fix(cli): Escape variable substitution in compose codegen (#6594)

Description: Catch invalid checkpointer objects early by validating any
checkpointer argument before compilation/execution, raising a clear
TypeError that instructs users to pass a proper BaseCheckpointSaver
(e.g., AsyncPostgresSaver) instead of stores like AsyncPostgresStore.
Includes shared validation logic and a regression test so we don’t see
AttributeError: 'AsyncPostgresStore' object has no attribute
'get_next_version' again.

Issue: Fixes #6585

Dependencies: None

Twitter handle: none

.github/CONTRIBUTING.md

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

.github/scripts/check_sdk_methods.py

@@ -38,7 +38,7 @@ def main():
         tree = ast.parse(file.read())
 
     classes = find_classes(tree)
-  
+
     def is_sync(class_spec: Tuple[str, List[str]]) -> bool:
         return class_spec[0].startswith("Sync")
 

.github/workflows/_integration_test.yml

@@ -32,7 +32,7 @@ jobs:
       run:
         working-directory: libs/cli
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Get changed files
         id: changed-files
         uses: Ana06/get-changed-files@v2.3.0

.github/workflows/_lint.yml

@@ -31,7 +31,7 @@ jobs:
           - "3.12"
     name: "lint #${{ matrix.python-version }}"
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Get changed files
         id: changed-files
         uses: Ana06/get-changed-files@v2.3.0

.github/workflows/_test.yml

@@ -25,7 +25,7 @@ jobs:
 
     name: "test #${{ matrix.python-version }}"
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Set up Python ${{ matrix.python-version }}
         uses: astral-sh/setup-uv@v7
         with:

.github/workflows/_test_langgraph.yml

@@ -23,7 +23,7 @@ jobs:
         working-directory: libs/langgraph
     name: "test #${{ matrix.python-version }}"
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Set up Python ${{ matrix.python-version }}
         uses: astral-sh/setup-uv@v7
         with:

.github/workflows/_test_release.yml

@@ -23,7 +23,7 @@ jobs:
       version: ${{ steps.check-version.outputs.version }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python $${ env.PYTHON_VERSION }}
         uses: astral-sh/setup-uv@v7
@@ -74,7 +74,7 @@ jobs:
       id-token: write
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - uses: actions/download-artifact@v6
         with:

.github/workflows/baseline.yml

@@ -17,7 +17,7 @@ jobs:
       run:
         working-directory: libs/langgraph
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - run: SHA=$(git rev-parse HEAD) && echo "SHA=$SHA" >> $GITHUB_ENV
       - name: Set up Python 3.11
         uses: astral-sh/setup-uv@v7

.github/workflows/bench.yml

@@ -15,7 +15,7 @@ jobs:
       run:
         working-directory: libs/langgraph
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - id: files
         name: Get changed files
         uses: Ana06/get-changed-files@v2.3.0

.github/workflows/ci.yml

@@ -27,7 +27,7 @@ jobs:
       python: ${{ steps.filter.outputs.python }}
       deps: ${{ steps.filter.outputs.deps }}
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - uses: dorny/paths-filter@v3
         id: filter
         with:
@@ -100,7 +100,7 @@ jobs:
     name: "Check SDK methods matching"
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
@@ -116,13 +116,13 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - "3.11"
+          - "3.13"
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Set up Python ${{ matrix.python-version }}
         uses: astral-sh/setup-uv@v7
         with:
-          python-version: "3.11"
+          python-version: "3.13"
           enable-cache: true
           cache-suffix: "schema-check-cli"
       - name: Install CLI dependencies

.github/workflows/codespell.yml

@@ -21,7 +21,7 @@
     
         steps:
           - name: Checkout
-            uses: actions/checkout@v5
+            uses: actions/checkout@v6
     
           - name: Install Dependencies
             run: |

.github/workflows/deploy_docs.yml

@@ -1,72 +0,0 @@
-name: Deploy Docs Redirects
-
-on:
-  push:
-    branches:
-      - main
-  workflow_dispatch:
-
-permissions:
-  contents: read
-  pages: write
-  id-token: write
-
-concurrency:
-  group: "pages"
-  cancel-in-progress: false
-
-defaults:
-  run:
-    working-directory: docs
-
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10 # Job will be cancelled if it runs for more than 10 minutes
-    env:
-      GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
-    steps:
-      - uses: actions/checkout@v5
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: astral-sh/setup-uv@v7
-        with:
-          python-version: "3.12"
-          enable-cache: true
-          cache-suffix: "docs"
-
-      - name: Install dependencies
-        run: |
-          yarn
-          uv sync --all-groups
-          # we run this installation only for internal PRs
-          # as GITHUB_TOKEN is not available for PRs from outside contributors
-          if [ -n "${GITHUB_TOKEN}" ]; then
-            uv run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
-          fi
-
-      - name: Build llms-text
-        run: make llms-text
-
-      - name: Build site (redirects only)
-        run: make build-docs
-        env:
-          OPENAI_API_KEY: sf-proj-1234567890 # fake placeholder, shouldn't actually be used
-          ANTHROPIC_API_KEY: sk-ant-api03-1234567890 # fake placeholder, shouldn't actually be used
-
-      - name: Configure GitHub Pages
-        if: github.ref == 'refs/heads/main'
-        uses: actions/configure-pages@v5
-
-      - name: Upload Pages Artifact
-        if: github.ref == 'refs/heads/main'
-        uses: actions/upload-pages-artifact@v4
-        with:
-          path: ./docs/site/
-
-      - name: Deploy to GitHub Pages
-        if: github.ref == 'refs/heads/main'
-        id: deployment
-        uses: actions/deploy-pages@v4

.github/workflows/link_check.yml

@@ -19,7 +19,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
 
@@ -36,7 +36,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           fetch-depth: 1
 

.github/workflows/release.yml

@@ -25,7 +25,7 @@ jobs:
       tag: ${{ steps.check-version.outputs.tag }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python
         uses: astral-sh/setup-uv@v7
@@ -86,7 +86,7 @@ jobs:
     outputs:
       release-body: ${{ steps.generate-release-body.outputs.release-body }}
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           repository: langchain-ai/langgraph
           path: langgraph
@@ -157,7 +157,7 @@ jobs:
       - test-pypi-publish
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       # We explicitly *don't* set up caching here. This ensures our tests are
       # maximally sensitive to catching breakage.
@@ -260,7 +260,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python
         uses: astral-sh/setup-uv@v7
@@ -301,7 +301,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - name: Set up Python
         uses: astral-sh/setup-uv@v7
@@ -322,5 +322,6 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           generateReleaseNotes: false
           tag: ${{needs.build.outputs.tag}}
+          name: ${{ needs.build.outputs.pkg-name }}==${{ needs.build.outputs.version }}
           body: ${{ needs.release-notes.outputs.release-body }}
           commit: ${{ github.sha }}

.github/workflows/run_notebooks.yml

@@ -28,7 +28,7 @@ jobs:
           - "latest"
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - name: Set up Python + Poetry
         uses: astral-sh/setup-uv@v7
         with:

.github/workflows/uv_lock_ugprade.yml

@@ -16,7 +16,7 @@ jobs:
     runs-on: ubuntu-latest
     
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       
       - name: Set up uv
         uses: astral-sh/setup-uv@v7

CLAUDE.md

@@ -0,0 +1,55 @@
+# AGENTS Instructions
+
+This repository is a monorepo. Each library lives in a subdirectory under `libs/`.
+
+When you modify code in any library, run the following commands in that library's directory before creating a pull request:
+
+- `make format` – run code formatters
+- `make lint` – run the linter
+- `make test` – execute the test suite
+
+To run a particular test file or to pass additional pytest options you can specify the `TEST` variable:
+
+```
+TEST=path/to/test.py make test
+```
+
+Other pytest arguments can also be supplied inside the `TEST` variable.
+
+## Libraries
+
+The repository contains several Python and JavaScript/TypeScript libraries.
+Below is a high-level overview:
+
+- **checkpoint** – base interfaces for LangGraph checkpointers.
+- **checkpoint-postgres** – Postgres implementation of the checkpoint saver.
+- **checkpoint-sqlite** – SQLite implementation of the checkpoint saver.
+- **cli** – official command-line interface for LangGraph.
+- **langgraph** – core framework for building stateful, multi-actor agents.
+- **prebuilt** – high-level APIs for creating and running agents and tools.
+- **sdk-js** – JS/TS SDK for interacting with the LangGraph REST API.
+- **sdk-py** – Python SDK for the LangGraph Server API.
+
+### Dependency map
+
+The diagram below lists downstream libraries for each production dependency as
+declared in that library's `pyproject.toml` (or `package.json`).
+
+```text
+checkpoint
+├── checkpoint-postgres
+├── checkpoint-sqlite
+├── prebuilt
+└── langgraph
+
+prebuilt
+└── langgraph
+
+sdk-py
+├── langgraph
+└── cli
+
+sdk-js (standalone)
+```
+
+Changes to a library may impact all of its dependents shown above.

CONTRIBUTING.md

@@ -1,293 +0,0 @@
-# Contributing to LangGraph
-
-Thank you for being interested in contributing to LangGraph!
-
-## General guidelines
-
-Here are some things to keep in mind for all types of contributions:
-
-- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
-- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
-- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
-  - If you would like comments or feedback, please tag a maintainer.
-- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
-- Look for duplicate PRs or issues that have already been opened before opening a new one.
-- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
-
-### Bugfixes
-
-For bug fixes, please open up an issue before proposing a fix to ensure the proposal properly addresses the underlying problem. In general, bug fixes should all have an accompanying unit test that fails before the fix.
-
-### New features
-
-For new features, please start a new [discussion](https://forum.langchain.com/), where the maintainers will help with scoping out the necessary changes.
-
-## Contribute Documentation
-
-Documentation is a vital part of LangGraph. We welcome both new documentation for new features and
-community improvements to our current documentation. Please read the resources below before getting started:
-
-- [Documentation style guide](#documentation-style-guide)
-- [Documentation setup](#setup)
-
-## Documentation Style Guide
-
-As LangGraph continues to grow, the surface area of documentation required to cover it continues to grow too.
-This page provides guidelines for anyone writing documentation for LangGraph, as well as some of our philosophies around organization and structure.
-
-## Philosophy
-
-LangGraph's documentation follows the [Diataxis framework](https://diataxis.fr).
-Under this framework, all documentation falls under one of four categories: [Tutorials](#tutorials),
-[How-to guides](#how-to-guides),
-[References](#references), and [Explanations (aka conceptual guides)](#conceptual-guide).
-
-### Tutorials
-
-Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
-gain understanding of concepts and how they interact by showing one way to achieve some goal in a hands-on way.
-
-They should **avoid** giving
-multiple permutations of ways to achieve that goal in-depth. Choice is burdensome. Instead, they should guide a new user through a recommended path to accomplishing a concrete goal. While the end result of a tutorial does not necessarily need to
-be completely production-ready, it should be useful and practically satisfy the goal that you clearly stated in the tutorial's introduction.
-
-To quote the Diataxis website:
-
-> A tutorial serves the user’s *acquisition* of skills and knowledge - their study. Its purpose is not to help the user get something done, but to help them learn.
-
-In LangGraph, these are often higher level guides that show off end-to-end use cases.
-
-Some examples include:
-
-- [Build a Customer Support Bot](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/)
-- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql/sql-agent/)
-
-Here are some high-level tips on writing a good tutorial:
-
-- Focus on guiding the user to get something done, but keep in mind the end-goal is more to impart principles than to create a perfect production system.
-- Be specific, not abstract and follow one path.
-  - No need to go deeply into alternative approaches, but it’s ok to reference them, ideally with a link to an appropriate how-to guide.
-- Get "a point on the board" as soon as possible - something the user can run that outputs something.
-  - You can iterate and expand afterwards.
-  - Try to frequently checkpoint at given steps where the user can run code and see progress.
-- Focus on results, not technical explanation.
-  - Crosslink heavily to appropriate conceptual/reference pages
-- The first time you mention a LangGraph concept, use its full name (e.g. "human-in-the-loop"), and link to its conceptual/other documentation page.
-  - It's also helpful to add a prerequisite callout that links to any pages with necessary background information.
-- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as related how-to guides.
-- Use phrases like "Next we can run X & Y. We will expect Z.". Then afterwards, use language like "Notice Z" that recalls our expectations and directs the reader's attention to the topic we are trying to teach.
-- Do not shy away from repetition.
-
-### How-to guides
-
-A how-to guide, as the name implies, demonstrates how to do something discrete and specific.
-It should assume that the user is already familiar with underlying concepts, and is trying to solve an immediate problem, but
-should still give some background or list the scenarios where the information contained within can be relevant.
-They can and should discuss alternatives if one approach may be better than another in certain cases.
-
-To quote the Diataxis website:
-
-> A how-to guide serves the work of the already-competent user, whom you can assume to know what they want to do, and to be able to follow your instructions correctly.
-
-Some examples include:
-
-- [How to add persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
-- [How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/)
-
-Here are some high-level tips on writing a good how-to guide:
-
-- Clearly explain what you are guiding the user through at the start
-- Assume higher intent than a tutorial and show what the user needs to do to get that task done
-- Assume familiarity of concepts, but explain why suggested actions are helpful
-  - Crosslink heavily to conceptual/reference pages
-- Discuss alternatives and responses to real-world tradeoffs that may arise when solving a problem
-- Use lots of example code, ideally within complete code blocks that the reader can copy and run.
-- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as other related how-to guides
-
-### Conceptual guides
-
-LangGraph's conceptual guides fall under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
-in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
-gaining a deeper understanding of the framework. Try to avoid excessively large code examples. The goal here is to
-impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work the way they do.
-
-To quote the Diataxis website:
-
-> The perspective of explanation is higher and wider than that of the other types. It does not take the user’s eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
-
-Some examples include:
-
-- [What does it mean to be agentic?](https://langchain-ai.github.io/langgraph/concepts/high_level/)
-- [Tool calling](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#tool-calling)
-
-Here are some high-level tips on writing a good conceptual guide:
-
-- Explain design decisions. Why does concept X exist and why was it designed this way?
-- Use analogies and reference other concepts and alternatives
-- Avoid blending in too much reference content
-- You can and should reference content covered in other guides, but make sure to link to them
-
-### References
-
-References contain detailed, low-level information that describes exactly what functionality exists and how to use it.
-In LangGraph, this is mainly our API reference pages, which are populated from docstrings within code.
-References pages are generally not read end-to-end, but are consulted as necessary when a user needs to know
-how to use something specific.
-
-To quote the Diataxis website:
-
-> The only purpose of a reference guide is to describe, as succinctly as possible, and in an orderly way. Whereas the content of tutorials and how-to guides are led by needs of the user, reference material is led by the product it describes.
-
-Many of the reference pages in LangChain are automatically generated from code,
-but here are some high-level tips on writing a good docstring:
-
-- Be concise
-- Discuss special cases and deviations from a user's expectations
-- Go into detail on required inputs and outputs
-- Light details on when one might use the feature are fine, but in-depth details belong in other sections.
-
-Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
-
-## General guidelines
-
-Here are some other guidelines you should think about when writing and organizing documentation.
-
-We generally do not merge new tutorials from outside contributors without an actual need.
-We welcome updates as well as new integration docs, how-tos, and references.
-
-### Avoid duplication
-
-Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
-be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
-
-### Link to other sections
-
-Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
-to allow a developer to learn more about an unfamiliar topic inline.
-
-This includes linking to the API references as well as conceptual sections!
-
-### Be concise
-
-In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
-re-explain it, unless the concept you are documenting presents some new wrinkle.
-
-Be concise, including in code samples.
-
-### General style
-
-- Use active voice and present tense whenever possible
-- Use examples and code snippets to illustrate concepts and usage
-- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically
-- Use fewer cells with more code to make copy/paste easier
-- Use bullet points and numbered lists to break down information into easily digestible chunks
-- Use tables (especially for **Reference** sections) and diagrams often to present information visually
-- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages
-
-## Setup
-
-LangGraph documentation consists of two components:
-
-1. Main Documentation: Hosted at [https://langchain-ai.github.io/langgraph/](https://langchain-ai.github.io/langgraph/),
-this comprehensive resource serves as the primary user-facing documentation.
-It covers a wide array of topics, including tutorials, use cases, integrations,
-and more, offering extensive guidance on building with LangGraph.
-The content for this documentation lives in the `/docs` directory of the monorepo.
-2. In-code Documentation: This is documentation of the codebase itself, which is also
-used to generate the externally facing [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/).
-The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that developers document their code well.
-
-We appreciate all contributions to the documentation, whether it be fixing a typo,
-adding a new tutorial or example and whether it be in the main documentation or the API Reference.
-
-### 📜 Main Documentation
-
-The content for the main documentation is located in the `/docs` directory of the monorepo.
-
-The documentation is written using a combination of ipython notebooks (`.ipynb` files)
-and markdown (`.md` files). The notebooks are converted to markdown
-and then built using [MkDocs](https://www.mkdocs.org/).
-
-Feel free to make contributions to the main documentation! 🥰
-
-After modifying the documentation:
-
-1. Run the linting and formatting commands (see below) to ensure that the documentation is well-formatted and free of errors.
-2. Optionally build the documentation locally to verify that the changes look good.
-3. Make a pull request with the changes.
-
-### ⚒️ Linting and Building Documentation Locally
-
-After writing up the documentation, you may want to lint and build the documentation
-locally to ensure that it looks good and is free of errors.
-
-If you're unable to build it locally that's okay as well, as you will be able to
-see a preview of the documentation on the pull request page.
-
-From the **monorepo root**, run the following command to install the dependencies:
-
-<!-- TODO -->
-```bash
-poetry install --with docs --no-root
-```
-
-#### Building
-
-The code that builds the documentation is located in the `/docs` directory of the monorepo.
-
-Before building the documentation, it is always a good idea to clean the build directory:
-
-```bash
-make clean-docs
-```
-
-You can build and preview the documentation as outlined below:
-
-```bash
-make serve-docs
-```
-
-#### Linting
-
-To spell check the docs, run the following from the `docs` directory:
-
-```bash
-codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map" --ignore-words-list="infor,thead,stdio,nd,jupyter,lets,lite,uis,deque" .
-```
-
-### ️In-code Documentation
-
-The in-code documentation is autogenerated from docstrings.
-
-For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangGraph because the API reference is the primary resource for developers to understand how to use the codebase.
-
-We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
-
-Here is an example of a well-documented function:
-
-```python
-
-def my_function(arg1: int, arg2: str) -> float:
-    """This is a short description of the function. (It should be a single sentence.)
-
-    This is a longer description of the function. It should explain what
-    the function does, what the arguments are, and what the return value is.
-    It should wrap at 88 characters.
-
-    Examples:
-        This is a section for examples of how to use the function.
-
-        ```python
-        my_function(1, "hello")
-        \```
-
-    Args:
-        arg1: This is a description of arg1. We do not need to specify the type since
-            it is already specified in the function signature.
-        arg2: This is a description of arg2.
-
-    Returns:
-        This is a description of the return value.
-    """
-    return 3.14
-```

README.md

@@ -11,7 +11,7 @@
 [![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
-[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
+[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://docs.langchain.com/oss/python/langgraph/overview)
 
 Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
 
@@ -23,60 +23,67 @@ Install LangGraph:
 pip install -U langgraph
 ```
 
-Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
+Create a simple workflow:
 
 ```python
-# pip install -qU "langchain[anthropic]" to call the model
+from langgraph.graph import START, StateGraph
+from typing_extensions import TypedDict
 
-from langgraph.prebuilt import create_react_agent
 
-def get_weather(city: str) -> str:
-    """Get weather for a given city."""
-    return f"It's always sunny in {city}!"
+class State(TypedDict):
+    text: str
 
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    prompt="You are a helpful assistant"
-)
 
-# Run the agent
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
-)
+def node_a(state: State) -> dict:
+    return {"text": state["text"] + "a"}
+
+
+def node_b(state: State) -> dict:
+    return {"text": state["text"] + "b"}
+
+
+graph = StateGraph(State)
+graph.add_node("node_a", node_a)
+graph.add_node("node_b", node_b)
+graph.add_edge(START, "node_a")
+graph.add_edge("node_a", "node_b")
+
+print(graph.compile().invoke({"text": ""}))
+# {'text': 'ab'}
 ```
 
-For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).
+Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
+
+To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
 
 ## Core benefits
 
 LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
 
-- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
-- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
-- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
+- [Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
+- [Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
+- [Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
 - [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
-- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
+- [Production-ready deployment](https://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
 
 ## LangGraph’s ecosystem
 
 While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
 
 - [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangSmith Deployment](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://docs.langchain.com/oss/python/langgraph/studio).
 - [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
 
 > [!NOTE]
-> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
+> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
 
 ## Additional resources
 
-- [Guides](https://langchain-ai.github.io/langgraph/guides/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
-- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
+- [Guides](https://docs.langchain.com/oss/python/langgraph/guides): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
+- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph.
 - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
-- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
 - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
 
 ## Acknowledgements

docs/_scripts/js_translation/codeblocks/yarn.lock

@@ -2108,9 +2108,9 @@ __metadata:
   linkType: hard
 
 "hono@npm:^4.5.4":
-  version: 4.9.7
-  resolution: "hono@npm:4.9.7"
-  checksum: 10c0/089184660a9211ea216ab95bafa45260e371651cb019db49828064b7982b0ae61cc3c4715324bfeb9037aa2460c39ffa2c91d84ad0c8d500fa77cbcc7fc07a8f
+  version: 4.10.3
+  resolution: "hono@npm:4.10.3"
+  checksum: 10c0/bdcc4c7066c74ba7cfa63ed6550768a0f43a420286c8f8f74b7012ea4901b8b06778fa8e98264b46f1a86920f056b7ede1f07814da4934912f9945def4977c29
   languageName: node
   linkType: hard
 
@@ -2340,13 +2340,13 @@ __metadata:
   linkType: hard
 
 "js-yaml@npm:^4.1.0":
-  version: 4.1.0
-  resolution: "js-yaml@npm:4.1.0"
+  version: 4.1.1
+  resolution: "js-yaml@npm:4.1.1"
   dependencies:
     argparse: "npm:^2.0.1"
   bin:
     js-yaml: bin/js-yaml.js
-  checksum: 10c0/184a24b4eaacfce40ad9074c64fd42ac83cf74d8c8cd137718d456ced75051229e5061b8633c3366b8aada17945a7a356b337828c19da92b51ae62126575018f
+  checksum: 10c0/561c7d7088c40a9bb53cc75becbfb1df6ae49b34b5e6e5a81744b14ae8667ec564ad2527709d1a6e7d5e5fa6d483aa0f373a50ad98d42fde368ec4a190d4fae7
   languageName: node
   linkType: hard
 

docs/_scripts/notebook_hooks.py

@@ -81,7 +81,7 @@ REDIRECT_MAP = {
     # cloud redirects
     "cloud/index.md": "https://docs.langchain.com/oss/python/langgraph/overview",
     "cloud/how-tos/index.md": "https://docs.langchain.com/langsmith/home",
-    "cloud/concepts/api.md": "https://docs.langchain.com/langsmith/langgraph-server",
+    "cloud/concepts/api.md": "https://docs.langchain.com/langsmith/agent-server",
     "cloud/concepts/cloud.md": "https://docs.langchain.com/langsmith/cloud",
     "cloud/faq/studio.md": "https://docs.langchain.com/langsmith/studio",
     "cloud/how-tos/human_in_the_loop_edit_state.md": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
@@ -111,8 +111,8 @@ REDIRECT_MAP = {
     "tutorials/introduction.ipynb": "https://docs.langchain.com/oss/python/langgraph/overview",
     "agents/deployment.md": "https://docs.langchain.com/oss/python/langgraph/local-server",
     # deployment redirects
-    "how-tos/deploy-self-hosted.md": "https://docs.langchain.com/langsmith/hosting",
-    "concepts/self_hosted.md": "https://docs.langchain.com/langsmith/hosting",
+    "how-tos/deploy-self-hosted.md": "https://docs.langchain.com/langsmith/platform-setup",
+    "concepts/self_hosted.md": "https://docs.langchain.com/langsmith/platform-setup",
     "tutorials/deployment.md": "https://docs.langchain.com/langsmith/deployments",
     # assistant redirects
     "cloud/how-tos/assistant_versioning.md": "https://docs.langchain.com/langsmith/configuration-cloud",
@@ -129,16 +129,30 @@ REDIRECT_MAP = {
     "how-tos/human_in_the_loop/edit-graph-state.ipynb": "https://docs.langchain.com/oss/python/langgraph/use-time-travel",
 
     # LGP mintlify migration redirects
+    "examples/index.md": "https://docs.langchain.com/oss/python/learn",
+    "guides/index.md": "https://docs.langchain.com/oss/python/langgraph/overview",
+    "concepts/index.md": "https://docs.langchain.com/oss/python/langgraph/overview",
+    "tutorials/index.md": "https://docs.langchain.com/oss/python/learn",
+    "llms-txt-overview.md": "https://docs.langchain.com/llms.txt",
+    "tutorials/rag/langgraph_adaptive_rag.md": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+    "tutorials/multi_agent/multi-agent-collaboration.ipynb": "https://docs.langchain.com/oss/python/langchain/multi-agent",
+    "how-tos/create-react-agent-manage-message-history.ipynb": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+    "how-tos/many-tools.ipynb": "https://docs.langchain.com/oss/python/langchain/tools",
+    "tutorials/customer-support/customer-support.ipynb": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+    "how-tos/react-agent-structured-output.ipynb": "https://docs.langchain.com/oss/python/langchain/agents#structured-output",
+    "tutorials/code_assistant/langgraph_code_assistant.ipynb": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+    "tutorials/multi_agent/hierarchical_agent_teams.ipynb": "https://docs.langchain.com/oss/python/langchain/supervisor",
     "tutorials/auth/getting_started.md": "https://docs.langchain.com/langsmith/auth",
     "tutorials/auth/resource_auth.md": "https://docs.langchain.com/langsmith/resource-auth",
     "tutorials/auth/add_auth_server.md": "https://docs.langchain.com/langsmith/add-auth-server",
     "how-tos/use-remote-graph.md": "https://docs.langchain.com/langsmith/use-remote-graph",
     "how-tos/autogen-integration.md": "https://docs.langchain.com/langsmith/autogen-integration",
+    "how-tos/human_in_the_loop/wait-user-input.ipynb": "https://docs.langchain.com/oss/python/langgraph/interrupts",
     "cloud/how-tos/use_stream_react.md": "https://docs.langchain.com/langsmith/use-stream-react",
     "cloud/how-tos/generative_ui_react.md": "https://docs.langchain.com/langsmith/generative-ui-react",
     "concepts/langgraph_platform.md": "https://docs.langchain.com/langsmith/deployments",
     "concepts/langgraph_components.md": "https://docs.langchain.com/langsmith/components",
-    "concepts/langgraph_server.md": "https://docs.langchain.com/langsmith/langgraph-server",
+    "concepts/langgraph_server.md": "https://docs.langchain.com/langsmith/agent-server",
     "concepts/langgraph_data_plane.md": "https://docs.langchain.com/langsmith/data-plane",
     "concepts/langgraph_control_plane.md": "https://docs.langchain.com/langsmith/control-plane",
     "concepts/langgraph_cli.md": "https://docs.langchain.com/langsmith/cli",
@@ -201,7 +215,7 @@ REDIRECT_MAP = {
     "cloud/deployment/egress.md": "https://docs.langchain.com/langsmith/env-var",
     "cloud/how-tos/streaming.md": "https://docs.langchain.com/langsmith/streaming",
     "cloud/reference/api/api_ref.md": "https://docs.langchain.com/langsmith/server-api-ref",
-    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langsmith/langgraph-server-changelog",
+    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langsmith/agent-server-changelog",
     "cloud/reference/api/api_ref_control_plane.md": "https://docs.langchain.com/langsmith/api-ref-control-plane",
     "cloud/reference/cli.md": "https://docs.langchain.com/langsmith/cli",
     "cloud/reference/env_var.md": "https://docs.langchain.com/langsmith/env-var",
@@ -219,12 +233,15 @@ REDIRECT_MAP = {
     "tutorials/get-started/6-time-travel.md": "https://docs.langchain.com/oss/python/langgraph/quickstart",
     "tutorials/langsmith/local-server.md": "https://docs.langchain.com/oss/python/langgraph/local-server",
     "tutorials/workflows.md": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+    "tutorials/plan-and-execute/plan-and-execute.ipynb": "https://docs.langchain.com/oss/python/langchain/middleware/built-in#to-do-list",
+    "tutorials/langgraph-platform/local-server/local-server.md": "https://docs.langchain.com/langsmith/local-server",
     "concepts/agentic_concepts.md": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
     "guides/index.md": "https://docs.langchain.com/oss/python/langchain/overview",
     "agents/overview.md": "https://docs.langchain.com/oss/python/langchain/agents",
     "agents/run_agents.md": "https://docs.langchain.com/oss/python/langgraph/quickstart",
     "concepts/low_level.md": "https://docs.langchain.com/oss/python/langgraph/graph-api",
     "how-tos/graph-api.md": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+    "how-tos/react-agent-from-scratch.ipynb": "https://docs.langchain.com/oss/python/langchain/quickstart",
     "concepts/functional_api.md": "https://docs.langchain.com/oss/python/langgraph/functional-api",
     "how-tos/use-functional-api.md": "https://docs.langchain.com/oss/python/langgraph/functional-api",
     "concepts/pregel.md": "https://docs.langchain.com/oss/python/langgraph/pregel",
@@ -282,8 +299,8 @@ REDIRECT_MAP = {
     "reference/supervisor.md": "https://reference.langchain.com/python/langgraph/supervisor/",
     "reference/swarm.md": "https://reference.langchain.com/python/langgraph/swarm/",
     "reference/mcp.md": "https://reference.langchain.com/python/langgraph/mcp/",
-    "cloud/reference/sdk/python_sdk_ref.md": "https://reference.langchain.com/python/platform/python_sdk/",
-    "reference/remote_graph.md": "https://reference.langchain.com/python/platform/remote_graph/",
+    "cloud/reference/sdk/python_sdk_ref.md": "https://reference.langchain.com/python/langsmith/deployment/sdk/",
+    "reference/remote_graph.md": "https://reference.langchain.com/python/langsmith/deployment/remote_graph/",
 
     # additional exclude-search entries from mkdocs.yml
     "additional-resources/index.md": "https://docs.langchain.com/oss/python/langchain/overview",
@@ -294,8 +311,8 @@ REDIRECT_MAP = {
     "cloud/deployment/custom_docker.md": "https://docs.langchain.com/langsmith/custom-docker",
     "cloud/deployment/egress.md": "https://docs.langchain.com/langsmith/env-var",
     "cloud/deployment/graph_rebuild.md": "https://docs.langchain.com/langsmith/graph-rebuild",
-    "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langsmith/hosting",
-    "cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langsmith/hosting",
+    "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langsmith/platform-setup",
+    "cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langsmith/platform-setup",
     "cloud/deployment/semantic_search.md": "https://docs.langchain.com/langsmith/semantic-search",
     "cloud/deployment/setup_javascript.md": "https://docs.langchain.com/langsmith/setup-javascript",
     "cloud/deployment/setup_pyproject.md": "https://docs.langchain.com/langsmith/setup-pyproject",
@@ -331,7 +348,7 @@ REDIRECT_MAP = {
     "cloud/reference/api/api_ref.md": "https://docs.langchain.com/langsmith/server-api-ref",
     "cloud/reference/cli.md": "https://docs.langchain.com/langsmith/cli",
     "cloud/reference/env_var.md": "https://docs.langchain.com/langsmith/env-var",
-    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langsmith/langgraph-server-changelog",
+    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langsmith/agent-server-changelog",
     "cloud/reference/sdk/js_ts_sdk_ref.md": "https://reference.langchain.com/javascript/modules/langsmith.html",
     "concepts/application_structure.md": "https://docs.langchain.com/langsmith/application-structure",
     "concepts/assistants.md": "https://docs.langchain.com/langsmith/assistants",
@@ -345,9 +362,9 @@ REDIRECT_MAP = {
     "concepts/langgraph_control_plane.md": "https://docs.langchain.com/langsmith/control-plane",
     "concepts/langgraph_data_plane.md": "https://docs.langchain.com/langsmith/data-plane",
     "concepts/langgraph_platform.md": "https://docs.langchain.com/langsmith/home",
-    "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langsmith/hosting",
-    "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langsmith/hosting",
-    "concepts/langgraph_server.md": "https://docs.langchain.com/langsmith/langgraph-server",
+    "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langsmith/platform-setup",
+    "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langsmith/platform-setup",
+    "concepts/langgraph_server.md": "https://docs.langchain.com/langsmith/agent-server",
     "concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langsmith/docker",
     "concepts/langgraph_studio.md": "https://docs.langchain.com/langsmith/studio",
     "concepts/plans.md": "https://docs.langchain.com/langsmith/home",
@@ -793,6 +810,17 @@ def on_post_build(config):
     # Track which paths have explicit redirects
     redirected_paths = set()
 
+    # Collect all existing HTML files in the site
+    all_html_files = set()
+    for root, dirs, files in os.walk(site_dir):
+        for file in files:
+            if file.endswith(".html"):
+                # Get relative path from site_dir
+                html_path = os.path.relpath(os.path.join(root, file), site_dir)
+                # Normalize path separators to forward slashes
+                html_path = html_path.replace(os.sep, "/")
+                all_html_files.add(html_path)
+
     # Process explicit redirects from REDIRECT_MAP
     for page_old, page_new in REDIRECT_MAP.items():
         # Convert .ipynb to .md for path calculation
@@ -847,6 +875,24 @@ def on_post_build(config):
 
