ci: update release workflow for prebuilt (#3621 )

docs: update getting started docs for studio (#3542 )
prebuilt: remove langgraph dependency (#3620 )
2026-08-30 11:49:38 +02:00 · 2025-02-27 09:49:15 -05:00 · 2025-02-27 15:41:52 +01:00 · 2025-02-27 09:41:43 -05:00 · 2025-02-26 21:47:32 -05:00 · 2025-02-26 21:33:45 -05:00
86 changed files with 4407 additions and 2694 deletions
@@ -39,6 +39,7 @@ jobs:
              - 'libs/checkpoint-sqlite/**'
              - 'libs/checkpoint-postgres/**'
              - 'libs/scheduler-kafka/**'
+              - 'libs/prebuilt/**'
            sdk-js:
              - 'libs/sdk-js/**'

@@ -56,6 +57,7 @@ jobs:
            "libs/checkpoint-sqlite",
            "libs/checkpoint-postgres",
            "libs/scheduler-kafka",
+            "libs/prebuilt",
          ]
    if: needs.changes.outputs.python == 'true'
    uses: ./.github/workflows/_lint.yml
@@ -74,6 +76,7 @@ jobs:
            "libs/checkpoint",
            "libs/checkpoint-sqlite",
            "libs/checkpoint-postgres",
+            "libs/prebuilt",
          ]
    if: needs.changes.outputs.python == 'true'
    uses: ./.github/workflows/_test.yml
@@ -195,7 +195,11 @@ jobs:
              "$PKG_NAME==$VERSION" \
          )

-          if [[ "$PKG_NAME" == *checkpoint* ]]; then
+          if [[ "$PKG_NAME" == *prebuilt* ]]; then
+            poetry run pip install langgraph
+          fi
+
+          if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
            # since checkpoint packages are namespace packages, import them with . convention
            # i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite
            IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)"
@@ -22,7 +22,7 @@ jobs:
      matrix:
        lib-version:
          - "development"
-          - "latest"
+          # - "latest"

    steps:
      - uses: actions/checkout@v4
@@ -70,7 +70,7 @@ pip install -U langgraph
 Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!

 ```shell
-pip install langchain-anthropic
+pip install langgraph-prebuilt langchain-anthropic
 ```

 ```shell
@@ -83,14 +83,18 @@ def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -
        resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
    )
    rows = [
-        "| Name | GitHub URL | Description | Weekly Downloads |",
-        "| --- | --- | --- | --- |",
+        "| Name | GitHub URL | Description | Weekly Downloads | Stars |",
+        "| --- | --- | --- | --- | --- |",
    ]
    for package in sorted_packages:
        name = f"**{package['name']}**"
        repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
+        stars_badge = (
+            f"https://img.shields.io/github/stars/{package['repo']}?style=social"
+        )
+        stars = f"![GitHub stars]({stars_badge})"
        downloads = package["weekly_downloads"] or "-"
-        row = f"| {name} | {repo_url} | {package['description']} | {downloads} |"
+        row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
        rows.append(row)
    markdown_content = MARKDOWN.format(
        library_list="\n".join(rows), langgraph_url=langgraph_url
@@ -2,13 +2,13 @@
 packages:
  - name: "trustcall"
    repo: "hinthornw/trustcall"
-    description: "Tenacious tool calling built on LangGraph"
+    description: "Tenacious tool calling built on LangGraph."
  - name: "breeze-agent"
    repo: "andrestorres123/breeze-agent"
-    description: "A streamlined research system built inspired on STORM and built on LangGraph"
+    description: "A streamlined research system built inspired on STORM and built on LangGraph."
  - name: "langgraph-supervisor"
-    repo: "langchain-ai/langgraph-supervisor"
-    description: "Build supervisor multi-agent systems with LangGraph"
+    repo: "langchain-ai/langgraph-supervisor-py"
+    description: "Build supervisor multi-agent systems with LangGraph."
  - name: "langmem"
    repo: "langchain-ai/langmem"
    description: "Build agents that learn and adapt from interactions over time."
@@ -18,3 +18,9 @@ packages:
  - name: "open-deep-research"
    repo: "langchain-ai/open_deep_research"
    description: "Open source assistant for iterative web research and report writing."
+  - name: "langgraph-swarm"
+    repo: "langchain-ai/langgraph-swarm-py"
+    description: "Build swarm-style multi-agent systems using LangGraph."
+  - name: "delve-taxonomy-generator"
+    repo: "andrestorres123/delve"
+    description: "A taxonomy generator for unstructured data"
@@ -9,6 +9,7 @@ This list of companies using LangGraph and their success stories is compiled fro
 | [AppFolio](https://www.appfolio.com/) | Real Estate | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-appfolio/) |
 | [Athena Intelligence](https://www.athenaintel.com/) | Software & Technology (GenAI Native) | Research & summarization | [Case study, 2024](https://blog.langchain.dev/customers-athena-intelligence/) |
 | [Captide](https://www.captide.co/) | Software & Technology (GenAI Native) | Data extraction | [Case study, 2025](https://blog.langchain.dev/how-captide-is-redefining-equity-research-with-agentic-workflows-built-on-langgraph-and-langsmith/) |
+| [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) |
 | [Elastic](https://www.elastic.co/) | Software & Technology | Copilot for domain-specific task | [Blog post, 2025](https://www.elastic.co/blog/elastic-security-generative-ai-features) |
 | [GitLab](https://about.gitlab.com/) | Software & Technology | Code generation | [Duo workflow docs](https://handbook.gitlab.com/handbook/engineering/architecture/design-documents/duo_workflow/) |
 | [Infor](https://infor.com/) | Software & Technology | GenAI embedded product experiences; customer support; copilot | [Case study, 2025](https://blog.langchain.dev/customers-infor/) |
@@ -1,10 +1,10 @@
-# Test Cloud Deployment
+# Test LangGraph Platform Deployment

-The LangGraph Studio UI connects directly to LangGraph Cloud deployments.
+The LangGraph Studio UI connects directly to LangGraph Platform deployments.

 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...

-1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
 1. Select an existing deployment to test with LangGraph Studio.
 1. In the top-right corner, select `Open LangGraph Studio`.
 1. [Invoke an assistant](./invoke_studio.md) or [view an existing thread](./threads_studio.md).
@@ -1,6 +1,6 @@
 # LangGraph CLI

-The LangGraph command line interface includes commands to build and run a LangGraph Cloud API server locally in [Docker](https://www.docker.com/). For development and testing, you can use the CLI to deploy a local API server as an alternative to the [Studio desktop app](../../concepts/langgraph_studio.md).
+The LangGraph command line interface includes commands to build and run a LangGraph Cloud API server locally in [Docker](https://www.docker.com/). For development and testing, you can use the CLI to deploy a local API server.

 ## Installation

@@ -2,6 +2,12 @@

 The LangGraph Cloud Server supports specific environment variables for configuring a deployment.

+## `DD_API_KEY`
+
+Specify `DD_API_KEY` (your [Datadog API Key](https://docs.datadoghq.com/account_management/api-app-keys/)) to automatically enable Datadog tracing for the deployment. Specify other [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) to configure the tracing instrumentation.
+
+If `DD_API_KEY` is specified, the application process is wrapped in the [`ddtrace-run` command](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html). Other `DD_*` environment variables (e.g. `DD_SITE`, `DD_ENV`, `DD_SERVICE`, `DD_TRACE_ENABLED`) are typically needed to properly configure the tracing instrumentation. See [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) for more details.
+
 ## `LANGCHAIN_TRACING_SAMPLING_RATE`

 Sampling rate for traces sent to LangSmith. Valid values: Any float between `0` and `1`.
@@ -36,7 +36,7 @@ LangGraph is a stateful, orchestration framework that brings added control to ag
 | Concurrency Control | Simple threading                                          | Supports double-texting                                                                                |
 | Scheduling          | None                                                      | Cron scheduling                                                                                        |
 | Monitoring          | None                                                      | Integrated with LangSmith for observability                                                            |
-| IDE integration     | LangGraph Studio for Desktop                              | LangGraph Studio for Desktop & Cloud                                                                   |
+| IDE integration     | LangGraph Studio                                          | LangGraph Studio                                                                                       |

 ## What are my deployment options for LangGraph Platform?

@@ -62,3 +62,9 @@ Yes! You can use LangGraph with any LLMs. The main reason we use LLMs that suppo
 ## Does LangGraph work with OSS LLMs?

 Yes! LangGraph is totally ambivalent to what LLMs are used under the hood. The main reason we use closed LLMs in most of the tutorials is that they seamlessly support tool calling, while OSS LLMs often don't. But tool calling is not necessary (see [this section](#does-langgraph-work-with-llms-that-dont-support-tool-calling)) so you can totally use LangGraph with OSS LLMs.
+
+## Can I use LangGraph Studio without logging to LangSmith
+
+Yes! You can use the [development version of LangGraph Server](../tutorials/langgraph-platform/local-server.md) to run the backend locally.
+This will connect to the studio frontend hosted as part of LangSmith.
+If you set an environment variable of `LANGSMITH_TRACING=false` then no traces will be sent to LangSmith.
@@ -4,7 +4,7 @@
    - [LangGraph Platform](./langgraph_platform.md)
    - [LangGraph Server](./langgraph_server.md)

-The LangGraph CLI is a multi-platform command-line tool for building and running the [LangGraph API server](./langgraph_server.md) locally. This offers an alternative to the [LangGraph Studio desktop app](./langgraph_studio.md) for developing and testing agents across all major operating systems (Linux, Windows, MacOS). The resulting server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.
+The LangGraph CLI is a multi-platform command-line tool for building and running the [LangGraph API server](./langgraph_server.md) locally. The resulting server includes all API endpoints for your graph's runs, threads, assistants, etc. as well as the other services required to run your agent, including a managed database for checkpointing and storage.

 ## Installation

@@ -7,7 +7,7 @@

 LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications.

-With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith allowing you to  collaborate with teammates to debug failure modes.
+With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith allowing you to collaborate with teammates to debug failure modes.

 ![](img/lg_studio.png)

@@ -15,7 +15,7 @@ With visual graphs and the ability to edit state, you can better understand agen

 The key features of LangGraph Studio are:

- Visualizes your graph
+- Visualize your graphs
 - Test your graph by running it from the UI
 - Debug your agent by [modifying its state and rerunning](human_in_the_loop.md)
 - Create and manage [assistants](assistants.md)
@@ -23,86 +23,54 @@ The key features of LangGraph Studio are:
 - View and manage [long term memory](memory.md)
 - Add node input/outputs to [LangSmith](https://smith.langchain.com/) datasets for testing

-## Types
+## Getting started

-### Development server with web UI
+There are two ways to connect your LangGraph app with the studio:

-You can [run a local in-memory development server](../tutorials/langgraph-platform/local-server.md) that can be used to connect a local LangGraph app with a web version of the studio.
-For example, if you start the local server with `langgraph dev` (running at `http://127.0.0.1:2024` by default), you can connect to the studio by navigating to:
+### Deployed Application
+
+If you have deployed your LangGraph application on LangGraph Platform, you can access the studio as part of that deployment. To do so, navigate to the deployment in LangGraph Platform within the LangSmith UI and click the "LangGraph Studio" button.
+
+### Local Development Server
+
+If you have a LangGraph application that is [running locally in-memory](../tutorials/langgraph-platform/local-server.md), you can connect it to LangGraph Studio in the browser within LangSmith.
+
+By default, starting the local server with `langgraph dev` will run the server at `http://127.0.0.1:2024` and automatically open Studio in your browser. However, you can also manually connect to Studio by either:
+
+1. In LangGraph Platform, clicking the "LangGraph Studio" button and entering the server URL in the dialog that appears.
+
+   or
+
+2. Navigating to the URL in your browser:

