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).

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.
 
@@ -23,47 +23,55 @@ 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]
@@ -71,12 +79,11 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 ## 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/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py

@@ -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

@@ -132,11 +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/shallow.py

@@ -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"],
@@ -636,10 +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

@@ -327,31 +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:
@@ -465,6 +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)[
@@ -1122,6 +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,))
@@ -1175,15 +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 = "3.0.1"
+version = "3.0.2"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"

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"

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]]
@@ -293,7 +293,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "3.0.0"
+version = "3.0.1"
 source = { editable = "." }
 dependencies = [
     { name = "aiosqlite" },

libs/cli/langgraph_cli/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.9"
+__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,
     )

libs/cli/langgraph_cli/config.py

@@ -824,6 +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")
@@ -1003,6 +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}",
@@ -1013,10 +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 --",
@@ -1202,26 +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(
@@ -1265,7 +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/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

@@ -420,7 +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
@@ -483,7 +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 = (
         """\
@@ -544,7 +544,7 @@ def test_config_to_docker_pipconfig():
                 "pip_config_file": "pipconfig.txt",
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     expected_docker_stdin = (
         """\
@@ -585,7 +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
@@ -594,7 +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",
         )
 
 
@@ -608,7 +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
@@ -654,7 +654,7 @@ dependencies = ["langchain"]"""
                 "graphs": graphs,
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
     os.remove(pyproject_path)
     expected_docker_stdin = (
@@ -689,7 +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
@@ -734,7 +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
@@ -786,7 +786,7 @@ def test_config_to_docker_python_encryption_bad_path():
 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, additional_contexts = config_to_docker(
+    actual_docker_stdin, _ = config_to_docker(
         PATH_TO_CONFIG,
         validate_config(
             {
@@ -796,7 +796,7 @@ def test_config_to_docker_python_encryption_formatted():
                 "encryption": {"path": "./agent.py:my_encryption"},
             }
         ),
-        "langchain/langgraph-api",
+        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
@@ -821,7 +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
@@ -875,7 +875,7 @@ def test_config_to_docker_webhooks_python():
                 "webhooks": webhooks,
             }
         ),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
 
     # Ensure the ENV line is present and the payload round-trips via JSON
@@ -900,7 +900,7 @@ def test_config_to_docker_webhooks_node():
                 "webhooks": webhooks,
             }
         ),
-        "langchain/langgraphjs-api",
+        base_image="langchain/langgraphjs-api",
     )
 
     parsed = _extract_env_json(dockerfile, "LANGGRAPH_WEBHOOKS")
@@ -912,7 +912,7 @@ def test_config_to_docker_no_webhooks():
     dockerfile, _ = config_to_docker(
         PATH_TO_CONFIG,
         validate_config({"dependencies": ["."], "graphs": graphs}),
-        "langchain/langgraph-api",
+        base_image="langchain/langgraph-api",
     )
 
     assert "ENV LANGGRAPH_WEBHOOKS=" not in dockerfile
@@ -930,7 +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
@@ -976,7 +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
@@ -1024,7 +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
@@ -1032,7 +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
@@ -1041,7 +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
@@ -1051,7 +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
@@ -1060,7 +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
 
@@ -1076,7 +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
@@ -1087,7 +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
@@ -1098,7 +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
@@ -1137,7 +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), "                ")}
@@ -1178,7 +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), "                ")}
@@ -1223,7 +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), "                ")}
@@ -1261,7 +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), "                ")}
@@ -1308,7 +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), "                ")}
@@ -1619,7 +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",
     )
 