             _write_html(site_dir, old_html_path, new_html_path)
 
+    # Create catch-all redirects for any HTML files not explicitly redirected
+    catchall_url = "https://docs.langchain.com/oss/python/langgraph/overview"
+    for html_file in all_html_files:
+        # Skip if this file is already explicitly redirected
+        if html_file in redirected_paths:
+            continue
+
+        # Skip the root index.html (we handle that separately)
+        if html_file == "index.html":
+            continue
+
+        # Skip reference documentation (keep those accessible)
+        if html_file.startswith("reference/"):
+            continue
+
+        # Create redirect for this unmapped file
+        _write_html(site_dir, html_file, catchall_url)
+
     # Create root index.html redirect
     root_redirect_html = """<!doctype html>
 <html lang="en">

libs/checkpoint-postgres/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py

@@ -97,7 +97,7 @@ class PostgresSaver(BasePostgresSaver):
                 strict=False,
             ):
                 cur.execute(migration)
-                cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
+                cur.execute("INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,))
         if self.pipe:
             self.pipe.sync()
 
@@ -143,11 +143,13 @@ class PostgresSaver(BasePostgresSaver):
         """
         where, args = self._search_where(config, filter, before)
         query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
         # if we change this to use .stream() we need to make sure to close the cursor
         with self._cursor() as cur:
-            cur.execute(query, args)
+            cur.execute(query, params)
             values = cur.fetchall()
             if not values:
                 return

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

@@ -102,7 +102,9 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 strict=False,
             ):
                 await cur.execute(migration)
-                await cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
+                await cur.execute(
+                    "INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,)
+                )
         if self.pipe:
             await self.pipe.sync()
 
@@ -130,11 +132,13 @@ class AsyncPostgresSaver(BasePostgresSaver):
         """
         where, args = self._search_where(config, filter, before)
         query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC"
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
         # if we change this to use .stream() we need to make sure to close the cursor
         async with self._cursor() as cur:
-            await cur.execute(query, args, binary=True)
+            await cur.execute(query, params, binary=True)
             values = await cur.fetchall()
             if not values:
                 return

libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py

@@ -81,7 +81,7 @@ MIGRATIONS = [
     """
     CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_writes_thread_id_idx ON checkpoint_writes(thread_id);
     """,
-    """ALTER TABLE checkpoint_writes ADD COLUMN task_path TEXT NOT NULL DEFAULT '';""",
+    """ALTER TABLE checkpoint_writes ADD COLUMN IF NOT EXISTS task_path TEXT NOT NULL DEFAULT '';""",
 ]
 
 SELECT_SQL = """

libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py

@@ -77,7 +77,7 @@ MIGRATIONS = [
     CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_writes_thread_id_idx ON checkpoint_writes(thread_id);
     """,
     """
-    ALTER TABLE checkpoint_writes ADD COLUMN task_path TEXT NOT NULL DEFAULT '';
+    ALTER TABLE checkpoint_writes ADD COLUMN IF NOT EXISTS task_path TEXT NOT NULL DEFAULT '';
     """,
 ]
 
@@ -252,7 +252,7 @@ class ShallowPostgresSaver(BasePostgresSaver):
                 strict=False,
             ):
                 cur.execute(migration)
-                cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
+                cur.execute("INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,))
         if self.pipe:
             self.pipe.sync()
 
@@ -272,10 +272,12 @@ class ShallowPostgresSaver(BasePostgresSaver):
         """
         where, args = self._search_where(config, filter, before)
         query = self.SELECT_SQL + where
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
         with self._cursor() as cur:
-            cur.execute(self.SELECT_SQL + where, args, binary=True)
+            cur.execute(query, params, binary=True)
             for value in cur:
                 checkpoint: Checkpoint = {
                     **value["checkpoint"],
@@ -614,7 +616,9 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
                 strict=False,
             ):
                 await cur.execute(migration)
-                await cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
+                await cur.execute(
+                    "INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,)
+                )
         if self.pipe:
             await self.pipe.sync()
 
@@ -634,10 +638,12 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
         """
         where, args = self._search_where(config, filter, before)
         query = self.SELECT_SQL + where
-        if limit:
-            query += f" LIMIT {limit}"
+        params = list(args)
+        if limit is not None:
+            query += " LIMIT %s"
+            params.append(int(limit))
         async with self._cursor() as cur:
-            await cur.execute(self.SELECT_SQL + where, args, binary=True)
+            await cur.execute(query, params, binary=True)
             async for value in cur:
                 checkpoint: Checkpoint = {
                     **value["checkpoint"],

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

@@ -266,6 +266,27 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
                             k: v(self) if v is not None and callable(v) else v
                             for k, v in migration.params.items()
                         }
+                        if "dims" in params:
+                            try:
+                                params["dims"] = int(params["dims"])
+                            except Exception as e:
+                                raise ValueError(
+                                    f"Invalid dims for vector index: {params['dims']}"
+                                ) from e
+                        if "vector_type" in params:
+                            vt = str(params["vector_type"])
+                            if vt not in ("vector", "halfvec"):
+                                raise ValueError(
+                                    f"Invalid vector_type for pgvector: {vt}"
+                                )
+                            params["vector_type"] = vt
+                        if "index_type" in params:
+                            it = str(params["index_type"])
+                            if it not in ("hnsw", "ivfflat"):
+                                raise ValueError(
+                                    f"Invalid index_type for pgvector: {it}"
+                                )
+                            params["index_type"] = it
                         sql = sql % params
                     await cur.execute(sql)
                     await cur.execute(

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

@@ -91,7 +91,12 @@ WHERE expires_at IS NOT NULL;
 VECTOR_MIGRATIONS: Sequence[Migration] = [
     Migration(
         """
-CREATE EXTENSION IF NOT EXISTS vector;
+DO $$
+BEGIN
+    IF NOT EXISTS (SELECT 1 FROM pg_extension WHERE extname = 'vector') THEN
+        CREATE EXTENSION vector;
+    END IF;
+END $$;
 """,
     ),
     Migration(
@@ -322,31 +327,36 @@ class BasePostgresStore(Generic[C]):
         embedding_request: tuple[str, Sequence[tuple[str, str, str, str]]] | None = None
         if inserts:
             values = []
-            insertion_params = []
+            insertion_params: list[Any] = []
             vector_values = []
             embedding_request_params = []
             # Handle TTL expiration
 
             # First handle main store insertions
             for op in inserts:
-                if op.ttl is not None:
-                    expires_at_str = f"NOW() + INTERVAL '{op.ttl * 60} seconds'"
-                    ttl_minutes = op.ttl
-                else:
-                    expires_at_str = "NULL"
-                    ttl_minutes = None
-
-                values.append(
-                    f"(%s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, {expires_at_str}, %s)"
-                )
                 insertion_params.extend(
-                    [
+                    (
                         _namespace_to_text(op.namespace),
                         op.key,
                         Jsonb(cast(dict, op.value)),
-                        ttl_minutes,
-                    ]
+                    )
                 )
+                if op.ttl is not None:
+                    values.append(
+                        "(%s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, NOW() + %s::interval, %s)"
+                    )
+                    ttl_minutes = float(op.ttl)
+                    insertion_params.extend(
+                        (
+                            f"{ttl_minutes * 60} seconds",
+                            ttl_minutes,
+                        )
+                    )
+                else:
+                    values.append(
+                        "(%s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, NULL, %s)"
+                    )
+                    insertion_params.append(None)
 
             # Then handle embeddings if configured
             if self.index_config:
@@ -460,6 +470,10 @@ class BasePostgresStore(Generic[C]):
                         cast(dict, self.index_config)["dims"],
                     )
                 else:
+                    if vector_type not in ("vector", "halfvec"):
+                        raise ValueError(
+                            f"Invalid vector_type for pgvector: {vector_type}"
+                        )
                     score_operator = score_operator % ("%s", vector_type)
 
                 vectors_per_doc_estimate = cast(dict, self.index_config)[
@@ -1117,6 +1131,27 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
                             k: v(self) if v is not None and callable(v) else v
                             for k, v in migration.params.items()
                         }
+                        if "dims" in params:
+                            try:
+                                params["dims"] = int(params["dims"])
+                            except Exception as e:
+                                raise ValueError(
+                                    f"Invalid dims for vector index: {params['dims']}"
+                                ) from e
+                        if "vector_type" in params:
+                            vt = str(params["vector_type"])
+                            if vt not in ("vector", "halfvec"):
+                                raise ValueError(
+                                    f"Invalid vector_type for pgvector: {vt}"
+                                )
+                            params["vector_type"] = vt
+                        if "index_type" in params:
+                            it = str(params["index_type"])
+                            if it not in ("hnsw", "ivfflat"):
+                                raise ValueError(
+                                    f"Invalid index_type for pgvector: {it}"
+                                )
+                            params["index_type"] = it
                         sql = sql % params
                     cur.execute(sql)
                     cur.execute("INSERT INTO vector_migrations (v) VALUES (%s)", (v,))
@@ -1170,15 +1205,44 @@ def _get_vector_type_ops(store: BasePostgresStore) -> str:
 
 
 def _get_index_params(store: Any) -> tuple[str, dict[str, Any]]:
-    """Get the index type and configuration based on config."""
+    """Get a sanitized index type and configuration based on config.
+
+    Only allow known-safe kinds and integer parameters to avoid SQL injection
+    when constructing DDL strings for index creation.
+    """
     if not store.index_config:
         return "hnsw", {}
 
     config = cast(PostgresIndexConfig, store.index_config)
-    index_config = config.get("ann_index_config", _DEFAULT_ANN_CONFIG).copy()
-    kind = index_config.pop("kind", "hnsw")
-    index_config.pop("vector_type", None)
-    return kind, index_config
+    raw = config.get("ann_index_config", _DEFAULT_ANN_CONFIG).copy()
+
+    kind = str(raw.pop("kind", "hnsw"))
+    if kind not in ("hnsw", "ivfflat", "flat"):
+        raise ValueError(
+            f"Invalid index kind for pgvector: {kind}. Expected 'hnsw', 'ivfflat', or 'flat'."
+        )
+
+    raw.pop("vector_type", None)
+
+    if kind == "hnsw":
+        allowed_keys = {"m", "ef_construction"}
+    else:  # ivfflat/flat
+        allowed_keys = {"lists", "nlist"}
+
+    sanitized: dict[str, int] = {}
+    for k, v in list(raw.items()):
+        if k not in allowed_keys:
+            continue
+        key = "lists" if k == "nlist" else k
+        try:
+            ivalue = int(v)  # type: ignore[call-overload]
+        except Exception as e:
+            raise ValueError(f"Invalid index parameter value for {k}: {v}") from e
+        if ivalue <= 0:
+            continue
+        sanitized[key] = ivalue
+
+    return kind, sanitized
 
 
 def _namespace_to_text(

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.0"
+version = "3.0.2"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"
@@ -19,7 +19,10 @@ dependencies = [
 ]
 
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-postgres"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [dependency-groups]
 test = [

libs/checkpoint-sqlite/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

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

@@ -329,8 +329,9 @@ class SqliteSaver(BaseCheckpointSaver[str]):
         FROM checkpoints
         {where}
         ORDER BY checkpoint_id DESC"""
-        if limit:
-            query += f" LIMIT {limit}"
+        if limit is not None:
+            query += " LIMIT ?"
+            param_values = (*param_values, limit)
         with self.cursor(transaction=False) as cur, closing(self.conn.cursor()) as wcur:
             cur.execute(query, param_values)
             for (

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

@@ -425,8 +425,9 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         FROM checkpoints
         {where}
         ORDER BY checkpoint_id DESC"""
-        if limit:
-            query += f" LIMIT {limit}"
+        if limit is not None:
+            query += " LIMIT ?"
+            params = (*params, limit)
         async with (
             self.lock,
             self.conn.execute(query, params) as cur,

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

@@ -1,12 +1,32 @@
 from __future__ import annotations
 
 import json
+import re
 from collections.abc import Sequence
 from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import get_checkpoint_id
 
+_FILTER_PATTERN = re.compile(r"^[a-zA-Z0-9_.-]+$")
+
+
+def _validate_filter_key(key: str) -> None:
+    """Validate that a filter key is safe for use in SQL queries.
+
+    Args:
+        key: The filter key to validate
+
+    Raises:
+        ValueError: If the key contains invalid characters that could enable SQL injection
+    """
+    # Allow alphanumeric characters, underscores, dots, and hyphens
+    # This covers typical JSON property names while preventing SQL injection
+    if not _FILTER_PATTERN.match(key):
+        raise ValueError(
+            f"Invalid filter key: '{key}'. Filter keys must contain only alphanumeric characters, underscores, dots, and hyphens."
+        )
+
 
 def _metadata_predicate(
     metadata_filter: dict[str, Any],
@@ -43,6 +63,7 @@ def _metadata_predicate(
 
     # process metadata query
     for query_key, query_value in metadata_filter.items():
+        _validate_filter_key(query_key)
         operator, param_value = _where_value(query_value)
         predicates.append(
             f"json_extract(CAST(metadata AS TEXT), '$.{query_key}') {operator}"

libs/checkpoint-sqlite/langgraph/store/sqlite/base.py

@@ -107,6 +107,9 @@ def _decode_ns_text(namespace: str) -> tuple[str, ...]:
     return tuple(namespace.split("."))
 
 
+_FILTER_PATTERN = re.compile(r"^[a-zA-Z0-9_.-]+$")
+
+
 def _validate_filter_key(key: str) -> None:
     """Validate that a filter key is safe for use in SQL queries.
 
@@ -118,7 +121,7 @@ def _validate_filter_key(key: str) -> None:
     """
     # Allow alphanumeric characters, underscores, dots, and hyphens
     # This covers typical JSON property names while preventing SQL injection
-    if not re.match(r"^[a-zA-Z0-9_.-]+$", key):
+    if not _FILTER_PATTERN.match(key):
         raise ValueError(
             f"Invalid filter key: '{key}'. Filter keys must contain only alphanumeric characters, underscores, dots, and hyphens."
         )
@@ -404,12 +407,9 @@ class BaseSqliteStore:
                         # SQLite json_extract returns unquoted string values
                         if isinstance(value, str):
                             filter_conditions.append(
-                                "json_extract(value, '$."
-                                + key
-                                + "') = '"
-                                + value.replace("'", "''")
-                                + "'"
+                                "json_extract(value, '$." + key + "') = ?"
                             )
+                            filter_params.append(value)
                         elif value is None:
                             filter_conditions.append(
                                 "json_extract(value, '$." + key + "') IS NULL"
@@ -423,9 +423,11 @@ class BaseSqliteStore:
                                 + ("1" if value else "0")
                             )
                         elif isinstance(value, (int, float)):
+                            # Use parameterized query to handle special floats and large integers
                             filter_conditions.append(
-                                "json_extract(value, '$." + key + "') = " + str(value)
+                                "json_extract(value, '$." + key + "') = ?"
                             )
+                            filter_params.append(float(value))
                         else:
                             # Complex objects (list, dict, …) – compare JSON text
                             filter_conditions.append(
@@ -636,85 +638,66 @@ class BaseSqliteStore:
         # We need to properly format values for SQLite JSON extraction comparison
         if op == "$eq":
             if isinstance(value, str):
-                # Direct string comparison with proper quoting for unquoted json_extract result
-                return (
-                    f"json_extract(value, '$.{key}') = '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') = ?", [value]
             elif value is None:
                 return f"json_extract(value, '$.{key}') IS NULL", []
             elif isinstance(value, bool):
                 # SQLite JSON stores booleans as integers
                 return f"json_extract(value, '$.{key}') = {1 if value else 0}", []
             elif isinstance(value, (int, float)):
-                return f"json_extract(value, '$.{key}') = {value}", []
+                # Convert to float to handle inf, -inf, nan, and very large integers
+                # SQLite REAL can handle these cases better than INTEGER
+                return f"json_extract(value, '$.{key}') = ?", [float(value)]
             else:
                 return f"json_extract(value, '$.{key}') = ?", [orjson.dumps(value)]
         elif op == "$gt":
             # For numeric values, SQLite needs to compare as numbers, not strings
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) > {value}", []
+                # Convert to float to handle special values and very large integers
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) > ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') > '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') > ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') > ?", [orjson.dumps(value)]
         elif op == "$gte":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) >= {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) >= ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') >= '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') >= ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') >= ?", [orjson.dumps(value)]
         elif op == "$lt":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) < {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) < ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') < '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') < ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') < ?", [orjson.dumps(value)]
         elif op == "$lte":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) <= {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) <= ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') <= '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') <= ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') <= ?", [orjson.dumps(value)]
         elif op == "$ne":
             if isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') != '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') != ?", [value]
             elif value is None:
                 return f"json_extract(value, '$.{key}') IS NOT NULL", []
             elif isinstance(value, bool):
                 return f"json_extract(value, '$.{key}') != {1 if value else 0}", []
             elif isinstance(value, (int, float)):
-                return f"json_extract(value, '$.{key}') != {value}", []
+                # Convert to float for consistency
+                return f"json_extract(value, '$.{key}') != ?", [float(value)]
             else:
                 return f"json_extract(value, '$.{key}') != ?", [orjson.dumps(value)]
         else:
@@ -792,8 +775,9 @@ class SqliteStore(BaseSqliteStore, BaseStore):
         self,
         conn: sqlite3.Connection,
         *,
-        deserializer: Callable[[bytes | str | orjson.Fragment], dict[str, Any]]
-        | None = None,
+        deserializer: (
+            Callable[[bytes | str | orjson.Fragment], dict[str, Any]] | None
+        ) = None,
         index: SqliteIndexConfig | None = None,
         ttl: TTLConfig | None = None,
     ):
@@ -874,85 +858,66 @@ class SqliteStore(BaseSqliteStore, BaseStore):
         # We need to properly format values for SQLite JSON extraction comparison
         if op == "$eq":
             if isinstance(value, str):
-                # Direct string comparison with proper quoting for unquoted json_extract result
-                return (
-                    f"json_extract(value, '$.{key}') = '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') = ?", [value]
             elif value is None:
                 return f"json_extract(value, '$.{key}') IS NULL", []
             elif isinstance(value, bool):
                 # SQLite JSON stores booleans as integers
                 return f"json_extract(value, '$.{key}') = {1 if value else 0}", []
             elif isinstance(value, (int, float)):
-                return f"json_extract(value, '$.{key}') = {value}", []
+                # Convert to float to handle inf, -inf, nan, and very large integers
+                # SQLite REAL can handle these cases better than INTEGER
+                return f"json_extract(value, '$.{key}') = ?", [float(value)]
             else:
                 return f"json_extract(value, '$.{key}') = ?", [orjson.dumps(value)]
         elif op == "$gt":
             # For numeric values, SQLite needs to compare as numbers, not strings
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) > {value}", []
+                # Convert to float to handle special values and very large integers
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) > ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') > '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') > ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') > ?", [orjson.dumps(value)]
         elif op == "$gte":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) >= {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) >= ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') >= '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') >= ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') >= ?", [orjson.dumps(value)]
         elif op == "$lt":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) < {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) < ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') < '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') < ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') < ?", [orjson.dumps(value)]
         elif op == "$lte":
             if isinstance(value, (int, float)):
-                return f"CAST(json_extract(value, '$.{key}') AS REAL) <= {value}", []
+                return f"CAST(json_extract(value, '$.{key}') AS REAL) <= ?", [
+                    float(value)
+                ]
             elif isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') <= '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') <= ?", [value]
             else:
                 return f"json_extract(value, '$.{key}') <= ?", [orjson.dumps(value)]
         elif op == "$ne":
             if isinstance(value, str):
-                return (
-                    f"json_extract(value, '$.{key}') != '"
-                    + value.replace("'", "''")
-                    + "'",
-                    [],
-                )
+                return f"json_extract(value, '$.{key}') != ?", [value]
             elif value is None:
                 return f"json_extract(value, '$.{key}') IS NOT NULL", []
             elif isinstance(value, bool):
                 return f"json_extract(value, '$.{key}') != {1 if value else 0}", []
             elif isinstance(value, (int, float)):
-                return f"json_extract(value, '$.{key}') != {value}", []
+                # Convert to float for consistency
+                return f"json_extract(value, '$.{key}') != ?", [float(value)]
             else:
                 return f"json_extract(value, '$.{key}') != ?", [orjson.dumps(value)]
         else:

libs/checkpoint-sqlite/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-sqlite"
-version = "3.0.0"
+version = "3.0.1"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"
@@ -18,7 +18,10 @@ dependencies = [
 ]
 
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-sqlite"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [dependency-groups]
 test = [

libs/checkpoint-sqlite/tests/test_aiosqlite.py

@@ -113,4 +113,78 @@ class TestAsyncSqliteSaver:
                 search_results_5[1].config["configurable"]["checkpoint_ns"],
             } == {"", "inner"}
 
-            # TODO: test before and limit params
+            # Test limit param
+            search_results_6 = [
+                c
+                async for c in saver.alist(
+                    {"configurable": {"thread_id": "thread-2"}}, limit=1
+                )
+            ]
+            assert len(search_results_6) == 1
+            assert search_results_6[0].config["configurable"]["thread_id"] == "thread-2"
+
+            # Test before param
+            search_results_7 = [
+                c async for c in saver.alist(None, before=search_results_5[1].config)
+            ]
+            assert len(search_results_7) == 1
+            assert search_results_7[0].config["configurable"]["thread_id"] == "thread-1"
+
+    async def test_limit_parameter_sql_injection_prevention(self) -> None:
+        """Test that the limit parameter properly uses parameterized queries to prevent SQL injection."""
+        async with AsyncSqliteSaver.from_conn_string(":memory:") as saver:
+            # Setup: Create multiple checkpoints
+            for i in range(5):
+                config: RunnableConfig = {
+                    "configurable": {
+                        "thread_id": f"thread-{i}",
+                        "checkpoint_ns": "",
+                    }
+                }
+                checkpoint = empty_checkpoint()
+                metadata: CheckpointMetadata = {"index": i}
+                await saver.aput(config, checkpoint, metadata, {})
+
+            # Test that limit works correctly with valid integer
+            results = [c async for c in saver.alist(None, limit=2)]
+            assert len(results) == 2
+
+            # Test that limit=0 returns no results
+            results = [c async for c in saver.alist(None, limit=0)]
+            assert len(results) == 0
+
+            # Test that limit=None returns all results
+            results = [c async for c in saver.alist(None, limit=None)]
+            assert len(results) == 5
+
+            # Test explicit SQL injection attempt via limit parameter
+            # Even if type checking is bypassed and a malicious string is passed,
+            # the parameterized query will treat it as a value, not SQL code
+            # This would cause an error (can't convert string to int for LIMIT),
+            # which is the correct secure behavior
+            malicious_limits = [
+                "1; DROP TABLE checkpoints; --",
+                "1 OR 1=1",
+                "999999 UNION SELECT * FROM checkpoints",
+            ]
+
+            for malicious_limit in malicious_limits:
+                # The parameterized query should safely reject non-integer limits
+                # or convert them in a way that prevents SQL injection
+                try:
+                    # Bypass type checking by casting
+                    results = [
+                        c
+                        async for c in saver.alist(None, limit=malicious_limit)  # type: ignore
+                    ]
+                    # If it doesn't raise an error, it should at least not execute the injection
+                    # SQLite's parameter binding will try to convert the string to an integer
+                    # which will either fail or treat it as 0
+                except Exception:
+                    # Expected: SQLite should reject invalid limit values
+                    pass
+
+            # Verify the checkpoints table still exists and has all data
+            # (would have been dropped if injection succeeded)
+            results = [c async for c in saver.alist(None, limit=None)]
+            assert len(results) == 5

libs/checkpoint-sqlite/tests/test_sqlite.py

@@ -182,3 +182,128 @@ class TestSqliteSaver:
             with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"):
                 async for _ in saver.alist(self.config_1):
                     pass
+
+    def test_metadata_predicate_sql_injection_prevention(self) -> None:
+        """Test that _metadata_predicate rejects malicious filter keys."""
+        # Test various SQL injection payloads
+        malicious_keys = [
+            "x') OR '1'='1",  # Boolean-based injection
+            "x') OR 1=1 --",  # Comment-based injection
+            "x') UNION SELECT 1,2,3,4,5,6,7 --",  # UNION-based injection
+            "access') = 'public' OR '1'='1' OR json_extract(value, '$.",  # Complex injection
+            "'; DROP TABLE checkpoints; --",  # Destructive injection
+        ]
+
+        for malicious_key in malicious_keys:
+            with pytest.raises(ValueError, match="Invalid filter key"):
+                _metadata_predicate({malicious_key: "dummy"})
+
+    def test_checkpoint_search_sql_injection_prevention(self) -> None:
+        """Test that SQL injection via malicious filter keys is prevented in checkpoint search."""
+        with SqliteSaver.from_conn_string(":memory:") as saver:
+            # Setup: Create checkpoints with different metadata
+            config_public: RunnableConfig = {
+                "configurable": {
+                    "thread_id": "thread-public",
+                    "checkpoint_ns": "",
+                }
+            }
+            config_private: RunnableConfig = {
+                "configurable": {
+                    "thread_id": "thread-private",
+                    "checkpoint_ns": "",
+                }
+            }
+
+            checkpoint_public = empty_checkpoint()
+            checkpoint_private = empty_checkpoint()
+
+            metadata_public: CheckpointMetadata = {
+                "access": "public",
+                "data": "public information",
+            }
+            metadata_private: CheckpointMetadata = {
+                "access": "private",
+                "data": "secret information",
+                "password": "secret123",
+            }
+
+            saver.put(config_public, checkpoint_public, metadata_public, {})
+            saver.put(config_private, checkpoint_private, metadata_private, {})
+
+            # Normal query - should return only public checkpoint
+            normal_results = list(saver.list(None, filter={"access": "public"}))
+            assert len(normal_results) == 1
+            assert normal_results[0].metadata["access"] == "public"
+
+            # SQL injection attempt should raise ValueError
+            malicious_key = (
+                "access') = 'public' OR '1'='1' OR json_extract(metadata, '$."
+            )
+
+            with pytest.raises(ValueError, match="Invalid filter key"):
+                list(saver.list(None, filter={malicious_key: "dummy"}))
+
+    def test_limit_parameter_sql_injection_prevention(self) -> None:
+        """Test that the limit parameter properly uses parameterized queries to prevent SQL injection."""
+        with SqliteSaver.from_conn_string(":memory:") as saver:
+            # Setup: Create multiple checkpoints
+            for i in range(5):
+                config: RunnableConfig = {
+                    "configurable": {
+                        "thread_id": f"thread-{i}",
+                        "checkpoint_ns": "",
+                    }
+                }
+                checkpoint = empty_checkpoint()
+                metadata: CheckpointMetadata = {"index": i}
+                saver.put(config, checkpoint, metadata, {})
+
+            # Test that limit works correctly with valid integer
+            results = list(saver.list(None, limit=2))
+            assert len(results) == 2
+
+            # Test that limit=0 returns no results
+            results = list(saver.list(None, limit=0))
+            assert len(results) == 0
+
+            # Test that limit=None returns all results
+            results = list(saver.list(None, limit=None))
+            assert len(results) == 5
+
+    def test_metadata_filter_keys_with_hyphens_and_digits(self) -> None:
+        """Metadata keys with hyphens and digit-start should be filterable.
+
+        This exposes incorrect JSON path handling (unquoted segments) by asserting
+        that such filters successfully match saved checkpoints.
+        """
+        with SqliteSaver.from_conn_string(":memory:") as saver:
+            config: RunnableConfig = {
+                "configurable": {
+                    "thread_id": "thread-hyphen-digit",
+                    "checkpoint_ns": "",
+                }
+            }
+            checkpoint = empty_checkpoint()
+            metadata: CheckpointMetadata = {
+                "access-level": "public",
+                "user": {"access-level": "nested", "123abc": "ok2"},
+                "123abc": "ok",
+            }
+            saver.put(config, checkpoint, metadata, {})
+
+            # Top-level hyphenated key
+            results = list(saver.list(None, filter={"access-level": "public"}))
+            assert len(results) == 1
+
+            # Nested hyphenated key via dotted path
+            results = list(saver.list(None, filter={"user.access-level": "nested"}))
+            assert len(results) == 1
+
+            # Top-level digit-starting key
+            results = list(saver.list(None, filter={"123abc": "ok"}))
+            assert len(results) == 1
+
+            # Nested digit-starting key via dotted path
+            results = list(saver.list(None, filter={"user.123abc": "ok2"}))
+            assert len(results) == 1

libs/checkpoint-sqlite/tests/test_store.py

@@ -1069,6 +1069,141 @@ def test_sql_injection_vulnerability(store: SqliteStore) -> None:
         store.search(("docs",), filter={malicious_key: "dummy"})
 
 
+def test_sql_injection_filter_values(store: SqliteStore) -> None:
+    """Test that SQL injection via malicious filter values is properly escaped."""
+    # Setup: Create documents with different access levels
+    store.put(("docs",), "doc1", {"access": "public", "title": "Public Document"})
+    store.put(("docs",), "doc2", {"access": "private", "title": "Private Document"})
+    store.put(("docs",), "doc3", {"access": "secret", "title": "Secret Document"})
+
+    # Test 1: Basic SQL injection attempt with single quote
+    malicious_value = "public' OR '1'='1"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    # Should return 0 results because the malicious value is escaped and won't match anything
+    assert len(results) == 0, "SQL injection via string value should be blocked"
+
+    # Test 2: SQL injection with comment
+    malicious_value = "public'; --"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    assert len(results) == 0, "SQL comment injection should be blocked"
+
+    # Test 3: UNION injection attempt
+    malicious_value = "public' UNION SELECT * FROM store --"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    assert len(results) == 0, "UNION injection should be blocked"
+
+    # Test 4: Parameterized queries handle strings with null bytes and SQL injection attempts safely
+    malicious_value = "public\x00' OR '1'='1"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    assert len(results) == 0, (
+        "Parameterized queries treat injection attempts as literal strings"
+    )
+
+    # Test 5: Multiple single quotes
+    malicious_value = "''''"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    assert len(results) == 0, "Multiple quotes should be handled safely"
+
+    # Test 6: Legitimate value with single quote should work
+    store.put(("docs",), "doc4", {"title": "O'Brien's Document", "access": "public"})
+    results = store.search(("docs",), filter={"title": "O'Brien's Document"})
+    assert len(results) == 1, "Legitimate single quotes should work"
+    assert results[0].value["title"] == "O'Brien's Document"
+
+    # Test 7: Unicode characters with injection attempt
+    malicious_value = "public' OR 'א'='א"
+    results = store.search(("docs",), filter={"access": malicious_value})
+    assert len(results) == 0, "Unicode-based injection should be blocked"
+
+
+def test_numeric_filter_safety(store: SqliteStore) -> None:
+    """Test that numeric filter values are handled safely."""
+    # Setup: Create documents with numeric fields
+    store.put(("items",), "item1", {"price": 10, "quantity": 5})
+    store.put(("items",), "item2", {"price": 20, "quantity": 3})
+    store.put(("items",), "item3", {"price": 30, "quantity": 1})
+
+    # Test 1: Normal numeric comparison
+    results = store.search(("items",), filter={"price": {"$gt": 15}})
+    assert len(results) == 2
+    assert all(r.value["price"] > 15 for r in results)
+
+    # Test 2: Special float values (infinity)
+    results = store.search(("items",), filter={"price": {"$lt": float("inf")}})
+    assert len(results) == 3, "All finite values should be less than infinity"
+
+    # Test 3: Special float values (negative infinity)
+    results = store.search(("items",), filter={"price": {"$gt": float("-inf")}})
+    assert len(results) == 3, (
+        "All finite values should be greater than negative infinity"
+    )
+
+    # Test 4: NaN handling - NaN comparisons should not cause errors
+    try:
+        results = store.search(("items",), filter={"price": {"$eq": float("nan")}})
+        # NaN never equals anything, including itself, so should return 0 results
+        assert len(results) == 0
+    except Exception as e:
+        pytest.fail(f"NaN handling should not raise exception: {e}")
+
+    # Test 5: Very large numbers
+    results = store.search(("items",), filter={"price": {"$lt": 10**100}})
+    assert len(results) == 3, "Very large numbers should be handled safely"
+
+    # Test 6: Negative numbers
+    store.put(("items",), "item4", {"price": -10, "quantity": 0})
+    results = store.search(("items",), filter={"price": {"$lt": 0}})
+    assert len(results) == 1
+    assert results[0].key == "item4"
+
+
+def test_boolean_filter_safety(store: SqliteStore) -> None:
+    """Test that boolean filter values are handled safely."""
+    store.put(("flags",), "flag1", {"active": True, "name": "Feature A"})
+    store.put(("flags",), "flag2", {"active": False, "name": "Feature B"})
+    store.put(("flags",), "flag3", {"active": True, "name": "Feature C"})
+
+    # Test boolean filters
+    results = store.search(("flags",), filter={"active": True})
+    assert len(results) == 2
+    assert all(r.value["active"] is True for r in results)
+
+    results = store.search(("flags",), filter={"active": False})
+    assert len(results) == 1
+    assert results[0].value["active"] is False
+
+
+def test_filter_keys_with_hyphens_and_digits(store: SqliteStore) -> None:
+    """Keys with hyphens or leading digits should be queryable via filters.
+
+    Current unquoted JSON path construction (e.g., '$.access-level' or '$.123abc')
+    is not valid JSON1 syntax, so this test will catch regressions in path handling.
+    """
+    # Documents with top-level and nested keys requiring bracket-quoted JSON paths
+    store.put(
+        ("docs",),
+        "hyphen",
+        {"access-level": "public", "user": {"access-level": "nested"}},
+    )
+    store.put(("docs",), "digit", {"123abc": "ok", "user": {"123abc": "ok2"}})
+
+    # Top-level hyphenated key
+    results = store.search(("docs",), filter={"access-level": "public"})
+    assert [r.key for r in results] == ["hyphen"]
+
+    # Nested hyphenated key via dotted path
+    results = store.search(("docs",), filter={"user.access-level": "nested"})
+    assert [r.key for r in results] == ["hyphen"]
+
+    # Top-level digit-starting key
+    results = store.search(("docs",), filter={"123abc": "ok"})
+    assert [r.key for r in results] == ["digit"]
+
+    # Nested digit-starting key via dotted path
+    results = store.search(("docs",), filter={"user.123abc": "ok2"})
+    assert [r.key for r in results] == ["digit"]
+
+
 @pytest.mark.parametrize("distance_type", VECTOR_TYPES)
 def test_non_ascii(
     fake_embeddings: CharacterEmbeddings,

libs/checkpoint-sqlite/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.10"
 
 [[package]]
@@ -246,7 +246,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "3.0.0"
+version = "3.0.1"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -256,7 +256,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "langchain-core", specifier = ">=0.2.38" },
-    { name = "ormsgpack", specifier = ">=1.10.0" },
+    { name = "ormsgpack", specifier = ">=1.12.0" },
 ]
 
 [package.metadata.requires-dev]
@@ -293,7 +293,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "3.0.0"
+version = "3.0.1"
 source = { editable = "." }
 dependencies = [
     { name = "aiosqlite" },
@@ -511,57 +511,57 @@ wheels = [
 
 [[package]]
 name = "ormsgpack"
-version = "1.11.0"
+version = "1.12.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/65/f8/224c342c0e03e131aaa1a1f19aa2244e167001783a433f4eed10eedd834b/ormsgpack-1.11.0.tar.gz", hash = "sha256:7c9988e78fedba3292541eb3bb274fa63044ef4da2ddb47259ea70c05dee4206", size = 49357, upload-time = "2025-10-08T17:29:15.621Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/67/d5ef41c3b4a94400be801984ef7c7fc9623e1a82b643e74eeec367e7462b/ormsgpack-1.12.0.tar.gz", hash = "sha256:94be818fdbb0285945839b88763b269987787cb2f7ef280cad5d6ec815b7e608", size = 49959, upload-time = "2025-11-04T18:30:10.083Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ff/3d/6996193cb2babc47fc92456223bef7d141065357ad4204eccf313f47a7b3/ormsgpack-1.11.0-cp310-cp310-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:03d4e658dd6e1882a552ce1d13cc7b49157414e7d56a4091fbe7823225b08cba", size = 367965, upload-time = "2025-10-08T17:28:06.736Z" },
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 ]
 
 [[package]]

libs/checkpoint/README.md

@@ -38,8 +38,10 @@ Each checkpointer should conform to `langgraph.checkpoint.base.BaseCheckpointSav
 - `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).
 - `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`).
 - `.list` - List checkpoints that match a given configuration and filter criteria.
+- `.delete_thread()` - Delete all checkpoints and writes associated with a thread.
+- `.get_next_version()` - Generate the next version ID for a channel.
 