 ```
 https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
 ```

-See [instructions here](../cloud/reference/cli.md#dev) for more information.
+## Related

-The web UI version of the studio will connect to your locally running server — your agent is still running locally and never leaves your device.
+For more information please see the following:

-### Cloud studio
+- [LangGraph Studio how-to guides](../how-tos/index.md#langgraph-studio)
+- [LangGraph CLI Documentation](../cloud/reference/cli.md)

-If you have deployed your LangGraph application on LangGraph Platform (Cloud), you can access the studio as part of that
-
-### Desktop app
-
-LangGraph Studio is available as a [desktop app](https://studio.langchain.com/) for MacOS users.
-
-While in Beta, LangGraph Studio is available for free to all [LangSmith](https://smith.langchain.com/) users on any plan tier.
-
-## Studio FAQs
+## LangGraph Studio FAQs

 ### Why is my project failing to start?

-There are a few reasons that your project might fail to start, here are some of the most common ones.
-
-#### Docker issues (desktop only)
-
-LangGraph Studio (desktop) requires Docker Desktop version 4.24 or higher. Please make sure you have a version of Docker installed that satisfies that requirement and also make sure you have the Docker Desktop app up and running before trying to use LangGraph Studio. In addition, make sure you have docker-compose updated to version 2.22.0 or higher.
-
-#### Configuration or environment issues
-
-Another reason your project might fail to start is because your configuration file is defined incorrectly, or you are missing required environment variables.
-
-!!! Important "Note (desktop only)"
-
-    LangGraph Studio Desktop automatically populates `LANGCHAIN_*` environment variables for license verification and tracing, regardless of the contents of the `.env` file. All other environment variables defined in `.env` will be read as normal.
-
-#### Incorrect data region (desktop only)
-
-If you receive a license verification error when attempting to start the LangGraph Server, you may be logged into the incorrect LangSmith data region. Ensure that you're logged into the correct LangSmith data region and ensure that the LangSmith account has access to LangGraph platform.
-
-1. In the top right-hand corner, click the user icon and select `Logout`.
-1. At the login screen, click the `Data Region` dropdown menu and select the appropriate data region. Then click `Login to LangSmith`.
+A project may fail to start if the configuration file is defined incorrectly, or if required environment variables are missing. See [here](../cloud/reference/cli.md#configuration-file) for how your configuration file should be defined.

 ### How does interrupt work?

 When you select the `Interrupts` dropdown and select a node to interrupt the graph will pause execution before and after (unless the node goes straight to `END`) that node has run. This means that you will be able to both edit the state before the node is ran and the state after the node has ran. This is intended to allow developers more fine-grained control over the behavior of a node and make it easier to observe how the node is behaving. You will not be able to edit the state after the node has ran if the node is the final node in the graph.

-### How do I reload the app?  (desktop only)
+For more information on interrupts and human in the loop, see [here](./human_in_the_loop.md).

-If you would like to reload the app, don't use Command+R as you might normally do. Instead, close and reopen the app for a full refresh.
-
-### How does automatic rebuilding work?  (desktop only)
-
-One of the key features of LangGraph Studio is that it automatically rebuilds your image when you change the source code. This allows for a super fast development and testing cycle which makes it easy to iterate on your graph. There are two different ways that LangGraph rebuilds your image: either by editing the image or completely rebuilding it.
-
-#### Rebuilds from source code changes
-
-If you modified the source code only (no configuration or dependency changes!) then the image does not require a full rebuild, and LangGraph Studio will only update the relevant parts. The UI status in the bottom left will switch from `Online` to `Stopping` temporarily while the image gets edited. The logs will be shown as this process is happening, and after the image has been edited the status will change back to `Online` and you will be able to run your graph with the modified code!
-
-
-#### Rebuilds from configuration or dependency changes
-
-If you edit your graph configuration file (`langgraph.json`) or the dependencies (either `pyproject.toml` or `requirements.txt`) then the entire image will be rebuilt. This will cause the UI to switch away from the graph view and start showing the logs of the new image building process. This can take a minute or two, and once it is done your updated image will be ready to use!
-
-### Why is my graph taking so long to startup?  (desktop only)
-
-The LangGraph Studio interacts with a local LangGraph API server. To stay aligned with ongoing updates, the LangGraph API requires regular rebuilding. As a result, you may occasionally experience slight delays when starting up your project.
-
-## Why are extra edges showing up in my graph?
+### Why are extra edges showing up in my graph?

 If you don't define your conditional edges carefully, you might notice extra edges appearing in your graph. This is because without proper definition, LangGraph Studio assumes the conditional edge could access all other nodes. In order for this to not be the case, you need to be explicit about how you define the nodes the conditional edge routes to. There are two ways you can do this:

-### Solution 1: Include a path map
+#### Solution 1: Include a path map

 The first way to solve this is to add path maps to your conditional edges. A path map is just a dictionary or array that maps the possible outputs of your router function with the names of the nodes that each output corresponds to. The path map is passed as the third argument to the `add_conditional_edges` function like so:

@@ -120,7 +88,7 @@ The first way to solve this is to add path maps to your conditional edges. A pat

 In this case, the routing function returns either True or False, which map to `node_b` and `node_c` respectively.

-### Solution 2: Update the typing of the router (Python only)
+#### Solution 2: Update the typing of the router (Python only)

 Instead of passing a path map, you can also be explicit about the typing of your routing function by specifying the nodes it can map to using the `Literal` python definition. Here is an example of how to define a routing function in that way:

@@ -132,9 +100,48 @@ def routing_function(state: GraphState) -> Literal["node_b","node_c"]:
        return "node_c"
 ```

+### Studio Desktop FAQs

-## Related
+!!! warning "Deprecation Warning"
+In order to support a wider range of platforms and users, we now recommend following the above instructions to connect to LangGraph Studio using the development server instead of the desktop app.

-For more information please see the following:
+The LangGraph Studio Desktop App is a standalone application that allows you to connect to your LangGraph application and visualize and interact with your graph. It is available for MacOS only and requires Docker to be installed.