@@ -1633,7 +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/cli/uv.lock

@@ -853,7 +853,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.1.2"
+version = "1.2.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch", marker = "python_full_version >= '3.11'" },
@@ -865,14 +865,14 @@ dependencies = [
     { name = "typing-extensions", marker = "python_full_version >= '3.11'" },
     { name = "uuid-utils", marker = "python_full_version >= '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/67/8d/99652acb7feaa4e16c9162429bc7a446f04749ef438aa02fce74b4319a00/langchain_core-1.1.2.tar.gz", hash = "sha256:75456c5cc10c3b53b80488bf5c6a4bcc3447b53e011533a8744bb0638b85dd78", size = 802560, upload-time = "2025-12-08T15:28:17.689Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f5/a0/2177f4ef4dfbea8edeba377b7b4889d177b8356ce186640e4651b240fd4d/langchain_core-1.2.1.tar.gz", hash = "sha256:131e6ad105b47ec2adc4d4d973f569276688f48cd890ba44603d48e76d9993ce", size = 802986, upload-time = "2025-12-15T14:32:50.845Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fb/68/2caf612e4b5e25d7938c96809b7ccbafb5906958bcad8c18d9211f092679/langchain_core-1.1.2-py3-none-any.whl", hash = "sha256:74dfd4dcc10a290e3701a64e35e0bea3f68420f5b7527820ced9414f5b2dc281", size = 475847, upload-time = "2025-12-08T15:28:16.467Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/95/98c47dbb4b6098934ff70e0f52efef3a85505dbcccc9eb63587e21fde4c9/langchain_core-1.2.1-py3-none-any.whl", hash = "sha256:2f63859f85dc3d95f768e35fed605702e3ff5aa3e92c7b253103119613e79768", size = 475972, upload-time = "2025-12-15T14:32:49.698Z" },
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 [[package]]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "langchain-core", marker = "python_full_version >= '3.11'" },
@@ -882,14 +882,14 @@ dependencies = [
     { name = "pydantic", marker = "python_full_version >= '3.11'" },
     { name = "xxhash", marker = "python_full_version >= '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d6/3c/af87902d300c1f467165558c8966d8b1e1f896dace271d3f35a410a5c26a/langgraph-1.0.4.tar.gz", hash = "sha256:86d08e25d7244340f59c5200fa69fdd11066aa999b3164b531e2a20036fac156", size = 484397, upload-time = "2025-11-25T20:31:48.608Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/47/28f4d4d33d88f69de26f7a54065961ac0c662cec2479b36a2db081ef5cb6/langgraph-1.0.5.tar.gz", hash = "sha256:7f6ae59622386b60fe9fa0ad4c53f42016b668455ed604329e7dc7904adbf3f8", size = 493969, upload-time = "2025-12-12T23:05:48.224Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/14/52/4eb25a3f60399da34ba34adff1b3e324cf0d87eb7a08cebf1882a9b5e0d5/langgraph-1.0.4-py3-none-any.whl", hash = "sha256:b1a835ceb0a8d69b9db48075e1939e28b1ad70ee23fa3fa8f90149904778bacf", size = 157271, upload-time = "2025-11-25T20:31:47.518Z" },
+    { url = "https://files.pythonhosted.org/packages/23/1b/e318ee76e42d28f515d87356ac5bd7a7acc8bad3b8f54ee377bef62e1cbf/langgraph-1.0.5-py3-none-any.whl", hash = "sha256:b4cfd173dca3c389735b47228ad8b295e6f7b3df779aba3a1e0c23871f81281e", size = 157056, upload-time = "2025-12-12T23:05:46.499Z" },
 ]
 