-If the checkpointer will be used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), checkpointer must implement asynchronous versions of the above methods (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`).
+If the checkpointer will be used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), checkpointer must implement asynchronous versions of the above methods (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`). Similarly, the checkpointer must implement `.adelete_thread()` if asynchronous thread cleanup is desired. The base class provides a default implementation of `.get_next_version()` that generates an integer sequence starting from 1, but this method should be overridden for custom versioning schemes.
 
 ## Usage
 

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

@@ -60,23 +60,28 @@ class Checkpoint(TypedDict):
     """State snapshot at a given point in time."""
 
     v: int
-    """The version of the checkpoint format. Currently 1."""
+    """The version of the checkpoint format. Currently `1`."""
     id: str
-    """The ID of the checkpoint. This is both unique and monotonically
-    increasing, so can be used for sorting checkpoints from first to last."""
+    """The ID of the checkpoint.
+    
+    This is both unique and monotonically increasing, so can be used for sorting
+    checkpoints from first to last."""
     ts: str
     """The timestamp of the checkpoint in ISO 8601 format."""
     channel_values: dict[str, Any]
     """The values of the channels at the time of the checkpoint.
+    
     Mapping from channel name to deserialized channel snapshot value.
     """
     channel_versions: ChannelVersions
     """The versions of the channels at the time of the checkpoint.
+    
     The keys are channel names and the values are monotonically increasing
     version strings for each channel.
     """
     versions_seen: dict[str, ChannelVersions]
     """Map from node ID to map from channel name to version seen.
+    
     This keeps track of the versions of the channels that each node has seen.
     Used to determine which nodes to execute next.
     """
@@ -352,8 +357,9 @@ class BaseCheckpointSaver(Generic[V]):
     def get_next_version(self, current: V | None, channel: None) -> V:
         """Generate the next version ID for a channel.
 
-        Default is to use integer versions, incrementing by `1`. If you override, you can use `str`/`int`/`float`
-        versions, as long as they are monotonically increasing.
+        Default is to use integer versions, incrementing by `1`.
+
+        If you override, you can use `str`/`int`/`float` versions, as long as they are monotonically increasing.
 
         Args:
             current: The current version identifier (`int`, `float`, or `str`).

libs/checkpoint/langgraph/checkpoint/memory/__init__.py

@@ -33,7 +33,7 @@ class InMemorySaver(
 ):
     """An in-memory checkpoint saver.
 
-    This checkpoint saver stores checkpoints in memory using a defaultdict.
+    This checkpoint saver stores checkpoints in memory using a `defaultdict`.
 
     Note:
         Only use `InMemorySaver` for debugging or testing purposes.
@@ -44,22 +44,23 @@ class InMemorySaver(
     Args:
         serde: The serializer to use for serializing and deserializing checkpoints.
 
-    Examples:
+    Example:
+        ```python
+        import asyncio
 
-            import asyncio
+        from langgraph.checkpoint.memory import InMemorySaver
+        from langgraph.graph import StateGraph
 
-            from langgraph.checkpoint.memory import InMemorySaver
-            from langgraph.graph import StateGraph
+        builder = StateGraph(int)
+        builder.add_node("add_one", lambda x: x + 1)
+        builder.set_entry_point("add_one")
+        builder.set_finish_point("add_one")
 
-            builder = StateGraph(int)
-            builder.add_node("add_one", lambda x: x + 1)
-            builder.set_entry_point("add_one")
-            builder.set_finish_point("add_one")
-
-            memory = InMemorySaver()
-            graph = builder.compile(checkpointer=memory)
-            coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
-            asyncio.run(coro)  # Output: 2
+        memory = InMemorySaver()
+        graph = builder.compile(checkpointer=memory)
+        coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
+        asyncio.run(coro)  # Output: 2
+        ```
     """
 
     # thread ID ->  checkpoint NS -> checkpoint ID -> checkpoint mapping
@@ -96,7 +97,8 @@ class InMemorySaver(
             self.stack.enter_context(self.blobs)  # type: ignore[arg-type]
 
     def __enter__(self) -> InMemorySaver:
-        return self.stack.__enter__()
+        self.stack.__enter__()
+        return self
 
     def __exit__(
         self,
@@ -107,7 +109,8 @@ class InMemorySaver(
         return self.stack.__exit__(exc_type, exc_value, traceback)
 
     async def __aenter__(self) -> InMemorySaver:
-        return self.stack.__enter__()
+        self.stack.__enter__()
+        return self
 
     async def __aexit__(
         self,

libs/checkpoint/langgraph/checkpoint/serde/base.py

@@ -15,9 +15,7 @@ class UntypedSerializerProtocol(Protocol):
 class SerializerProtocol(Protocol):
     """Protocol for serialization and deserialization of objects.
 
-    - `dumps`: Serialize an object to bytes.
     - `dumps_typed`: Serialize an object to a tuple `(type, bytes)`.
-    - `loads`: Deserialize an object from bytes.
     - `loads_typed`: Deserialize an object from a tuple `(type, bytes)`.
 
     Valid implementations include the `pickle`, `json` and `orjson` modules.
@@ -52,12 +50,13 @@ def maybe_add_typed_methods(
 
 class CipherProtocol(Protocol):
     """Protocol for encryption and decryption of data.
+
     - `encrypt`: Encrypt plaintext.
     - `decrypt`: Decrypt ciphertext.
     """
 
     def encrypt(self, plaintext: bytes) -> tuple[str, bytes]:
-        """Encrypt plaintext. Returns a tuple (cipher name, ciphertext)."""
+        """Encrypt plaintext. Returns a tuple `(cipher name, ciphertext)`."""
         ...
 
     def decrypt(self, ciphername: str, ciphertext: bytes) -> bytes:

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

@@ -41,10 +41,12 @@ logger = logging.getLogger(__name__)
 class JsonPlusSerializer(SerializerProtocol):
     """Serializer that uses ormsgpack, with optional fallbacks.
 
-    Security note: this serializer is intended for use within the BaseCheckpointSaver
-    class and called within the Pregel loop. It should not be used on untrusted
-    python objects. If an attacker can write directly to your checkpoint database,
-    they may be able to trigger code execution when data is deserialized.
+    !!! warning
+
+        Security note: This serializer is intended for use within the `BaseCheckpointSaver`
+        class and called within the Pregel loop. It should not be used on untrusted
+        python objects. If an attacker can write directly to your checkpoint database,
+        they may be able to trigger code execution when data is deserialized.
     """
 
     def __init__(
@@ -640,6 +642,7 @@ _option = (
     | ormsgpack.OPT_PASSTHROUGH_DATETIME
     | ormsgpack.OPT_PASSTHROUGH_ENUM
     | ormsgpack.OPT_PASSTHROUGH_UUID
+    | ormsgpack.OPT_REPLACE_SURROGATES
 )
 
 

libs/checkpoint/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint"
-version = "3.0.0"
+version = "3.0.1"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.10"
@@ -13,11 +13,14 @@ license = "MIT"
 license-files = ['LICENSE']
 dependencies = [
     "langchain-core>=0.2.38",
-    "ormsgpack>=1.10.0",
+    "ormsgpack>=1.12.0",
 ]
 
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [dependency-groups]
 test = [

libs/checkpoint/tests/test_jsonplus.py

@@ -149,20 +149,20 @@ def test_serde_jsonplus() -> None:
         assert serde.loads_typed(serde.dumps_typed(value)) == value
 
     surrogates = [
-        "Hello\ud83d\ude00",
-        "Python\ud83d\udc0d",
-        "Surrogate\ud834\udd1e",
-        "Example\ud83c\udf89",
-        "String\ud83c\udfa7",
-        "With\ud83c\udf08",
-        "Surrogates\ud83d\ude0e",
-        "Embedded\ud83d\udcbb",
-        "In\ud83c\udf0e",
-        "The\ud83d\udcd6",
-        "Text\ud83d\udcac",
+        "Hello??",
+        "Python??",
+        "Surrogate??",
+        "Example??",
+        "String??",
+        "With??",
+        "Surrogates??",
+        "Embedded??",
+        "In??",
+        "The??",
+        "Text??",
         "收花🙄·到",
     ]
-    serde = JsonPlusSerializer(pickle_fallback=True)
+    serde = JsonPlusSerializer(pickle_fallback=False)
 
     assert serde.loads_typed(serde.dumps_typed(surrogates)) == surrogates
 

libs/checkpoint/tests/test_memory.py

@@ -188,7 +188,14 @@ class TestMemorySaver:
         assert len(search_results_4) == 0
 
 
-def test_memory_saver() -> None:
+async def test_memory_saver() -> None:
     from langgraph.checkpoint.memory import InMemorySaver
 
-    assert isinstance(InMemorySaver(), InMemorySaver)
+    memory_saver = InMemorySaver()
+    assert isinstance(memory_saver, InMemorySaver)
+
+    async with memory_saver as async_memory_saver:
+        assert async_memory_saver is memory_saver
+
+    with memory_saver as sync_memory_saver:
+        assert sync_memory_saver is memory_saver

libs/checkpoint/uv.lock

@@ -51,75 +51,100 @@ wheels = [
 
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+version = "2025.10.5"
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+    { url = "https://files.pythonhosted.org/packages/e4/37/af0d2ef3967ac0d6113837b44a4f0bfe1328c2b9763bd5b1744520e5cfed/certifi-2025.10.5-py3-none-any.whl", hash = "sha256:0f212c2744a9bb6de0c56639a6f68afe01ecd92d91f14ae897c4fe7bbeeef0de", size = 163286, upload-time = "2025-10-05T04:12:14.03Z" },
 ]
 
 [[package]]
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-version = "3.4.3"
+version = "3.4.4"
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 ]
 
 [[package]]

libs/cli/.gitignore

@@ -0,0 +1 @@
+.langgraph_api/

libs/cli/Makefile

@@ -1,4 +1,4 @@
-.PHONY: test lint format test-integration update-schema
+.PHONY: test lint format test-integration update-schema bump-version
 
 ######################
 # TESTING AND COVERAGE
@@ -35,3 +35,6 @@ format format_diff:
 
 update-schema:
 	uv run python generate_schema.py
+
+bump-version:
+	uv run hatch version patch

libs/cli/generate_schema.py

@@ -15,6 +15,7 @@ import msgspec
 
 from langgraph_cli.schemas import (
     AuthConfig,
+    CacheConfig,
     CheckpointerConfig,
     Config,
     ConfigurableHeaderConfig,
@@ -26,6 +27,8 @@ from langgraph_cli.schemas import (
     StoreConfig,
     ThreadTTLConfig,
     TTLConfig,
+    WebhooksConfig,
+    WebhookUrlPolicy,
 )
 
 
@@ -111,11 +114,14 @@ def add_descriptions_to_schema(schema, cls):
                 SecurityConfig,
                 HttpConfig,
                 CorsConfig,
+                CacheConfig,
                 ThreadTTLConfig,
                 CheckpointerConfig,
                 SerdeConfig,
                 TTLConfig,
                 ConfigurableHeaderConfig,
+                WebhooksConfig,
+                WebhookUrlPolicy,
             ]:
                 if potential_cls.__name__ == def_name:
                     add_descriptions_to_schema(def_schema, potential_cls)

libs/cli/langgraph_cli/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.6"
+__version__ = "0.4.10"

libs/cli/langgraph_cli/cli.py

@@ -336,13 +336,13 @@ def _build(
 
     # apply config
     stdin, additional_contexts = langgraph_cli.config.config_to_docker(
-        config,
-        config_json,
-        base_image,
-        api_version,
-        install_command,
-        build_command,
-        build_context,
+        config_path=config,
+        config=config_json,
+        base_image=base_image,
+        api_version=api_version,
+        install_command=install_command,
+        build_command=build_command,
+        build_context=build_context,
     )
     # add additional_contexts
     if additional_contexts:
@@ -522,8 +522,8 @@ def dockerfile(
 
     secho(f"📝 Generating Dockerfile at {save_path}", fg="yellow")
     dockerfile, additional_contexts = langgraph_cli.config.config_to_docker(
-        config,
-        config_json,
+        config_path=config,
+        config=config_json,
         base_image=base_image,
         api_version=api_version,
     )
@@ -760,6 +760,7 @@ def dev(
         http=config_json.get("http"),
         ui=config_json.get("ui"),
         ui_config=config_json.get("ui_config"),
+        webhooks=config_json.get("webhooks"),
         studio_url=studio_url,
         allow_blocking=allow_blocking,
         tunnel=tunnel,

libs/cli/langgraph_cli/config.py

@@ -154,7 +154,10 @@ def validate_config(config: Config) -> Config:
         "env": config.get("env", {}),
         "store": config.get("store"),
         "auth": config.get("auth"),
+        "encryption": config.get("encryption"),
         "http": config.get("http"),
+        # Pass through webhooks config so it can be injected into the image
+        "webhooks": config.get("webhooks"),
         "checkpointer": config.get("checkpointer"),
         "ui": config.get("ui"),
         "ui_config": config.get("ui_config"),
@@ -228,6 +231,14 @@ def validate_config(config: Config) -> Config:
                     f"Invalid auth.path format: '{auth_conf['path']}'. "
                     "Must be in format './path/to/file.py:attribute_name'"
                 )
+    # Validate encryption config
+    if encryption_conf := config.get("encryption"):
+        if "path" in encryption_conf:
+            if ":" not in encryption_conf["path"]:
+                raise ValueError(
+                    f"Invalid encryption.path format: '{encryption_conf['path']}'. "
+                    "Must be in format './path/to/file.py:attribute_name'"
+                )
     if http_conf := config.get("http"):
         if "app" in http_conf:
             if ":" not in http_conf["app"]:
@@ -614,6 +625,49 @@ def _update_auth_path(
     )
 
 
+def _update_encryption_path(
+    config_path: pathlib.Path, config: Config, local_deps: LocalDeps
+) -> None:
+    """Update encryption.path to use Docker container paths."""
+    encryption_conf = config.get("encryption")
+    if not encryption_conf or not (path_str := encryption_conf.get("path")):
+        return
+
+    module_str, sep, attr_str = path_str.partition(":")
+    if not sep or not module_str.startswith("."):
+        return  # Already validated or absolute path
+
+    resolved = config_path.parent / module_str
+    if not resolved.exists():
+        raise FileNotFoundError(
+            f"Encryption file not found: {resolved} (from {path_str})"
+        )
+    if not resolved.is_file():
+        raise IsADirectoryError(f"Encryption path must be a file: {resolved}")
+
+    # Check faux packages first (higher priority)
+    for faux_path, (_, destpath) in local_deps.faux_pkgs.items():
+        if resolved.is_relative_to(faux_path):
+            new_path = f"{destpath}/{resolved.relative_to(faux_path)}:{attr_str}"
+            encryption_conf["path"] = new_path
+            return
+
+    # Check real packages
+    for real_path in local_deps.real_pkgs:
+        if resolved.is_relative_to(real_path):
+            new_path = (
+                f"/deps/{real_path.name}/{resolved.relative_to(real_path)}:{attr_str}"
+            )
+            encryption_conf["path"] = new_path
+            return
+
+    raise ValueError(
+        f"Encryption file '{resolved}' not covered by dependencies.\n"
+        "Add its parent directory to the 'dependencies' array in your config.\n"
+        f"Current dependencies: {config['dependencies']}"
+    )
+
+
 def _update_http_app_path(
     config_path: pathlib.Path, config: Config, local_deps: LocalDeps
 ) -> None:
@@ -770,6 +824,8 @@ def python_config_to_docker(
     config: Config,
     base_image: str,
     api_version: str | None = None,
+    *,
+    escape_variables: bool = False,
 ) -> tuple[str, dict[str, str]]:
     """Generate a Dockerfile from the configuration."""
     pip_installer = config.get("pip_installer", "auto")
@@ -809,6 +865,8 @@ def python_config_to_docker(
     _update_graph_paths(config_path, config, local_deps)
     # Rewrite auth path, so it points to the correct location in the Docker container
     _update_auth_path(config_path, config, local_deps)
+    # Rewrite encryption path, so it points to the correct location in the Docker container
+    _update_encryption_path(config_path, config, local_deps)
     # Rewrite HTTP app path, so it points to the correct location in the Docker container
     _update_http_app_path(config_path, config, local_deps)
 
@@ -899,9 +957,16 @@ ADD {relpath} /deps/{name}
     if (auth_config := config.get("auth")) is not None:
         env_vars.append(f"ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'")
 
+    if (encryption_config := config.get("encryption")) is not None:
+        env_vars.append(f"ENV LANGGRAPH_ENCRYPTION='{json.dumps(encryption_config)}'")
+
     if (http_config := config.get("http")) is not None:
         env_vars.append(f"ENV LANGGRAPH_HTTP='{json.dumps(http_config)}'")
 
+    # Inject webhooks configuration if provided
+    if (webhooks_config := config.get("webhooks")) is not None:
+        env_vars.append(f"ENV LANGGRAPH_WEBHOOKS='{json.dumps(webhooks_config)}'")
+
     if (checkpointer_config := config.get("checkpointer")) is not None:
         env_vars.append(
             f"ENV LANGGRAPH_CHECKPOINTER='{json.dumps(checkpointer_config)}'"
@@ -940,6 +1005,7 @@ ADD {relpath} /deps/{name}
         )
 
     # Add main dockerfile content
+    dep_vname = "$$dep" if escape_variables else "$dep"
     docker_file_contents.extend(
         [
             f"FROM {image_str}",
@@ -950,10 +1016,10 @@ ADD {relpath} /deps/{name}
             "",
             "# -- Installing all local dependencies --",
             f"""RUN for dep in /deps/*; do \
-            echo "Installing $dep"; \
-            if [ -d "$dep" ]; then \
-                echo "Installing $dep"; \
-                (cd "$dep" && {global_reqs_pip_install} -e .); \
+            echo "Installing {dep_vname}"; \
+            if [ -d "{dep_vname}" ]; then \
+                echo "Installing {dep_vname}"; \
+                (cd "{dep_vname}" && {global_reqs_pip_install} -e .); \
             fi; \
         done""",
             "# -- End of local dependencies install --",
@@ -1022,9 +1088,16 @@ def node_config_to_docker(
     if (auth_config := config.get("auth")) is not None:
         env_vars.append(f"ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'")
 
+    if (encryption_config := config.get("encryption")) is not None:
+        env_vars.append(f"ENV LANGGRAPH_ENCRYPTION='{json.dumps(encryption_config)}'")
+
     if (http_config := config.get("http")) is not None:
         env_vars.append(f"ENV LANGGRAPH_HTTP='{json.dumps(http_config)}'")
 
+    # Inject webhooks configuration if provided
+    if (webhooks_config := config.get("webhooks")) is not None:
+        env_vars.append(f"ENV LANGGRAPH_WEBHOOKS='{json.dumps(webhooks_config)}'")
+
     if (checkpointer_config := config.get("checkpointer")) is not None:
         env_vars.append(
             f"ENV LANGGRAPH_CHECKPOINTER='{json.dumps(checkpointer_config)}'"
@@ -1132,26 +1205,34 @@ def _calculate_relative_workdir(config_path: pathlib.Path, build_context: str) -
 def config_to_docker(
     config_path: pathlib.Path,
     config: Config,
+    *,
     base_image: str | None = None,
     api_version: str | None = None,
     install_command: str | None = None,
     build_command: str | None = None,
     build_context: str | None = None,
+    escape_variables: bool = False,
 ) -> tuple[str, dict[str, str]]:
     base_image = base_image or default_base_image(config)
 
     if config.get("node_version") and not config.get("python_version"):
         return node_config_to_docker(
-            config_path,
-            config,
-            base_image,
-            api_version,
-            install_command,
-            build_command,
-            build_context,
+            config_path=config_path,
+            config=config,
+            base_image=base_image,
+            api_version=api_version,
+            install_command=install_command,
+            build_command=build_command,
+            build_context=build_context,
         )
 
-    return python_config_to_docker(config_path, config, base_image, api_version)
+    return python_config_to_docker(
+        config_path=config_path,
+        config=config,
+        base_image=base_image,
+        api_version=api_version,
+        escape_variables=escape_variables,
+    )
 
 
 def config_to_compose(
@@ -1195,7 +1276,11 @@ def config_to_compose(
 
     else:
         dockerfile, additional_contexts = config_to_docker(
-            config_path, config, base_image, api_version
+            config_path=config_path,
+            config=config,
+            base_image=base_image,
+            api_version=api_version,
+            escape_variables=True,
         )
 
         additional_contexts_str = "\n".join(

libs/cli/langgraph_cli/schemas.py

@@ -233,6 +233,27 @@ class SecurityConfig(TypedDict, total=False):
     """
 
 
+class CacheConfig(TypedDict, total=False):
+    cache_keys: list[str]
+    """Optional. List of header keys to use for caching.
+    
+    Example:
+        ["user_id", "workspace_id"]
+    """
+    ttl_seconds: int
+    """Optional. Time-to-live in seconds for cached items.
+    
+    Example:
+        3600
+    """
+    max_size: int
+    """Optional. Maximum size of the cache.
+    
+    Example:
+        100
+    """
+
+
 class AuthConfig(TypedDict, total=False):
     """Configuration for custom authentication logic and how it integrates into the OpenAPI spec."""
 
@@ -269,6 +290,37 @@ class AuthConfig(TypedDict, total=False):
             ]
         }
     """
+    cache: CacheConfig
+    """Optional. Cache configuration for the server.
+    
+    Example:
+        {
+            "cache_keys": ["user_id", "workspace_id"],
+            "ttl_seconds": 3600,
+            "max_size": 100
+        }
+    """
+
+
+class EncryptionConfig(TypedDict, total=False):
+    """Configuration for custom at-rest encryption logic.
+
+    Allows you to implement custom encryption for sensitive data stored in the database,
+    including metadata fields and checkpoint blobs.
+    """
+
+    path: str
+    """Required. Path to an instance of the Encryption() class that implements custom encryption handlers.
+
+    Format: "path/to/file.py:my_encryption"
+
+    Example:
+        {
+            "encryption": {
+                "path": "./encryption.py:my_encryption"
+            }
+        }
+    """
 
 
 class CorsConfig(TypedDict, total=False):
@@ -310,7 +362,7 @@ class CorsConfig(TypedDict, total=False):
     """
 
 
-class ConfigurableHeaderConfig(TypedDict):
+class ConfigurableHeaderConfig(TypedDict, total=False):
     """Customize which headers to include as configurable values in your runs.
 
     By default, omits x-api-key, x-tenant-id, and x-service-key.
@@ -321,7 +373,7 @@ class ConfigurableHeaderConfig(TypedDict):
     """
 
     includes: list[str] | None
-    """Headers to include (if not also matches against an 'exludes' pattern.
+    """Headers to include (if not also matched against an 'excludes' pattern).
 
     Examples:
         - 'user-agent'
@@ -367,7 +419,12 @@ class HttpConfig(TypedDict, total=False):
     Default is False.
     """
     disable_mcp: bool
-    """Optional. If `True`, /mcp routes are removed, disabling the MCP server.
+    """Optional. If `True`, /mcp routes are removed, disabling default support to expose the deployment as an MCP server.
+    
+    Default is False.
+    """
+    disable_a2a: bool
+    """Optional. If `True`, /a2a routes are removed, disabling default support to expose the deployment as an agent-to-agent (A2A) server.
     
     Default is False.
     """
@@ -377,6 +434,17 @@ class HttpConfig(TypedDict, total=False):
     Set to True to disable the following endpoints: /openapi.json, /info, /metrics, /docs.
     This will also make the /ok endpoint skip any DB or other checks, always returning {"ok": True}.
     
+    Default is False.
+    """
+    disable_ui: bool
+    """Optional. If `True`, /ui routes are removed, disabling the UI server.
+    
+    Default is False.
+    """
+    disable_webhooks: bool
+    """Optional. If `True`, webhooks are disabled. Runs created with an associated webhook will
+    still be executed, but the webhook event will not be sent.
+    
     Default is False.
     """
     cors: CorsConfig | None
@@ -409,6 +477,52 @@ class HttpConfig(TypedDict, total=False):
     Default is False. This flag only affects authentication behavior
     if `app` is provided and contains custom routes.
     """
+    mount_prefix: str
+    """Optional. URL prefix to prepend to all the routes.
+    
+    Example:
+        "/api"
+    """
+
+
+class WebhookUrlPolicy(TypedDict, total=False):
+    require_https: bool
+    """Enforce HTTPS scheme for absolute URLs; reject `http://` when true."""
+    allowed_domains: list[str]
+    """Hostname allowlist. Supports exact hosts and wildcard subdomains.
+
+    Use entries like "hooks.example.com" or "*.mycorp.com". The wildcard only
+    matches subdomains ("foo.mycorp.com"), not the apex ("mycorp.com"). When
+    empty or omitted, any public host is allowed (subject to SSRF IP checks).
+    """
+    allowed_ports: list[int]
+    """Explicit port allowlist for absolute URLs.
+
+    If set, requests must use one of these ports. Defaults are respected when
+    a port is not present in the URL (443 for https, 80 for http).
+    """
+    max_url_length: int
+    """Maximum permitted URL length in characters; longer inputs are rejected early."""
+    disable_loopback: bool
+    """Disallow relative URLs (internal loopback calls) when true."""
+
+
+class WebhooksConfig(TypedDict, total=False):
+    env_prefix: str
+    """Required prefix for environment variables referenced in header templates.
+
+    Acts as an allowlist boundary to prevent leaking arbitrary environment
+    variables. Defaults to "LG_WEBHOOK_" when omitted.
+    """
+    url: WebhookUrlPolicy
+    """URL validation policy for user-supplied webhook endpoints."""
+    headers: dict[str, str]
+    """Static headers to include with webhook requests.
+
+    Values may contain templates of the form "${{ env.VAR }}". On startup, these
+    are resolved via the process environment after verifying `VAR` starts with
+    `env_prefix`. Mixed literals and multiple templates are allowed.
+    """
 
 
 class Config(TypedDict, total=False):
@@ -524,15 +638,28 @@ class Config(TypedDict, total=False):
     """
 
     auth: AuthConfig | None
-    """Optional. Custom authentication config, including the path to your Python auth logic and 
+    """Optional. Custom authentication config, including the path to your Python auth logic and
     the OpenAPI security definitions it uses.
     """
 
+    encryption: EncryptionConfig | None
+    """Optional. Custom at-rest encryption config, including the path to your Python encryption logic.
+
+    Allows you to implement custom encryption for sensitive data stored in the database.
+    """
+
     http: HttpConfig | None
     """Optional. Configuration for the built-in HTTP server, controlling which custom routes are exposed
     and how cross-origin requests are handled.
     """
 
+    webhooks: WebhooksConfig | None
+    """Optional. Webhooks configuration for outbound event delivery.
+
+    Forwarded into the container as `LANGGRAPH_WEBHOOKS`. See `WebhooksConfig`
+    for URL policy and header templating details.
+    """
+
     ui: dict[str, str] | None
     """Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.
     """
@@ -550,6 +677,7 @@ __all__ = [
     "StoreConfig",
     "CheckpointerConfig",
     "AuthConfig",
+    "EncryptionConfig",
     "HttpConfig",
     "MiddlewareOrders",
     "Distros",

libs/cli/pyproject.toml

@@ -19,13 +19,16 @@ dependencies = [
 path = "langgraph_cli/__init__.py"
 [project.optional-dependencies]
 inmem = [
-    "langgraph-api>=0.3,<0.5.0 ; python_version >= '3.11'",
+    "langgraph-api>=0.5.35,<0.6.0 ; python_version >= '3.11'",
     "langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
     "python-dotenv>=0.8.0",
 ]
 
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/cli"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [project.scripts]
 langgraph = "langgraph_cli.cli:cli"
@@ -46,6 +49,7 @@ lint = [
 dev = [
     {include-group = "test"},
     {include-group = "lint"},
+    "hatch>=1.16.2",
 ]
 