-*  [LangGraph Studio how-to guides](../how-tos/index.md#langgraph-studio)
+#### Why is my project failing to start?
+
+In addition to the reasons listed above, for the desktop app there are a few more reasons that your project might fail to start:
+
+!!! Important "Note "
+
+    LangGraph Studio Desktop automatically populates `LANGCHAIN_*` environment variables for license verification and tracing, regardless of the contents of the `.env` file. All other environment variables defined in `.env` will be read as normal.
+
+##### Docker issues
+
+LangGraph Studio (desktop) requires Docker Desktop version 4.24 or higher. Please make sure you have a version of Docker installed that satisfies that requirement and also make sure you have the Docker Desktop app up and running before trying to use LangGraph Studio. In addition, make sure you have docker-compose updated to version 2.22.0 or higher.
+
+##### Incorrect data region
+
+If you receive a license verification error when attempting to start the LangGraph Server, you may be logged into the incorrect LangSmith data region. Ensure that you're logged into the correct LangSmith data region and ensure that the LangSmith account has access to LangGraph platform.
+
+1. In the top right-hand corner, click the user icon and select `Logout`.
+1. At the login screen, click the `Data Region` dropdown menu and select the appropriate data region. Then click `Login to LangSmith`.
+
+### How do I reload the app?
+
+If you would like to reload the app, don't use Command+R as you might normally do. Instead, close and reopen the app for a full refresh.
+
+### How does automatic rebuilding work?
+
+One of the key features of LangGraph Studio is that it automatically rebuilds your image when you change the source code. This allows for a super fast development and testing cycle which makes it easy to iterate on your graph. There are two different ways that LangGraph rebuilds your image: either by editing the image or completely rebuilding it.
+
+#### Rebuilds from source code changes
+
+If you modified the source code only (no configuration or dependency changes!) then the image does not require a full rebuild, and LangGraph Studio will only update the relevant parts. The UI status in the bottom left will switch from `Online` to `Stopping` temporarily while the image gets edited. The logs will be shown as this process is happening, and after the image has been edited the status will change back to `Online` and you will be able to run your graph with the modified code!
+
+#### Rebuilds from configuration or dependency changes
+
+If you edit your graph configuration file (`langgraph.json`) or the dependencies (either `pyproject.toml` or `requirements.txt`) then the entire image will be rebuilt. This will cause the UI to switch away from the graph view and start showing the logs of the new image building process. This can take a minute or two, and once it is done your updated image will be ready to use!
+
+### Why is my graph taking so long to startup?
+
+The LangGraph Studio interacts with a local LangGraph API server. To stay aligned with ongoing updates, the LangGraph API requires regular rebuilding. As a result, you may occasionally experience slight delays when starting up your project.
@@ -310,7 +310,7 @@ graph.add_conditional_edges(START, routing_function, {True: "node_b", False: "no

 ## `Send`

-By default, `Nodes` and `Edges` are defined ahead of time and operate on the same shared state. However, there can be cases where the exact edges are not known ahead of time and/or you may want different versions of `State` to exist at the same time. A common example of this is with `map-reduce` design patterns. In this design pattern, a first node may generate a list of objects, and you may want to apply some other node to all those objects. The number of objects may be unknown ahead of time (meaning the number of edges may not be known) and the input `State` to the downstream `Node` should be different (one for each generated object).
+By default, `Nodes` and `Edges` are defined ahead of time and operate on the same shared state. However, there can be cases where the exact edges are not known ahead of time and/or you may want different versions of `State` to exist at the same time. A common example of this is with [map-reduce](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) design patterns. In this design pattern, a first node may generate a list of objects, and you may want to apply some other node to all those objects. The number of objects may be unknown ahead of time (meaning the number of edges may not be known) and the input `State` to the downstream `Node` should be different (one for each generated object).

 To support this design pattern, LangGraph supports returning [`Send`][langgraph.types.Send] objects from conditional edges. `Send` takes two arguments: first is the name of the node, and second is the state to pass to that node.

@@ -78,7 +78,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-anthropic"
+    "%pip install -U langgraph langgraph-prebuilt langchain-anthropic"
   ]
  },
  {
@@ -67,7 +67,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_anthropic"
+    "%pip install --quiet -U langgraph langgraph-prebuilt langchain_anthropic"
   ]
  },
  {
@@ -59,7 +59,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
   ]
  },
  {
@@ -64,7 +64,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
   ]
  },
  {
@@ -52,7 +52,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
   ]
  },
  {
@@ -60,7 +60,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
   ]
  },
  {
@@ -68,7 +68,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
   ]
  },
  {
@@ -71,7 +71,7 @@ If you navigate to `localhost:2024/hello` in your browser (2024 is the default d

 ## Deploying

-You can deploy this app as-is to the managed langgraph cloud or to your self-hsoted platform.
+You can deploy this app as-is to the managed langgraph cloud or to your self-hosted platform.

 ## Next steps

@@ -59,12 +59,10 @@ LangGraph makes it easy to manage conversation [memory](../concepts/memory.md) i
 [Human-in-the-loop](../concepts/human_in_the_loop.md) functionality allows
 you to involve humans in the decision-making process of your graph. These how-to guides show how to implement human-in-the-loop workflows in your graph.

-
 Key workflows:

 - [How to wait for user input](human_in_the_loop/wait-user-input.ipynb): A basic example that shows how to implement a human-in-the-loop workflow in your graph using the `interrupt` function.
 - [How to review tool calls](human_in_the_loop/review-tool-calls.ipynb): Incorporate human-in-the-loop for reviewing/editing/accepting tool call requests before they executed using the `interrupt` function.
- 

 Other methods:

@@ -290,10 +288,9 @@ Graph execution can take a while, and sometimes users may change their mind abou

 LangGraph Studio is a built-in UI for visualizing, testing, and debugging your agents.

- [How to connect to a LangGraph Cloud deployment](../cloud/how-tos/test_deployment.md)
+- [How to connect to a LangGraph Platform deployment](../cloud/how-tos/test_deployment.md)
 - [How to connect to a local dev server](../how-tos/local-studio.md)
 - [How to connect to a local deployment (Docker)](../cloud/how-tos/test_local_deployment.md)
- [How to test your graph in LangGraph Studio (MacOS only)](../cloud/how-tos/invoke_studio.md)
 - [How to interact with threads in LangGraph Studio](../cloud/how-tos/threads_studio.md)
 - [How to add nodes as dataset examples in LangGraph Studio](../cloud/how-tos/datasets_studio.md)
 - [How to engineer prompts in LangGraph Studio](../cloud/how-tos/iterate_graph_studio.md)
@@ -312,4 +309,4 @@ These are the guides for resolving common errors you may find while building wit

 These guides provide troubleshooting information for errors that are specific to the LangGraph Platform.

- [INVALID_LICENSE](../troubleshooting/errors/INVALID_LICENSE.md)
+- [INVALID_LICENSE](../troubleshooting/errors/INVALID_LICENSE.md)
@@ -1,15 +1,6 @@
 # How to connect a local agent to LangGraph Studio

-This guide shows you how to connect your local agent to [LangGraph Studio](../concepts/langgraph_studio.md) for visualization, interaction, and debugging.
-
-## Connection Options
-
-There are two ways to connect your local agent to LangGraph Studio:
-
- [Development Server](../concepts/langgraph_studio.md#development-server-with-web-ui): Python package, all platforms, no Docker
- [LangGraph Desktop](../concepts/langgraph_studio.md#desktop-app): Application, Mac only, requires Docker
-
-In this guide we will cover how to use the development server as that is generally an easier and better experience.
+This guide shows you how to connect your local agent to [LangGraph Studio](../concepts/langgraph_studio.md) for visualization, interaction, and debugging using the development server.

 ## Setup your application

@@ -24,9 +15,8 @@ You will need to make sure to install the `inmem` extras.

 ???+ note "Minimum version"

-  The minimum version to use the `inmem` extra with `langgraph-cli` is `0.1.55`.
-  Python 3.11 or higher is required.
-  
+The minimum version to use the `inmem` extra with `langgraph-cli` is `0.1.55`.
+Python 3.11 or higher is required.

 ```shell
 pip install -U "langgraph-cli[inmem]"
@@ -41,7 +31,7 @@ pip install -U "langgraph-cli[inmem]"
   langgraph dev
   ```

-This will look for the `langgraph.json` file in your current directory. 
+This will look for the `langgraph.json` file in your current directory.
 In there, it will find the paths to the graph(s), and start those up.
 It will then automatically connect to the cloud-hosted studio.

