-import urllib.parse
-import urllib.request
-from typing import Any
-
-import click
-
-
-class HostBackendError(click.ClickException):
-    """Raised when the host backend returns an error response."""
-
-
-class HostBackendClient:
-    """Minimal JSON HTTP client for the host backend deployment service."""
-
-    def __init__(self, base_url: str, api_key: str):
-        if not base_url:
-            raise click.UsageError("Host backend URL is required")
-        base_url = base_url.rstrip("/")
-        self._base_url = base_url
-        self._api_key = api_key
-
-    def _request(
-        self, method: str, path: str, payload: dict[str, Any] | None = None
-    ) -> Any:
-        url = f"{self._base_url}{path}"
-        data: bytes | None
-        if payload is not None:
-            data = json.dumps(payload).encode("utf-8")
-        else:
-            data = None
-        headers: dict[str, str] = {
-            "X-Api-Key": self._api_key,
-            "Accept": "application/json",
-        }
-        if data is not None:
-            headers["Content-Type"] = "application/json"
-        req = urllib.request.Request(url, data=data, headers=headers, method=method)
-        try:
-            with urllib.request.urlopen(req, timeout=30) as resp:
-                body = resp.read()
-        except urllib.error.HTTPError as err:
-            detail = err.read().decode("utf-8", errors="ignore")
-            message = detail or err.reason
-            raise HostBackendError(
-                f"{method} {path} failed with status {err.code}: {message}"
-            ) from None
-        except urllib.error.URLError as err:
-            raise HostBackendError(str(err.reason)) from None
-
-        if not body:
-            return None
-        try:
-            return json.loads(body)
-        except json.JSONDecodeError as err:
-            raise HostBackendError(
-                f"Failed to decode response from {path}: {err.msg}"
-            ) from None
-
-    def create_deployment(self, payload: dict[str, Any]) -> dict[str, Any]:
-        return self._request("POST", "/v2/deployments", payload)
-
-    def list_deployments(self, name_contains: str) -> dict[str, Any]:
-        encoded = urllib.parse.quote(name_contains, safe="")
-        return self._request("GET", f"/v2/deployments?name_contains={encoded}")
-
-    def get_deployment(self, deployment_id: str) -> dict[str, Any]:
-        return self._request("GET", f"/v2/deployments/{deployment_id}")
-
-    def request_push_token(self, deployment_id: str) -> dict[str, Any]:
-        return self._request(
-            "POST",
-            f"/v2/deployments/{deployment_id}/push-token",
-        )
-
-    def update_deployment(
-        self,
-        deployment_id: str,
-        image_uri: str,
-        secrets: list[dict[str, str]] | None = None,
-    ) -> dict[str, Any]:
-        payload: dict[str, Any] = {
-            "source_revision_config": {"image_uri": image_uri},
-        }
-        if secrets is not None:
-            payload["secrets"] = secrets
-        return self._request(
-            "PATCH",
-            f"/v2/deployments/{deployment_id}",
-            payload,
-        )
-
-    def list_revisions(self, deployment_id: str, limit: int = 1) -> dict[str, Any]:
-        return self._request(
-            "GET",
-            f"/v2/deployments/{deployment_id}/revisions?limit={limit}",
-        )
-
-    def get_revision(self, deployment_id: str, revision_id: str) -> dict[str, Any]:
-        return self._request(
-            "GET",
-            f"/v2/deployments/{deployment_id}/revisions/{revision_id}",
-        )
-
-    def request_upload_url(self, deployment_id: str) -> dict[str, Any]:
-        """Get a signed GCS URL for uploading the source tarball."""
-        return self._request(
-            "POST",
-            f"/v2/deployments/{deployment_id}/upload-url",
-        )
-
-    def update_deployment_internal_source(
-        self,
-        deployment_id: str,
-        source_tarball_path: str,
-        secrets: list[dict[str, str]] | None = None,
-        config_path: str | None = None,
-        install_command: str | None = None,
-        build_command: str | None = None,
-    ) -> dict[str, Any]:
-        """Trigger a remote build revision with the uploaded tarball."""
-        src_config: dict[str, Any] = {
-            "source_tarball_path": source_tarball_path,
-        }
-        if config_path is not None:
-            src_config["langgraph_config_path"] = config_path
-
-        payload: dict[str, Any] = {"source_revision_config": src_config}
-
-        source_config: dict[str, Any] = {}
-        if install_command is not None:
-            source_config["install_command"] = install_command
-        if build_command is not None:
-            source_config["build_command"] = build_command
-        if source_config:
-            payload["source_config"] = source_config
-
-        if secrets is not None:
-            payload["secrets"] = secrets
-        return self._request("PATCH", f"/v2/deployments/{deployment_id}", payload)
-
-    def list_build_logs(
-        self,
-        deployment_id: str,
-        revision_id: str,
-        order: str = "asc",
-        limit: int = 50,
-        offset: str | None = None,
-    ) -> dict[str, Any]:
-        """Fetch build logs for a revision."""
-        payload: dict[str, Any] = {"order": order, "limit": limit}
-        if offset:
-            payload["offset"] = offset
-        return self._request(
-            "POST",
-            f"/v1/projects/{deployment_id}/revisions/{revision_id}/build_logs",
-            payload,
-        )