 [tool.uv]
@@ -68,4 +72,4 @@ lint.select = [
   "UP", # pyupgrade
 ]
 lint.ignore = ["E501", "B008"]
-target-version = "py310"
+target-version = "py310"

libs/cli/schemas/schema.json

@@ -99,6 +99,17 @@
               },
               "description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
             },
+            "encryption": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/EncryptionConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Custom at-rest encryption config, including the path to your Python encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database.\n"
+            },
             "env": {
               "anyOf": [
                 {
@@ -199,6 +210,17 @@
                 }
               ],
               "description": "Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.\n"
+            },
+            "webhooks": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/WebhooksConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Webhooks configuration for outbound event delivery.\n\nForwarded into the container as `LANGGRAPH_WEBHOOKS`. See `WebhooksConfig`\nfor URL policy and header templating details.\n"
             }
           },
           "required": [
@@ -292,6 +314,17 @@
               },
               "description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
             },
+            "encryption": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/EncryptionConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Custom at-rest encryption config, including the path to your Python encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database.\n"
+            },
             "env": {
               "anyOf": [
                 {
@@ -391,6 +424,17 @@
                 }
               ],
               "description": "Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.\n"
+            },
+            "webhooks": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/WebhooksConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Webhooks configuration for outbound event delivery.\n\nForwarded into the container as `LANGGRAPH_WEBHOOKS`. See `WebhooksConfig`\nfor URL policy and header templating details.\n"
             }
           },
           "required": [
@@ -405,6 +449,10 @@
       "description": "Configuration for custom authentication logic and how it integrates into the OpenAPI spec.",
       "type": "object",
       "properties": {
+        "cache": {
+          "$ref": "#/$defs/CacheConfig",
+          "description": "Optional. Cache configuration for the server.\n"
+        },
         "disable_studio_auth": {
           "type": "boolean",
           "description": "Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio.\n\nDefaults to False, meaning that if a particular header is set, the server will verify the `x-api-key` header\nvalue is a valid API key for the deployment's workspace. If `True`, all requests will go through your custom\nauthentication logic, regardless of origin of the request.\n"
@@ -420,6 +468,29 @@
       },
       "required": []
     },
+    "CacheConfig": {
+      "title": "CacheConfig",
+      "type": "object",
+      "properties": {
+        "cache_keys": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Optional. List of header keys to use for caching.\n"
+        },
+        "max_size": {
+          "type": "integer",
+          "description": "Optional. Maximum size of the cache.\n"
+        },
+        "ttl_seconds": {
+          "type": "integer",
+          "description": "Optional. Time-to-live in seconds for cached items.\n"
+        }
+      },
+      "required": [],
+      "description": "dict() -> new empty dictionary\ndict(mapping) -> new dictionary initialized from a mapping object's\n    (key, value) pairs\ndict(iterable) -> new dictionary initialized as if via:\n    d = {}\n    for k, v in iterable:\n        d[k] = v\ndict(**kwargs) -> new dictionary initialized with the name=value pairs\n    in the keyword argument list.  For example:  dict(one=1, two=2)"
+    },
     "SecurityConfig": {
       "title": "SecurityConfig",
       "description": "Configuration for OpenAPI security definitions and requirements.\n\nUseful for specifying global or path-level authentication and authorization flows\n(e.g., OAuth2, API key headers, etc.).",
@@ -565,6 +636,17 @@
       },
       "required": []
     },
+    "EncryptionConfig": {
+      "title": "EncryptionConfig",
+      "description": "Configuration for custom at-rest encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database,\nincluding metadata fields and checkpoint blobs.",
+      "type": "object",
+      "properties": {
+        "path": {
+          "type": "string"
+        }
+      },
+      "required": []
+    },
     "HttpConfig": {
       "title": "HttpConfig",
       "description": "Configuration for the built-in HTTP server that powers your deployment's routes and endpoints.",
@@ -596,13 +678,17 @@
           ],
           "description": "Optional. Defines CORS restrictions. If omitted, no special rules are set and\ncross-origin behavior depends on default server settings.\n"
         },
+        "disable_a2a": {
+          "type": "boolean",
+          "description": "Optional. If `True`, /a2a routes are removed, disabling default support to expose the deployment as an agent-to-agent (A2A) server.\n\nDefault is False.\n"
+        },
         "disable_assistants": {
           "type": "boolean",
           "description": "Optional. If `True`, /assistants routes are removed from the server.\n\nDefault is False (meaning /assistants is enabled).\n"
         },
         "disable_mcp": {
           "type": "boolean",
-          "description": "Optional. If `True`, /mcp routes are removed, disabling the MCP server.\n\nDefault is False.\n"
+          "description": "Optional. If `True`, /mcp routes are removed, disabling default support to expose the deployment as an MCP server.\n\nDefault is False.\n"
         },
         "disable_meta": {
           "type": "boolean",
@@ -620,6 +706,14 @@
           "type": "boolean",
           "description": "Optional. If `True`, /threads routes are removed.\n\nDefault is False.\n"
         },
+        "disable_ui": {
+          "type": "boolean",
+          "description": "Optional. If `True`, /ui routes are removed, disabling the UI server.\n\nDefault is False.\n"
+        },
+        "disable_webhooks": {
+          "type": "boolean",
+          "description": "Optional. If `True`, webhooks are disabled. Runs created with an associated webhook will\nstill be executed, but the webhook event will not be sent.\n\nDefault is False.\n"
+        },
         "enable_custom_route_auth": {
           "type": "boolean",
           "description": "Optional. If `True`, authentication is enabled for custom routes,\nnot just the routes that are protected by default.\n(Routes protected by default include /assistants, /threads, and /runs).\n\nDefault is False. This flag only affects authentication behavior\nif `app` is provided and contains custom routes.\n"
@@ -648,6 +742,10 @@
             }
           ],
           "description": "Optional. Defines the order in which to apply server customizations.\n"
+        },
+        "mount_prefix": {
+          "type": "string",
+          "description": "Optional. URL prefix to prepend to all the routes.\n"
         }
       },
       "required": []
@@ -683,13 +781,10 @@
               "type": "null"
             }
           ],
-          "description": "Headers to include (if not also matches against an 'exludes' pattern.\n"
+          "description": "Headers to include (if not also matched against an 'excludes' pattern).\n"
         }
       },
-      "required": [
-        "excludes",
-        "includes"
-      ]
+      "required": []
     },
     "CorsConfig": {
       "title": "CorsConfig",
@@ -832,6 +927,63 @@
         }
       },
       "required": []
+    },
+    "WebhooksConfig": {
+      "title": "WebhooksConfig",
+      "type": "object",
+      "properties": {
+        "env_prefix": {
+          "type": "string",
+          "description": "Required prefix for environment variables referenced in header templates.\n\nActs as an allowlist boundary to prevent leaking arbitrary environment\nvariables. Defaults to \"LG_WEBHOOK_\" when omitted.\n"
+        },
+        "headers": {
+          "type": "object",
+          "additionalProperties": {
+            "type": "string"
+          },
+          "description": "Static headers to include with webhook requests.\n\nValues may contain templates of the form \"${{ env.VAR }}\". On startup, these\nare resolved via the process environment after verifying `VAR` starts with\n`env_prefix`. Mixed literals and multiple templates are allowed.\n"
+        },
+        "url": {
+          "$ref": "#/$defs/WebhookUrlPolicy",
+          "description": "URL validation policy for user-supplied webhook endpoints."
+        }
+      },
+      "required": [],
+      "description": "dict() -> new empty dictionary\ndict(mapping) -> new dictionary initialized from a mapping object's\n    (key, value) pairs\ndict(iterable) -> new dictionary initialized as if via:\n    d = {}\n    for k, v in iterable:\n        d[k] = v\ndict(**kwargs) -> new dictionary initialized with the name=value pairs\n    in the keyword argument list.  For example:  dict(one=1, two=2)"
+    },
+    "WebhookUrlPolicy": {
+      "title": "WebhookUrlPolicy",
+      "type": "object",
+      "properties": {
+        "allowed_domains": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Hostname allowlist. Supports exact hosts and wildcard subdomains.\n\nUse entries like \"hooks.example.com\" or \"*.mycorp.com\". The wildcard only\nmatches subdomains (\"foo.mycorp.com\"), not the apex (\"mycorp.com\"). When\nempty or omitted, any public host is allowed (subject to SSRF IP checks).\n"
+        },
+        "allowed_ports": {
+          "type": "array",
+          "items": {
+            "type": "integer"
+          },
+          "description": "Explicit port allowlist for absolute URLs.\n\nIf set, requests must use one of these ports. Defaults are respected when\na port is not present in the URL (443 for https, 80 for http).\n"
+        },
+        "disable_loopback": {
+          "type": "boolean",
+          "description": "Disallow relative URLs (internal loopback calls) when true."
+        },
+        "max_url_length": {
+          "type": "integer",
+          "description": "Maximum permitted URL length in characters; longer inputs are rejected early."
+        },
+        "require_https": {
+          "type": "boolean",
+          "description": "Enforce HTTPS scheme for absolute URLs; reject `http://` when true."
+        }
+      },
+      "required": [],
+      "description": "dict() -> new empty dictionary\ndict(mapping) -> new dictionary initialized from a mapping object's\n    (key, value) pairs\ndict(iterable) -> new dictionary initialized as if via:\n    d = {}\n    for k, v in iterable:\n        d[k] = v\ndict(**kwargs) -> new dictionary initialized with the name=value pairs\n    in the keyword argument list.  For example:  dict(one=1, two=2)"
     }
   },
   "title": "LangGraph CLI Configuration",

libs/cli/schemas/schema.v0.json

@@ -99,6 +99,17 @@
               },
               "description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
             },
+            "encryption": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/EncryptionConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Custom at-rest encryption config, including the path to your Python encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database.\n"
+            },
             "env": {
               "anyOf": [
                 {
@@ -199,6 +210,17 @@
                 }
               ],
               "description": "Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.\n"
+            },
+            "webhooks": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/WebhooksConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Webhooks configuration for outbound event delivery.\n\nForwarded into the container as `LANGGRAPH_WEBHOOKS`. See `WebhooksConfig`\nfor URL policy and header templating details.\n"
             }
           },
           "required": [
@@ -292,6 +314,17 @@
               },
               "description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
             },
+            "encryption": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/EncryptionConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Custom at-rest encryption config, including the path to your Python encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database.\n"
+            },
             "env": {
               "anyOf": [
                 {
@@ -391,6 +424,17 @@
                 }
               ],
               "description": "Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.\n"
+            },
+            "webhooks": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/WebhooksConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Webhooks configuration for outbound event delivery.\n\nForwarded into the container as `LANGGRAPH_WEBHOOKS`. See `WebhooksConfig`\nfor URL policy and header templating details.\n"
             }
           },
           "required": [
@@ -405,6 +449,10 @@
       "description": "Configuration for custom authentication logic and how it integrates into the OpenAPI spec.",
       "type": "object",
       "properties": {
+        "cache": {
+          "$ref": "#/$defs/CacheConfig",
+          "description": "Optional. Cache configuration for the server.\n"
+        },
         "disable_studio_auth": {
           "type": "boolean",
           "description": "Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio.\n\nDefaults to False, meaning that if a particular header is set, the server will verify the `x-api-key` header\nvalue is a valid API key for the deployment's workspace. If `True`, all requests will go through your custom\nauthentication logic, regardless of origin of the request.\n"
@@ -420,6 +468,29 @@
       },
       "required": []
     },
+    "CacheConfig": {
+      "title": "CacheConfig",
+      "type": "object",
+      "properties": {
+        "cache_keys": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Optional. List of header keys to use for caching.\n"
+        },
+        "max_size": {
+          "type": "integer",
+          "description": "Optional. Maximum size of the cache.\n"
+        },
+        "ttl_seconds": {
+          "type": "integer",
+          "description": "Optional. Time-to-live in seconds for cached items.\n"
+        }
+      },
+      "required": [],
+      "description": "dict() -> new empty dictionary\ndict(mapping) -> new dictionary initialized from a mapping object's\n    (key, value) pairs\ndict(iterable) -> new dictionary initialized as if via:\n    d = {}\n    for k, v in iterable:\n        d[k] = v\ndict(**kwargs) -> new dictionary initialized with the name=value pairs\n    in the keyword argument list.  For example:  dict(one=1, two=2)"
+    },
     "SecurityConfig": {
       "title": "SecurityConfig",
       "description": "Configuration for OpenAPI security definitions and requirements.\n\nUseful for specifying global or path-level authentication and authorization flows\n(e.g., OAuth2, API key headers, etc.).",
@@ -565,6 +636,17 @@
       },
       "required": []
     },
+    "EncryptionConfig": {
+      "title": "EncryptionConfig",
+      "description": "Configuration for custom at-rest encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database,\nincluding metadata fields and checkpoint blobs.",
+      "type": "object",
+      "properties": {
+        "path": {
+          "type": "string"
+        }
+      },
+      "required": []
+    },
     "HttpConfig": {
       "title": "HttpConfig",
       "description": "Configuration for the built-in HTTP server that powers your deployment's routes and endpoints.",
@@ -596,13 +678,17 @@
           ],
           "description": "Optional. Defines CORS restrictions. If omitted, no special rules are set and\ncross-origin behavior depends on default server settings.\n"
         },
+        "disable_a2a": {
+          "type": "boolean",
+          "description": "Optional. If `True`, /a2a routes are removed, disabling default support to expose the deployment as an agent-to-agent (A2A) server.\n\nDefault is False.\n"
+        },
         "disable_assistants": {
           "type": "boolean",
           "description": "Optional. If `True`, /assistants routes are removed from the server.\n\nDefault is False (meaning /assistants is enabled).\n"
         },
         "disable_mcp": {
           "type": "boolean",
-          "description": "Optional. If `True`, /mcp routes are removed, disabling the MCP server.\n\nDefault is False.\n"
+          "description": "Optional. If `True`, /mcp routes are removed, disabling default support to expose the deployment as an MCP server.\n\nDefault is False.\n"
         },
         "disable_meta": {
           "type": "boolean",
@@ -620,6 +706,14 @@
           "type": "boolean",
           "description": "Optional. If `True`, /threads routes are removed.\n\nDefault is False.\n"
         },
+        "disable_ui": {
+          "type": "boolean",
+          "description": "Optional. If `True`, /ui routes are removed, disabling the UI server.\n\nDefault is False.\n"
+        },
+        "disable_webhooks": {
+          "type": "boolean",
+          "description": "Optional. If `True`, webhooks are disabled. Runs created with an associated webhook will\nstill be executed, but the webhook event will not be sent.\n\nDefault is False.\n"
+        },
         "enable_custom_route_auth": {
           "type": "boolean",
           "description": "Optional. If `True`, authentication is enabled for custom routes,\nnot just the routes that are protected by default.\n(Routes protected by default include /assistants, /threads, and /runs).\n\nDefault is False. This flag only affects authentication behavior\nif `app` is provided and contains custom routes.\n"
@@ -648,6 +742,10 @@
             }
           ],
           "description": "Optional. Defines the order in which to apply server customizations.\n"
+        },
+        "mount_prefix": {
+          "type": "string",
+          "description": "Optional. URL prefix to prepend to all the routes.\n"
         }
       },
       "required": []
@@ -683,13 +781,10 @@
               "type": "null"
             }
           ],
-          "description": "Headers to include (if not also matches against an 'exludes' pattern.\n"
+          "description": "Headers to include (if not also matched against an 'excludes' pattern).\n"
         }
       },
-      "required": [
-        "excludes",
-        "includes"
-      ]
+      "required": []
     },
     "CorsConfig": {
       "title": "CorsConfig",
@@ -832,6 +927,63 @@
         }
       },
       "required": []
+    },
+    "WebhooksConfig": {
+      "title": "WebhooksConfig",
+      "type": "object",
+      "properties": {
+        "env_prefix": {
+          "type": "string",
+          "description": "Required prefix for environment variables referenced in header templates.\n\nActs as an allowlist boundary to prevent leaking arbitrary environment\nvariables. Defaults to \"LG_WEBHOOK_\" when omitted.\n"
+        },
+        "headers": {
+          "type": "object",
+          "additionalProperties": {
+            "type": "string"
+          },
+          "description": "Static headers to include with webhook requests.\n\nValues may contain templates of the form \"${{ env.VAR }}\". On startup, these\nare resolved via the process environment after verifying `VAR` starts with\n`env_prefix`. Mixed literals and multiple templates are allowed.\n"
+        },
+        "url": {
+          "$ref": "#/$defs/WebhookUrlPolicy",
+          "description": "URL validation policy for user-supplied webhook endpoints."
+        }
+      },
+      "required": [],
+      "description": "dict() -> new empty dictionary\ndict(mapping) -> new dictionary initialized from a mapping object's\n    (key, value) pairs\ndict(iterable) -> new dictionary initialized as if via:\n    d = {}\n    for k, v in iterable:\n        d[k] = v\ndict(**kwargs) -> new dictionary initialized with the name=value pairs\n    in the keyword argument list.  For example:  dict(one=1, two=2)"
+    },
+    "WebhookUrlPolicy": {
+      "title": "WebhookUrlPolicy",
+      "type": "object",
+      "properties": {
+        "allowed_domains": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Hostname allowlist. Supports exact hosts and wildcard subdomains.\n\nUse entries like \"hooks.example.com\" or \"*.mycorp.com\". The wildcard only\nmatches subdomains (\"foo.mycorp.com\"), not the apex (\"mycorp.com\"). When\nempty or omitted, any public host is allowed (subject to SSRF IP checks).\n"
+        },
+        "allowed_ports": {
+          "type": "array",
+          "items": {
+            "type": "integer"
+          },
+          "description": "Explicit port allowlist for absolute URLs.\n\nIf set, requests must use one of these ports. Defaults are respected when\na port is not present in the URL (443 for https, 80 for http).\n"
+        },
+        "disable_loopback": {
+          "type": "boolean",
+          "description": "Disallow relative URLs (internal loopback calls) when true."
+        },
+        "max_url_length": {
+          "type": "integer",
+          "description": "Maximum permitted URL length in characters; longer inputs are rejected early."
+        },
+        "require_https": {
+          "type": "boolean",
+          "description": "Enforce HTTPS scheme for absolute URLs; reject `http://` when true."
+        }
+      },
+      "required": [],
+      "description": "dict() -> new empty dictionary\ndict(mapping) -> new dictionary initialized from a mapping object's\n    (key, value) pairs\ndict(iterable) -> new dictionary initialized as if via:\n    d = {}\n    for k, v in iterable:\n        d[k] = v\ndict(**kwargs) -> new dictionary initialized with the name=value pairs\n    in the keyword argument list.  For example:  dict(one=1, two=2)"
     }
   },
   "title": "LangGraph CLI Configuration",

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

@@ -150,7 +150,7 @@ services:
                 COPY --from=cli_1 . /deps/cli_1
                 # -- End of local package ../../.. --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}

libs/cli/tests/unit_tests/test_config.py

@@ -48,6 +48,8 @@ def test_validate_config():
         "env": {},
         "store": None,
         "auth": None,
+        "encryption": None,
+        "webhooks": None,
         "checkpointer": None,
         "http": None,
         "ui": None,
@@ -74,6 +76,8 @@ def test_validate_config():
         "env": env,
         "store": None,
         "auth": None,
+        "encryption": None,
+        "webhooks": None,
         "checkpointer": None,
         "http": None,
         "ui": None,
@@ -416,7 +420,7 @@ def test_config_to_docker_simple():
                 "http": {"app": "../../examples/my_app.py:app"},
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     expected_docker_stdin = f"""\
 # syntax=docker/dockerfile:1.4
@@ -479,7 +483,7 @@ def test_config_to_docker_outside_path():
     actual_docker_stdin, additional_contexts = config_to_docker(
         PATH_TO_CONFIG,
         validate_config({"dependencies": [".", ".."], "graphs": graphs}),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     expected_docker_stdin = (
         """\
@@ -540,7 +544,7 @@ def test_config_to_docker_pipconfig():
                 "pip_config_file": "pipconfig.txt",
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     expected_docker_stdin = (
         """\
@@ -581,7 +585,7 @@ def test_config_to_docker_invalid_inputs():
         config_to_docker(
             PATH_TO_CONFIG,
             validate_config({"dependencies": ["./missing"], "graphs": graphs}),
-            "langchain/langgraph-api",
+            base_image="langchain/langgraph-api",
         )
 
     # test missing local module
@@ -590,7 +594,7 @@ def test_config_to_docker_invalid_inputs():
         config_to_docker(
             PATH_TO_CONFIG,
             validate_config({"dependencies": ["."], "graphs": graphs}),
-            "langchain/langgraph-api",
+            base_image="langchain/langgraph-api",
         )
 
 
@@ -604,7 +608,7 @@ def test_config_to_docker_local_deps():
                 "graphs": graphs,
             }
         ),
-        "langchain/langgraph-api-custom",
+        base_image="langchain/langgraph-api-custom",
     )
     expected_docker_stdin = f"""\
 FROM langchain/langgraph-api-custom:3.11
@@ -650,7 +654,7 @@ dependencies = ["langchain"]"""
                 "graphs": graphs,
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     os.remove(pyproject_path)
     expected_docker_stdin = (
@@ -685,7 +689,7 @@ def test_config_to_docker_end_to_end():
                 "dockerfile_lines": ["ARG meow", "ARG foo"],
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     expected_docker_stdin = f"""FROM langchain/langgraph-api:3.12
 ARG meow
@@ -730,7 +734,7 @@ def test_config_to_docker_nodejs():
                 "ui_config": {"shared": ["nuqs"]},
             }
         ),
-        "langchain/langgraphjs-api",
+        base_image="langchain/langgraphjs-api",
     )
     expected_docker_stdin = """FROM langchain/langgraphjs-api:20
 ARG meow
@@ -748,6 +752,60 @@ RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not foun
     assert additional_contexts == {}
 
 
+def test_config_to_docker_python_encryption():
+    # Test that encryption config is included in validation
+    graphs = {"agent": "./agent.py:graph"}
+    validated = validate_config(
+        {
+            "python_version": "3.11",
+            "graphs": graphs,
+            "dependencies": ["."],
+            "encryption": {"path": "./encryption.py:encryption"},
+        }
+    )
+
+    # Verify that encryption config is preserved after validation
+    assert validated.get("encryption") is not None
+    assert validated["encryption"]["path"] == "./encryption.py:encryption"
+
+
+def test_config_to_docker_python_encryption_bad_path():
+    # Test that invalid encryption path format raises ValueError
+    graphs = {"agent": "./agent.py:graph"}
+    with pytest.raises(ValueError, match="Invalid encryption.path format"):
+        validate_config(
+            {
+                "python_version": "3.11",
+                "graphs": graphs,
+                "dependencies": ["."],
+                "encryption": {"path": "./encryption.py"},  # Missing :attribute
+            }
+        )
+
+
+def test_config_to_docker_python_encryption_formatted():
+    # Test that encryption config is properly formatted in Docker output
+    graphs = {"agent": "./graphs/agent.py:graph"}
+    actual_docker_stdin, _ = config_to_docker(
+        PATH_TO_CONFIG,
+        validate_config(
+            {
+                "python_version": "3.11",
+                "dependencies": ["."],
+                "graphs": graphs,
+                "encryption": {"path": "./agent.py:my_encryption"},
+            }
+        ),
+        base_image="langchain/langgraph-api",
+    )
+    # Verify that LANGGRAPH_ENCRYPTION is in the docker output with the correct path
+    assert "LANGGRAPH_ENCRYPTION=" in actual_docker_stdin
+    assert (
+        "/deps/outer-unit_tests/unit_tests/agent.py:my_encryption"
+        in actual_docker_stdin
+    )
+
+
 def test_config_to_docker_nodejs_internal_docker_tag():
     graphs = {"agent": "./graphs/agent.js:graph"}
     actual_docker_stdin, additional_contexts = config_to_docker(
@@ -763,7 +821,7 @@ def test_config_to_docker_nodejs_internal_docker_tag():
                 "_INTERNAL_docker_tag": "my-tag",
             }
         ),
-        "langchain/langgraphjs-api",
+        base_image="langchain/langgraphjs-api",
     )
     expected_docker_stdin = """FROM langchain/langgraphjs-api:my-tag
 ARG meow
@@ -781,6 +839,85 @@ RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not foun
     assert additional_contexts == {}
 
 
+def _extract_env_json(dockerfile: str, var_name: str) -> dict:
+    """Helper to extract and parse a JSON value from an ENV line in a Dockerfile."""
+    line_prefix = f"ENV {var_name}='"
+    for line in dockerfile.splitlines():
+        if line.startswith(line_prefix) and line.endswith("'"):
+            json_str = line[len(line_prefix) : -1]
+            return json.loads(json_str)
+    raise AssertionError(f"{var_name} not found in Dockerfile env lines")
+
+
+def test_config_to_docker_webhooks_python():
+    graphs = {"agent": "./agent.py:graph"}
+    webhooks = {
+        "env_prefix": "LG_WEBHOOK_",
+        "url": {
+            "require_https": True,
+            "allowed_domains": ["hooks.example.com", "*.example.org"],
+            "allowed_ports": [443],
+            "max_url_length": 1024,
+            "disable_loopback": False,
+        },
+        "headers": {
+            "x-auth": "${{ env.LG_WEBHOOK_TOKEN }}",
+            "x-mixed": "Bearer ${{ env.LG_WEBHOOK_TOKEN }}-suffix",
+        },
+    }
+
+    dockerfile, _ = config_to_docker(
+        PATH_TO_CONFIG,
+        validate_config(
+            {
+                "dependencies": ["."],
+                "graphs": graphs,
+                "webhooks": webhooks,
+            }
+        ),
+        base_image="langchain/langgraph-api",
+    )
+
+    # Ensure the ENV line is present and the payload round-trips via JSON
+    parsed = _extract_env_json(dockerfile, "LANGGRAPH_WEBHOOKS")
+    assert parsed == webhooks
+
+
+def test_config_to_docker_webhooks_node():
+    graphs = {"agent": "./graphs/agent.js:graph"}
+    webhooks = {
+        "env_prefix": "LG_WEBHOOK_",
+        "url": {"require_https": True},
+        "headers": {"x-auth": "${{ env.LG_WEBHOOK_TOKEN }}"},
+    }
+
+    dockerfile, _ = config_to_docker(
+        PATH_TO_CONFIG,
+        validate_config(
+            {
+                "node_version": "20",
+                "graphs": graphs,
+                "webhooks": webhooks,
+            }
+        ),
+        base_image="langchain/langgraphjs-api",
+    )
+
+    parsed = _extract_env_json(dockerfile, "LANGGRAPH_WEBHOOKS")
+    assert parsed == webhooks
+
+
+def test_config_to_docker_no_webhooks():
+    graphs = {"agent": "./agent.py:graph"}
+    dockerfile, _ = config_to_docker(
+        PATH_TO_CONFIG,
+        validate_config({"dependencies": ["."], "graphs": graphs}),
+        base_image="langchain/langgraph-api",
+    )
+
+    assert "ENV LANGGRAPH_WEBHOOKS=" not in dockerfile
+
+
 def test_config_to_docker_gen_ui_python():
     graphs = {"agent": "./agent.py:graph"}
     actual_docker_stdin, additional_contexts = config_to_docker(
@@ -793,7 +930,7 @@ def test_config_to_docker_gen_ui_python():
                 "ui_config": {"shared": ["nuqs"]},
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
 
     expected_docker_stdin = f"""FROM langchain/langgraph-api:3.11
@@ -839,7 +976,7 @@ def test_config_to_docker_multiplatform():
         validate_config(
             {"node_version": "22", "dependencies": ["."], "graphs": graphs}
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
 
     expected_docker_stdin = f"""FROM langchain/langgraph-api:3.11
@@ -887,7 +1024,7 @@ def test_config_to_docker_pip_installer():
         {**copy.deepcopy(base_config), "pip_installer": "auto"}
     )
     docker_auto, _ = config_to_docker(
-        PATH_TO_CONFIG, config_auto, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_auto, base_image="langchain/langgraph-api:0.2.47"
     )
     assert "uv pip install --system " in docker_auto
     assert "rm /usr/bin/uv /usr/bin/uvx" in docker_auto
@@ -895,7 +1032,7 @@ def test_config_to_docker_pip_installer():
     # Test explicit pip setting
     config_pip = validate_config({**copy.deepcopy(base_config), "pip_installer": "pip"})
     docker_pip, _ = config_to_docker(
-        PATH_TO_CONFIG, config_pip, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_pip, base_image="langchain/langgraph-api:0.2.47"
     )
     assert "uv pip install --system " not in docker_pip
     assert "pip install" in docker_pip
@@ -904,7 +1041,7 @@ def test_config_to_docker_pip_installer():
     # Test explicit uv setting
     config_uv = validate_config({**copy.deepcopy(base_config), "pip_installer": "uv"})
     docker_uv, _ = config_to_docker(
-        PATH_TO_CONFIG, config_uv, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_uv, base_image="langchain/langgraph-api:0.2.47"
     )
     assert "uv pip install --system " in docker_uv
     assert "rm /usr/bin/uv /usr/bin/uvx" in docker_uv
@@ -914,7 +1051,7 @@ def test_config_to_docker_pip_installer():
         {**copy.deepcopy(base_config), "pip_installer": "auto"}
     )
     docker_auto_old, _ = config_to_docker(
-        PATH_TO_CONFIG, config_auto_old, "langchain/langgraph-api:0.2.46"
+        PATH_TO_CONFIG, config_auto_old, base_image="langchain/langgraph-api:0.2.46"
     )
     assert "uv pip install --system " not in docker_auto_old
     assert "pip install" in docker_auto_old
@@ -923,7 +1060,7 @@ def test_config_to_docker_pip_installer():
     # Test that missing pip_installer defaults to auto behavior
     config_default = validate_config(copy.deepcopy(base_config))
     docker_default, _ = config_to_docker(
-        PATH_TO_CONFIG, config_default, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_default, base_image="langchain/langgraph-api:0.2.47"
     )
     assert "uv pip install --system " in docker_default
 
@@ -939,7 +1076,7 @@ def test_config_retain_build_tools():
         {**copy.deepcopy(base_config), "keep_pkg_tools": True}
     )
     docker_true, _ = config_to_docker(
-        PATH_TO_CONFIG, config_true, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_true, base_image="langchain/langgraph-api:0.2.47"
     )
     assert not any(
         "/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_true
@@ -950,7 +1087,7 @@ def test_config_retain_build_tools():
         {**copy.deepcopy(base_config), "keep_pkg_tools": False}
     )
     docker_false, _ = config_to_docker(
-        PATH_TO_CONFIG, config_false, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_false, base_image="langchain/langgraph-api:0.2.47"
     )
     assert all(
         "/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_false
@@ -961,7 +1098,7 @@ def test_config_retain_build_tools():
         {**copy.deepcopy(base_config), "keep_pkg_tools": ["pip", "setuptools"]}
     )
     docker_list, _ = config_to_docker(
-        PATH_TO_CONFIG, config_list, "langchain/langgraph-api:0.2.47"
+        PATH_TO_CONFIG, config_list, base_image="langchain/langgraph-api:0.2.47"
     )
     assert all(
         "/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_list
@@ -1000,7 +1137,7 @@ def test_config_to_compose_simple_config():
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-unit_tests/unit_tests/agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
@@ -1041,7 +1178,7 @@ def test_config_to_compose_env_vars():
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-unit_tests/unit_tests/agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
@@ -1086,7 +1223,7 @@ def test_config_to_compose_env_file():
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-unit_tests/unit_tests/agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
@@ -1124,7 +1261,7 @@ def test_config_to_compose_watch():
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-unit_tests/unit_tests/agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
@@ -1171,7 +1308,7 @@ def test_config_to_compose_end_to_end():
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
+                RUN for dep in /deps/*; do             echo "Installing $$dep";             if [ -d "$$dep" ]; then                 echo "Installing $$dep";                 (cd "$$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/outer-unit_tests/unit_tests/agent.py:graph"}}'
 {textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
@@ -1482,7 +1619,7 @@ def test_config_to_docker_with_api_version():
     actual_docker_stdin, additional_contexts = config_to_docker(
         PATH_TO_CONFIG,
         validate_config({"dependencies": ["."], "graphs": graphs}),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
         api_version="0.2.74",
     )
 
@@ -1496,7 +1633,7 @@ def test_config_to_docker_with_api_version():
     actual_docker_stdin, additional_contexts = config_to_docker(
         PATH_TO_CONFIG,
         validate_config({"node_version": "20", "graphs": graphs}),
-        "langchain/langgraphjs-api",
+        base_image="langchain/langgraphjs-api",
         api_version="0.2.74",
     )
 

libs/langgraph/.claude/settings.local.json

@@ -1,16 +0,0 @@
-{
-  "permissions": {
-    "allow": [
-      "Bash(rg:*)",
-      "Bash(python:*)",
-      "Bash(grep:*)",
-      "Bash(sed:*)",
-      "Bash(awk:*)",
-      "Bash(uv run mypy:*)",
-      "Bash(uv run:*)",
-      "Bash(make test:*)",
-      "Bash(make test_parallel:*)"
-    ],
-    "deny": []
-  }
-}

libs/langgraph/README.md

@@ -11,7 +11,7 @@
 [![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
-[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
+[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://docs.langchain.com/oss/python/langgraph/overview)
 
 Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
 
@@ -23,60 +23,67 @@ Install LangGraph:
 pip install -U langgraph
 ```
 
-Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
+Create a simple workflow:
 
 ```python
-# pip install -qU "langchain[anthropic]" to call the model
+from langgraph.graph import START, StateGraph
+from typing_extensions import TypedDict
 
-from langgraph.prebuilt import create_react_agent
 
-def get_weather(city: str) -> str:
-    """Get weather for a given city."""
-    return f"It's always sunny in {city}!"
+class State(TypedDict):
+    text: str
 
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    prompt="You are a helpful assistant"
-)
 