@@ -89,4 +79,4 @@ Then attach your preferred debugger:
    2. Click + and select "Python Debug Server"
    3. Set IDE host name: `localhost`
    4. Set port: `5678` (or the port number you chose in the previous step)
-    5. Click "OK" and start debugging
+    5. Click "OK" and start debugging
@@ -21,7 +21,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai langchain"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai langchain"
   ]
  },
  {
@@ -89,7 +89,7 @@
   "outputs": [],
   "source": [
    "# %%capture --no-stderr\n",
-    "# %pip install -U langgraph langchain-anthropic"
+    "# %pip install -U langgraph langgraph-prebuilt langchain-anthropic"
   ]
  },
  {
@@ -72,7 +72,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-anthropic"
+    "%pip install -U langgraph langgraph-prebuilt langchain-anthropic"
   ]
  },
  {
@@ -79,7 +79,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-anthropic"
+    "%pip install -U langgraph langgraph-prebuilt langchain-anthropic"
   ]
  },
  {
@@ -57,7 +57,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-anthropic"
+    "%pip install -U langgraph langgraph-prebuilt langchain-anthropic"
   ]
  },
  {
@@ -70,7 +70,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_anthropic"
+    "%pip install --quiet -U langgraph langgraph-prebuilt langchain_anthropic"
   ]
  },
  {
@@ -110,7 +110,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain-openai"
+    "%pip install --quiet -U langgraph langgraph-prebuilt langchain-openai"
   ]
  },
  {
@@ -65,7 +65,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U pymongo langgraph langgraph-checkpoint-mongodb"
+    "%pip install -U pymongo langgraph langgraph-prebuilt langgraph-checkpoint-mongodb"
   ]
  },
  {
@@ -44,8 +44,7 @@
    "...\n",
    "```\n",
    "\n",
-    "!!! info \"Setup\"",
-    "\n",
+    "!!! info \"Setup\"\n",
    "    You need to run `.setup()` once on your checkpointer to initialize the database before you can use it."
   ]
  },
@@ -70,7 +69,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U psycopg psycopg-pool langgraph langgraph-checkpoint-postgres"
+    "%pip install -U psycopg psycopg-pool langgraph langgraph-prebuilt langgraph-checkpoint-postgres"
   ]
  },
  {
@@ -70,7 +70,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U redis langgraph langchain_openai"
+    "%pip install -U redis langgraph langgraph-prebuilt langchain_openai"
   ]
  },
  {
@@ -28,7 +28,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
   ]
  },
  {
@@ -81,7 +81,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_openai"
+    "%pip install --quiet -U langgraph langgraph-prebuilt langchain_openai"
   ]
  },
  {
@@ -34,7 +34,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai langchain-community"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai langchain-community"
   ]
  },
  {
@@ -93,7 +93,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
   ]
  },
  {
@@ -66,7 +66,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
+    "%pip install -U langgraph langgraph-prebuilt langchain-openai"
   ]
  },
  {
@@ -35,7 +35,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain_community langchain_anthropic langchain_experimental"
+    "%pip install -U langgraph langgraph-prebuilt langchain_community langchain_anthropic langchain_experimental"
   ]
  },
  {
@@ -49,7 +49,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain_community langchain_anthropic langchain_experimental"
+    "%pip install -U langgraph langgraph-prebuilt langchain_community langchain_anthropic langchain_experimental"
   ]
  },
  {
@@ -37,7 +37,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install -U langchain_community langchain_anthropic langchain_experimental matplotlib langgraph"
+    "%pip install -U langchain_community langchain_anthropic langchain_experimental matplotlib langgraph langgraph-prebuilt"
   ]
  },
  {
@@ -51,7 +51,7 @@
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain-community langchain-openai tavily-python"
+    "%pip install --quiet -U langgraph langgraph-prebuilt langchain-community langchain-openai tavily-python"
   ]
  },
  {
@@ -184,7 +184,7 @@ As noted in the [Anthropic blog](https://www.anthropic.com/research/building-eff

    See our lesson on Prompt Chaining [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/chain.ipynb).

-=== "Functional API (beta)"
+=== "Functional API"

    ```python
    from langgraph.func import entrypoint, task
@@ -335,7 +335,7 @@ With parallelization, LLMs work simultaneously on a task:

    See our lesson on parallelization [here](https://github.com/langchain-ai/langchain-academy/blob/main/module-1/simple-graph.ipynb).

-=== "Functional API (beta)"
+=== "Functional API"

    ```python
    @task
@@ -524,7 +524,7 @@ Routing classifies an input and directs it to a followup task. As noted in the [

    [Here](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/) is RAG workflow that routes questions. See our video [here](https://www.youtube.com/watch?v=bq1Plo2RhYI).

-=== "Functional API (beta)"
+=== "Functional API"

    ```python
    from typing_extensions import Literal
@@ -761,7 +761,7 @@ With orchestrator-worker, an orchestrator breaks down a task and delegates each
    [Here](https://github.com/langchain-ai/report-mAIstro) is a project that uses orchestrator-worker for report planning and writing. See our video [here](https://www.youtube.com/watch?v=wSxZ7yFbbas).


-=== "Functional API (beta)"
+=== "Functional API"

    ```python
    from typing import List
@@ -952,7 +952,7 @@ In the evaluator-optimizer workflow, one LLM call generates a response while ano

    [Here](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_adaptive_rag_local/) is a RAG workflow that grades answers for hallucinations or errors. See our video [here](https://www.youtube.com/watch?v=bq1Plo2RhYI).

-=== "Functional API (beta)"
+=== "Functional API"

    ```python
    # Schema for structured output to use in evaluation
@@ -1161,7 +1161,7 @@ llm_with_tools = llm.bind_tools(tools)

    [Here](https://github.com/langchain-ai/memory-agent) is a project that uses a tool calling agent to create / store long-term memories.

-=== "Functional API (beta)"
+=== "Functional API"

    ```python
    from langgraph.graph import add_messages
@@ -1270,4 +1270,4 @@ LangGraph provides several ways to stream workflow / agent outputs or intermedia

 ### Deployment

-LangGraph provides an easy on-ramp for deployment, observability, and evaluation. See [module 6](https://github.com/langchain-ai/langchain-academy/tree/main/module-6) of LangChain Academy.
+LangGraph provides an easy on-ramp for deployment, observability, and evaluation. See [module 6](https://github.com/langchain-ai/langchain-academy/tree/main/module-6) of LangChain Academy.
@@ -169,15 +169,15 @@ files = [

 [[package]]
 name = "anthropic"
-version = "0.45.2"
+version = "0.47.2"
 description = "The official Python library for the anthropic API"
 optional = false
 python-versions = ">=3.8"
 groups = ["test"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "anthropic-0.45.2-py3-none-any.whl", hash = "sha256:ecd746f7274451dfcb7e1180571ead624c7e1195d1d46cb7c70143d2aedb4d35"},
-    {file = "anthropic-0.45.2.tar.gz", hash = "sha256:32a18b9ecd12c91b2be4cae6ca2ab46a06937b5aa01b21308d97a6d29794fb5e"},
+    {file = "anthropic-0.47.2-py3-none-any.whl", hash = "sha256:61b712a56308fce69f04d92ba0230ab2bc187b5bce17811d400843a8976bb67f"},
+    {file = "anthropic-0.47.2.tar.gz", hash = "sha256:452f4ca0c56ffab8b6ce9928bf8470650f88106a7001b250895eb65c54cfa44c"},
 ]

 [package.dependencies]
@@ -1299,7 +1299,7 @@ version = "0.7.1"
 description = "XML bomb protection for Python stdlib modules"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-groups = ["docs", "test"]
+groups = ["docs"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
    {file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"},
@@ -3288,21 +3288,20 @@ together = ["langchain-together"]

 [[package]]
 name = "langchain-anthropic"
-version = "0.2.4"
+version = "0.3.8"
 description = "An integration package connecting AnthropicMessages and LangChain"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["test"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "langchain_anthropic-0.2.4-py3-none-any.whl", hash = "sha256:bcb6c2d0df4a67aff52816621079d6e743b260911caccf313a72b33b7edece6f"},
-    {file = "langchain_anthropic-0.2.4.tar.gz", hash = "sha256:0382d4c7b5236839b703f7b72b3e06de4bb5be99104b193f719adbe34c49562b"},
+    {file = "langchain_anthropic-0.3.8-py3-none-any.whl", hash = "sha256:05a70f51500d3c4e0f3e463730e193a25b6244e06b3bda3d7b2ec21d83d081ae"},
+    {file = "langchain_anthropic-0.3.8.tar.gz", hash = "sha256:1932977b8105744739ffdcb39861b041b73ae93846d0896a775fcea9a29e4b2b"},
 ]

 [package.dependencies]
-anthropic = ">=0.30.0,<1"
-defusedxml = ">=0.7.1,<0.8.0"
-langchain-core = ">=0.3.15,<0.4.0"
+anthropic = ">=0.47.0,<1"
+langchain-core = ">=0.3.39,<1.0.0"
 pydantic = ">=2.7.4,<3.0.0"

 [[package]]
@@ -3357,15 +3356,15 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"

 [[package]]
 name = "langchain-core"
-version = "0.3.34"
+version = "0.3.40"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["docs", "test"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "langchain_core-0.3.34-py3-none-any.whl", hash = "sha256:a057ebeddd2158d3be14bde341b25640ddf958b6989bd6e47160396f5a8202ae"},
-    {file = "langchain_core-0.3.34.tar.gz", hash = "sha256:26504cf1e8e6c310adad907b890d4e3c147581cfa7434114f6dc1134fe4bc6d3"},
+    {file = "langchain_core-0.3.40-py3-none-any.whl", hash = "sha256:9f31358741f10a13db8531e8288b8a5ae91904018c5c2e6f739d6645a98fca03"},
+    {file = "langchain_core-0.3.40.tar.gz", hash = "sha256:893a238b38491967c804662c1ec7c3e6ebaf223d1125331249c3cf3862ff2746"},
 ]