 [[package]]
 name = "langgraph-api"
-version = "0.5.35"
+version = "0.5.42"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cloudpickle", marker = "python_full_version >= '3.11'" },
@@ -919,9 +919,9 @@ dependencies = [
     { name = "uvicorn", marker = "python_full_version >= '3.11'" },
     { name = "watchfiles", marker = "python_full_version >= '3.11'" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/80/22/454e81273f77fe33c20b75e046f5a3ca2f3f7fc18f13df7d95bddad372d2/langgraph_api-0.5.42.tar.gz", hash = "sha256:b2e71dbb7392b459ee6218f9fe8deca1be97c0d89891e867990a311912257fe6", size = 392953, upload-time = "2025-12-12T19:40:43.199Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/a0/05/6fc28638791fd021989279c90a2053783170ad273b79fbb487c241022ade/langgraph_api-0.5.42-py3-none-any.whl", hash = "sha256:42560a57870d7f2df3d0726392b1ad52670e5b4170e23790f77fea1610891446", size = 312558, upload-time = "2025-12-12T19:40:42.107Z" },
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 [[package]]
@@ -1027,7 +1027,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-runtime-inmem"
-version = "0.19.1"
+version = "0.20.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "blockbuster", marker = "python_full_version >= '3.11'" },
@@ -1037,27 +1037,27 @@ dependencies = [
     { name = "starlette", marker = "python_full_version >= '3.11'" },
     { name = "structlog", marker = "python_full_version >= '3.11'" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/31/12/219cc68f58e69fe9a92518ae2563cbf20b118e1a6abb8827daafb26a83a2/langgraph_runtime_inmem-0.20.1.tar.gz", hash = "sha256:6ee918f0a839f3044cbed69580b44eb9cadf31a4771fc2e6d66842c7b7722fb0", size = 101516, upload-time = "2025-12-09T22:01:54.588Z" }
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+    { url = "https://files.pythonhosted.org/packages/4c/9c/b3883f862c46379bdd5fe4bd1eb8dce6c3897d7ec2d226b019e0866fad47/langgraph_runtime_inmem-0.20.1-py3-none-any.whl", hash = "sha256:27f4401372679c1132a31937524ca9fded071fc8e9a6cd5ac25e3860ebc8238b", size = 35183, upload-time = "2025-12-09T22:01:53.462Z" },
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 [[package]]
 name = "langgraph-sdk"
-version = "0.2.14"
+version = "0.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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+    { url = "https://files.pythonhosted.org/packages/69/48/ee4d7afb3c3d38bd2ebe51a4d37f1ed7f1058dd242f35994b562203067aa/langgraph_sdk-0.3.0-py3-none-any.whl", hash = "sha256:c1ade483fba17ae354ee920e4779042b18d5aba875f2a858ba569f62f628f26f", size = 66489, upload-time = "2025-12-12T22:19:29.228Z" },
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 [[package]]
 name = "langsmith"
-version = "0.4.56"
+version = "0.4.59"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -1069,82 +1069,82 @@ dependencies = [
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+version = "0.7.4"
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 [[package]]

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.
 
@@ -23,47 +23,55 @@ 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]
@@ -71,12 +79,11 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 ## 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/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
@@ -853,6 +854,8 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
         Returns:
             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/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
@@ -642,7 +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] = (),
@@ -665,6 +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()
         }

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/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.10"
@@ -26,7 +26,7 @@ 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",

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

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

libs/langgraph/uv.lock

@@ -1345,7 +1345,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -1572,7 +1572,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.1"
+version = "3.0.2"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -1619,7 +1619,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "3.0.0"
+version = "3.0.1"
 source = { editable = "../checkpoint-sqlite" }
 dependencies = [
     { name = "aiosqlite" },

libs/prebuilt/tests/test_react_agent.py

@@ -638,7 +638,7 @@ def test_react_agent_parallel_tool_calls(
     for event in agent.stream(
         {"messages": [("user", query)]}, config, stream_mode="values"
     ):
-        if "__interrupt__" not in event: 
+        if "__interrupt__" not in event:
             if messages := event.get("messages"):
                 message_types.append([m.type for m in messages])
 

libs/prebuilt/uv.lock

@@ -271,7 +271,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "1.0.4"
+version = "1.0.5"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -402,7 +402,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.1"
+version = "3.0.2"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -449,7 +449,7 @@ test = [
 
 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "3.0.0"
+version = "3.0.1"
 source = { editable = "../checkpoint-sqlite" }
 dependencies = [
     { name = "aiosqlite" },

libs/sdk-py/langgraph_sdk/__init__.py

@@ -3,6 +3,6 @@ from langgraph_sdk.client import get_client, get_sync_client
 from langgraph_sdk.encryption import Encryption
 from langgraph_sdk.encryption.types import EncryptionContext
 