-# Run the agent
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
-)
+def node_a(state: State) -> dict:
+    return {"text": state["text"] + "a"}
+
+
+def node_b(state: State) -> dict:
+    return {"text": state["text"] + "b"}
+
+
+graph = StateGraph(State)
+graph.add_node("node_a", node_a)
+graph.add_node("node_b", node_b)
+graph.add_edge(START, "node_a")
+graph.add_edge("node_a", "node_b")
+
+print(graph.compile().invoke({"text": ""}))
+# {'text': 'ab'}
 ```
 
-For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).
+Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
+
+To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
 
 ## Core benefits
 
 LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
 
-- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
-- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
-- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
+- [Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
+- [Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
+- [Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
 - [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
-- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
+- [Production-ready deployment](https://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
 
 ## LangGraph’s ecosystem
 
 While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
 
 - [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangSmith Deployment](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://docs.langchain.com/oss/python/langgraph/studio).
 - [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
 
 > [!NOTE]
-> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
+> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
 
 ## Additional resources
 
-- [Guides](https://langchain-ai.github.io/langgraph/guides/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
-- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
+- [Guides](https://docs.langchain.com/oss/python/langgraph/guides): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
+- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph.
 - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
-- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
 - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
 
 ## Acknowledgements

libs/langgraph/langgraph/channels/base.py

@@ -39,14 +39,19 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
 
     def copy(self) -> Self:
         """Return a copy of the channel.
+
         By default, delegates to `checkpoint()` and `from_checkpoint()`.
-        Subclasses can override this method with a more efficient implementation."""
+
+        Subclasses can override this method with a more efficient implementation.
+        """
         return self.from_checkpoint(self.checkpoint())
 
     def checkpoint(self) -> Checkpoint | Any:
         """Return a serializable representation of the channel's current state.
+
         Raises `EmptyChannelError` if the channel is empty (never updated yet),
-        or doesn't support checkpoints."""
+        or doesn't support checkpoints.
+        """
         try:
             return self.get()
         except EmptyChannelError:
@@ -55,7 +60,9 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
     @abstractmethod
     def from_checkpoint(self, checkpoint: Checkpoint | Any) -> Self:
         """Return a new identical channel, optionally initialized from a checkpoint.
-        If the checkpoint contains complex data structures, they should be copied."""
+
+        If the checkpoint contains complex data structures, they should be copied.
+        """
 
     # read methods
 
@@ -67,6 +74,7 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
 
     def is_available(self) -> bool:
         """Return `True` if the channel is available (not empty), `False` otherwise.
+
         Subclasses should override this method to provide a more efficient
         implementation than calling `get()` and catching `EmptyChannelError`.
         """
@@ -83,21 +91,29 @@ class BaseChannel(Generic[Value, Update, Checkpoint], ABC):
         """Update the channel's value with the given sequence of updates.
         The order of the updates in the sequence is arbitrary.
         This method is called by Pregel for all channels at the end of each step.
+
         If there are no updates, it is called with an empty sequence.
+
         Raises `InvalidUpdateError` if the sequence of updates is invalid.
+
         Returns `True` if the channel was updated, `False` otherwise."""
 
     def consume(self) -> bool:
-        """Notify the channel that a subscribed task ran. By default, no-op.
-        A channel can use this method to modify its state, preventing the value
-        from being consumed again.
+        """Notify the channel that a subscribed task ran.
+
+        By default, no-op.
+
+        A channel can use this method to modify its state, preventing the value from being consumed again.
 
         Returns `True` if the channel was updated, `False` otherwise.
         """
         return False
 
     def finish(self) -> bool:
-        """Notify the channel that the Pregel run is finishing. By default, no-op.
+        """Notify the channel that the Pregel run is finishing.
+
+        By default, no-op.
+
         A channel can use this method to modify its state, preventing finish.
 
         Returns `True` if the channel was updated, `False` otherwise.

libs/langgraph/langgraph/config.py

@@ -32,8 +32,8 @@ def get_config() -> RunnableConfig:
 def get_store() -> BaseStore:
     """Access LangGraph store from inside a graph node or entrypoint task at runtime.
 
-    Can be called from inside any [StateGraph][langgraph.graph.StateGraph] node or
-    functional API [task][langgraph.func.task], as long as the StateGraph or the [entrypoint][langgraph.func.entrypoint]
+    Can be called from inside any [`StateGraph`][langgraph.graph.StateGraph] node or
+    functional API [`task`][langgraph.func.task], as long as the `StateGraph` or the [`entrypoint`][langgraph.func.entrypoint]
     was initialized with a store, e.g.:
 
     ```python
@@ -53,10 +53,10 @@ def get_store() -> BaseStore:
     !!! warning "Async with Python < 3.11"
 
         If you are using Python < 3.11 and are running LangGraph asynchronously,
-        `get_store()` won't work since it uses [contextvar](https://docs.python.org/3/library/contextvars.html) propagation (only available in [Python >= 3.11](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)).
+        `get_store()` won't work since it uses [`contextvar`](https://docs.python.org/3/library/contextvars.html) propagation (only available in [Python >= 3.11](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)).
 
 
-    Example: Using with StateGraph
+    Example: Using with `StateGraph`
         ```python
         from typing_extensions import TypedDict
         from langgraph.graph import StateGraph, START
@@ -124,17 +124,17 @@ def get_store() -> BaseStore:
 
 
 def get_stream_writer() -> StreamWriter:
-    """Access LangGraph [StreamWriter][langgraph.types.StreamWriter] from inside a graph node or entrypoint task at runtime.
+    """Access LangGraph [`StreamWriter`][langgraph.types.StreamWriter] from inside a graph node or entrypoint task at runtime.
 
-    Can be called from inside any [StateGraph][langgraph.graph.StateGraph] node or
-    functional API [task][langgraph.func.task].
+    Can be called from inside any [`StateGraph`][langgraph.graph.StateGraph] node or
+    functional API [`task`][langgraph.func.task].
 
     !!! warning "Async with Python < 3.11"
 
         If you are using Python < 3.11 and are running LangGraph asynchronously,
-        `get_stream_writer()` won't work since it uses [contextvar](https://docs.python.org/3/library/contextvars.html) propagation (only available in [Python >= 3.11](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)).
+        `get_stream_writer()` won't work since it uses [`contextvar`](https://docs.python.org/3/library/contextvars.html) propagation (only available in [Python >= 3.11](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)).
 
-    Example: Using with StateGraph
+    Example: Using with `StateGraph`
         ```python
         from typing_extensions import TypedDict
         from langgraph.graph import StateGraph, START

libs/langgraph/langgraph/func/__init__.py

@@ -151,13 +151,13 @@ def task(
 
 
         @task
-        def add_one(a: int) -> int:
+        def add_one_task(a: int) -> int:
             return a + 1
 
 
         @entrypoint()
         def add_one(numbers: list[int]) -> list[int]:
-            futures = [add_one(n) for n in numbers]
+            futures = [add_one_task(n) for n in numbers]
             results = [f.result() for f in futures]
             return results
 
@@ -173,13 +173,13 @@ def task(
 
 
         @task
-        async def add_one(a: int) -> int:
+        async def add_one_task(a: int) -> int:
             return a + 1
 
 
         @entrypoint()
         async def add_one(numbers: list[int]) -> list[int]:
-            futures = [add_one(n) for n in numbers]
+            futures = [add_one_task(n) for n in numbers]
             return asyncio.gather(*futures)
 
 
@@ -357,7 +357,7 @@ class entrypoint(Generic[ContextT]):
         my_workflow.invoke("hello", config)
         ```
 
-    Example: Using entrypoint.final to save a value
+    Example: Using `entrypoint.final` to save a value
         The `entrypoint.final` object allows you to return a value while saving
         a different value to the checkpoint. This value will be accessible
         in the next invocation of the entrypoint via the `previous` parameter, as

libs/langgraph/langgraph/graph/message.py

@@ -30,6 +30,7 @@ __all__ = (
     "add_messages",
     "MessagesState",
     "MessageGraph",
+    "REMOVE_ALL_MESSAGES",
 )
 
 Messages = list[MessageLikeRepresentation] | MessageLikeRepresentation
@@ -87,8 +88,8 @@ def add_messages(
         If a message in `right` has the same ID as a message in `left`, the
             message from `right` will replace the message from `left`.
 
-    Example:
-        ```python title="Basic usage"
+    Example: Basic usage
+        ```python
         from langchain_core.messages import AIMessage, HumanMessage
 
         msgs1 = [HumanMessage(content="Hello", id="1")]
@@ -97,14 +98,16 @@ def add_messages(
         # [HumanMessage(content='Hello', id='1'), AIMessage(content='Hi there!', id='2')]
         ```
 
-        ```python title="Overwrite existing message"
+    Example: Overwrite existing message
+        ```python
         msgs1 = [HumanMessage(content="Hello", id="1")]
         msgs2 = [HumanMessage(content="Hello again", id="1")]
         add_messages(msgs1, msgs2)
         # [HumanMessage(content='Hello again', id='1')]
         ```
 
-        ```python title="Use in a StateGraph"
+    Example: Use in a StateGraph
+        ```python
         from typing import Annotated
         from typing_extensions import TypedDict
         from langgraph.graph import StateGraph
@@ -123,7 +126,8 @@ def add_messages(
         # {'messages': [AIMessage(content='Hello', id=...)]}
         ```
 