 [package.dependencies]
@@ -3474,19 +3473,19 @@ ollama = ">=0.4.4,<1"

 [[package]]
 name = "langchain-openai"
-version = "0.3.4"
+version = "0.3.7"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["test"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "langchain_openai-0.3.4-py3-none-any.whl", hash = "sha256:58d0c014620eb92f4f46ff9daf584c2a7794896b1379eb85ad7be8d9f3493b61"},
-    {file = "langchain_openai-0.3.4.tar.gz", hash = "sha256:c6645745a1d1bf19f21ea6fa473a746bd464053ff57ce563215e6165a0c4b9f1"},
+    {file = "langchain_openai-0.3.7-py3-none-any.whl", hash = "sha256:0aefc7bdf8e7398d41e09c4313cace816df6438f2aa93d34f79523487310f0da"},
+    {file = "langchain_openai-0.3.7.tar.gz", hash = "sha256:b8b51a3aaa1cc3bda060651ea41145f7728219e8a7150b5404fb1e8446de9cef"},
 ]

 [package.dependencies]
-langchain-core = ">=0.3.34,<1.0.0"
+langchain-core = ">=0.3.39,<1.0.0"
 openai = ">=1.58.1,<2.0.0"
 tiktoken = ">=0.7,<1"

@@ -3508,17 +3507,17 @@ langchain-core = ">=0.3.34,<1.0.0"

 [[package]]
 name = "langgraph"
-version = "0.2.71"
+version = "0.3.0"
 description = "Building stateful, multi-actor applications with LLMs"
 optional = false
 python-versions = ">=3.9.0,<4.0"
-groups = ["docs", "test"]
+groups = ["docs"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = []
 develop = true

 [package.dependencies]
-langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
+langchain-core = ">=0.1,<0.4"
 langgraph-checkpoint = "^2.0.10"
 langgraph-sdk = "^0.1.42"

@@ -3528,7 +3527,7 @@ url = "../libs/langgraph"

 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.13"
+version = "2.0.16"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -3547,26 +3546,25 @@ url = "../libs/checkpoint"

 [[package]]
 name = "langgraph-checkpoint-mongodb"
-version = "0.1.0"
+version = "0.1.1"
 description = "Library with a MongoDB implementation of LangGraph checkpoint saver."
 optional = false
-python-versions = "<4.0.0,>=3.9.0"
+python-versions = ">=3.9"
 groups = ["test"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "langgraph_checkpoint_mongodb-0.1.0-py3-none-any.whl", hash = "sha256:52f20956b36e0275ff805a1eea1db4c1a7e5e0ffe0a1ade65969004fa1654703"},
-    {file = "langgraph_checkpoint_mongodb-0.1.0.tar.gz", hash = "sha256:3165c134ad5c82a3fe02fef04c81dcd48a3f5d031e07a9d1cb84457241f76793"},
+    {file = "langgraph_checkpoint_mongodb-0.1.1-py3-none-any.whl", hash = "sha256:1ff2c3cb2a9139c38ea9cf398659b8b32d6bbfcc4999713b62014431477c5ac5"},
+    {file = "langgraph_checkpoint_mongodb-0.1.1.tar.gz", hash = "sha256:350d347b0458fb7977231ac1295095bef512458ee0debe09fd394d913b8d89d3"},
 ]

 [package.dependencies]
-langgraph = ">=0.2.38,<0.3.0"
-langgraph-checkpoint = ">=2.0.0,<3.0.0"
+langgraph-checkpoint = ">=2.0.0"
 motor = ">3.5.0"
-pymongo = ">=4.9.0,<4.10.0"
+pymongo = ">=4.9,<4.12"

 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.14"
+version = "2.0.15"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -3576,7 +3574,7 @@ files = []
 develop = true

 [package.dependencies]
-langgraph-checkpoint = "^2.0.10"
+langgraph-checkpoint = "^2.0.15"
 orjson = ">=3.10.1"
 psycopg = "^3.2.0"
 psycopg-pool = "^3.2.0"
@@ -3587,7 +3585,7 @@ url = "../libs/checkpoint-postgres"

 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "2.0.4"
+version = "2.0.5"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0"
@@ -3597,20 +3595,40 @@ files = []
 develop = true

 [package.dependencies]
-aiosqlite = "^0.20.0"
-langgraph-checkpoint = "^2.0.10"
+aiosqlite = ">=0.20,<0.22"
+langgraph-checkpoint = "^2.0.15"

 [package.source]
 type = "directory"
 url = "../libs/checkpoint-sqlite"

+[[package]]
+name = "langgraph-prebuilt"
+version = "1.0.0"
+description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
+optional = false
+python-versions = "^3.9.0,<4.0"
+groups = ["docs"]
+markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
+files = []
+develop = true
+
+[package.dependencies]
+langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
+langgraph = ">=0.3,<0.4"
+langgraph-checkpoint = "^2.0.10"
+
+[package.source]
+type = "directory"
+url = "../libs/prebuilt"
+
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.51"
+version = "0.1.53"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
-groups = ["docs", "test"]
+groups = ["docs"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = []
 develop = true
@@ -5939,7 +5957,6 @@ python-versions = ">=3.8"
 groups = ["test"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
    {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
 ]

@@ -5952,7 +5969,6 @@ python-versions = ">=3.8"
 groups = ["test"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
    {file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
 ]

@@ -8634,4 +8650,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.10"
-content-hash = "06debb82135affdb2baf1fdcc028c062c236121508d787588cd0de1db2da11e4"
+content-hash = "ac9af57c6abaddd1f181551a7bb8194ef3e4491391a0f2dc71417d68e85cb5b3"
@@ -13,6 +13,7 @@ hub = "^3.0.1"

 [tool.poetry.group.docs.dependencies]
 langgraph = { path = "../libs/langgraph/", develop = true }
+langgraph-prebuilt = {path = "../libs/prebuilt", develop = true}
 langgraph-checkpoint = { path = "../libs/checkpoint/", develop = true }
 langgraph-checkpoint-sqlite = { path = "../libs/checkpoint-sqlite", develop = true }
 langgraph-checkpoint-postgres = { path = "../libs/checkpoint-postgres", develop = true }
@@ -40,8 +41,8 @@ langchain-cohere = "^0.4.2"

 [tool.poetry.group.test.dependencies]
 langchain = "^0.3.8"
-langchain-openai = "^0.3.0"
-langchain-anthropic = "^0.2.1"
+langchain-openai = "^0.3.7"
+langchain-anthropic = "^0.3.8"
 langchain-nomic = "^0.1.3"
 langchain-fireworks = "^0.2.0"
 langchain-community = "^0.3.0"
@@ -1,23 +1,23 @@
-# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.

 [[package]]
 name = "aiosqlite"
-version = "0.20.0"
+version = "0.21.0"
 description = "asyncio bridge to the standard sqlite3 module"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"},
-    {file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"},
+    {file = "aiosqlite-0.21.0-py3-none-any.whl", hash = "sha256:2549cf4057f95f53dcba16f2b64e8e2791d7e1adedb13197dd8ed77bb226d7d0"},
+    {file = "aiosqlite-0.21.0.tar.gz", hash = "sha256:131bb8056daa3bc875608c631c678cda73922a2d4ba8aec373b19f18c17e7aa3"},
 ]

 [package.dependencies]
 typing_extensions = ">=4.0"

 [package.extras]
-dev = ["attribution (==1.7.0)", "black (==24.2.0)", "coverage[toml] (==7.4.1)", "flake8 (==7.0.0)", "flake8-bugbear (==24.2.6)", "flit (==3.9.0)", "mypy (==1.8.0)", "ufmt (==2.3.0)", "usort (==1.0.8.post1)"]
-docs = ["sphinx (==7.2.6)", "sphinx-mdinclude (==0.5.3)"]
+dev = ["attribution (==1.7.1)", "black (==24.3.0)", "build (>=1.2)", "coverage[toml] (==7.6.10)", "flake8 (==7.0.0)", "flake8-bugbear (==24.12.12)", "flit (==3.10.1)", "mypy (==1.14.1)", "ufmt (==2.5.1)", "usort (==1.0.8.post1)"]
+docs = ["sphinx (==8.1.3)", "sphinx-mdinclude (==0.6.1)"]

 [[package]]
 name = "annotated-types"
@@ -1043,4 +1043,4 @@ watchmedo = ["PyYAML (>=3.10)"]
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.9.0"
-content-hash = "e6d3ca9bce723c05f4c5ae9dc4bee872f7581b7763680b34112f1d280f5a9b0a"
+content-hash = "21896b8d3d283d95bc3988aa93f06faf5c47dadc2a8822e5a35672b9cb054693"
@@ -11,7 +11,7 @@ packages = [{ include = "langgraph" }]
 [tool.poetry.dependencies]
 python = "^3.9.0"
 langgraph-checkpoint = "^2.0.15"
-aiosqlite = "^0.20.0"
+aiosqlite = ">=0.20,<0.22"