-__version__ = "0.2.14"
+__version__ = "0.3.0"
 
 __all__ = ["Auth", "Encryption", "EncryptionContext", "get_client", "get_sync_client"]

libs/sdk-py/langgraph_sdk/client.py

@@ -1193,6 +1193,7 @@ class AssistantsClient:
         *,
         metadata: Json = None,
         graph_id: str | None = None,
+        name: str | None = None,
         headers: Mapping[str, str] | None = None,
         params: QueryParamTypes | None = None,
     ) -> int:
@@ -1201,6 +1202,8 @@ class AssistantsClient:
         Args:
             metadata: Metadata to filter by. Exact match for each key/value.
             graph_id: Optional graph id to filter by.
+            name: Optional name to filter by.
+                The filtering logic will match assistants where 'name' is a substring (case insensitive) of the assistant name.
             headers: Optional custom headers to include with the request.
             params: Optional query parameters to include with the request.
 
@@ -1212,6 +1215,8 @@ class AssistantsClient:
             payload["metadata"] = metadata
         if graph_id:
             payload["graph_id"] = graph_id
+        if name:
+            payload["name"] = name
         return await self.http.post(
             "/assistants/count", json=payload, headers=headers, params=params
         )
@@ -4526,6 +4531,7 @@ class SyncAssistantsClient:
         *,
         metadata: Json = None,
         graph_id: str | None = None,
+        name: str | None = None,
         headers: Mapping[str, str] | None = None,
         params: QueryParamTypes | None = None,
     ) -> int:
@@ -4534,6 +4540,8 @@ class SyncAssistantsClient:
         Args:
             metadata: Metadata to filter by. Exact match for each key/value.
             graph_id: Optional graph id to filter by.
+            name: Optional name to filter by.
+                The filtering logic will match assistants where 'name' is a substring (case insensitive) of the assistant name.
             headers: Optional custom headers to include with the request.
             params: Optional query parameters to include with the request.
 
@@ -4545,6 +4553,8 @@ class SyncAssistantsClient:
             payload["metadata"] = metadata
         if graph_id:
             payload["graph_id"] = graph_id
+        if name:
+            payload["name"] = name
         return self.http.post(
             "/assistants/count", json=payload, headers=headers, params=params
         )

libs/sdk-py/langgraph_sdk/schema.py

@@ -167,7 +167,7 @@ class Config(TypedDict, total=False):
     """
     Runtime values for attributes previously made configurable on this Runnable,
     or sub-Runnables, through .configurable_fields() or .configurable_alternatives().
-    Check .output_schema() for a description of the attributes that have been made 
+    Check .output_schema() for a description of the attributes that have been made
     configurable.
     """
 
@@ -301,7 +301,7 @@ class ThreadState(TypedDict):
     values: list[dict] | dict[str, Any]
     """The state values."""
     next: Sequence[str]
-    """The next nodes to execute. If empty, the thread is done until new input is 
+    """The next nodes to execute. If empty, the thread is done until new input is
     received."""
     checkpoint: Checkpoint
     """The ID of the checkpoint."""
@@ -476,7 +476,7 @@ class Item(TypedDict):
     """The namespace of the item. A namespace is analogous to a document's directory."""
     key: str
     """The unique identifier of the item within its namespace.
-    
+
     In general, keys needn't be globally unique.
     """
     value: dict[str, Any]
@@ -519,6 +519,8 @@ class StreamPart(NamedTuple):
     """The type of event for this stream part."""
     data: dict
     """The data payload associated with the event."""
+    id: str | None = None
+    """The ID of the event."""
 
 
 class Send(TypedDict):

libs/sdk-py/langgraph_sdk/sse.py

@@ -103,6 +103,7 @@ class SSEDecoder:
             sse = StreamPart(
                 event=self._event,
                 data=orjson.loads(self._data) if self._data else None,  # type: ignore[invalid-argument-type]
+                id=self.last_event_id,
             )
 