-        ```python title="Use OpenAI message format"
+    Example: Use OpenAI message format
+        ```python
         from typing import Annotated
         from typing_extensions import TypedDict
         from langgraph.graph import StateGraph, add_messages

libs/langgraph/langgraph/graph/state.py

@@ -78,6 +78,7 @@ from langgraph.types import (
     Command,
     RetryPolicy,
     Send,
+    ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, NodeInputT, OutputT, StateT
 from langgraph.warnings import LangGraphDeprecatedSinceV05, LangGraphDeprecatedSinceV10
@@ -110,15 +111,24 @@ def _get_node_name(node: StateNode[Any, ContextT]) -> str:
 
 class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
     """A graph whose nodes communicate by reading and writing to a shared state.
-    The signature of each node is State -> Partial<State>.
+
+    The signature of each node is `State -> Partial<State>`.
 
     Each state key can optionally be annotated with a reducer function that
     will be used to aggregate the values of that key received from multiple nodes.
     The signature of a reducer function is `(Value, Value) -> Value`.
 
+    !!! warning
+
+        `StateGraph` is a builder class and cannot be used directly for execution.
+        You must first call `.compile()` to create an executable graph that supports
+        methods like `invoke()`, `stream()`, `astream()`, and `ainvoke()`. See the
+        `CompiledStateGraph` documentation for more details.
+
     Args:
         state_schema: The schema class that defines the state.
         context_schema: The schema class that defines the runtime context.
+
             Use this to expose immutable context data to your nodes, like `user_id`, `db_conn`, etc.
         input_schema: The schema class that defines the input to the graph.
         output_schema: The schema class that defines the output from the graph.
@@ -289,7 +299,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the state graph, input schema is inferred as the state schema.
+        """Add a new node to the `StateGraph`, input schema is inferred as the state schema.
         Will take the name of the function/runnable as the node name.
         """
         ...
@@ -307,7 +317,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the state graph, input schema is specified.
+        """Add a new node to the `StateGraph`, input schema is specified.
         Will take the name of the function/runnable as the node name.
         """
         ...
@@ -326,7 +336,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the state graph, input schema is inferred as the state schema."""
+        """Add a new node to the `StateGraph`, input schema is inferred as the state schema."""
         ...
 
     @overload
@@ -343,7 +353,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the state graph, input schema is specified."""
+        """Add a new node to the `StateGraph`, input schema is specified."""
         ...
 
     def add_node(
@@ -359,22 +369,27 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
-        """Add a new node to the state graph.
+        """Add a new node to the `StateGraph`.
 
         Args:
             node: The function or runnable this node will run.
+
                 If a string is provided, it will be used as the node name, and action will be used as the function or runnable.
             action: The action associated with the node.
                 Will be used as the node function or runnable if `node` is a string (node name).
             defer: Whether to defer the execution of the node until the run is about to end.
             metadata: The metadata associated with the node.
-            input_schema: The input schema for the node. (default: the graph's state schema)
+            input_schema: The input schema for the node. (Default: the graph's state schema)
             retry_policy: The retry policy for the node.
+
                 If a sequence is provided, the first matching policy will be applied.
             cache_policy: The cache policy for the node.
             destinations: Destinations that indicate where a node can route to.
-                This is useful for edgeless graphs with nodes that return `Command` objects.
+
+                Useful for edgeless graphs with nodes that return `Command` objects.
+
                 If a `dict` is provided, the keys will be used as the target node names and the values will be used as the labels for the edges.
+
                 If a `tuple` is provided, the values will be used as the target node names.
 
                 !!! note
@@ -416,7 +431,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             ```
 
         Returns:
-            Self: The instance of the state graph, allowing for method chaining.
+            Self: The instance of the `StateGraph`, allowing for method chaining.
         """
         if (retry := kwargs.get("retry", MISSING)) is not MISSING:
             warnings.warn(
@@ -571,7 +586,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             ValueError: If the start key is `'END'` or if the start key or end key is not present in the graph.
 
         Returns:
-            Self: The instance of the state graph, allowing for method chaining.
+            Self: The instance of the `StateGraph`, allowing for method chaining.
         """
         if self.compiled:
             logger.warning(
@@ -623,11 +638,14 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         Args:
             source: The starting node. This conditional edge will run when
                 exiting this node.
-            path: The callable that determines the next
-                node or nodes. If not specifying `path_map` it should return one or
-                more nodes. If it returns `'END'`, the graph will stop execution.
-            path_map: Optional mapping of paths to node
-                names. If omitted the paths returned by `path` should be node names.
+            path: The callable that determines the next node or nodes.
+
+                If not specifying `path_map` it should return one or more nodes.
+
+                If it returns `'END'`, the graph will stop execution.
+            path_map: Optional mapping of paths to node names.
+
+                If omitted the paths returned by `path` should be node names.
 
         Returns:
             Self: The instance of the graph, allowing for method chaining.
@@ -668,7 +686,9 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
 
         Args:
             nodes: A sequence of `StateNode` (callables that accept a `state` arg) or `(name, StateNode)` tuples.
+
                 If no names are provided, the name will be inferred from the node object (e.g. a `Runnable` or a `Callable` name).
+
                 Each node will be executed in the order provided.
 
         Raises:
@@ -676,7 +696,7 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             ValueError: If the sequence contains duplicate node names.
 
         Returns:
-            Self: The instance of the state graph, allowing for method chaining.
+            Self: The instance of the `StateGraph`, allowing for method chaining.
         """
         if len(nodes) < 1:
             raise ValueError("Sequence requires at least one node.")
@@ -725,11 +745,14 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         """Sets a conditional entry point in the graph.
 
         Args:
-            path: The callable that determines the next
-                node or nodes. If not specifying `path_map` it should return one or
-                more nodes. If it returns END, the graph will stop execution.
-            path_map: Optional mapping of paths to node
-                names. If omitted the paths returned by `path` should be node names.
+            path: The callable that determines the next node or nodes.
+
+                If not specifying `path_map` it should return one or more nodes.
+
+                If it returns END, the graph will stop execution.
+            path_map: Optional mapping of paths to node names.
+
+                If omitted the paths returned by `path` should be node names.
 
         Returns:
             Self: The instance of the graph, allowing for method chaining.
@@ -809,16 +832,19 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         debug: bool = False,
         name: str | None = None,
     ) -> CompiledStateGraph[StateT, ContextT, InputT, OutputT]:
-        """Compiles the state graph into a `CompiledStateGraph` object.
+        """Compiles the `StateGraph` into a `CompiledStateGraph` object.
 
         The compiled graph implements the `Runnable` interface and can be invoked,
         streamed, batched, and run asynchronously.
 
         Args:
             checkpointer: A checkpoint saver object or flag.
+
                 If provided, this `Checkpointer` serves as a fully versioned "short-term memory" for the graph,
                 allowing it to be paused, resumed, and replayed from any point.
+
                 If `None`, it may inherit the parent graph's checkpointer when used as a subgraph.
+
                 If `False`, it will not use or inherit any checkpointer.
             interrupt_before: An optional list of node names to interrupt before.
             interrupt_after: An optional list of node names to interrupt after.
@@ -826,8 +852,10 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
             name: The name to use for the compiled graph.
 
         Returns:
-            CompiledStateGraph: The compiled state graph.
+            CompiledStateGraph: The compiled `StateGraph`.
         """
+        checkpointer = ensure_valid_checkpointer(checkpointer)
+
         # assign default values
         interrupt_before = interrupt_before or []
         interrupt_after = interrupt_after or []

libs/langgraph/langgraph/pregel/_algo.py

@@ -258,9 +258,11 @@ def apply_writes(
         next_version = None
     else:
         next_version = get_next_version(
-            max(checkpoint["channel_versions"].values())
-            if checkpoint["channel_versions"]
-            else None,
+            (
+                max(checkpoint["channel_versions"].values())
+                if checkpoint["channel_versions"]
+                else None
+            ),
             None,
         )
 
@@ -491,6 +493,11 @@ def prepare_next_tasks(
 PUSH_TRIGGER = (PUSH,)
 
 
+class _TaskIDFn(Protocol):
+    def __call__(self, namespace: bytes, *parts: str | bytes) -> str:
+        pass
+
+
 def prepare_single_task(
     task_path: tuple[Any, ...],
     task_id_checksum: str | None,
@@ -520,250 +527,50 @@ def prepare_single_task(
     task_id_func = _xxhash_str if checkpoint["v"] > 1 else _uuid5_str
 
     if task_path[0] == PUSH and isinstance(task_path[-1], Call):
-        # (PUSH, parent task path, idx of PUSH write, id of parent task, Call)
-        task_path_t = cast(tuple[str, tuple, int, str, Call], task_path)
-        call = task_path_t[-1]
-        proc_ = get_runnable_for_task(call.func)
-        name = proc_.name
-        if name is None:
-            raise ValueError("`call` functions must have a `__name__` attribute")
-        # create task id
-        triggers: Sequence[str] = PUSH_TRIGGER
-        checkpoint_ns = f"{parent_ns}{NS_SEP}{name}" if parent_ns else name
-        task_id = task_id_func(
-            checkpoint_id_bytes,
-            checkpoint_ns,
-            str(step),
-            name,
-            PUSH,
-            task_path_str(task_path[1]),
-            str(task_path[2]),
+        return prepare_push_task_functional(
+            cast(tuple[str, tuple, int, str, Call], task_path),
+            task_id_checksum,
+            checkpoint=checkpoint,
+            checkpoint_id_bytes=checkpoint_id_bytes,
+            pending_writes=pending_writes,
+            channels=channels,
+            managed=managed,
+            config=config,
+            step=step,
+            stop=stop,
+            for_execution=for_execution,
+            store=store,
+            checkpointer=checkpointer,
+            manager=manager,
+            cache_policy=cache_policy,
+            retry_policy=retry_policy,
+            parent_ns=parent_ns,
+            task_id_func=task_id_func,
+        )
+
+    elif task_path[0] == PUSH:
+        return prepare_push_task_send(
+            cast(tuple[str, tuple], task_path),
+            task_id_checksum,
+            checkpoint=checkpoint,
+            checkpoint_id_bytes=checkpoint_id_bytes,
+            pending_writes=pending_writes,
+            channels=channels,
+            managed=managed,
+            config=config,
+            step=step,
+            processes=processes,
+            stop=stop,
+            for_execution=for_execution,
+            store=store,
+            checkpointer=checkpointer,
+            manager=manager,
+            cache_policy=cache_policy,
+            retry_policy=retry_policy,
+            parent_ns=parent_ns,
+            task_id_func=task_id_func,
         )
-        task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
-        # we append True to the task path to indicate that a call is being
-        # made, so we should not return interrupts from this task (responsibility lies with the parent)
-        task_path = (*task_path[:3], True)
-        metadata = {
-            "langgraph_step": step,
-            "langgraph_node": name,
-            "langgraph_triggers": triggers,
-            "langgraph_path": task_path,
-            "langgraph_checkpoint_ns": task_checkpoint_ns,
-        }
-        if task_id_checksum is not None:
-            assert task_id == task_id_checksum, f"{task_id} != {task_id_checksum}"
-        if for_execution:
-            writes: deque[tuple[str, Any]] = deque()
-            cache_policy = call.cache_policy or cache_policy
-            if cache_policy:
-                args_key = cache_policy.key_func(*call.input[0], **call.input[1])
-                cache_key: CacheKey | None = CacheKey(
-                    (
-                        CACHE_NS_WRITES,
-                        (identifier(call.func) or "__dynamic__"),
-                    ),
-                    xxh3_128_hexdigest(
-                        args_key.encode() if isinstance(args_key, str) else args_key,
-                    ),
-                    cache_policy.ttl,
-                )
-            else:
-                cache_key = None
-            scratchpad = _scratchpad(
-                config[CONF].get(CONFIG_KEY_SCRATCHPAD),
-                pending_writes,
-                task_id,
-                xxh3_128_hexdigest(task_checkpoint_ns.encode()),
-                config[CONF].get(CONFIG_KEY_RESUME_MAP),
-                step,
-                stop,
-            )
-            runtime = cast(
-                Runtime, configurable.get(CONFIG_KEY_RUNTIME, DEFAULT_RUNTIME)
-            )
-            runtime = runtime.override(store=store)
-            return PregelExecutableTask(
-                name,
-                call.input,
-                proc_,
-                writes,
-                patch_config(
-                    merge_configs(config, {"metadata": metadata}),
-                    run_name=name,
-                    callbacks=call.callbacks
-                    or (manager.get_child(f"graph:step:{step}") if manager else None),
-                    configurable={
-                        CONFIG_KEY_TASK_ID: task_id,
-                        # deque.extend is thread-safe
-                        CONFIG_KEY_SEND: writes.extend,
-                        CONFIG_KEY_READ: partial(
-                            local_read,
-                            scratchpad,
-                            channels,
-                            managed,
-                            PregelTaskWrites(task_path, name, writes, triggers),
-                        ),
-                        CONFIG_KEY_CHECKPOINTER: (
-                            checkpointer or configurable.get(CONFIG_KEY_CHECKPOINTER)
-                        ),
-                        CONFIG_KEY_CHECKPOINT_MAP: {
-                            **configurable.get(CONFIG_KEY_CHECKPOINT_MAP, {}),
-                            parent_ns: checkpoint["id"],
-                        },
-                        CONFIG_KEY_CHECKPOINT_ID: None,
-                        CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
-                        CONFIG_KEY_SCRATCHPAD: scratchpad,
-                        CONFIG_KEY_RUNTIME: runtime,
-                    },
-                ),
-                triggers,
-                call.retry_policy or retry_policy,
-                cache_key,
-                task_id,
-                task_path,
-            )
-        else:
-            return PregelTask(task_id, name, task_path)
-    elif task_path[0] == PUSH:
-        if len(task_path) == 2:
-            # SEND tasks, executed in superstep n+1
-            # (PUSH, idx of pending send)
-            idx = cast(int, task_path[1])
-            if not channels[TASKS].is_available():
-                return
-            sends: Sequence[Send] = channels[TASKS].get()
-            if idx < 0 or idx >= len(sends):
-                return
-            packet = sends[idx]
-            if not isinstance(packet, Send):
-                logger.warning(
-                    f"Ignoring invalid packet type {type(packet)} in pending sends"
-                )
-                return
 
-            if packet.node not in processes:
-                logger.warning(
-                    f"Ignoring unknown node name {packet.node} in pending sends"
-                )
-                return
-            # find process
-            proc = processes[packet.node]
-            proc_node = proc.node
-            if proc_node is None:
-                return
-            # create task id
-            triggers = PUSH_TRIGGER
-            checkpoint_ns = (
-                f"{parent_ns}{NS_SEP}{packet.node}" if parent_ns else packet.node
-            )
-            task_id = task_id_func(
-                checkpoint_id_bytes,
-                checkpoint_ns,
-                str(step),
-                packet.node,
-                PUSH,
-                str(idx),
-            )
-        else:
-            logger.warning(f"Ignoring invalid PUSH task path {task_path}")
-            return
-        task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
-        # we append False to the task path to indicate that a call is not being made
-        # so we should return interrupts from this task
-        task_path = (*task_path[:3], False)
-        metadata = {
-            "langgraph_step": step,
-            "langgraph_node": packet.node,
-            "langgraph_triggers": triggers,
-            "langgraph_path": task_path,
-            "langgraph_checkpoint_ns": task_checkpoint_ns,
-        }
-        if task_id_checksum is not None:
-            assert task_id == task_id_checksum, f"{task_id} != {task_id_checksum}"
-        if for_execution:
-            if proc.metadata:
-                metadata.update(proc.metadata)
-            writes = deque()
-            cache_policy = proc.cache_policy or cache_policy
-            if cache_policy:
-                args_key = cache_policy.key_func(packet.arg)
-                cache_key = CacheKey(
-                    (
-                        CACHE_NS_WRITES,
-                        (identifier(proc) or "__dynamic__"),
-                        packet.node,
-                    ),
-                    xxh3_128_hexdigest(
-                        args_key.encode() if isinstance(args_key, str) else args_key,
-                    ),
-                    cache_policy.ttl,
-                )
-            else:
-                cache_key = None
-            scratchpad = _scratchpad(
-                config[CONF].get(CONFIG_KEY_SCRATCHPAD),
-                pending_writes,
-                task_id,
-                xxh3_128_hexdigest(task_checkpoint_ns.encode()),
-                config[CONF].get(CONFIG_KEY_RESUME_MAP),
-                step,
-                stop,
-            )
-            runtime = cast(
-                Runtime, configurable.get(CONFIG_KEY_RUNTIME, DEFAULT_RUNTIME)
-            )
-            runtime = runtime.override(
-                store=store, previous=checkpoint["channel_values"].get(PREVIOUS, None)
-            )
-            additional_config: RunnableConfig = {
-                "metadata": metadata,
-                "tags": proc.tags,
-            }
-            return PregelExecutableTask(
-                packet.node,
-                packet.arg,
-                proc_node,
-                writes,
-                patch_config(
-                    merge_configs(config, additional_config),
-                    run_name=packet.node,
-                    callbacks=(
-                        manager.get_child(f"graph:step:{step}") if manager else None
-                    ),
-                    configurable={
-                        CONFIG_KEY_TASK_ID: task_id,
-                        # deque.extend is thread-safe
-                        CONFIG_KEY_SEND: writes.extend,
-                        CONFIG_KEY_READ: partial(
-                            local_read,
-                            scratchpad,
-                            channels,
-                            managed,
-                            PregelTaskWrites(task_path, packet.node, writes, triggers),
-                        ),
-                        CONFIG_KEY_CHECKPOINTER: (
-                            checkpointer or configurable.get(CONFIG_KEY_CHECKPOINTER)
-                        ),
-                        CONFIG_KEY_CHECKPOINT_MAP: {
-                            **configurable.get(CONFIG_KEY_CHECKPOINT_MAP, {}),
-                            parent_ns: checkpoint["id"],
-                        },
-                        CONFIG_KEY_CHECKPOINT_ID: None,
-                        CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
-                        CONFIG_KEY_SCRATCHPAD: scratchpad,
-                        CONFIG_KEY_RUNTIME: runtime,
-                    },
-                ),
-                triggers,
-                proc.retry_policy or retry_policy,
-                cache_key,
-                task_id,
-                task_path,
-                writers=proc.flat_writers,
-                subgraphs=proc.subgraphs,
-            )
-        else:
-            return PregelTask(task_id, packet.node, task_path)
     elif task_path[0] == PULL:
         # (PULL, node name)
         name = cast(str, task_path[1])
@@ -834,7 +641,7 @@ def prepare_single_task(
                 if node := proc.node:
                     if proc.metadata:
                         metadata.update(proc.metadata)
-                    writes = deque()
+                    writes: deque[tuple[str, Any]] = deque()
                     cache_policy = proc.cache_policy or cache_policy
                     if cache_policy:
                         args_key = cache_policy.key_func(val)
@@ -845,9 +652,11 @@ def prepare_single_task(
                                 name,
                             ),
                             xxh3_128_hexdigest(
-                                args_key.encode()
-                                if isinstance(args_key, str)
-                                else args_key,
+                                (
+                                    args_key.encode()
+                                    if isinstance(args_key, str)
+                                    else args_key
+                                ),
                             ),
                             cache_policy.ttl,
                         )
@@ -870,7 +679,9 @@ def prepare_single_task(
                         node,
                         writes,
                         patch_config(
-                            merge_configs(config, additional_config),
+                            merge_configs(
+                                config, cast(RunnableConfig, additional_config)
+                            ),
                             run_name=name,
                             callbacks=(
                                 manager.get_child(f"graph:step:{step}")
@@ -919,6 +730,297 @@ def prepare_single_task(
                 return PregelTask(task_id, name, task_path[:3])
 
 
+def prepare_push_task_functional(
+    task_path: tuple[str, tuple, int, str, Call],
+    # (PUSH, parent task path, idx of PUSH write, id of parent task, Call)
+    task_id_checksum: str | None,
+    *,
+    checkpoint: Checkpoint,
+    checkpoint_id_bytes: bytes,
+    pending_writes: list[PendingWrite],
+    channels: Mapping[str, BaseChannel],
+    managed: ManagedValueMapping,
+    config: RunnableConfig,
+    step: int,
+    stop: int,
+    for_execution: bool,
+    store: BaseStore | None = None,
+    checkpointer: BaseCheckpointSaver | None = None,
+    manager: None | ParentRunManager | AsyncParentRunManager = None,
+    cache_policy: CachePolicy | None = None,
+    retry_policy: Sequence[RetryPolicy] = (),
+    parent_ns: str,
+    # namespace: bytes, *parts: str | bytes
+    task_id_func: _TaskIDFn,
+) -> PregelTask | PregelExecutableTask:
+    """Prepare a push task with an attached caller. Used for the functional API."""
+    configurable = config.get(CONF, {})
+
+    call = task_path[-1]
+    proc_ = get_runnable_for_task(call.func)
+    name = proc_.name
+    if name is None:
+        raise ValueError("`call` functions must have a `__name__` attribute")
+    # create task id
+    triggers: Sequence[str] = PUSH_TRIGGER
+    checkpoint_ns = f"{parent_ns}{NS_SEP}{name}" if parent_ns else name
+    task_id = task_id_func(
+        checkpoint_id_bytes,
+        checkpoint_ns,
+        str(step),
+        name,
+        PUSH,
+        task_path_str(task_path[1]),
+        str(task_path[2]),
+    )
+    task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
+    # we append True to the task path to indicate that a call is being
+    # made, so we should not return interrupts from this task (responsibility lies with the parent)
+    in_progress_task_path = (*task_path[:3], True)
+    metadata = {
+        "langgraph_step": step,
+        "langgraph_node": name,
+        "langgraph_triggers": triggers,
+        "langgraph_path": in_progress_task_path,
+        "langgraph_checkpoint_ns": task_checkpoint_ns,
+    }
+    if task_id_checksum is not None:
+        assert task_id == task_id_checksum, f"{task_id} != {task_id_checksum}"
+    if for_execution:
+        writes: deque[tuple[str, Any]] = deque()
+        cache_policy = call.cache_policy or cache_policy
+        if cache_policy:
+            args_key = cache_policy.key_func(*call.input[0], **call.input[1])
+            cache_key: CacheKey | None = CacheKey(
+                (
+                    CACHE_NS_WRITES,
+                    (identifier(call.func) or "__dynamic__"),
+                ),
+                xxh3_128_hexdigest(
+                    args_key.encode() if isinstance(args_key, str) else args_key,
+                ),
+                cache_policy.ttl,
+            )
+        else:
+            cache_key = None
+        scratchpad = _scratchpad(
+            configurable.get(CONFIG_KEY_SCRATCHPAD),
+            pending_writes,
+            task_id,
+            xxh3_128_hexdigest(task_checkpoint_ns.encode()),
+            configurable.get(CONFIG_KEY_RESUME_MAP),
+            step,
+            stop,
+        )
+        runtime = cast(Runtime, configurable.get(CONFIG_KEY_RUNTIME, DEFAULT_RUNTIME))
+        runtime = runtime.override(store=store)
+        return PregelExecutableTask(
+            name,
+            call.input,
+            proc_,
+            writes,
+            patch_config(
+                merge_configs(config, {"metadata": metadata}),
+                run_name=name,
+                callbacks=call.callbacks
+                or (manager.get_child(f"graph:step:{step}") if manager else None),
+                configurable={
+                    CONFIG_KEY_TASK_ID: task_id,
+                    # deque.extend is thread-safe
+                    CONFIG_KEY_SEND: writes.extend,
+                    CONFIG_KEY_READ: partial(
+                        local_read,
+                        scratchpad,
+                        channels,
+                        managed,
+                        PregelTaskWrites(in_progress_task_path, name, writes, triggers),
+                    ),
+                    CONFIG_KEY_CHECKPOINTER: (
+                        checkpointer or configurable.get(CONFIG_KEY_CHECKPOINTER)
+                    ),
+                    CONFIG_KEY_CHECKPOINT_MAP: {
+                        **configurable.get(CONFIG_KEY_CHECKPOINT_MAP, {}),
+                        parent_ns: checkpoint["id"],
+                    },
+                    CONFIG_KEY_CHECKPOINT_ID: None,
+                    CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
+                    CONFIG_KEY_SCRATCHPAD: scratchpad,
+                    CONFIG_KEY_RUNTIME: runtime,
+                },
+            ),
+            triggers,
+            call.retry_policy or retry_policy,
+            cache_key,
+            task_id,
+            in_progress_task_path,
+        )
+    else:
+        return PregelTask(task_id, name, in_progress_task_path)
+
+
+def prepare_push_task_send(
+    task_path: tuple[str, tuple],
+    # (PUSH, parent task path)
+    task_id_checksum: str | None,
+    *,
+    checkpoint: Checkpoint,
+    checkpoint_id_bytes: bytes,
+    pending_writes: list[PendingWrite],
+    channels: Mapping[str, BaseChannel],
+    managed: ManagedValueMapping,
+    config: RunnableConfig,
+    step: int,
+    stop: int,
+    for_execution: bool,
+    store: BaseStore | None = None,
+    checkpointer: BaseCheckpointSaver | None = None,
+    manager: None | ParentRunManager | AsyncParentRunManager = None,
+    cache_policy: CachePolicy | None = None,
+    retry_policy: Sequence[RetryPolicy] = (),
+    parent_ns: str,
+    task_id_func: _TaskIDFn,
+    processes: Mapping[str, PregelNode],
+) -> PregelTask | PregelExecutableTask | None:
+    if len(task_path) == 2:
+        # SEND tasks, executed in superstep n+1
+        # (PUSH, idx of pending send)
+        idx = cast(int, task_path[1])
+        if not channels[TASKS].is_available():
+            return
+        sends: Sequence[Send] = channels[TASKS].get()
+        if idx < 0 or idx >= len(sends):
+            return
+        packet = sends[idx]
+        if not isinstance(packet, Send):
+            logger.warning(
+                f"Ignoring invalid packet type {type(packet)} in pending sends"
+            )
+            return
+
+        if packet.node not in processes:
+            logger.warning(f"Ignoring unknown node name {packet.node} in pending sends")
+            return
+        # find process
+        proc = processes[packet.node]
+        proc_node = proc.node
+        if proc_node is None:
+            return
+        # create task id
+        triggers = PUSH_TRIGGER
+        checkpoint_ns = (
+            f"{parent_ns}{NS_SEP}{packet.node}" if parent_ns else packet.node
+        )
+        task_id = task_id_func(
+            checkpoint_id_bytes,
+            checkpoint_ns,
+            str(step),
+            packet.node,
+            PUSH,
+            str(idx),
+        )
+    else:
+        logger.warning(f"Ignoring invalid PUSH task path {task_path}")
+        return
+    configurable = config.get(CONF, {})
+    task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
+    # we append False to the task path to indicate that a call is not being made
+    # so we should return interrupts from this task
+    translated_task_path = (*task_path[:3], False)
+    metadata = {
+        "langgraph_step": step,
+        "langgraph_node": packet.node,
+        "langgraph_triggers": triggers,
+        "langgraph_path": translated_task_path,
+        "langgraph_checkpoint_ns": task_checkpoint_ns,
+    }
+    if task_id_checksum is not None:
+        assert task_id == task_id_checksum, f"{task_id} != {task_id_checksum}"
+    if for_execution:
+        if proc.metadata:
+            metadata.update(proc.metadata)
+        writes: deque[tuple[str, Any]] = deque()
+        cache_policy = proc.cache_policy or cache_policy
+        if cache_policy:
+            args_key = cache_policy.key_func(packet.arg)
+            cache_key = CacheKey(
+                (
+                    CACHE_NS_WRITES,
+                    (identifier(proc) or "__dynamic__"),
+                    packet.node,
+                ),
+                xxh3_128_hexdigest(
+                    args_key.encode() if isinstance(args_key, str) else args_key,
+                ),
+                cache_policy.ttl,
+            )
+        else:
+            cache_key = None
+        scratchpad = _scratchpad(
+            config[CONF].get(CONFIG_KEY_SCRATCHPAD),
+            pending_writes,
+            task_id,
+            xxh3_128_hexdigest(task_checkpoint_ns.encode()),
+            config[CONF].get(CONFIG_KEY_RESUME_MAP),
+            step,
+            stop,
+        )
+        runtime = cast(Runtime, configurable.get(CONFIG_KEY_RUNTIME, DEFAULT_RUNTIME))
+        runtime = runtime.override(
+            store=store, previous=checkpoint["channel_values"].get(PREVIOUS, None)
+        )
+        additional_config: RunnableConfig = {
+            "metadata": metadata,
+            "tags": proc.tags,
+        }
+        return PregelExecutableTask(
+            packet.node,
+            packet.arg,
+            proc_node,
+            writes,
+            patch_config(
+                merge_configs(config, additional_config),
+                run_name=packet.node,
+                callbacks=(
+                    manager.get_child(f"graph:step:{step}") if manager else None
+                ),
+                configurable={
+                    CONFIG_KEY_TASK_ID: task_id,
+                    # deque.extend is thread-safe
+                    CONFIG_KEY_SEND: writes.extend,
+                    CONFIG_KEY_READ: partial(
+                        local_read,
+                        scratchpad,
+                        channels,
+                        managed,
+                        PregelTaskWrites(
+                            translated_task_path, packet.node, writes, triggers
+                        ),
+                    ),
+                    CONFIG_KEY_CHECKPOINTER: (
+                        checkpointer or configurable.get(CONFIG_KEY_CHECKPOINTER)
+                    ),
+                    CONFIG_KEY_CHECKPOINT_MAP: {
+                        **configurable.get(CONFIG_KEY_CHECKPOINT_MAP, {}),
+                        parent_ns: checkpoint["id"],
+                    },
+                    CONFIG_KEY_CHECKPOINT_ID: None,
+                    CONFIG_KEY_CHECKPOINT_NS: task_checkpoint_ns,
+                    CONFIG_KEY_SCRATCHPAD: scratchpad,
+                    CONFIG_KEY_RUNTIME: runtime,
+                },
+            ),
+            triggers,
+            proc.retry_policy or retry_policy,
+            cache_key,
+            task_id,
+            translated_task_path,
+            writers=proc.flat_writers,
+            subgraphs=proc.subgraphs,
+        )
+    else:
+        return PregelTask(task_id, packet.node, translated_task_path)
+
+
 def checkpoint_null_version(
     checkpoint: Checkpoint,
 ) -> V | None:

libs/langgraph/langgraph/pregel/_loop.py

@@ -459,9 +459,6 @@ class PregelLoop:
     def tick(self) -> bool:
         """Execute a single iteration of the Pregel loop.
 
-        Args:
-            input_keys: The key(s) to read input from.
-
         Returns:
             True if more iterations are needed.
         """
@@ -769,6 +766,7 @@ class PregelLoop:
             ]
             self.checkpoint["channel_values"][TASKS] = sanitized_tasks
         # bail if no checkpointer
+
         if do_checkpoint and self._checkpointer_put_after_previous is not None:
             self.prev_checkpoint_config = (
                 self.checkpoint_config
@@ -938,8 +936,15 @@ class PregelLoop:
                 stream_modes = self.stream.modes if self.stream else []
                 if "updates" in stream_modes:
                     self._emit("updates", lambda: iter(interrupts))
-                elif "values" in stream_modes:
-                    self._emit("values", lambda: iter(interrupts))
+                if "values" in stream_modes:
+                    current_values = read_channels(self.channels, self.output_keys)
+                    # self.output_keys is a sequence, stream chunk contains entire state and interrupts
+                    if isinstance(current_values, dict):
+                        current_values[INTERRUPT] = interrupts[0][INTERRUPT]
+                        self._emit("values", lambda: iter([current_values]))
+                    # self.output_keys is a string, stream chunk contains only interrupts
+                    else:
+                        self._emit("values", lambda: iter(interrupts))
             elif writes[0][0] != ERROR:
                 self._emit(
                     "updates",

libs/langgraph/langgraph/pregel/main.py

@@ -142,6 +142,7 @@ from langgraph.types import (
     StateSnapshot,
     StateUpdate,
     StreamMode,
+    ensure_valid_checkpointer,
 )
 from langgraph.typing import ContextT, InputT, OutputT, StateT
 from langgraph.warnings import LangGraphDeprecatedSinceV10
@@ -210,8 +211,10 @@ class NodeBuilder:
         *channels: str,
         read: bool = True,
     ) -> Self:
-        """Add channels to subscribe to. Node will be invoked when any of these
-        channels are updated, with a dict of the channel values as input.
+        """Add channels to subscribe to.
+
+        Node will be invoked when any of these channels are updated, with a dict of the
+        channel values as input.
 
         Args:
             channels: Channel name(s) to subscribe to
@@ -270,8 +273,8 @@ class NodeBuilder:
         """Add channel writes.
 
         Args:
-            *channels: Channel names to write to
-            **kwargs: Channel name and value mappings
+            *channels: Channel names to write to.
+            **kwargs: Channel name and value mappings.
 
         Returns:
             Self for chaining
@@ -375,12 +378,12 @@ class Pregel(
     ### Advanced channels: Context and BinaryOperatorAggregate
 
     - `Context`: exposes the value of a context manager, managing its lifecycle.
-      Useful for accessing external resources that require setup and/or teardown. eg.
-      `client = Context(httpx.Client)`
+        Useful for accessing external resources that require setup and/or teardown. e.g.
+        `client = Context(httpx.Client)`
     - `BinaryOperatorAggregate`: stores a persistent value, updated by applying
-       a binary operator to the current value and each update
-       sent to the channel, useful for computing aggregates over multiple steps. eg.
-      `total = BinaryOperatorAggregate(int, operator.add)`
+        a binary operator to the current value and each update
+        sent to the channel, useful for computing aggregates over multiple steps. e.g.
+        `total = BinaryOperatorAggregate(int, operator.add)`
 
     ## Examples
 
@@ -495,7 +498,7 @@ class Pregel(
         {"c": ["foofoo", "foofoofoofoo"]}
         ```
 
-    Example: Using a BinaryOperatorAggregate channel
+    Example: Using a `BinaryOperatorAggregate` channel
         ```python
         from langgraph.channels import EphemeralValue, BinaryOperatorAggregate
         from langgraph.pregel import Pregel, NodeBuilder
@@ -541,8 +544,9 @@ class Pregel(
 
     Example: Introducing a cycle
         This example demonstrates how to introduce a cycle in the graph, by having
-        a chain write to a channel it subscribes to. Execution will continue
-        until a None value is written to the channel.
+        a chain write to a channel it subscribes to.
+
+        Execution will continue until a `None` value is written to the channel.
 
         ```python
         from langgraph.channels import EphemeralValue
@@ -639,7 +643,7 @@ class Pregel(
         input_channels: str | Sequence[str],
         step_timeout: float | None = None,
         debug: bool | None = None,
-        checkpointer: BaseCheckpointSaver | None = None,
+        checkpointer: Checkpointer = None,
         store: BaseStore | None = None,
         cache: BaseCache | None = None,
         retry_policy: RetryPolicy | Sequence[RetryPolicy] = (),
@@ -662,6 +666,8 @@ class Pregel(
             if context_schema is None:
                 context_schema = cast(type[ContextT], config_type)
 
+        checkpointer = ensure_valid_checkpointer(checkpointer)
+
         self.nodes = {
             k: v.build() if isinstance(v, NodeBuilder) else v for k, v in nodes.items()
         }
@@ -1426,7 +1432,8 @@ class Pregel(
         Args:
             config: The config to apply the updates to.
             supersteps: A list of supersteps, each including a list of updates to apply sequentially to a graph state.
-                        Each update is a tuple of the form `(values, as_node, task_id)` where `task_id` is optional.
+
+                Each update is a tuple of the form `(values, as_node, task_id)` where `task_id` is optional.
 
         Raises:
             ValueError: If no checkpointer is set or no updates are provided.
@@ -1702,7 +1709,9 @@ class Pregel(
             # we use the task id generated by prepare_next_tasks
             node_to_task_ids: dict[str, deque[str]] = defaultdict(deque)
             if saved is not None and saved.pending_writes is not None:
-                # tasks for this checkpoint
+                # we call prepare_next_tasks to discover the task IDs that
+                # would have been generated, so we can reuse them and
+                # properly populate task.result in state history
                 next_tasks = prepare_next_tasks(
                     checkpoint,
                     saved.pending_writes,
@@ -1721,32 +1730,6 @@ class Pregel(
                 for t in next_tasks.values():
                     node_to_task_ids[t.name].append(t.id)
 
-                # apply null writes
-                if null_writes := [
-                    w[1:] for w in saved.pending_writes or [] if w[0] == NULL_TASK_ID
-                ]:
-                    apply_writes(
-                        checkpoint,
-                        channels,
-                        [PregelTaskWrites((), INPUT, null_writes, [])],
-                        checkpointer.get_next_version,
-                        self.trigger_to_nodes,
-                    )
-                # apply writes
-                for tid, k, v in saved.pending_writes:
-                    if k in (ERROR, INTERRUPT):
-                        continue
-                    if tid not in next_tasks:
-                        continue
-                    next_tasks[tid].writes.append((k, v))
-                if tasks := [t for t in next_tasks.values() if t.writes]:
-                    apply_writes(
-                        checkpoint,
-                        channels,
-                        tasks,
-                        checkpointer.get_next_version,
-                        self.trigger_to_nodes,
-                    )
             valid_updates: list[tuple[str, dict[str, Any] | None, str | None]] = []
             if len(updates) == 1:
                 values, as_node, task_id = updates[0]
@@ -1893,7 +1876,8 @@ class Pregel(
         Args:
             config: The config to apply the updates to.
             supersteps: A list of supersteps, each including a list of updates to apply sequentially to a graph state.
-                        Each update is a tuple of the form `(values, as_node, task_id)` where `task_id` is optional.
+
+                Each update is a tuple of the form `(values, as_node, task_id)` where `task_id` is optional.
 
         Raises:
             ValueError: If no checkpointer is set or no updates are provided.
@@ -2167,7 +2151,9 @@ class Pregel(
             # we use the task id generated by prepare_next_tasks
             node_to_task_ids: dict[str, deque[str]] = defaultdict(deque)
             if saved is not None and saved.pending_writes is not None:
-                # tasks for this checkpoint
+                # we call prepare_next_tasks to discover the task IDs that
+                # would have been generated, so we can reuse them and
+                # properly populate task.result in state history
                 next_tasks = prepare_next_tasks(
                     checkpoint,
                     saved.pending_writes,
@@ -2186,31 +2172,6 @@ class Pregel(
                 for t in next_tasks.values():
                     node_to_task_ids[t.name].append(t.id)
 
-                # apply null writes
-                if null_writes := [
-                    w[1:] for w in saved.pending_writes or [] if w[0] == NULL_TASK_ID
-                ]:
-                    apply_writes(
-                        checkpoint,
-                        channels,
-                        [PregelTaskWrites((), INPUT, null_writes, [])],
-                        checkpointer.get_next_version,
-                        self.trigger_to_nodes,
-                    )
-                for tid, k, v in saved.pending_writes:
-                    if k in (ERROR, INTERRUPT):
-                        continue
-                    if tid not in next_tasks:
-                        continue
-                    next_tasks[tid].writes.append((k, v))
-                if tasks := [t for t in next_tasks.values() if t.writes]:
-                    apply_writes(
-                        checkpoint,
-                        channels,
-                        tasks,
-                        checkpointer.get_next_version,
-                        self.trigger_to_nodes,
-                    )
             valid_updates: list[tuple[str, dict[str, Any] | None, str | None]] = []
             if len(updates) == 1:
                 values, as_node, task_id = updates[0]
@@ -2401,7 +2362,7 @@ class Pregel(
             validate_keys(output_keys, self.channels)
         interrupt_before = interrupt_before or self.interrupt_before_nodes
         interrupt_after = interrupt_after or self.interrupt_after_nodes
-        if not isinstance(stream_mode, list):
+        if isinstance(stream_mode, str):
             stream_modes = {stream_mode}
         else:
             stream_modes = set(stream_mode)
@@ -2467,6 +2428,7 @@ class Pregel(
             context: The static context to use for the run.
                 !!! version-added "Added in version 0.6.0"
             stream_mode: The mode to stream output, defaults to `self.stream_mode`.
+
                 Options are:
 
                 - `"values"`: Emit all values in the state after each step, including interrupts.
@@ -2475,31 +2437,36 @@ class Pregel(
                     If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.
                 - `"custom"`: Emit custom data from inside nodes or tasks using `StreamWriter`.
                 - `"messages"`: Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks.
-                    Will be emitted as 2-tuples `(LLM token, metadata)`.
+                    - Will be emitted as 2-tuples `(LLM token, metadata)`.
                 - `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by `get_state()`.
                 - `"tasks"`: Emit events when tasks start and finish, including their results and errors.
+                - `"debug"`: Emit debug events with as much information as possible for each step.
 
                 You can pass a list as the `stream_mode` parameter to stream multiple modes at once.
                 The streamed outputs will be tuples of `(mode, data)`.
 
-                See [LangGraph streaming guide](https://langchain-ai.github.io/langgraph/how-tos/streaming/) for more details.
-            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes. Does not affect the output of the graph in any way.
+                See [LangGraph streaming guide](https://docs.langchain.com/oss/python/langgraph/streaming) for more details.
+            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes.
+
+                Does not affect the output of the graph in any way.
             output_keys: The keys to stream, defaults to all non-context channels.
             interrupt_before: Nodes to interrupt before, defaults to all nodes in the graph.
             interrupt_after: Nodes to interrupt after, defaults to all nodes in the graph.
             durability: The durability mode for the graph execution, defaults to `"async"`.
+
                 Options are:
 
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
                 - `"async"`: Changes are persisted asynchronously while the next step executes.
                 - `"exit"`: Changes are persisted only when the graph exits.
-            subgraphs: Whether to stream events from inside subgraphs, defaults to False.
+            subgraphs: Whether to stream events from inside subgraphs, defaults to `False`.
+
                 If `True`, the events will be emitted as tuples `(namespace, data)`,
                 or `(namespace, mode, data)` if `stream_mode` is a list,
                 where `namespace` is a tuple with the path to the node where a subgraph is invoked,
                 e.g. `("parent_node:<task_id>", "child_node:<task_id>")`.
 
-                See [LangGraph streaming guide](https://langchain-ai.github.io/langgraph/how-tos/streaming/) for more details.
+                See [LangGraph streaming guide](https://docs.langchain.com/oss/python/langgraph/streaming) for more details.
 
         Yields:
             The output of each step in the graph. The output shape depends on the `stream_mode`.
@@ -2735,6 +2702,7 @@ class Pregel(
             context: The static context to use for the run.
                 !!! version-added "Added in version 0.6.0"
             stream_mode: The mode to stream output, defaults to `self.stream_mode`.
+
                 Options are:
 
                 - `"values"`: Emit all values in the state after each step, including interrupts.
@@ -2743,30 +2711,36 @@ class Pregel(
                     If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.
                 - `"custom"`: Emit custom data from inside nodes or tasks using `StreamWriter`.
                 - `"messages"`: Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks.
-                    Will be emitted as 2-tuples `(LLM token, metadata)`.
+                    - Will be emitted as 2-tuples `(LLM token, metadata)`.
+                - `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by `get_state()`.
+                - `"tasks"`: Emit events when tasks start and finish, including their results and errors.
                 - `"debug"`: Emit debug events with as much information as possible for each step.
 
                 You can pass a list as the `stream_mode` parameter to stream multiple modes at once.
                 The streamed outputs will be tuples of `(mode, data)`.
 
-                See [LangGraph streaming guide](https://langchain-ai.github.io/langgraph/how-tos/streaming/) for more details.
-            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes. Does not affect the output of the graph in any way.
+                See [LangGraph streaming guide](https://docs.langchain.com/oss/python/langgraph/streaming) for more details.
+            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes.
+
+                Does not affect the output of the graph in any way.
             output_keys: The keys to stream, defaults to all non-context channels.
             interrupt_before: Nodes to interrupt before, defaults to all nodes in the graph.
             interrupt_after: Nodes to interrupt after, defaults to all nodes in the graph.
             durability: The durability mode for the graph execution, defaults to `"async"`.
+
                 Options are:
 
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
                 - `"async"`: Changes are persisted asynchronously while the next step executes.
                 - `"exit"`: Changes are persisted only when the graph exits.
-            subgraphs: Whether to stream events from inside subgraphs, defaults to False.
+            subgraphs: Whether to stream events from inside subgraphs, defaults to `False`.
+
                 If `True`, the events will be emitted as tuples `(namespace, data)`,
                 or `(namespace, mode, data)` if `stream_mode` is a list,
                 where `namespace` is a tuple with the path to the node where a subgraph is invoked,
                 e.g. `("parent_node:<task_id>", "child_node:<task_id>")`.
 
-                See [LangGraph streaming guide](https://langchain-ai.github.io/langgraph/how-tos/streaming/) for more details.
+                See [LangGraph streaming guide](https://docs.langchain.com/oss/python/langgraph/streaming) for more details.
 
         Yields:
             The output of each step in the graph. The output shape depends on the `stream_mode`.
@@ -3069,11 +3043,14 @@ class Pregel(
             context: The static context to use for the run.
                 !!! version-added "Added in version 0.6.0"
             stream_mode: The stream mode for the graph run.
-            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes. Does not affect the output of the graph in any way.
+            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes.
+
+                Does not affect the output of the graph in any way.
             output_keys: The output keys to retrieve from the graph run.
             interrupt_before: The nodes to interrupt the graph run before.
             interrupt_after: The nodes to interrupt the graph run after.
             durability: The durability mode for the graph execution, defaults to `"async"`.
+
                 Options are:
 
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
@@ -3148,31 +3125,33 @@ class Pregel(
         durability: Durability | None = None,
         **kwargs: Any,
     ) -> dict[str, Any] | Any:
-        """Asynchronously invoke the graph on a single input.
+        """Asynchronously run the graph with a single input and config.
 
         Args:
-            input: The input data for the computation. It can be a dictionary or any other type.
-            config: The configuration for the computation.
+            input: The input data for the graph. It can be a dictionary or any other type.
+            config: The configuration for the graph run.
             context: The static context to use for the run.
                 !!! version-added "Added in version 0.6.0"
-            stream_mode: The stream mode for the computation.
-            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes. Does not affect the output of the graph in any way.
-            output_keys: The output keys to include in the result.
-            interrupt_before: The nodes to interrupt before.
-            interrupt_after: The nodes to interrupt after.
+            stream_mode: The stream mode for the graph run.
+            print_mode: Accepts the same values as `stream_mode`, but only prints the output to the console, for debugging purposes.
+
+                Does not affect the output of the graph in any way.
+            output_keys: The output keys to retrieve from the graph run.
+            interrupt_before: The nodes to interrupt the graph run before.
+            interrupt_after: The nodes to interrupt the graph run after.
             durability: The durability mode for the graph execution, defaults to `"async"`.
+
                 Options are:
 
                 - `"sync"`: Changes are persisted synchronously before the next step starts.
                 - `"async"`: Changes are persisted asynchronously while the next step executes.
                 - `"exit"`: Changes are persisted only when the graph exits.
-            **kwargs: Additional keyword arguments.
+            **kwargs: Additional keyword arguments to pass to the graph run.
 
         Returns:
-            The result of the computation. If `stream_mode` is `"values"`, it returns the latest value.
-            If `stream_mode` is `"chunks"`, it returns a list of chunks.
+            The output of the graph run. If `stream_mode` is `"values"`, it returns the latest output.
+            If `stream_mode` is not `"values"`, it returns a list of output chunks.
         """
-
         output_keys = output_keys if output_keys is not None else self.output_channels
 
         latest: dict[str, Any] | Any = None

libs/langgraph/langgraph/pregel/remote.py

@@ -725,9 +725,10 @@ class RemoteGraph(PregelProtocol):
             input = None
         else:
             command = None
+        thread_id = sanitized_config.get("configurable", {}).pop("thread_id", None)
 
         for chunk in sync_client.runs.stream(
-            thread_id=sanitized_config["configurable"].get("thread_id"),
+            thread_id=thread_id,
             assistant_id=self.assistant_id,
             input=input,
             command=command,
@@ -834,9 +835,10 @@ class RemoteGraph(PregelProtocol):
             input = None
         else:
             command = None
+        thread_id = sanitized_config.get("configurable", {}).pop("thread_id", None)
 
         async for chunk in client.runs.stream(
-            thread_id=sanitized_config["configurable"].get("thread_id"),
+            thread_id=thread_id,
             assistant_id=self.assistant_id,
             input=input,
             command=command,

libs/langgraph/langgraph/runtime.py

@@ -111,7 +111,7 @@ class Runtime(Generic[ContextT]):
             stream_writer=other.stream_writer
             if other.stream_writer is not _no_op_stream_writer
             else self.stream_writer,
-            previous=other.previous or self.previous,
+            previous=self.previous if other.previous is None else other.previous,
         )
 
     def override(

libs/langgraph/langgraph/types.py

@@ -56,6 +56,7 @@ __all__ = (
     "Durability",
     "interrupt",
     "Overwrite",
+    "ensure_valid_checkpointer",
 )
 
 Durability = Literal["sync", "async", "exit"]
@@ -73,6 +74,20 @@ Checkpointer = None | bool | BaseCheckpointSaver
 - False disables checkpointing, even if the parent graph has a checkpointer.
 - None inherits checkpointer from the parent graph."""
 
+
+def ensure_valid_checkpointer(checkpointer: Checkpointer) -> Checkpointer:
+    if checkpointer not in (None, True, False) and not isinstance(
+        checkpointer, BaseCheckpointSaver
+    ):
+        raise TypeError(
+            "Invalid checkpointer provided. Expected an instance of "
+            "`BaseCheckpointSaver`, `True`, `False`, or `None`. "
+            f"Received {type(checkpointer).__name__!s}. "
+            "Pass a proper saver (e.g., InMemorySaver, AsyncPostgresSaver)."
+        )
+    return checkpointer
+
+
 StreamMode = Literal[
     "values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"
 ]

libs/langgraph/langgraph/typing.py

@@ -31,13 +31,13 @@ ContextT_contra = TypeVar(
 )
 
 InputT = TypeVar("InputT", bound=StateLike, default=StateT)
-"""Type variable used to represent the input to a state graph.
+"""Type variable used to represent the input to a `StateGraph`.
 
 Defaults to `StateT`.
 """
 
 OutputT = TypeVar("OutputT", bound=StateLike, default=StateT)
-"""Type variable used to represent the output of a state graph.
+"""Type variable used to represent the output of a `StateGraph`.
 
 Defaults to `StateT`.
 """

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "1.0.2"
+version = "1.0.5"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -26,14 +26,21 @@ classifiers = [
 dependencies = [
     "langchain-core>=0.1",
     "langgraph-checkpoint>=2.1.0,<4.0.0",
-    "langgraph-sdk>=0.2.2,<0.3.0",
+    "langgraph-sdk>=0.3.0,<0.4.0",
     "langgraph-prebuilt>=1.0.2,<1.1.0",
     "xxhash>=3.5.0",
     "pydantic>=2.7.4",
 ]
 
+
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Homepage = "https://docs.langchain.com/oss/python/langgraph/overview"
+Documentation = "https://reference.langchain.com/python/langgraph/"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/langgraph"
+Changelog = "https://github.com/langchain-ai/langgraph/releases"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [dependency-groups]
 test = [

libs/langgraph/tests/test_large_cases.py

@@ -3271,7 +3271,7 @@ def test_root_graph(
                     content="result for query",
                     name="search_api",
                     tool_call_id="tool_call123",
-                    id="00000000-0000-4000-8000-000000000024",
+                    id="00000000-0000-4000-8000-000000000004",
                 )
             ]
         },
@@ -3294,7 +3294,7 @@ def test_root_graph(
                     content="result for another",
                     name="search_api",
                     tool_call_id="tool_call456",
-                    id="00000000-0000-4000-8000-000000000030",
+                    id="00000000-0000-4000-8000-000000000005",
                 )
             ]
         },
@@ -3898,7 +3898,7 @@ def test_root_graph(
         "__root__": [
             HumanMessage(
                 content="what is weather in sf",
-                id="00000000-0000-4000-8000-000000000051",
+                id="00000000-0000-4000-8000-000000000008",
             ),
             AIMessage(
                 content="",
@@ -3918,7 +3918,7 @@ def test_root_graph(
             ),
             AIMessage(content="answer", id="ai2"),
             AIMessage(
-                content="an extra message", id="00000000-0000-4000-8000-000000000066"
+                content="an extra message", id="00000000-0000-4000-8000-000000000010"
             ),
             HumanMessage(content="what is weather in la"),
         ],

libs/langgraph/tests/test_pregel.py

@@ -120,6 +120,22 @@ def test_graph_validation() -> None:
         graph.invoke({"hello": "there"})
 
 
+def test_invalid_checkpointer_type() -> None:
+    class State(TypedDict):
+        foo: str
+
+    builder = StateGraph(State)
+    builder.add_node("start", lambda state: state)
+    builder.set_entry_point("start")
+    builder.set_finish_point("start")
+
+    class NotACheckpointer:
+        pass
+
+    with pytest.raises(TypeError, match="Invalid checkpointer provided"):
+        builder.compile(checkpointer=NotACheckpointer())
+
+
 def test_graph_validation_with_command() -> None:
     class State(TypedDict):
         foo: str
@@ -8422,23 +8438,55 @@ def test_null_resume_disallowed_with_multiple_interrupts(
     }
 
 
-def test_interrupt_stream_mode_values():
-    """Test that interrupts are surfaced when steam_mode='values'"""
+def test_interrupt_stream_mode_values(sync_checkpointer: BaseCheckpointSaver):
+    """Test that interrupts are surfaced on 'values' stream mode"""
 
     class State(TypedDict):
+        robot_input: str
         human_input: str
 
+    def robot_input_node(state: State) -> State:
+        return {"robot_input": "beep boop i am a robot"}
+
     def human_input_node(state: State) -> Command:
         human_input = interrupt("interrupt")
         return Command(update={"human_input": human_input})
 
     builder = StateGraph(State)
+    builder.add_node(robot_input_node)
     builder.add_node(human_input_node)
-    builder.add_edge(START, "human_input_node")
-    app = builder.compile()
+    builder.add_edge(START, "robot_input_node")
+    builder.add_edge("robot_input_node", "human_input_node")
+    app = builder.compile(checkpointer=sync_checkpointer)
+    config = {"configurable": {"thread_id": str(uuid.uuid4())}}
 
-    result = [*app.stream(State(), stream_mode="values")]
-    assert "__interrupt__" in result[-1]
+    result = [*app.stream(State(), config, stream_mode=["updates", "values"])]
+    assert len(result) == 4
+    assert result == [
+        ("updates", {"robot_input_node": {"robot_input": "beep boop i am a robot"}}),
+        ("values", {"robot_input": "beep boop i am a robot"}),
+        ("updates", {"__interrupt__": (Interrupt(value="interrupt", id=AnyStr()),)}),
+        (
+            "values",
+            {
+                "robot_input": "beep boop i am a robot",
+                "__interrupt__": (Interrupt(value="interrupt", id=AnyStr()),),
+            },
+        ),
+    ]
+    resume_result = [
+        *app.stream(
+            Command(resume="i am a human"), config, stream_mode=["updates", "values"]
+        )
+    ]
+    assert resume_result == [
+        ("values", {"robot_input": "beep boop i am a robot"}),
+        ("updates", {"human_input_node": {"human_input": "i am a human"}}),
+        (
+            "values",
+            {"robot_input": "beep boop i am a robot", "human_input": "i am a human"},
+        ),
+    ]
 
 
 def test_supersteps_populate_task_results(
@@ -8805,3 +8853,43 @@ def test_overwrite_parallel_error(
         InvalidUpdateError, match="Can receive only one Overwrite value per super-step."
     ):
         graph.invoke({"messages": ["START"]}, config)
+
+
+def test_fork_does_not_apply_pending_writes(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that forking with update_state does not apply pending writes from original execution."""
+
+    class State(TypedDict):
+        value: Annotated[int, operator.add]
+
+    def node_a(state: State) -> State:
+        return {"value": 10}
+
+    def node_b(state: State) -> State:
+        return {"value": 100}
+
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge("node_a", "node_b")
+        .compile(checkpointer=sync_checkpointer)
+    )
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    graph.invoke({"value": 1}, thread1)
+
+    history = list(graph.get_state_history(thread1))
+    checkpoint_before_a = next(s for s in history if s.next == ("node_a",))
+
+    fork_config = graph.update_state(
+        checkpoint_before_a.config, {"value": 20}, as_node="node_a"
+    )
+
+    # Continue from fork (should run node_b)
+    result = graph.invoke(None, fork_config)
+
+    # Should be: 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
+    assert result == {"value": 121}

libs/langgraph/tests/test_pregel_async.py

@@ -9196,6 +9196,64 @@ async def test_astream_waiter_cleanup_on_cancel(
     assert all(t.done() for t in recorded_tasks)
 
 
+@NEEDS_CONTEXTVARS
+async def test_interrupt_stream_mode_values(async_checkpointer: BaseCheckpointSaver):
+    """Test that interrupts are surfaced on 'values' stream mode"""
+
+    class State(TypedDict):
+        robot_input: str
+        human_input: str
+
+    def robot_input_node(state: State) -> State:
+        return {"robot_input": "beep boop i am a robot"}
+
+    def human_input_node(state: State) -> Command:
+        human_input = interrupt("interrupt")
+        return Command(update={"human_input": human_input})
+
+    builder = StateGraph(State)
+    builder.add_node(robot_input_node)
+    builder.add_node(human_input_node)
+    builder.add_edge(START, "robot_input_node")
+    builder.add_edge("robot_input_node", "human_input_node")
+    app = builder.compile(checkpointer=async_checkpointer)
+    config = {"configurable": {"thread_id": str(uuid.uuid4())}}
+
+    result = [
+        (mode, e)
+        async for mode, e in app.astream(
+            State(), config, stream_mode=["updates", "values"]
+        )
+    ]
+    assert len(result) == 4
+    assert result == [
+        ("updates", {"robot_input_node": {"robot_input": "beep boop i am a robot"}}),
+        ("values", {"robot_input": "beep boop i am a robot"}),
+        ("updates", {"__interrupt__": (Interrupt(value="interrupt", id=AnyStr()),)}),
+        (
+            "values",
+            {
+                "robot_input": "beep boop i am a robot",
+                "__interrupt__": (Interrupt(value="interrupt", id=AnyStr()),),
+            },
+        ),
+    ]
+    resume_result = [
+        (mode, e)
+        async for mode, e in app.astream(
+            Command(resume="i am a human"), config, stream_mode=["updates", "values"]
+        )
+    ]
+    assert resume_result == [
+        ("values", {"robot_input": "beep boop i am a robot"}),
+        ("updates", {"human_input_node": {"human_input": "i am a human"}}),
+        (
+            "values",