 [tool.poetry.group.dev.dependencies]
 ruff = "^0.6.2"
@@ -70,7 +70,7 @@ pip install -U langgraph
 Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!

 ```shell
-pip install langchain-anthropic
+pip install langgraph-prebuilt langchain-anthropic
 ```

 ```shell
@@ -1,134 +0,0 @@
-from typing import Any, Callable, Sequence, Union
-
-from langchain_core.load.serializable import Serializable
-from langchain_core.runnables import RunnableConfig
-from langchain_core.tools import BaseTool
-from langchain_core.tools import tool as create_tool
-
-from langgraph._api.deprecation import deprecated
-from langgraph.utils.runnable import RunnableCallable
-
-INVALID_TOOL_MSG_TEMPLATE = (
-    "{requested_tool_name} is not a valid tool, "
-    "try one of [{available_tool_names_str}]."
-)
-
-
-@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
-class ToolInvocationInterface:
-    """Interface for invoking a tool.
-
-    Attributes:
-        tool (str): The name of the tool to invoke.
-        tool_input (Union[str, dict]): The input to pass to the tool.
-
-    """
-
-    tool: str
-    tool_input: Union[str, dict]
-
-
-@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
-class ToolInvocation(Serializable):
-    """Information about how to invoke a tool.
-
-    Attributes:
-        tool (str): The name of the Tool to execute.
-        tool_input (Union[str, dict]): The input to pass in to the Tool.
-
-    Examples:
-        Basic usage:
-        ```pycon
-        >>> invocation = ToolInvocation(
-        ...    tool="search",
-        ...     tool_input="What is the capital of France?"
-        ... )
-        ```
-    """
-
-    tool: str
-    tool_input: Union[str, dict]
-
-
-@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0")
-class ToolExecutor(RunnableCallable):
-    """Executes a tool invocation.
-
-    Args:
-        tools (Sequence[BaseTool]): A sequence of tools that can be invoked.
-        invalid_tool_msg_template (str, optional): The template for the error message
-            when an invalid tool is requested. Defaults to INVALID_TOOL_MSG_TEMPLATE.
-
-    Examples:
-        Basic usage:
-
-        ```pycon
-        >>> from langchain_core.tools import tool
-        >>> from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation
-        ...
-        ...
-        >>> @tool
-        ... def search(query: str) -> str:
-        ...     \"\"\"Search engine.\"\"\"
-        ...     return f"Searching for: {query}"
-        ...
-        ...
-        >>> tools = [search]
-        >>> executor = ToolExecutor(tools)
-        ...
-        >>> invocation = ToolInvocation(tool="search", tool_input="What is the capital of France?")
-        >>> result = executor.invoke(invocation)
-        >>> print(result)
-        "Searching for: What is the capital of France?"
-        ```
-        Handling invalid tool:
-
-        ```pycon
-        >>> invocation = ToolInvocation(
-        ...     tool="nonexistent", tool_input="What is the capital of France?"
-        ... )
-        >>> result = executor.invoke(invocation)
-        >>> print(result)
-        "nonexistent is not a valid tool, try one of [search]."
-        ```
-    """
-
-    def __init__(
-        self,
-        tools: Sequence[Union[BaseTool, Callable]],
-        *,
-        invalid_tool_msg_template: str = INVALID_TOOL_MSG_TEMPLATE,
-    ) -> None:
-        super().__init__(self._execute, afunc=self._aexecute, trace=False)
-        tools_ = [
-            tool if isinstance(tool, BaseTool) else create_tool(tool) for tool in tools
-        ]
-        self.tools = tools_
-        self.tool_map = {t.name: t for t in tools_}
-        self.invalid_tool_msg_template = invalid_tool_msg_template
-
-    def _execute(
-        self, tool_invocation: ToolInvocationInterface, config: RunnableConfig
-    ) -> Any:
-        if tool_invocation.tool not in self.tool_map:
-            return self.invalid_tool_msg_template.format(
-                requested_tool_name=tool_invocation.tool,
-                available_tool_names_str=", ".join([t.name for t in self.tools]),
-            )
-        else:
-            tool = self.tool_map[tool_invocation.tool]
-            output = tool.invoke(tool_invocation.tool_input, config)
-            return output
-
-    async def _aexecute(
-        self, tool_invocation: ToolInvocationInterface, config: RunnableConfig
-    ) -> Any:
-        if tool_invocation.tool not in self.tool_map:
-            return self.invalid_tool_msg_template.format(
-                requested_tool_name=tool_invocation.tool,
-                available_tool_names_str=", ".join([t.name for t in self.tools]),
-            )
-        else:
-            tool = self.tool_map[tool_invocation.tool]
-            output = await tool.ainvoke(tool_invocation.tool_input, config)
-            return output
@@ -971,6 +971,12 @@ class Pregel(PregelProtocol):
                raise ValueError(f"Subgraph {recast} not found")

        config = merge_configs(self.config, config) if self.config else config
+        if self.checkpointer is True:
+            ns = cast(str, config[CONF][CONFIG_KEY_CHECKPOINT_NS])
+            config = merge_configs(
+                config, {CONF: {CONFIG_KEY_CHECKPOINT_NS: recast_checkpoint_ns(ns)}}
+            )
+
        saved = checkpointer.get_tuple(config)
        return self._prepare_state_snapshot(
            config,
@@ -1004,6 +1010,12 @@ class Pregel(PregelProtocol):
                raise ValueError(f"Subgraph {recast} not found")

        config = merge_configs(self.config, config) if self.config else config
+        if self.checkpointer is True:
+            ns = cast(str, config[CONF][CONFIG_KEY_CHECKPOINT_NS])
+            config = merge_configs(
+                config, {CONF: {CONFIG_KEY_CHECKPOINT_NS: recast_checkpoint_ns(ns)}}
+            )
+
        saved = await checkpointer.aget_tuple(config)
        return await self._aprepare_state_snapshot(
            config,
@@ -1911,9 +1923,7 @@ class Pregel(PregelProtocol):
            # set up subgraph checkpointing
            if self.checkpointer is True:
                ns = cast(str, config[CONF][CONFIG_KEY_CHECKPOINT_NS])
-                config[CONF][CONFIG_KEY_CHECKPOINT_NS] = NS_SEP.join(
-                    part.split(NS_END)[0] for part in ns.split(NS_SEP)
-                )
+                config[CONF][CONFIG_KEY_CHECKPOINT_NS] = recast_checkpoint_ns(ns)
            # set up messages stream mode
            if "messages" in stream_modes:
                run_manager.inheritable_handlers.append(
@@ -2201,9 +2211,7 @@ class Pregel(PregelProtocol):
            # set up subgraph checkpointing
            if self.checkpointer is True:
                ns = cast(str, config[CONF][CONFIG_KEY_CHECKPOINT_NS])
-                config[CONF][CONFIG_KEY_CHECKPOINT_NS] = NS_SEP.join(
-                    part.split(NS_END)[0] for part in ns.split(NS_SEP)
-                )
+                config[CONF][CONFIG_KEY_CHECKPOINT_NS] = recast_checkpoint_ns(ns)
            # set up messages stream mode
            if "messages" in stream_modes:
                run_manager.inheritable_handlers.append(
@@ -127,6 +127,16 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
                tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP)),
                metadata,
            )
+            if isinstance(inputs, dict):
+                for key, value in inputs.items():
+                    if isinstance(value, BaseMessage):
+                        if value.id is not None:
+                            self.seen.add(value.id)
+                    elif isinstance(value, Sequence) and not isinstance(value, str):
+                        for item in value:
+                            if isinstance(item, BaseMessage):
+                                if item.id is not None:
+                                    self.seen.add(item.id)

    def on_chain_end(
        self,
@@ -32,11 +32,17 @@ def validate_graph(

    for chan in subscribed_channels:
        if chan not in channels:
-            raise ValueError(f"Subscribed channel '{chan}' not in 'channels'")
+            raise ValueError(
+                f"Subscribed channel '{chan}' not "
+                f"in known channels: '{repr(sorted(channels))[:100]}'"
+            )

    if isinstance(input_channels, str):
        if input_channels not in channels:
-            raise ValueError(f"Input channel '{input_channels}' not in 'channels'")
+            raise ValueError(
+                f"Input channel '{input_channels}' not "
+                f"in known channels: '{repr(sorted(channels))[:100]}'"
+            )
        if input_channels not in subscribed_channels:
            raise ValueError(
                f"Input channel {input_channels} is not subscribed to by any node"
@@ -44,10 +50,13 @@ def validate_graph(
    else:
        for chan in input_channels:
            if chan not in channels:
-                raise ValueError(f"Input channel '{chan}' not in 'channels'")
+                raise ValueError(
+                    f"Input channel '{chan}' not in '{repr(sorted(channels))[:100]}'"
+                )