libs/cli/langgraph_cli/progress.py

@@ -12,11 +12,8 @@ class Progress:
         while True:
             yield from "|/-\\"
 
-    def __init__(self, *, message="", elapsed: bool = False):
+    def __init__(self, *, message=""):
         self.message = message
-        self._base_message = message
-        self._show_elapsed = elapsed
-        self._stop = threading.Event()
         self.spinner_generator = self.spinning_cursor()
 
     def spinner_iteration(self):
@@ -24,6 +21,7 @@ class Progress:
         sys.stdout.write(next(self.spinner_generator) + " " + message)
         sys.stdout.flush()
         time.sleep(self.delay)
+        # clear the spinner and message
         sys.stdout.write(
             "\b" * (len(message) + 2)
             + " " * (len(message) + 2)
@@ -31,26 +29,13 @@ class Progress:
         )
         sys.stdout.flush()
 
-    def _format_elapsed(self, seconds: float) -> str:
-        mins, secs = divmod(int(seconds), 60)
-        if mins:
-            return f"{self._base_message} ({mins}m {secs:02d}s)"
-        return f"{self._base_message} ({secs}s)"
-
     def spinner_task(self):
-        start = time.monotonic()
-        while not self._stop.is_set():
-            if not self.message:
-                time.sleep(self.delay)
-                continue
-            if self._show_elapsed:
-                self.message = self._format_elapsed(time.monotonic() - start)
+        while self.message:
             message = self.message
-            if not message:
-                continue
             sys.stdout.write(next(self.spinner_generator) + " " + message)
             sys.stdout.flush()
             time.sleep(self.delay)
+            # clear the spinner and message
             sys.stdout.write(
                 "\b" * (len(message) + 2)
                 + " " * (len(message) + 2)
@@ -65,22 +50,21 @@ class Progress:
 
             def set_message(message):
                 self.message = message
-                self._base_message = message or self._base_message
+                if not message:
+                    self.thread.join()
 
             return set_message
         else:
 
             def set_message(message):
-                if message:
-                    sys.stderr.write(message + "\n")
-                    sys.stderr.flush()
+                sys.stderr.write(message + "\n")
+                sys.stderr.flush()
 
             return set_message
 
     def __exit__(self, exception, value, tb):
         if sys.stdout.isatty():
             self.message = ""
-            self._stop.set()
             try:
                 self.thread.join()
             finally:

libs/cli/langgraph_cli/schemas.py

@@ -179,6 +179,8 @@ class CheckpointerConfig(TypedDict, total=False):
 
     This configuration requires server version 0.5 or later to take effect.
     """
+    sweep_limit: int | None
+    """Maximum number of threads to process per sweep iteration. Defaults to 1000."""
 
 
 class SecurityConfig(TypedDict, total=False):

libs/cli/pyproject.toml

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

libs/cli/schemas/schema.json

@@ -554,6 +554,17 @@
           ],
           "description": "Optional. Defines the serde configuration.\n\nIf provided, the checkpointer will apply serde settings according to the configuration.\nIf omitted, no serde behavior is configured.\n\nThis configuration requires server version 0.5 or later to take effect.\n"
         },
+        "sweep_limit": {
+          "anyOf": [
+            {
+              "type": "integer"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Maximum number of threads to process per sweep iteration. Defaults to 1000."
+        },
         "ttl": {
           "anyOf": [
             {

libs/cli/schemas/schema.v0.json

@@ -554,6 +554,17 @@
           ],
           "description": "Optional. Defines the serde configuration.\n\nIf provided, the checkpointer will apply serde settings according to the configuration.\nIf omitted, no serde behavior is configured.\n\nThis configuration requires server version 0.5 or later to take effect.\n"
         },
+        "sweep_limit": {
+          "anyOf": [
+            {
+              "type": "integer"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Maximum number of threads to process per sweep iteration. Defaults to 1000."
+        },
         "ttl": {
           "anyOf": [
             {

libs/langgraph/langgraph/_internal/_queue.py

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