+            {"robot_input": "beep boop i am a robot", "human_input": "i am a human"},
+        ),
+    ]
+
+
 async def test_supersteps_populate_task_results(
     async_checkpointer: BaseCheckpointSaver,
 ) -> None:
@@ -9249,3 +9307,41 @@ async def test_supersteps_populate_task_results(
 
     assert bulk_start_result == ref_start_result == {"num": 1, "text": "one"}
     assert bulk_double_result == ref_double_result == {"num": 2, "text": "oneone"}
+
+
+async def test_fork_does_not_apply_pending_writes(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    """Test that forking with aupdate_state does not apply pending writes from original execution."""
+
+    class State(TypedDict):
+        value: Annotated[int, operator.add]
+
+    def node_a(state: State) -> State:
+        return {"value": 10}
+
+    def node_b(state: State) -> State:
+        return {"value": 100}
+
+    graph = (
+        StateGraph(State)
+        .add_node("node_a", node_a)
+        .add_node("node_b", node_b)
+        .add_edge(START, "node_a")
+        .add_edge("node_a", "node_b")
+        .compile(checkpointer=async_checkpointer)
+    )
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    await graph.ainvoke({"value": 1}, thread1)
+
+    history = [c async for c in graph.aget_state_history(thread1)]
+    checkpoint_before_a = next(s for s in history if s.next == ("node_a",))
+
+    fork_config = await graph.aupdate_state(
+        checkpoint_before_a.config, {"value": 20}, as_node="node_a"
+    )
+    result = await graph.ainvoke(None, fork_config)
+
+    # 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
+    assert result == {"value": 121}

libs/langgraph/tests/test_remote_graph.py

@@ -567,9 +567,9 @@ def test_stream():
         stream_parts.append(stream_part)
 
     assert stream_parts == [
-        ("updates", {"chunk": "data3"}),
-        ("updates", {"chunk": "data4"}),
-        ("updates", {"__interrupt__": ()}),
+        ("updates", {"chunk": "data3"}, None),
+        ("updates", {"chunk": "data4"}, None),
+        ("updates", {"__interrupt__": ()}, None),
     ]
 
     # subgraphs + list modes
@@ -739,9 +739,9 @@ async def test_astream():
         stream_parts.append(stream_part)
 
     assert stream_parts == [
-        ("updates", {"chunk": "data3"}),
-        ("updates", {"chunk": "data4"}),
-        ("updates", {"__interrupt__": ()}),
+        ("updates", {"chunk": "data3"}, None),
+        ("updates", {"chunk": "data4"}, None),
+        ("updates", {"__interrupt__": ()}, None),
     ]
 
     # subgraphs + list modes
@@ -840,6 +840,46 @@ def test_invoke():
     assert result == {"messages": [{"type": "human", "content": "world"}]}
 
 
+def test_invoke_sanitizes_thread_id():
+    # Ensure that invoking with thread_id passes thread_id as a top-level arg
+    # and removes it from the config body.
+    mock_sync_client = MagicMock()
+    mock_sync_client.runs.stream.return_value = []
+    remote_pregel = RemoteGraph("test_graph_id", sync_client=mock_sync_client)
+
+    config = {"configurable": {"thread_id": "thread_1"}}
+    remote_pregel.invoke(
+        {"input": {"messages": [{"type": "human", "content": "hello"}]}}, config
+    )
+
+    assert mock_sync_client.runs.stream.called
+    _, kwargs = mock_sync_client.runs.stream.call_args
+    assert kwargs.get("thread_id") == "thread_1"
+    passed_config = kwargs.get("config") or {}
+    assert "configurable" in passed_config
+    assert "thread_id" not in passed_config["configurable"]
+    assert not passed_config["configurable"]
+
+
+def test_stream_sanitizes_thread_id():
+    # Ensure that streaming with thread_id passes thread_id as a top-level arg
+    # and removes it from the config body.
+    mock_sync_client = MagicMock()
+    mock_sync_client.runs.stream.return_value = []
+    remote_pregel = RemoteGraph("test_graph_id", sync_client=mock_sync_client)
+
+    config = {"configurable": {"thread_id": "thread_2"}}
+    list(remote_pregel.stream({"input": {"messages": []}}, config))
+
+    assert mock_sync_client.runs.stream.called
+    _, kwargs = mock_sync_client.runs.stream.call_args
+    assert kwargs.get("thread_id") == "thread_2"
+    passed_config = kwargs.get("config") or {}
+    assert "configurable" in passed_config
+    assert "thread_id" not in passed_config["configurable"]
+    assert not passed_config["configurable"]
+
+
 @pytest.mark.anyio
 async def test_ainvoke():
     # set up test
@@ -879,8 +919,8 @@ async def test_langgraph_cloud_integration():
     from langgraph.graph import END, START, MessagesState, StateGraph
 
     # create RemotePregel instance
-    client = get_client()
-    sync_client = get_sync_client()
+    client = get_client(url="http://localhost:8123")
+    sync_client = get_sync_client(url="http://localhost:8123")
     remote_pregel = RemoteGraph(
         "agent",
         client=client,

libs/prebuilt/.claude/settings.local.json

@@ -0,0 +1,21 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(make test:*)",
+      "Bash(uv run pytest:*)",
+      "Bash(LANGGRAPH_TEST_FAST=0 make start-services:*)",
+      "Bash(LANGGRAPH_TEST_FAST=0 uv run:*)",
+      "Bash(EXIT_CODE=$?)",
+      "Bash(make stop-services:*)",
+      "Bash(exit $EXIT_CODE)",
+      "Read(//Users/sydney_runkle/oss/langgraph/**)",
+      "Bash(python3:*)",
+      "Bash(find:*)",
+      "Bash(python -m pytest:*)",
+      "Bash(python:*)",
+      "Read(//tmp/**)"
+    ],
+    "deny": [],
+    "ask": []
+  }
+}

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -63,7 +63,7 @@ class AgentState(TypedDict):
 
 
 @deprecated(
-    "AgentStatePydantic has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentStatePydantic`.",
+    "AgentStatePydantic has been deprecated in favor of AgentState in `langchain.agents`.",
     category=LangGraphDeprecatedSinceV10,
 )
 class AgentStatePydantic(BaseModel):
@@ -78,11 +78,11 @@ with warnings.catch_warnings():
     warnings.filterwarnings(
         "ignore",
         category=LangGraphDeprecatedSinceV10,
-        message="AgentState has been moved to langchain.agents.*",
+        message="AgentState has been moved to `langchain.agents`.*",
     )
 
     @deprecated(
-        "AgentStateWithStructuredResponse has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentStateWithStructuredResponse`.",
+        "AgentStateWithStructuredResponse has been deprecated in favor of AgentState in `langchain.agents`.",
         category=LangGraphDeprecatedSinceV10,
     )
     class AgentStateWithStructuredResponse(AgentState):
@@ -95,11 +95,11 @@ with warnings.catch_warnings():
     warnings.filterwarnings(
         "ignore",
         category=LangGraphDeprecatedSinceV10,
-        message="AgentStatePydantic has been moved to langchain.agents.*",
+        message="AgentStatePydantic has been deprecated in favor of AgentState in `langchain.agents`.",
     )
 
     @deprecated(
-        "AgentStateWithStructuredResponsePydantic has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentStateWithStructuredResponsePydantic`.",
+        "AgentStateWithStructuredResponsePydantic has been deprecated in favor of AgentState in `langchain.agents`.",
         category=LangGraphDeprecatedSinceV10,
     )
     class AgentStateWithStructuredResponsePydantic(AgentStatePydantic):

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -142,6 +142,25 @@ class ToolCallRequest:
     state: Any
     runtime: ToolRuntime
 
+    def __setattr__(self, name: str, value: Any) -> None:
+        """Raise deprecation warning when setting attributes directly.
+
+        Direct attribute assignment is deprecated. Use the `override()` method instead.
+        """
+        import warnings
+
+        # Allow setting attributes during initialization
+        if not hasattr(self, "__dataclass_fields__") or not hasattr(self, name):
+            object.__setattr__(self, name, value)
+        else:
+            warnings.warn(
+                f"Setting attribute '{name}' on ToolCallRequest is deprecated. "
+                "Use the override() method instead to create a new instance with modified values.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            object.__setattr__(self, name, value)
+
     def override(
         self, **overrides: Unpack[_ToolCallRequestOverrides]
     ) -> ToolCallRequest:
@@ -202,8 +221,9 @@ Examples:
 
     ```python
     def handler(request, execute):
-        request.tool_call["args"]["value"] *= 2
-        return execute(request)
+        modified_call = {**request.tool_call, "args": {**request.tool_call["args"], "value": request.tool_call["args"]["value"] * 2}}
+        modified_request = request.override(tool_call=modified_call)
+        return execute(modified_request)
     ```
 
     Retry on error (execute multiple times):
@@ -479,9 +499,7 @@ def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception],
 
 def _filter_validation_errors(
     validation_error: ValidationError,
-    tool_to_state_args: dict[str, str | None],
-    tool_to_store_arg: str | None,
-    tool_to_runtime_arg: str | None,
+    injected_args: _InjectedArgs | None,
 ) -> list[ErrorDetails]:
     """Filter validation errors to only include LLM-controlled arguments.
 
@@ -496,25 +514,28 @@ def _filter_validation_errors(
 
     Args:
         validation_error: The Pydantic ValidationError raised during tool invocation.
-        tool_to_state_args: Mapping of state argument names to state field names.
-        tool_to_store_arg: Name of the store argument, if any.
-        tool_to_runtime_arg: Name of the runtime argument, if any.
+        injected_args: The _InjectedArgs structure containing all injected arguments,
+            or None if there are no injected arguments.
 
     Returns:
         List of ErrorDetails containing only errors for LLM-controlled arguments,
         with system-injected argument values removed from the input field.
     """
-    injected_args = set(tool_to_state_args.keys())
-    if tool_to_store_arg:
-        injected_args.add(tool_to_store_arg)
-    if tool_to_runtime_arg:
-        injected_args.add(tool_to_runtime_arg)
+    # Collect all injected argument names
+    injected_arg_names: set[str] = set()
+    if injected_args:
+        if injected_args.state:
+            injected_arg_names.update(injected_args.state.keys())
+        if injected_args.store:
+            injected_arg_names.add(injected_args.store)
+        if injected_args.runtime:
+            injected_arg_names.add(injected_args.runtime)
 
     filtered_errors: list[ErrorDetails] = []
     for error in validation_error.errors():
         # Check if error location contains any injected argument
         # error['loc'] is a tuple like ('field_name',) or ('field_name', 'nested_field')
-        if error["loc"] and error["loc"][0] not in injected_args:
+        if error["loc"] and error["loc"][0] not in injected_arg_names:
             # Create a copy of the error dict to avoid mutating the original
             error_copy: dict[str, Any] = {**error}
 
@@ -522,7 +543,7 @@ def _filter_validation_errors(
             if isinstance(error_copy.get("input"), dict):
                 input_dict = error_copy["input"]
                 input_copy = {
-                    k: v for k, v in input_dict.items() if k not in injected_args
+                    k: v for k, v in input_dict.items() if k not in injected_arg_names
                 }
                 error_copy["input"] = input_copy
 
@@ -532,6 +553,60 @@ def _filter_validation_errors(
     return filtered_errors
 
 
+@dataclass
+class _InjectedArgs:
+    """Internal structure for tracking injected arguments for a tool.
+
+    This data structure is built once during ToolNode initialization by analyzing
+    the tool's signature and args schema, then reused during execution for efficient
+    injection without repeated reflection.
+
+    The structure maps from tool parameter names to their injection sources, enabling
+    the ToolNode to know exactly which arguments need to be injected and where to
+    get their values from.
+
+    Attributes:
+        state: Mapping from tool parameter names to state field names for injection.
+            Keys are tool parameter names, values are either:
+            - str: Name of the state field to extract and inject
+            - None: Inject the entire state object
+            Empty dict if no state injection is needed.
+        store: Name of the tool parameter where the store should be injected,
+            or None if no store injection is needed.
+        runtime: Name of the tool parameter where the runtime should be injected,
+            or None if no runtime injection is needed.
+
+    Example:
+        For a tool with signature:
+        ```python
+        def my_tool(
+            x: int,
+            messages: Annotated[list, InjectedState("messages")],
+            full_state: Annotated[dict, InjectedState()],
+            store: Annotated[BaseStore, InjectedStore()],
+            runtime: ToolRuntime,
+        ) -> str:
+            ...
+        ```
+
+        The resulting `_InjectedArgs` would be:
+        ```python
+        _InjectedArgs(
+            state={
+                "messages": "messages",  # Extract state["messages"]
+                "full_state": None,      # Inject entire state
+            },
+            store="store",               # Inject into "store" parameter
+            runtime="runtime",           # Inject into "runtime" parameter
+        )
+        ```
+    """
+
+    state: dict[str, str | None]
+    store: str | None
+    runtime: str | None
+
+
 class ToolNode(RunnableCallable):
     """A node for executing tools in LangGraph workflows.
 
@@ -676,9 +751,7 @@ class ToolNode(RunnableCallable):
         """
         super().__init__(self._func, self._afunc, name=name, tags=tags, trace=False)
         self._tools_by_name: dict[str, BaseTool] = {}
-        self._tool_to_state_args: dict[str, dict[str, str | None]] = {}
-        self._tool_to_store_arg: dict[str, str | None] = {}
-        self._tool_to_runtime_arg: dict[str, str | None] = {}
+        self._injected_args: dict[str, _InjectedArgs] = {}
         self._handle_tool_errors = handle_tool_errors
         self._messages_key = messages_key
         self._wrap_tool_call = wrap_tool_call
@@ -689,9 +762,8 @@ class ToolNode(RunnableCallable):
             else:
                 tool_ = tool
             self._tools_by_name[tool_.name] = tool_
-            self._tool_to_state_args[tool_.name] = _get_state_args(tool_)
-            self._tool_to_store_arg[tool_.name] = _get_store_arg(tool_)
-            self._tool_to_runtime_arg[tool_.name] = _get_runtime_arg(tool_)
+            # Build injected args mapping once during initialization in a single pass
+            self._injected_args[tool_.name] = _get_all_injected_args(tool_)
 
     @property
     def tools_by_name(self) -> dict[str, BaseTool]:
@@ -844,12 +916,8 @@ class ToolNode(RunnableCallable):
                 response = tool.invoke(call_args, config)
             except ValidationError as exc:
                 # Filter out errors for injected arguments
-                filtered_errors = _filter_validation_errors(
-                    exc,
-                    self._tool_to_state_args.get(call["name"], {}),
-                    self._tool_to_store_arg.get(call["name"]),
-                    self._tool_to_runtime_arg.get(call["name"]),
-                )
+                injected = self._injected_args.get(call["name"])
+                filtered_errors = _filter_validation_errors(exc, injected)
                 # Use original call["args"] without injected values for error reporting
                 raise ToolInvocationError(
                     call["name"], exc, call["args"], filtered_errors
@@ -1001,12 +1069,8 @@ class ToolNode(RunnableCallable):
                 response = await tool.ainvoke(call_args, config)
             except ValidationError as exc:
                 # Filter out errors for injected arguments
-                filtered_errors = _filter_validation_errors(
-                    exc,
-                    self._tool_to_state_args.get(call["name"], {}),
-                    self._tool_to_store_arg.get(call["name"]),
-                    self._tool_to_runtime_arg.get(call["name"]),
-                )
+                injected = self._injected_args.get(call["name"])
+                filtered_errors = _filter_validation_errors(exc, injected)
                 # Use original call["args"] without injected values for error reporting
                 raise ToolInvocationError(
                     call["name"], exc, call["args"], filtered_errors
@@ -1199,86 +1263,6 @@ class ToolNode(RunnableCallable):
             return input["state"]
         return input
 
-    def _inject_state(
-        self,
-        tool_call: ToolCall,
-        state: list[AnyMessage] | dict[str, Any] | BaseModel,
-    ) -> ToolCall:
-        state_args = self._tool_to_state_args[tool_call["name"]]
-
-        if state_args and isinstance(state, list):
-            required_fields = list(state_args.values())
-            if (
-                len(required_fields) == 1 and required_fields[0] == self._messages_key
-            ) or required_fields[0] is None:
-                state = {self._messages_key: state}
-            else:
-                err_msg = (
-                    f"Invalid input to ToolNode. Tool {tool_call['name']} requires "
-                    f"graph state dict as input."
-                )
-                if any(state_field for state_field in state_args.values()):
-                    required_fields_str = ", ".join(f for f in required_fields if f)
-                    err_msg += f" State should contain fields {required_fields_str}."
-                raise ValueError(err_msg)
-
-        if isinstance(state, dict):
-            tool_state_args = {
-                tool_arg: state[state_field] if state_field else state
-                for tool_arg, state_field in state_args.items()
-            }
-        else:
-            tool_state_args = {
-                tool_arg: getattr(state, state_field) if state_field else state
-                for tool_arg, state_field in state_args.items()
-            }
-
-        tool_call["args"] = {
-            **tool_call["args"],
-            **tool_state_args,
-        }
-        return tool_call
-
-    def _inject_store(self, tool_call: ToolCall, store: BaseStore | None) -> ToolCall:
-        store_arg = self._tool_to_store_arg[tool_call["name"]]
-        if not store_arg:
-            return tool_call
-
-        if store is None:
-            msg = (
-                "Cannot inject store into tools with InjectedStore annotations - "
-                "please compile your graph with a store."
-            )
-            raise ValueError(msg)
-
-        tool_call["args"] = {
-            **tool_call["args"],
-            store_arg: store,
-        }
-        return tool_call
-
-    def _inject_runtime(
-        self, tool_call: ToolCall, tool_runtime: ToolRuntime
-    ) -> ToolCall:
-        """Inject ToolRuntime into tool call arguments.
-
-        Args:
-            tool_call: The tool call to inject runtime into.
-            tool_runtime: The ToolRuntime instance to inject.
-
-        Returns:
-            The tool call with runtime injected if needed.
-        """
-        runtime_arg = self._tool_to_runtime_arg.get(tool_call["name"])
-        if not runtime_arg:
-            return tool_call
-
-        tool_call["args"] = {
-            **tool_call["args"],
-            runtime_arg: tool_runtime,
-        }
-        return tool_call
-
     def _inject_tool_args(
         self,
         tool_call: ToolCall,
@@ -1317,12 +1301,64 @@ class ToolNode(RunnableCallable):
         if tool_call["name"] not in self.tools_by_name:
             return tool_call
 
+        injected = self._injected_args.get(tool_call["name"])
+        if not injected:
+            return tool_call
+
         tool_call_copy: ToolCall = copy(tool_call)
-        tool_call_with_state = self._inject_state(tool_call_copy, tool_runtime.state)
-        tool_call_with_store = self._inject_store(
-            tool_call_with_state, tool_runtime.store
-        )
-        return self._inject_runtime(tool_call_with_store, tool_runtime)
+        injected_args = {}
+
+        # Inject state
+        if injected.state:
+            state = tool_runtime.state
+            # Handle list state by converting to dict
+            if isinstance(state, list):
+                required_fields = list(injected.state.values())
+                if (
+                    len(required_fields) == 1
+                    and required_fields[0] == self._messages_key
+                ) or required_fields[0] is None:
+                    state = {self._messages_key: state}
+                else:
+                    err_msg = (
+                        f"Invalid input to ToolNode. Tool {tool_call['name']} requires "
+                        f"graph state dict as input."
+                    )
+                    if any(state_field for state_field in injected.state.values()):
+                        required_fields_str = ", ".join(f for f in required_fields if f)
+                        err_msg += (
+                            f" State should contain fields {required_fields_str}."
+                        )
+                    raise ValueError(err_msg)
+
+            # Extract state values
+            if isinstance(state, dict):
+                for tool_arg, state_field in injected.state.items():
+                    injected_args[tool_arg] = (
+                        state[state_field] if state_field else state
+                    )
+            else:
+                for tool_arg, state_field in injected.state.items():
+                    injected_args[tool_arg] = (
+                        getattr(state, state_field) if state_field else state
+                    )
+
+        # Inject store
+        if injected.store:
+            if tool_runtime.store is None:
+                msg = (
+                    "Cannot inject store into tools with InjectedStore annotations - "
+                    "please compile your graph with a store."
+                )
+                raise ValueError(msg)
+            injected_args[injected.store] = tool_runtime.store
+
+        # Inject runtime
+        if injected.runtime:
+            injected_args[injected.runtime] = tool_runtime
+
+        tool_call_copy["args"] = {**tool_call_copy["args"], **injected_args}
+        return tool_call_copy
 
     def _validate_tool_command(
         self,
@@ -1715,120 +1751,86 @@ def _is_injection(
     return False
 
 
-def _get_state_args(tool: BaseTool) -> dict[str, str | None]:
-    """Extract state injection mappings from tool annotations.
-
-    This function analyzes a tool's input schema to identify arguments that should
-    be injected with graph state. It processes InjectedState annotations to build
-    a mapping of tool argument names to state field names.
+def _get_injection_from_type(
+    type_: Any, injection_type: type[InjectedState | InjectedStore | ToolRuntime]
+) -> Any | None:
+    """Extract injection instance from a type annotation.
 
     Args:
-        tool: The tool to analyze for state injection requirements.
+        type_: The type annotation to check.
+        injection_type: The injection type to look for.
 
     Returns:
-        A dictionary mapping tool argument names to state field names. If a field
-        name is None, the entire state should be injected for that argument.
+        The injection instance if found, True if injection marker found without instance, None otherwise.
     """
-    full_schema = tool.get_input_schema()
-    tool_args_to_state_fields: dict = {}
+    type_args = get_args(type_)
+    matches = [arg for arg in type_args if _is_injection(arg, injection_type)]
 
-    for name, type_ in get_all_basemodel_annotations(full_schema).items():
-        injections = [
-            type_arg
-            for type_arg in get_args(type_)
-            if _is_injection(type_arg, InjectedState)
-        ]
-        if len(injections) > 1:
-            msg = (
-                "A tool argument should not be annotated with InjectedState more than "
-                f"once. Received arg {name} with annotations {injections}."
-            )
-            raise ValueError(msg)
-        if len(injections) == 1:
-            injection = injections[0]
-            if isinstance(injection, InjectedState) and injection.field:
-                tool_args_to_state_fields[name] = injection.field
-            else:
-                tool_args_to_state_fields[name] = None
-        else:
-            pass
-    return tool_args_to_state_fields
+    if len(matches) > 1:
+        msg = (
+            f"A tool argument should not be annotated with {injection_type.__name__} "
+            f"more than once. Found: {matches}"
+        )
+        raise ValueError(msg)
 
-
-def _get_store_arg(tool: BaseTool) -> str | None:
-    """Extract store injection argument from tool annotations.
-
-    This function analyzes a tool's input schema to identify the argument that
-    should be injected with the graph store. Only one store argument is supported
-    per tool.
-
-    Args:
-        tool: The tool to analyze for store injection requirements.
-
-    Returns:
-        The name of the argument that should receive the store injection, or None
-        if no store injection is required.
-
-    Raises:
-        ValueError: If a tool argument has multiple InjectedStore annotations.
-    """
-    full_schema = tool.get_input_schema()
-    for name, type_ in get_all_basemodel_annotations(full_schema).items():
-        injections = [
-            type_arg
-            for type_arg in get_args(type_)
-            if _is_injection(type_arg, InjectedStore)
-        ]
-        if len(injections) > 1:
-            msg = (
-                "A tool argument should not be annotated with InjectedStore more than "
-                f"once. Received arg {name} with annotations {injections}."
-            )
-            raise ValueError(msg)
-        if len(injections) == 1:
-            return name
+    if len(matches) == 1:
+        return matches[0]
+    elif _is_injection(type_, injection_type):
+        return True
 
     return None
 
 
-def _get_runtime_arg(tool: BaseTool) -> str | None:
-    """Extract runtime injection argument from tool annotations.
+def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
+    """Extract all injected arguments from tool in a single pass.
 
-    This function analyzes a tool's input schema to identify the argument that
-    should be injected with the ToolRuntime instance. Only one runtime argument
-    is supported per tool.
+    This function analyzes both the tool's input schema and function signature
+    to identify all arguments that should be injected (state, store, runtime).
 
     Args:
-        tool: The tool to analyze for runtime injection requirements.
+        tool: The tool to analyze for injection requirements.
 
     Returns:
-        The name of the argument that should receive the runtime injection, or None
-        if no runtime injection is required.
-
-    Raises:
-        ValueError: If a tool argument has multiple ToolRuntime annotations.
+        _InjectedArgs structure containing all detected injections.
     """
+    # Get annotations from both schema and function signature
     full_schema = tool.get_input_schema()
-    for name, type_ in get_all_basemodel_annotations(full_schema).items():
-        # Check if the parameter name is "runtime" (regardless of type)
+    schema_annotations = get_all_basemodel_annotations(full_schema)
+
+    func = getattr(tool, "func", None) or getattr(tool, "coroutine", None)
+    func_annotations = get_type_hints(func, include_extras=True) if func else {}
+
+    # Combine both annotation sources, preferring schema annotations
+    # In the future, we might want to add more restrictions here...
+    all_annotations = {**func_annotations, **schema_annotations}
+
+    # Track injected args
+    state_args: dict[str, str | None] = {}
+    store_arg: str | None = None
+    runtime_arg: str | None = None
+
+    for name, type_ in all_annotations.items():
+        # Check for runtime (special case: parameter named "runtime")
         if name == "runtime":
-            return name
-        # Check if the type itself is ToolRuntime (direct usage)
-        if _is_injection(type_, ToolRuntime):
-            return name
-        # Check if ToolRuntime is in Annotated args
-        injections = [
-            type_arg
-            for type_arg in get_args(type_)
-            if _is_injection(type_arg, ToolRuntime)
-        ]
-        if len(injections) > 1:
-            msg = (
-                "A tool argument should not be annotated with ToolRuntime more than "
-                f"once. Received arg {name} with annotations {injections}."
-            )
-            raise ValueError(msg)
-        if len(injections) == 1:
-            return name
+            runtime_arg = name
 
-    return None
+        # Check for InjectedState
+        if state_inj := _get_injection_from_type(type_, InjectedState):
+            if isinstance(state_inj, InjectedState) and state_inj.field:
+                state_args[name] = state_inj.field
+            else:
+                state_args[name] = None
+
+        # Check for InjectedStore
+        if _get_injection_from_type(type_, InjectedStore):
+            store_arg = name
+
+        # Check for ToolRuntime
+        if _get_injection_from_type(type_, ToolRuntime):
+            runtime_arg = name
+
+    return _InjectedArgs(
+        state=state_args,
+        store=store_arg,
+        runtime=runtime_arg,
+    )

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "1.0.2"
+version = "1.0.5"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.10"
@@ -29,7 +29,10 @@ dependencies = [
 ]
 
 [project.urls]
-Repository = "https://www.github.com/langchain-ai/langgraph"
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/prebuilt"
+Twitter = "https://x.com/LangChainAI"
+Slack = "https://www.langchain.com/join-community"
+Reddit = "https://www.reddit.com/r/LangChain/"
 
 [dependency-groups]
 test = [

libs/prebuilt/tests/test_on_tool_call.py

@@ -130,11 +130,17 @@ def test_modify_arguments() -> None:
         execute: Callable[[ToolCallRequest], ToolMessage | Command],
     ) -> ToolMessage | Command:
         """Handler that doubles the input arguments."""
-        # Modify the arguments
-        request.tool_call["args"]["a"] *= 2
-        request.tool_call["args"]["b"] *= 2
-
-        return execute(request)
+        # Modify the arguments using override method
+        modified_call = {
+            **request.tool_call,
+            "args": {
+                **request.tool_call["args"],
+                "a": request.tool_call["args"]["a"] * 2,
+                "b": request.tool_call["args"]["b"] * 2,
+            },
+        }
+        modified_request = request.override(tool_call=modified_call)
+        return execute(modified_request)
 
     tool_node = ToolNode([add], wrap_tool_call=modify_args_handler)
 
@@ -362,10 +368,17 @@ async def test_handler_with_async_execution() -> None:
         execute: Callable[[ToolCallRequest], ToolMessage | Command],
     ) -> ToolMessage | Command:
         """Handler that modifies arguments."""
-        # Add 10 to both arguments
-        request.tool_call["args"]["a"] += 10
-        request.tool_call["args"]["b"] += 10
-        return execute(request)
+        # Add 10 to both arguments using override method
+        modified_call = {
+            **request.tool_call,
+            "args": {
+                **request.tool_call["args"],
+                "a": request.tool_call["args"]["a"] + 10,
+                "b": request.tool_call["args"]["b"] + 10,
+            },
+        }
+        modified_request = request.override(tool_call=modified_call)
+        return execute(modified_request)
 
     tool_node = ToolNode([async_add], wrap_tool_call=modifying_handler)
 
@@ -1305,3 +1318,64 @@ async def test_state_extraction_with_tool_call_with_context_async() -> None:
     assert state_seen[0] == actual_state
     assert "__type" not in state_seen[0]
     assert "tool_call" not in state_seen[0]
+
+
+def test_tool_call_request_is_frozen() -> None:
+    """Test that ToolCallRequest raises deprecation warnings on direct attribute reassignment."""
+    tool_call: ToolCall = {"name": "add", "args": {"a": 1, "b": 2}, "id": "call_1"}
+    state: dict = {"messages": []}
+    runtime = None
+
+    request = ToolCallRequest(
+        tool_call=tool_call, tool=add, state=state, runtime=runtime
+    )  # type: ignore[arg-type]
+
+    # Test that direct attribute reassignment raises DeprecationWarning
+    with pytest.warns(
+        DeprecationWarning,
+        match="Setting attribute 'tool_call' on ToolCallRequest is deprecated",
+    ):
+        request.tool_call = {"name": "other", "args": {}, "id": "call_2"}  # type: ignore[misc]
+
+    with pytest.warns(
+        DeprecationWarning,
+        match="Setting attribute 'tool' on ToolCallRequest is deprecated",
+    ):
+        request.tool = None  # type: ignore[misc]
+
+    with pytest.warns(
+        DeprecationWarning,
+        match="Setting attribute 'state' on ToolCallRequest is deprecated",
+    ):
+        request.state = {}  # type: ignore[misc]
+
+    with pytest.warns(
+        DeprecationWarning,
+        match="Setting attribute 'runtime' on ToolCallRequest is deprecated",
+    ):
+        request.runtime = None  # type: ignore[misc]
+
+    # Test that override method works correctly
+    new_tool_call: ToolCall = {
+        "name": "multiply",
+        "args": {"x": 5, "y": 10},
+        "id": "call_3",
+    }
+
+    # Original request should be unchanged (note: it was modified by the warnings tests above)
+    # So we create a fresh request to test override properly
+    fresh_request = ToolCallRequest(
+        tool_call=tool_call, tool=add, state=state, runtime=runtime
+    )  # type: ignore[arg-type]
+    fresh_new_request = fresh_request.override(tool_call=new_tool_call)
+
+    # Original request should be unchanged
+    assert fresh_request.tool_call == tool_call
+    assert fresh_request.tool_call["name"] == "add"
+
+    # New request should have the updated tool_call
+    assert fresh_new_request.tool_call == new_tool_call
+    assert fresh_new_request.tool_call["name"] == "multiply"
+    assert fresh_new_request.tool == add  # Other fields should remain the same
+    assert fresh_new_request.state == state
+    assert fresh_new_request.runtime is None

libs/prebuilt/tests/test_react_agent.py

@@ -53,7 +53,6 @@ from langgraph.prebuilt.chat_agent_executor import (
 from langgraph.prebuilt.tool_node import (
     InjectedState,
     InjectedStore,
-    _get_state_args,
     _infer_handled_types,
 )
 from tests.any_str import AnyStr
@@ -639,8 +638,9 @@ def test_react_agent_parallel_tool_calls(
     for event in agent.stream(
         {"messages": [("user", query)]}, config, stream_mode="values"
     ):
-        if messages := event.get("messages"):
-            message_types.append([m.type for m in messages])
+        if "__interrupt__" not in event:
+            if messages := event.get("messages"):
+                message_types.append([m.type for m in messages])
 
     if version == "v1":
         assert message_types == [
@@ -1084,21 +1084,6 @@ async def test_return_direct(version: str) -> None:
     ]
 
 
-def test__get_state_args() -> None:
-    class Schema1(BaseModel):
-        a: Annotated[str, InjectedState]
-
-    class Schema2(Schema1):
-        b: Annotated[int, InjectedState("bar")]
-
-    @dec_tool(args_schema=Schema2)
-    def foo(a: str, b: int) -> float:
-        """return"""
-        return 0.0
-
-    assert _get_state_args(foo) == {"a": None, "b": "bar"}
-
-
 def test_inspect_react() -> None:
     model = FakeToolCallingModel(tool_calls=[])
     agent = create_react_agent(model, [])

libs/prebuilt/tests/test_tool_node.py

@@ -42,6 +42,7 @@ from langgraph.prebuilt import (
 from langgraph.prebuilt.tool_node import (
     TOOL_CALL_ERROR_TEMPLATE,
     ToolInvocationError,
+    ToolRuntime,
     tools_condition,
 )
 
@@ -1610,3 +1611,252 @@ def test_tool_node_stream_writer() -> None:
             },
         ),
     ]
+
+
+def test_tool_call_request_setattr_deprecation_warning():
+    """Test that ToolCallRequest raises a deprecation warning on direct attribute modification."""
+    import warnings
+
+    from langgraph.prebuilt.tool_node import ToolCallRequest
+
+    # Create a mock ToolCall
+    tool_call = {"name": "test", "args": {"a": 1}, "id": "call_1", "type": "tool_call"}
+
+    # Create a ToolCallRequest
+    request = ToolCallRequest(
+        tool_call=tool_call,
+        tool=None,
+        state={"messages": []},
+        runtime=None,
+    )
+
+    # Test 1: Direct attribute assignment should raise deprecation warning but still work
+    with pytest.warns(DeprecationWarning, match="deprecated.*override"):
+        request.tool_call = {"name": "other", "args": {}, "id": "call_2"}
+
+    # Verify the attribute was actually modified
+    assert request.tool_call == {"name": "other", "args": {}, "id": "call_2"}
+
+    # Reset for further tests
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        request.tool_call = tool_call
+
+    # Test 2: override method should work without warnings
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        new_tool_call = {
+            "name": "new_tool",
+            "args": {"b": 2},
+            "id": "call_3",
+            "type": "tool_call",
+        }
+        new_request = request.override(tool_call=new_tool_call)
+
+        # Verify no warning was raised
+        assert len(w) == 0
+
+    # Verify original is unchanged
+    assert request.tool_call == tool_call
+
+    # Verify new request has updated values
+    assert new_request.tool_call == new_tool_call
+
+    # Test 3: Initialization should not trigger warning
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        ToolCallRequest(
+            tool_call=tool_call,
+            tool=None,
+            state={"messages": []},
+            runtime=None,
+        )
+        # Verify no warning was raised during initialization
+        assert len(w) == 0
+
+
+async def test_tool_node_inject_async_all_types_signature_only() -> None:
+    """Test all injection types without @tool decorator."""
+    store = InMemoryStore()
+    namespace = ("test",)
+    store.put(namespace, "test_key", {"store_data": "from_store"})
+
+    class TestState(TypedDict):
+        messages: list
+        foo: str
+        bar: int
+
+    async def comprehensive_async_tool(
+        x: int,
+        whole_state: Annotated[TestState, InjectedState],
+        foo_field: Annotated[str, InjectedState("foo")],
+        store: Annotated[BaseStore, InjectedStore()],
+        runtime: ToolRuntime,
+    ) -> str:
+        """Async tool that uses all injection types."""
+        bar_from_whole = whole_state["bar"]
+        foo_value = foo_field
+        store_val = store.get(namespace, "test_key").value["store_data"]
+        foo_from_runtime = runtime.state["foo"]
+        tool_call_id = runtime.tool_call_id
+
+        return (
+            f"x={x}, "
+            f"bar_from_whole={bar_from_whole}, "
+            f"foo_field={foo_value}, "
+            f"store={store_val}, "
+            f"foo_from_runtime={foo_from_runtime}, "
+            f"tool_call_id={tool_call_id}"
+        )
+
+    node = ToolNode([comprehensive_async_tool], handle_tool_errors=True)
+    tool_call = {
+        "name": "comprehensive_async_tool",
+        "args": {"x": 42},
+        "id": "test_call_123",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+
+    config = _create_config_with_runtime(store=store)
+    result = await node.ainvoke(
+        {"messages": [msg], "foo": "foo_value", "bar": 99}, config=config
+    )
+
+    tool_message = result["messages"][-1]
+    assert tool_message.content == (
+        "x=42, "
+        "bar_from_whole=99, "
+        "foo_field=foo_value, "
+        "store=from_store, "
+        "foo_from_runtime=foo_value, "
+        "tool_call_id=test_call_123"
+    )
+
+
+async def test_tool_node_inject_async_all_types_with_decorator() -> None:
+    """Test all injection types with @tool decorator."""
+    store = InMemoryStore()
+    namespace = ("test",)
+    store.put(namespace, "test_key", {"store_data": "from_store"})
+
+    class TestState(TypedDict):
+        messages: list
+        foo: str
+        bar: int
+
+    @dec_tool
+    async def comprehensive_async_tool(
+        x: int,
+        whole_state: Annotated[TestState, InjectedState],
+        foo_field: Annotated[str, InjectedState("foo")],
+        store: Annotated[BaseStore, InjectedStore()],
+        runtime: ToolRuntime,
+    ) -> str:
+        """Async tool that uses all injection types."""
+        bar_from_whole = whole_state["bar"]
+        foo_value = foo_field
+        store_val = store.get(namespace, "test_key").value["store_data"]
+        foo_from_runtime = runtime.state["foo"]
+        tool_call_id = runtime.tool_call_id
+
+        return (
+            f"x={x}, "
+            f"bar_from_whole={bar_from_whole}, "
+            f"foo_field={foo_value}, "
+            f"store={store_val}, "
+            f"foo_from_runtime={foo_from_runtime}, "
+            f"tool_call_id={tool_call_id}"
+        )
+
+    node = ToolNode([comprehensive_async_tool], handle_tool_errors=True)
+    tool_call = {
+        "name": "comprehensive_async_tool",
+        "args": {"x": 42},
+        "id": "test_call_456",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+
+    config = _create_config_with_runtime(store=store)
+    result = await node.ainvoke(
+        {"messages": [msg], "foo": "foo_value", "bar": 99}, config=config
+    )
+
+    tool_message = result["messages"][-1]
+    assert tool_message.content == (
+        "x=42, "
+        "bar_from_whole=99, "
+        "foo_field=foo_value, "
+        "store=from_store, "
+        "foo_from_runtime=foo_value, "
+        "tool_call_id=test_call_456"
+    )
+
+
+async def test_tool_node_inject_async_all_types_with_schema() -> None:
+    """Test all injection types with explicit schema."""
+    store = InMemoryStore()
+    namespace = ("test",)
+    store.put(namespace, "test_key", {"store_data": "from_store"})
+
+    class TestState(TypedDict):
+        messages: list
+        foo: str
+        bar: int
+
+    class ComprehensiveToolSchema(BaseModel):
+        model_config = {"arbitrary_types_allowed": True}
+        x: int
+        whole_state: Annotated[TestState, InjectedState]
+        foo_field: Annotated[str, InjectedState("foo")]
+        store: Annotated[BaseStore, InjectedStore()]
+        runtime: ToolRuntime
+
+    @dec_tool(args_schema=ComprehensiveToolSchema)
+    async def comprehensive_async_tool(
+        x: int,
+        whole_state: Annotated[TestState, InjectedState],
+        foo_field: Annotated[str, InjectedState("foo")],
+        store: Annotated[BaseStore, InjectedStore()],
+        runtime: ToolRuntime,
+    ) -> str:
+        """Async tool that uses all injection types."""
+        bar_from_whole = whole_state["bar"]
+        foo_value = foo_field
+        store_val = store.get(namespace, "test_key").value["store_data"]
+        foo_from_runtime = runtime.state["foo"]
+        tool_call_id = runtime.tool_call_id
+
+        return (
+            f"x={x}, "
+            f"bar_from_whole={bar_from_whole}, "
+            f"foo_field={foo_value}, "
+            f"store={store_val}, "
+            f"foo_from_runtime={foo_from_runtime}, "
+            f"tool_call_id={tool_call_id}"
+        )
+
+    node = ToolNode([comprehensive_async_tool], handle_tool_errors=True)
+    tool_call = {
+        "name": "comprehensive_async_tool",
+        "args": {"x": 42},
+        "id": "test_call_789",
+        "type": "tool_call",
+    }
+    msg = AIMessage("hi?", tool_calls=[tool_call])
+
+    config = _create_config_with_runtime(store=store)
+    result = await node.ainvoke(
+        {"messages": [msg], "foo": "foo_value", "bar": 99}, config=config
+    )
+
+    tool_message = result["messages"][-1]
+    assert tool_message.content == (
+        "x=42, "
+        "bar_from_whole=99, "
+        "foo_field=foo_value, "
+        "store=from_store, "
+        "foo_from_runtime=foo_value, "
+        "tool_call_id=test_call_789"
+    )

libs/sdk-py/Makefile

@@ -17,7 +17,7 @@ lint lint_diff:
 	uv run ruff check .
 	[ "$(PYTHON_FILES)" = "" ] || uv run ruff format $(PYTHON_FILES) --diff
 	[ "$(PYTHON_FILES)" = "" ] || uv run ruff check --select I $(PYTHON_FILES)
-	uvx ty check .
+	uv run ty check .
 
 format format_diff:
 	uv run ruff check --select I --fix $(PYTHON_FILES)

libs/sdk-py/langgraph_sdk/__init__.py

@@ -1,6 +1,8 @@
 from langgraph_sdk.auth import Auth
 from langgraph_sdk.client import get_client, get_sync_client
+from langgraph_sdk.encryption import Encryption
+from langgraph_sdk.encryption.types import EncryptionContext
 
-__version__ = "0.2.9"
+__version__ = "0.3.0"
 
-__all__ = ["Auth", "get_client", "get_sync_client"]
+__all__ = ["Auth", "Encryption", "EncryptionContext", "get_client", "get_sync_client"]

libs/sdk-py/langgraph_sdk/auth/__init__.py

@@ -90,11 +90,11 @@ class Auth:
     """
 
     __slots__ = (
-        "on",
-        "_handlers",
-        "_global_handlers",
         "_authenticate_handler",
+        "_global_handlers",
         "_handler_cache",
+        "_handlers",
+        "on",
     )
     types = types
     """Reference to auth type definitions.
@@ -259,7 +259,7 @@ class Auth:
         """
         if self._authenticate_handler is not None:
             raise ValueError(
-                "Authentication handler already set as {self._authenticate_handler}."
+                f"Authentication handler already set as {self._authenticate_handler}."
             )
         self._authenticate_handler = fn
         return fn
@@ -383,7 +383,7 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
         if fn is not None:
             _validate_handler(fn)
             return typing.cast(
-                _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch],
+                "_ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]",
                 _register_handler(self.auth, self.resource, "*", fn),
             )
 
@@ -392,7 +392,7 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
         ) -> _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]:
             _validate_handler(handler)
             return typing.cast(
-                _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch],
+                "_ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]",
                 _register_handler(self.auth, self.resource, "*", handler),
             )
 
@@ -612,10 +612,10 @@ class _On:
     __slots__ = (
         "_auth",
         "assistants",
-        "threads",
-        "runs",
         "crons",
+        "runs",
         "store",
+        "threads",
         "value",
     )
 
@@ -740,9 +740,8 @@ def is_studio_user(
 ) -> bool:
     return (
         isinstance(user, types.StudioUser)
-        or isinstance(user, dict)
-        and user.get("kind") == "StudioUser"  # ty: ignore[invalid-argument-type]
+        or (isinstance(user, dict) and user.get("kind") == "StudioUser")  # ty: ignore[invalid-argument-type]
     )
 
 
-__all__ = ["Auth", "types", "exceptions"]
+__all__ = ["Auth", "exceptions", "types"]

libs/sdk-py/langgraph_sdk/auth/types.py

@@ -243,7 +243,7 @@ class StudioUser:
         ```
     """
 
-    __slots__ = ("username", "_is_authenticated", "_permissions")
+    __slots__ = ("_is_authenticated", "_permissions", "username")
 
     def __init__(self, username: str, is_authenticated: bool = False) -> None:
         self.username = username
@@ -1093,22 +1093,22 @@ class on:
 
 
 __all__ = [
-    "on",
+    "AssistantsCreate",
+    "AssistantsDelete",
+    "AssistantsRead",
+    "AssistantsSearch",
+    "AssistantsUpdate",
     "MetadataInput",
     "RunsCreate",
-    "ThreadsCreate",
-    "ThreadsRead",
-    "ThreadsUpdate",
-    "ThreadsDelete",
-    "ThreadsSearch",
-    "AssistantsCreate",
-    "AssistantsRead",
-    "AssistantsUpdate",
-    "AssistantsDelete",
-    "AssistantsSearch",
+    "StoreDelete",
     "StoreGet",
-    "StoreSearch",
     "StoreListNamespaces",
     "StorePut",
-    "StoreDelete",
+    "StoreSearch",
+    "ThreadsCreate",
+    "ThreadsDelete",
+    "ThreadsRead",
+    "ThreadsSearch",
+    "ThreadsUpdate",
+    "on",
 ]

libs/sdk-py/langgraph_sdk/encryption/__init__.py

@@ -0,0 +1,554 @@
+"""Custom encryption support for LangGraph.
+
+.. warning::
+    This API is in beta and may change in future versions.
+
+This module provides a framework for implementing custom at-rest encryption
+in LangGraph applications. Similar to the Auth system, it allows developers
+to define custom encryption and decryption handlers that are executed
+server-side.
+"""
+
+from __future__ import annotations
+
+import functools
+import inspect
+import typing
+import warnings
+
+from langgraph_sdk.encryption import types
+
+
+class LangGraphBetaWarning(UserWarning):
+    """Warning for beta features in LangGraph SDK."""
+
+
+@functools.lru_cache(maxsize=1)
+def _warn_encryption_beta() -> None:
+    warnings.warn(
+        "The Encryption API is in beta and may change in future versions.",
+        LangGraphBetaWarning,
+        stacklevel=4,
+    )
+
+
+class DuplicateHandlerError(Exception):
+    """Raised when attempting to register a duplicate encryption/decryption handler."""
+
+    pass
+
+
+def _validate_handler(fn: typing.Callable, handler_type: str) -> None:
+    """Validate that a handler function has the correct signature.
+
+    Args:
+        fn: The handler function to validate
+        handler_type: Description of the handler for error messages
+
+    Raises:
+        TypeError: If the handler is not an async function or has wrong parameter count
+    """
+    if not inspect.iscoroutinefunction(fn):
+        raise TypeError(f"{handler_type} must be an async function, got {type(fn)}")
+
+    sig = inspect.signature(fn)
+    params = [
+        p
+        for p in sig.parameters.values()
+        if p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD)
+    ]
+    if len(params) != 2:
+        raise TypeError(
+            f"{handler_type} must accept exactly 2 parameters "
+            f"(ctx, data), got {len(params)}"
+        )
+
+
+class _JsonEncryptDecorators:
+    """Dynamic decorator factory for JSON encryption handlers.
+
+    Supports both default and model-specific handlers:
+    - @encrypt.json - default handler for all models
+    - @encrypt.json.thread - handler for thread model
+    """
+
+    def __init__(self, parent: Encryption):
+        self._parent = parent
+
+    def __call__(self, fn: types.JsonEncryptor) -> types.JsonEncryptor:
+        """Register the default JSON encryption handler.
+
+        Args:
+            fn: The handler function
+
+        Returns:
+            The registered handler function
+
+        Raises:
+            DuplicateHandlerError: If handler already registered
+            TypeError: If handler has invalid signature
+        """
+        if self._parent._json_encryptor is not None:
+            raise DuplicateHandlerError("Default JSON encryptor already registered")
+        _validate_handler(fn, "Default JSON encryptor")
+        self._parent._json_encryptor = fn
+        return fn
+
+    def __getattr__(
+        self, model: str
+    ) -> typing.Callable[[types.JsonEncryptor], types.JsonEncryptor]:
+        """Dynamic attribute access for model-specific handlers.
+
+        Allows @encryption.encrypt.json.thread, @encryption.encrypt.json.assistant, etc.
+
+        Raises:
+            DuplicateHandlerError: If handler already registered for this model
+            TypeError: If handler has invalid signature
+        """
+
+        def decorator(fn: types.JsonEncryptor) -> types.JsonEncryptor:
+            if model in self._parent._json_encryptors:
+                raise DuplicateHandlerError(
+                    f"JSON encryptor for model '{model}' already registered"
+                )
+            _validate_handler(fn, f"JSON encryptor for model '{model}'")
+            self._parent._json_encryptors[model] = fn
+            return fn
+
+        return decorator
+
+
+class _JsonDecryptDecorators:
+    """Dynamic decorator factory for JSON decryption handlers.
+
+    Supports both default and model-specific handlers:
+    - @encryption.decrypt.json - default handler for all models
+    - @encryption.decrypt.json.thread - handler for thread model
+    """
+
+    def __init__(self, parent: Encryption):
+        self._parent = parent
+
+    def __call__(self, fn: types.JsonDecryptor) -> types.JsonDecryptor:
+        """Register the default JSON decryption handler.
+
+        Args:
+            fn: The handler function
+
+        Returns:
+            The registered handler function
+
+        Raises:
+            DuplicateHandlerError: If handler already registered
+            TypeError: If handler has invalid signature
+        """
+        if self._parent._json_decryptor is not None:
+            raise DuplicateHandlerError("Default JSON decryptor already registered")
+        _validate_handler(fn, "Default JSON decryptor")
+        self._parent._json_decryptor = fn
+        return fn
+
+    def __getattr__(
+        self, model: str
+    ) -> typing.Callable[[types.JsonDecryptor], types.JsonDecryptor]:
+        """Dynamic attribute access for model-specific handlers.
+
+        Allows @encryption.decrypt.json.thread, @encryption.decrypt.json.assistant, etc.
+
+        Raises:
+            DuplicateHandlerError: If handler already registered for this model
+            TypeError: If handler has invalid signature
+        """
+
+        def decorator(fn: types.JsonDecryptor) -> types.JsonDecryptor:
+            if model in self._parent._json_decryptors:
+                raise DuplicateHandlerError(
+                    f"JSON decryptor for model '{model}' already registered"
+                )
+            _validate_handler(fn, f"JSON decryptor for model '{model}'")
+            self._parent._json_decryptors[model] = fn
+            return fn
+
+        return decorator
+
+
+class _EncryptDecorators:
+    """Decorators for encryption handlers.
+
+    Provides @encryption.encrypt.blob and @encryption.encrypt.json decorators for
+    registering encryption functions.
+    """
+
+    def __init__(self, parent: Encryption):
+        self._parent = parent
+        self._json = _JsonEncryptDecorators(parent)
+
+    def blob(self, fn: types.BlobEncryptor) -> types.BlobEncryptor:
+        """Register a blob encryption handler.
+
+        The handler will be called to encrypt opaque data like checkpoint blobs.
+
+        Example:
+            ```python
+            @encryption.encrypt.blob
+            async def encrypt_blob(ctx: EncryptionContext, blob: bytes) -> bytes:
+                # Encrypt the blob using your encryption service
+                return encrypted_blob
+            ```
+
+        Args:
+            fn: The encryption handler function
+
+        Returns:
+            The registered handler function
+
+        Raises:
+            DuplicateHandlerError: If blob encryptor already registered
+            TypeError: If handler has invalid signature
+        """
+        if self._parent._blob_encryptor is not None:
+            raise DuplicateHandlerError("Blob encryptor already registered")
+        _validate_handler(fn, "Blob encryptor")
+        self._parent._blob_encryptor = fn
+        return fn
+
+    @property
+    def json(self) -> _JsonEncryptDecorators:
+        """Access JSON encryption decorators.
+
+        Supports model-specific handlers:
+        - @encryption.encrypt.json - default handler for all models
+        - @encryption.encrypt.json.thread - handler for thread model only
+        - @encryption.encrypt.json.assistant - handler for assistant model only
+
+        Example:
+            ```python
+            @encryption.encrypt.json
+            async def default_encrypt(ctx: EncryptionContext, data: dict) -> dict:
+                # Default encryption for all models
+                return encrypt_data(data)
+
+            @encryption.encrypt.json.thread
+            async def encrypt_thread(ctx: EncryptionContext, data: dict) -> dict:
+                # Special encryption for thread model only
+                return encrypt_thread_data(data)
+            ```
+        """
+        return self._json
+
+
+class _DecryptDecorators:
+    """Decorators for decryption handlers.
+
+    Provides @encryption.decrypt.blob and @encryption.decrypt.json decorators for
+    registering decryption functions.
+    """
+
+    def __init__(self, parent: Encryption):
+        self._parent = parent
+        self._json = _JsonDecryptDecorators(parent)
+
+    def blob(self, fn: types.BlobDecryptor) -> types.BlobDecryptor:
+        """Register a blob decryption handler.
+
+        The handler will be called to decrypt opaque data like checkpoint blobs.
+
+        Example:
+            ```python
+            @encryption.decrypt.blob
+            async def decrypt_blob(ctx: EncryptionContext, blob: bytes) -> bytes:
+                # Decrypt the blob using your encryption service
+                return decrypted_blob
+            ```
+
+        Args:
+            fn: The decryption handler function
+
+        Returns:
+            The registered handler function
+
+        Raises:
+            DuplicateHandlerError: If blob decryptor already registered
+            TypeError: If handler has invalid signature
+        """
+        if self._parent._blob_decryptor is not None:
+            raise DuplicateHandlerError("Blob decryptor already registered")
+        _validate_handler(fn, "Blob decryptor")
+        self._parent._blob_decryptor = fn
+        return fn
+
+    @property
+    def json(self) -> _JsonDecryptDecorators:
+        """Access JSON decryption decorators.
+
+        Supports model-specific handlers:
+        - @encryption.decrypt.json - default handler for all models
+        - @encryption.decrypt.json.thread - handler for thread model only
+        - @encryption.decrypt.json.assistant - handler for assistant model only
+
+        Example:
+            ```python
+            @encryption.decrypt.json
+            async def default_decrypt(ctx: EncryptionContext, data: dict) -> dict:
+                # Default decryption for all models
+                return decrypt_data(data)
+
+            @encryption.decrypt.json.thread
+            async def decrypt_thread(ctx: EncryptionContext, data: dict) -> dict:
+                # Special decryption for thread model only
+                return decrypt_thread_data(data)
+            ```
+        """
+        return self._json
+
+
+class Encryption:
+    """Add custom at-rest encryption to your LangGraph application.
+
+    .. warning::
+        This API is in beta and may change in future versions.
+
+    The Encryption class provides a system for implementing custom encryption
+    of data at rest in LangGraph applications. It supports encryption of
+    both opaque blobs (like checkpoints) and structured JSON data (like
+    metadata, context, kwargs, values, etc.).
+
+    To use, create a separate Python file and add the path to the file to your
+    LangGraph API configuration file (`langgraph.json`). Within that file, create
+    an instance of the Encryption class and register encryption and decryption
+    handlers as needed.
+
+    Example `langgraph.json` file:
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "agent": "./my_agent/agent.py:graph"
+      },
+      "env": ".env",
+      "encryption": {
+        "path": "./encryption.py:my_encryption"
+      }
+    }
+    ```
+
+    Then the LangGraph server will load your encryption file and use it to
+    encrypt/decrypt data at rest.
+
+    ???+ example "Basic Usage"
+
+        ```python
+        from langgraph_sdk import Encryption, EncryptionContext
+
+        my_encryption = Encryption()
+
+        @my_encryption.encrypt.blob
+        async def encrypt_blob(ctx: EncryptionContext, blob: bytes) -> bytes:
+            # Call your encryption service
+            return encrypted_blob
+
+        @my_encryption.decrypt.blob
+        async def decrypt_blob(ctx: EncryptionContext, blob: bytes) -> bytes:
+            # Call your decryption service
+            return decrypted_blob
+
+        @my_encryption.encrypt.json
+        async def encrypt_json(ctx: EncryptionContext, data: dict) -> dict:
+            # Practical encryption strategy:
+            # - "owner" field: unencrypted (for search/filtering)
+            # - "my.customer.org/" prefixed fields: encrypt VALUES only
+            # - All other fields: pass through unencrypted
+            encrypted = {}
+            for key, value in data.items():
+                if key.startswith("my.customer.org/"):
+                    # Encrypt VALUE for sensitive customer data
+                    encrypted[key] = encrypt_value(value)
+                else:
+                    # Pass through (including "owner" for search)
+                    encrypted[key] = value
+            return encrypted
+
+        @my_encryption.decrypt.json
+        async def decrypt_json(ctx: EncryptionContext, data: dict) -> dict:
+            # Decrypt VALUES for "my.customer.org/" prefixed fields
+            decrypted = {}
+            for key, value in data.items():
+                if key.startswith("my.customer.org/"):
+                    decrypted[key] = decrypt_value(value)
+                else:
+                    decrypted[key] = value
+            return decrypted
+        ```
+
+    ???+ example "Model-Specific Handlers"
+
+        You can register different encryption handlers for different model types
+        (thread, assistant, run, cron, checkpoint, etc.):
+
+        ```python
+        from langgraph_sdk import Encryption, EncryptionContext
+
+        my_encryption = Encryption()
+
+        # Default handler for models without specific handlers
+        @my_encryption.encrypt.json
+        async def default_encrypt(ctx: EncryptionContext, data: dict) -> dict:
+            return standard_encrypt(data)
+
+        # Thread-specific handler (uses different KMS key)
+        @my_encryption.encrypt.json.thread
+        async def encrypt_thread(ctx: EncryptionContext, data: dict) -> dict:
+            return encrypt_with_thread_key(data)
+
+        # Assistant-specific handler
+        @my_encryption.encrypt.json.assistant
+        async def encrypt_assistant(ctx: EncryptionContext, data: dict) -> dict:
+            return encrypt_with_assistant_key(data)
+
+        # Same pattern for decryption
+        @my_encryption.decrypt.json
+        async def default_decrypt(ctx: EncryptionContext, data: dict) -> dict:
+            return standard_decrypt(data)
+
+        @my_encryption.decrypt.json.thread
+        async def decrypt_thread(ctx: EncryptionContext, data: dict) -> dict:
+            return decrypt_with_thread_key(data)
+        ```
+
+    ???+ example "Field-Specific Logic"
+
+        The `ctx.field` attribute tells you which specific field is being encrypted,
+        allowing different logic within the same model:
+
+        ```python
+        @my_encryption.encrypt.json.thread
+        async def encrypt_thread(ctx: EncryptionContext, data: dict) -> dict:
+            if ctx.field == "metadata":
+                # Thread metadata - standard encryption
+                return encrypt_standard(data)
+            elif ctx.field == "values":
+                # Thread values - more sensitive, use stronger encryption
+                return encrypt_sensitive(data)
+            else:
+                return encrypt_standard(data)
+        ```
+    """
+
+    __slots__ = (
+        "_blob_decryptor",
+        "_blob_encryptor",
+        "_context_handler",
+        "_json_decryptor",
+        "_json_decryptors",
+        "_json_encryptor",
+        "_json_encryptors",
+        "decrypt",
+        "encrypt",
+    )
+
+    types = types
+    """Reference to encryption type definitions.
+
+    Provides access to all type definitions used in the encryption system,
+    including EncryptionContext, BlobEncryptor, BlobDecryptor,
+    JsonEncryptor, and JsonDecryptor.
+    """
+
+    def __init__(self) -> None:
+        """Initialize the Encryption instance."""
+        _warn_encryption_beta()
+        self.encrypt = _EncryptDecorators(self)
+        self.decrypt = _DecryptDecorators(self)
+        self._blob_encryptor: types.BlobEncryptor | None = None
+        self._blob_decryptor: types.BlobDecryptor | None = None
+        self._json_encryptor: types.JsonEncryptor | None = None
+        self._json_decryptor: types.JsonDecryptor | None = None
+        self._json_encryptors: dict[str, types.JsonEncryptor] = {}
+        self._json_decryptors: dict[str, types.JsonDecryptor] = {}
+        self._context_handler: types.ContextHandler | None = None
+
+    def context(self, fn: types.ContextHandler) -> types.ContextHandler:
+        """Register a context handler to derive encryption context from auth.
+
+        The handler receives the authenticated user and current EncryptionContext,
+        and returns a dict that becomes ctx.metadata for encrypt/decrypt handlers.
+
+        This allows encryption context to be derived from JWT claims or other
+        auth-derived data instead of requiring a separate X-Encryption-Context header.
+
+        Note: The context handler is called once per request in middleware,
+        so ctx.model and ctx.field will be None in the handler.
+
+        Example:
+            ```python
+            from langgraph_sdk import Encryption, EncryptionContext
+            from starlette.authentication import BaseUser
+
+            encryption = Encryption()
+
+            @encryption.context
+            async def get_context(user: BaseUser, ctx: EncryptionContext) -> dict:
+                # Derive encryption context from authenticated user
+                return {
+                    **ctx.metadata,  # preserve X-Encryption-Context header if present
+                    "tenant_id": user.tenant_id,
+                }
+            ```
+
+        Args:
+            fn: The context handler function
+
+        Returns:
+            The registered handler function
+        """
+        self._context_handler = fn
+        return fn
+
+    def get_json_encryptor(
+        self, model: str | None = None
+    ) -> types.JsonEncryptor | None:
+        """Get the JSON encryptor for a specific model.
+
+        Args:
+            model: The model type (e.g., "thread", "assistant"). If None, returns default.
+
+        Returns:
+            Model-specific encryptor if registered, otherwise default encryptor, or None.
+        """
+        if model and model in self._json_encryptors:
+            return self._json_encryptors[model]
+        return self._json_encryptor
+
+    def get_json_decryptor(
+        self, model: str | None = None
+    ) -> types.JsonDecryptor | None:
+        """Get the JSON decryptor for a specific model.
+
+        Args:
+            model: The model type (e.g., "thread", "assistant"). If None, returns default.
+
+        Returns:
+            Model-specific decryptor if registered, otherwise default decryptor, or None.
+        """
+        if model and model in self._json_decryptors:
+            return self._json_decryptors[model]
+        return self._json_decryptor
+
+    def __repr__(self) -> str:
+        handlers = []
+        if self._blob_encryptor:
+            handlers.append("blob_encryptor")
+        if self._blob_decryptor:
+            handlers.append("blob_decryptor")
+        if self._json_encryptor:
+            handlers.append("json_encryptor")
+        if self._json_decryptor:
+            handlers.append("json_decryptor")
+        if self._json_encryptors:
+            handlers.append(f"json_encryptors({list(self._json_encryptors.keys())})")
+        if self._json_decryptors:
+            handlers.append(f"json_decryptors({list(self._json_decryptors.keys())})")
+        if self._context_handler:
+            handlers.append("context_handler")
+        return f"Encryption(handlers=[{', '.join(handlers)}])"