             # NOTE: as per the SSE spec, do not reset last_event_id.

libs/sdk-py/tests/test_client_stream.py

@@ -50,7 +50,7 @@ def iter_lines_raw(payload: list[bytes]) -> Iterator[BytesLike]:
     yield from decoder.flush()
 
 
-def test_stream_see():
+def test_stream_sse():
     for groups in (
         [RESPONSE_PAYLOAD],
         RESPONSE_PAYLOAD.splitlines(keepends=True),
@@ -150,9 +150,9 @@ def test_sync_http_client_stream_recovers_after_disconnect():
 
     assert call_count == 2
     assert parts == [
-        StreamPart(event="values", data={"step": 1}),
-        StreamPart(event="values", data={"step": 2}),
-        StreamPart(event="end", data=None),  # ty: ignore
+        StreamPart(event="values", data={"step": 1}, id="1"),
+        StreamPart(event="values", data={"step": 2}, id="2"),
+        StreamPart(event="end", data=None, id="2"),
     ]
 
 
@@ -222,9 +222,9 @@ async def test_http_client_stream_recovers_after_disconnect():
 
     assert call_count == 2
     assert parts == [
-        StreamPart(event="values", data={"step": 1}),
-        StreamPart(event="values", data={"step": 2}),
-        StreamPart(event="end", data=None),
+        StreamPart(event="values", data={"step": 1}, id="1"),
+        StreamPart(event="values", data={"step": 2}, id="2"),
+        StreamPart(event="end", data=None, id="2"),
     ]
 
 

security.md

@@ -1,61 +0,0 @@
-# Security Policy
-
-## Reporting OSS Vulnerabilities
-
-LangChain is partnered with [huntr by Protect AI](https://huntr.com/) to provide
-a bounty program for our open source projects.
-
-Please report security vulnerabilities associated with the LangChain
-open source projects by visiting the following link:
-
-[https://huntr.com/bounties/disclose/](https://huntr.com/bounties/disclose/?target=https%3A%2F%2Fgithub.com%2Flangchain-ai%2Flangchain&validSearch=true)
-
-Before reporting a vulnerability, please review:
-
-1) In-Scope Targets and Out-of-Scope Targets below.
-2) The [langchain-ai/langchain](https://github.com/langchain-ai/langchain) monorepo structure.
-3) LangChain [security guidelines](https://python.langchain.com/docs/security) to
-   understand what we consider to be a security vulnerability vs. developer
-   responsibility.
-
-### In-Scope Targets
-
-The following packages and repositories are eligible for bug bounties:
-
-- langchain-core
-- langchain (see exceptions)
-- langchain-community (see exceptions)
-- langgraph
-- langserve
-
-### Out of Scope Targets
-
-All out of scope targets defined by huntr as well as:
-
-- **langchain-experimental**: This repository is for experimental code and is not
-  eligible for bug bounties, bug reports to it will be marked as interesting or waste of
-  time and published with no bounty attached.
-- **tools**: Tools in either langchain or langchain-community are not eligible for bug
-  bounties. This includes the following directories
-  - langchain/tools
-  - langchain-community/tools
-  - Please review our [security guidelines](https://python.langchain.com/docs/security)
-    for more details, but generally tools interact with the real world. Developers are
-    expected to understand the security implications of their code and are responsible
-    for the security of their tools.
-- Code documented with security notices. This will be decided done on a case by
-  case basis, but likely will not be eligible for a bounty as the code is already
-  documented with guidelines for developers that should be followed for making their
-  application secure.
-- Any LangSmith related repositories or APIs see below.
-
-## Reporting LangSmith Vulnerabilities
-
-Please report security vulnerabilities associated with LangSmith by email to `security@langchain.dev`.
-
-- LangSmith site: <https://smith.langchain.com>
-- SDK client: <https://github.com/langchain-ai/langsmith-sdk>
-
-### Other Security Concerns
-
-For any other security concerns, please contact us at `security@langchain.dev`.