        if all(chan not in subscribed_channels for chan in input_channels):
            raise ValueError(
-                f"None of the input channels {input_channels} are subscribed to by any node"
+                f"None of the input channels {input_channels} "
+                f"are subscribed to by any node"
            )

    all_output_channels = set[str]()
@@ -62,7 +71,10 @@ def validate_graph(

    for chan in all_output_channels:
        if chan not in channels:
-            raise ValueError(f"Output channel '{chan}' not in 'channels'")
+            raise ValueError(
+                f"Output channel '{chan}' not "
+                f"in known channels: '{repr(sorted(channels))[:100]}'"
+            )

    if interrupt_after_nodes != "*":
        for n in interrupt_after_nodes:
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.

 [[package]]
 name = "aiosqlite"
@@ -1348,7 +1348,7 @@ typing-extensions = ">=4.7"

 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.10"
+version = "2.0.16"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1366,7 +1366,7 @@ url = "../checkpoint"

 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.12"
+version = "2.0.15"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -1375,7 +1375,7 @@ files = []
 develop = true

 [package.dependencies]
-langgraph-checkpoint = "^2.0.10"
+langgraph-checkpoint = "^2.0.15"
 orjson = ">=3.10.1"
 psycopg = "^3.2.0"
 psycopg-pool = "^3.2.0"
@@ -1386,7 +1386,7 @@ url = "../checkpoint-postgres"

 [[package]]
 name = "langgraph-checkpoint-sqlite"
-version = "2.0.3"
+version = "2.0.5"
 description = "Library with a SQLite implementation of LangGraph checkpoint saver."
 optional = false
 python-versions = "^3.9.0"
@@ -1395,16 +1395,35 @@ files = []
 develop = true

 [package.dependencies]
-aiosqlite = "^0.20.0"
-langgraph-checkpoint = "^2.0.10"
+aiosqlite = ">=0.20,<0.22"
+langgraph-checkpoint = "^2.0.15"

 [package.source]
 type = "directory"
 url = "../checkpoint-sqlite"

+[[package]]
+name = "langgraph-prebuilt"
+version = "0.1.0"
+description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
+optional = false
+python-versions = "^3.9.0,<4.0"
+groups = ["dev"]
+files = []
+develop = true
+
+[package.dependencies]
+langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
+langgraph = ">=0.3,<0.4"
+langgraph-checkpoint = "^2.0.10"
+
+[package.source]
+type = "directory"
+url = "../prebuilt"
+
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.51"
+version = "0.1.53"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
@@ -3491,4 +3510,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9.0,<4.0"
-content-hash = "caf943b02b6913c05d15c37fda6d216669f789e2a059b7e8e2490b2bdcd23e0e"
+content-hash = "967adb1fe0f48b76a53b79c8604768b2ce74042f88d1791f57ea1ed0a0ede0ad"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.2.74"
+version = "0.3.0"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"
@@ -9,7 +9,7 @@ repository = "https://www.github.com/langchain-ai/langgraph"

 [tool.poetry.dependencies]
 python = ">=3.9.0,<4.0"
-langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
+langchain-core = ">=0.1,<0.4"
 langgraph-checkpoint = "^2.0.10"
 langgraph-sdk = "^0.1.42"

@@ -26,6 +26,7 @@ ruff = "^0.6.2"
 jupyter = "^1.0.0"
 pytest-xdist = {extras = ["psutil"], version = "^3.6.1"}
 pytest-repeat = "^0.9.3"
+langgraph-prebuilt = {path = "../prebuilt", develop = true}
 langgraph-checkpoint = {path = "../checkpoint", develop = true}
 langgraph-checkpoint-sqlite = {path = "../checkpoint-sqlite", develop = true}
 langgraph-checkpoint-postgres = {path = "../checkpoint-postgres", develop = true}
@@ -3371,6 +3371,59 @@ def test_subgraph_checkpoint_true(
    ]


+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_subgraph_checkpoint_true_interrupt(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    checkpointer = request.getfixturevalue("checkpointer_" + checkpointer_name)
+
+    # Define subgraph
+    class SubgraphState(TypedDict):
+        # note that none of these keys are shared with the parent graph state
+        bar: str
+        baz: str
+
+    def subgraph_node_1(state: SubgraphState):
+        baz_value = interrupt("Provide baz value")
+        return {"baz": baz_value}
+
+    def subgraph_node_2(state: SubgraphState):
+        return {"bar": state["bar"] + state["baz"]}
+
+    subgraph_builder = StateGraph(SubgraphState)
+    subgraph_builder.add_node(subgraph_node_1)
+    subgraph_builder.add_node(subgraph_node_2)
+    subgraph_builder.add_edge(START, "subgraph_node_1")
+    subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2")
+    subgraph = subgraph_builder.compile(checkpointer=True)
+
+    class ParentState(TypedDict):
+        foo: str
+
+    def node_1(state: ParentState):
+        return {"foo": "hi! " + state["foo"]}
+
+    def node_2(state: ParentState):
+        response = subgraph.invoke({"bar": state["foo"]})
+        return {"foo": response["bar"]}
+
+    builder = StateGraph(ParentState)
+    builder.add_node("node_1", node_1)
+    builder.add_node("node_2", node_2)
+    builder.add_edge(START, "node_1")
+    builder.add_edge("node_1", "node_2")
+
+    checkpointer = MemorySaver()
+    graph = builder.compile(checkpointer=checkpointer)
+    config = {"configurable": {"thread_id": "1"}}
+
+    assert graph.invoke({"foo": "foo"}, config) == {"foo": "hi! foo"}
+    assert graph.get_state(config, subgraphs=True).tasks[0].state.values == {
+        "bar": "hi! foo"
+    }
+    assert graph.invoke(Command(resume="baz"), config) == {"foo": "hi! foobaz"}
+
+
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
 def test_stream_subgraphs_during_execution(
    request: pytest.FixtureRequest, checkpointer_name: str
@@ -6588,3 +6641,101 @@ def test_get_stream_writer() -> None:
            },
        ),
    ]
+
+
+def test_stream_messages_dedupe_inputs() -> None:
+    from langchain_core.messages import AIMessage
+
+    def call_model(state):
+        return {"messages": AIMessage("hi", id="1")}
+
+    def route(state):
+        return Command(goto="node_2", graph=Command.PARENT)
+
+    subgraph = (
+        StateGraph(MessagesState)
+        .add_node(call_model)
+        .add_node(route)
+        .add_edge(START, "call_model")
+        .add_edge("call_model", "route")
+        .compile()
+    )
+
+    graph = (
+        StateGraph(MessagesState)
+        .add_node("node_1", subgraph)
+        .add_node("node_2", lambda state: state)
+        .add_edge(START, "node_1")
+        .compile()
+    )
+
+    chunks = [
+        chunk
+        for ns, chunk in graph.stream(
+            {"messages": "hi"}, stream_mode="messages", subgraphs=True
+        )
+    ]
+
+    assert len(chunks) == 1
+    assert chunks[0][0] == AIMessage("hi", id="1")
+    assert chunks[0][1]["langgraph_node"] == "call_model"
+
+
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
+def test_stream_messages_dedupe_state(
+    request: pytest.FixtureRequest, checkpointer_name: str
+) -> None:
+    from langchain_core.messages import AIMessage
+
+    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
+    to_emit = [AIMessage("bye", id="1"), AIMessage("bye again", id="2")]
+
+    def call_model(state):
+        return {"messages": to_emit.pop(0)}
+
+    def route(state):
+        return Command(goto="node_2", graph=Command.PARENT)
+
+    subgraph = (
+        StateGraph(MessagesState)
+        .add_node(call_model)
+        .add_node(route)
+        .add_edge(START, "call_model")
+        .add_edge("call_model", "route")
+        .compile()
+    )
+
+    graph = (
+        StateGraph(MessagesState)
+        .add_node("node_1", subgraph)
+        .add_node("node_2", lambda state: state)
+        .add_edge(START, "node_1")
+        .compile(checkpointer=checkpointer)
+    )
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+
+    chunks = [
+        chunk
+        for ns, chunk in graph.stream(
+            {"messages": "hi"}, thread1, stream_mode="messages", subgraphs=True
+        )
+    ]
+
+    assert len(chunks) == 1
+    assert chunks[0][0] == AIMessage("bye", id="1")
+    assert chunks[0][1]["langgraph_node"] == "call_model"
+
+    chunks = [
+        chunk
+        for ns, chunk in graph.stream(
+            {"messages": "hi again"},
+            thread1,
+            stream_mode="messages",
+            subgraphs=True,
+        )
+    ]
+
+    assert len(chunks) == 1
+    assert chunks[0][0] == AIMessage("bye again", id="2")
+    assert chunks[0][1]["langgraph_node"] == "call_model"
@@ -7282,9 +7282,7 @@ async def test_multiple_subgraphs_mixed_state_graph(

@NEEDS_CONTEXTVARS
@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
-async def test_multiple_subgraphs_checkpointer(
-    request: pytest.FixtureRequest, checkpointer_name: str
-) -> None:
+async def test_multiple_subgraphs_checkpointer(checkpointer_name: str) -> None:
    async with awith_checkpointer(checkpointer_name) as checkpointer:

        class SubgraphState(TypedDict):
@@ -7513,3 +7511,99 @@ async def test_tags_stream_mode_messages() -> None:
            },
        )
    ]
+
+
+async def test_stream_messages_dedupe_inputs() -> None:
+    from langchain_core.messages import AIMessage
+