libs/sdk-py/langgraph_sdk/encryption/types.py

@@ -0,0 +1,147 @@
+"""Encryption and decryption types for LangGraph.
+
+This module defines the core types used for custom at-rest encryption
+in LangGraph. It includes context types and typed dictionaries for
+encryption operations.
+"""
+
+from __future__ import annotations
+
+import typing
+from collections.abc import Awaitable, Callable
+
+Json = dict[str, typing.Any]
+"""JSON-serializable dictionary type for structured data encryption."""
+
+
+class EncryptionContext:
+    """Context passed to encryption/decryption handlers.
+
+    Contains arbitrary non-secret key-values that will be stored on encrypt.
+    These key-values are intended to be sent to an external service that
+    manages keys and handles the actual encryption and decryption of data.
+
+    Attributes:
+        model: The model type being encrypted (e.g., "assistant", "thread", "run", "checkpoint")
+        field: The specific field being encrypted (e.g., "metadata", "context", "kwargs", "values")
+        metadata: Additional context metadata that can be used for encryption decisions
+    """
+
+    __slots__ = ("field", "metadata", "model")
+
+    def __init__(
+        self,
+        model: str | None = None,
+        metadata: dict[str, typing.Any] | None = None,
+        field: str | None = None,
+    ):
+        self.model = model
+        self.field = field
+        self.metadata = metadata or {}
+
+    def __repr__(self) -> str:
+        return f"EncryptionContext(model={self.model!r}, field={self.field!r}, metadata={self.metadata!r})"
+
+
+BlobEncryptor = Callable[[EncryptionContext, bytes], Awaitable[bytes]]
+"""Handler for encrypting opaque blob data like checkpoints.
+
+Note: Must be an async function. Encryption typically involves I/O operations
+(calling external KMS services), which should be async.
+
+Args:
+    ctx: Encryption context with model type and metadata
+    blob: The raw bytes to encrypt
+
+Returns:
+    Awaitable that resolves to encrypted bytes
+"""
+
+BlobDecryptor = Callable[[EncryptionContext, bytes], Awaitable[bytes]]
+"""Handler for decrypting opaque blob data like checkpoints.
+
+Note: Must be an async function. Decryption typically involves I/O operations
+(calling external KMS services), which should be async.
+
+Args:
+    ctx: Encryption context with model type and metadata
+    blob: The encrypted bytes to decrypt
+
+Returns:
+    Awaitable that resolves to decrypted bytes
+"""
+
+JsonEncryptor = Callable[[EncryptionContext, Json], Awaitable[Json]]
+"""Handler for encrypting structured JSON data.
+
+Note: Must be an async function. Encryption typically involves I/O operations
+(calling external KMS services), which should be async.
+
+Used for encrypting structured data like metadata, context, kwargs, values,
+and other JSON-serializable fields across different model types.
+
+Maps plaintext fields to encrypted fields. A practical approach:
+- Keep "owner" field unencrypted for search/filtering
+- Encrypt VALUES (not keys) for fields with specific prefix (e.g., "my.customer.org/")
+- Pass through all other fields unencrypted
+
+Example:
+    Input:  {"owner": "user123", "my.customer.org/email": "john@example.com", "tenant_id": "t-456"}
+    Output: {"owner": "user123", "my.customer.org/email": "ENCRYPTED", "tenant_id": "t-456"}
+
+Note: Encrypted field VALUES cannot be reliably searched, as most real-world
+encryption implementations use nonces (non-deterministic encryption).
+Only unencrypted fields can be used in search queries.
+
+Args:
+    ctx: Encryption context with model type, field name, and metadata
+    data: The plaintext JSON dictionary
+
+Returns:
+    Awaitable that resolves to encrypted JSON dictionary
+"""
+
+JsonDecryptor = Callable[[EncryptionContext, Json], Awaitable[Json]]
+"""Handler for decrypting structured JSON data.
+
+Note: Must be an async function. Decryption typically involves I/O operations
+(calling external KMS services), which should be async.
+
+Inverse of JsonEncryptor. Must be able to decrypt data that
+was encrypted by the corresponding encryptor.
+
+Args:
+    ctx: Encryption context with model type, field name, and metadata
+    data: The encrypted JSON dictionary
+
+Returns:
+    Awaitable that resolves to decrypted JSON dictionary
+"""
+
+if typing.TYPE_CHECKING:
+    from starlette.authentication import BaseUser
+
+ContextHandler = Callable[
+    ["BaseUser", EncryptionContext], Awaitable[dict[str, typing.Any]]
+]
+"""Handler for deriving encryption context from authenticated user info.
+
+Note: Must be an async function as it may involve I/O operations.
+
+The context handler is called once per request in middleware (after auth),
+allowing encryption context to be derived from JWT claims, user properties,
+or other auth-derived data instead of requiring a separate X-Encryption-Context header.
+
+The return value becomes ctx.metadata for subsequent encrypt/decrypt operations
+and is persisted with encrypted data for later decryption.
+
+Note: ctx.model and ctx.field will be None in context handlers since
+the handler runs once per request before any specific model/field is known.
+
+Args:
+    user: The authenticated user (from Starlette's AuthenticationMiddleware)
+    ctx: Current encryption context with metadata from X-Encryption-Context header
+
+Returns:
+    Awaitable that resolves to dict that becomes the new ctx.metadata
+"""