+    async def call_model(state):
+        return {"messages": AIMessage("hi", id="1")}
+
+    async def route(state):
+        return Command(goto="node_2", graph=Command.PARENT)
+
+    subgraph = (
+        StateGraph(MessagesState)
+        .add_node(call_model)
+        .add_node(route)
+        .add_edge(START, "call_model")
+        .add_edge("call_model", "route")
+        .compile()
+    )
+
+    graph = (
+        StateGraph(MessagesState)
+        .add_node("node_1", subgraph)
+        .add_node("node_2", lambda state: state)
+        .add_edge(START, "node_1")
+        .compile()
+    )
+
+    chunks = [
+        chunk
+        async for ns, chunk in graph.astream(
+            {"messages": "hi"}, stream_mode="messages", subgraphs=True
+        )
+    ]
+
+    assert len(chunks) == 1
+    assert chunks[0][0] == AIMessage("hi", id="1")
+    assert chunks[0][1]["langgraph_node"] == "call_model"
+
+
+@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
+async def test_stream_messages_dedupe_state(checkpointer_name: str) -> None:
+    async with awith_checkpointer(checkpointer_name) as checkpointer:
+        from langchain_core.messages import AIMessage
+
+        to_emit = [AIMessage("bye", id="1"), AIMessage("bye again", id="2")]
+
+        async def call_model(state):
+            return {"messages": to_emit.pop(0)}
+
+        async def route(state):
+            return Command(goto="node_2", graph=Command.PARENT)
+
+        subgraph = (
+            StateGraph(MessagesState)
+            .add_node(call_model)
+            .add_node(route)
+            .add_edge(START, "call_model")
+            .add_edge("call_model", "route")
+            .compile()
+        )
+
+        graph = (
+            StateGraph(MessagesState)
+            .add_node("node_1", subgraph)
+            .add_node("node_2", lambda state: state)
+            .add_edge(START, "node_1")
+            .compile(checkpointer=checkpointer)
+        )
+
+        thread1 = {"configurable": {"thread_id": "1"}}
+
+        chunks = [
+            chunk
+            async for ns, chunk in graph.astream(
+                {"messages": "hi"}, thread1, stream_mode="messages", subgraphs=True
+            )
+        ]
+
+        assert len(chunks) == 1
+        assert chunks[0][0] == AIMessage("bye", id="1")
+        assert chunks[0][1]["langgraph_node"] == "call_model"
+
+        chunks = [
+            chunk
+            async for ns, chunk in graph.astream(
+                {"messages": "hi again"},
+                thread1,
+                stream_mode="messages",
+                subgraphs=True,
+            )
+        ]
+
+        assert len(chunks) == 1
+        assert chunks[0][0] == AIMessage("bye again", id="2")
+        assert chunks[0][1]["langgraph_node"] == "call_model"
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
@@ -0,0 +1,78 @@
+.PHONY: all format lint test test_watch integration_tests spell_check spell_fix benchmark profile
+
+# Default target executed when no arguments are given to make.
+all: help
+
+######################
+# TESTING AND COVERAGE
+######################
+
+start-postgres:
+	docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait --remove-orphans
+
+stop-postgres:
+	docker compose -f tests/compose-postgres.yml down -v
+
+TEST ?= .
+
+test:
+	make start-postgres && poetry run pytest $(TEST); \
+	EXIT_CODE=$$?; \
+	make stop-postgres; \
+	exit $$EXIT_CODE
+
+test_watch:
+	make start-postgres && poetry run ptw $(TEST); \
+	EXIT_CODE=$$?; \
+	make stop-postgres; \
+	exit $$EXIT_CODE
+
+######################
+# LINTING AND FORMATTING
+######################
+
+# Define a variable for Python and notebook files.
+PYTHON_FILES=.
+MYPY_CACHE=.mypy_cache
+lint format: PYTHON_FILES=.
+lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=langgraph
+lint_tests: PYTHON_FILES=tests
+lint_tests: MYPY_CACHE=.mypy_cache_test
+
+lint lint_diff lint_package lint_tests:
+	poetry run ruff check .
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE)
+	[ "$(PYTHON_FILES)" = "" ] || poetry run mypy langgraph --cache-dir $(MYPY_CACHE)
+
+format format_diff:
+	poetry run ruff format $(PYTHON_FILES)
+	poetry run ruff check --select I --fix $(PYTHON_FILES)
+
+spell_check:
+	poetry run codespell --toml pyproject.toml
+
+spell_fix:
+	poetry run codespell --toml pyproject.toml -w
+
+
+######################
+# HELP
+######################
+
+help:
+	@echo '===================='
+	@echo '-- DOCUMENTATION --'
+	
+	@echo '-- LINTING --'
+	@echo 'format                       - run code formatters'
+	@echo 'lint                         - run linters'
+	@echo 'spell_check               	- run codespell on the project'
+	@echo 'spell_fix               		- run codespell on the project and fix the errors'
+	@echo '-- TESTS --'
+	@echo 'coverage                     - run unit tests and generate coverage report'
+	@echo 'test                         - run unit tests'
+	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
+	@echo 'test_watch                   - run unit tests in watch mode'
@@ -0,0 +1,117 @@
+# LangGraph Prebuilt
+
+This library defines high-level APIs for creating and executing LangGraph agents and tools.
+
+> [!IMPORTANT]
+> This library is meant to be bundled with `langgraph`, don't install it directly
+
+## Agents
+
+`langgraph-prebuilt` provides an [implementation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) of a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent - `create_react_agent`:
+
+```bash
+pip install langchain-anthropic
+```
+
+```python
+from langchain_anthropic import ChatAnthropic
+from langgraph.prebuilt import create_react_agent
+
+# Define the tools for the agent to use
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
+
+tools = [search]
+model = ChatAnthropic(model="claude-3-7-sonnet-latest")
+
+app = create_react_agent(model, tools)
+# run the agent
+app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+)
+```
+
+## Tools
+
+### ToolNode
+
+`langgraph-prebuilt` provides an [implementation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.ToolNode) of a node that executes tool calls - `ToolNode`:
+
+```python
+from langgraph.prebuilt import ToolNode
+from langchain_core.messages import AIMessage
+
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
+
+tool_node = ToolNode([search])
+tool_calls = [{"name": "search", "args": {"query": "what is the weather in sf"}, "id": "1"}]
+ai_message = AIMessage(content="", tool_calls=tool_calls)
+# execute tool call
+tool_node.invoke({"messages": [ai_message]})
+```
+
+### ValidationNode
+
+`langgraph-prebuilt` provides an [implementation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_validator.ValidationNode) of a node that validates tool calls against a pydantic schema - `ValidationNode`:
+
+```python
+from pydantic import BaseModel, field_validator
+from langgraph.prebuilt import ValidationNode
+from langchain_core.messages import AIMessage
+
+
+class SelectNumber(BaseModel):
+    a: int
+
+    @field_validator("a")
+    def a_must_be_meaningful(cls, v):
+        if v != 37:
+            raise ValueError("Only 37 is allowed")
+        return v
+
+validation_node = ValidationNode([SelectNumber])
+validation_node.invoke({
+    "messages": [AIMessage("", tool_calls=[{"name": "SelectNumber", "args": {"a": 42}, "id": "1"}])]
+})
+```
+
+## Agent Inbox
+
+The library contains schemas for using the [Agent Inbox](https://github.com/langchain-ai/agent-inbox) with LangGraph agents. Learn more about how to use Agent Inbox [here](https://github.com/langchain-ai/agent-inbox#interrupts).
+
+```python
+from langgraph.types import interrupt
+from langgraph.prebuilt.interrupt import HumanInterrupt, HumanResponse
+
+def my_graph_function():
+    # Extract the last tool call from the `messages` field in the state
+    tool_call = state["messages"][-1].tool_calls[0]
+    # Create an interrupt
+    request: HumanInterrupt = {
+        "action_request": {
+            "action": tool_call['name'],
+            "args": tool_call['args']
+        },
+        "config": {
+            "allow_ignore": True,
+            "allow_respond": True,
+            "allow_edit": False,
+            "allow_accept": False
+        },
+        "description": _generate_email_markdown(state) # Generate a detailed markdown description.
+    }
+    # Send the interrupt request inside a list, and extract the first response
+    response = interrupt([request])[0]
+    if response['type'] == "response":
+        # Do something with the response
+    ...
+```
@@ -1,7 +1,6 @@
 """langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools."""

 from langgraph.prebuilt.chat_agent_executor import create_react_agent
-from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation
 from langgraph.prebuilt.tool_node import (
    InjectedState,
    InjectedStore,
@@ -12,8 +11,6 @@ from langgraph.prebuilt.tool_validator import ValidationNode

 __all__ = [
    "create_react_agent",
-    "ToolExecutor",
-    "ToolInvocation",
    "ToolNode",
    "tools_condition",
    "ValidationNode",
@@ -32,7 +32,6 @@ from langgraph.graph import END, StateGraph
 from langgraph.graph.graph import CompiledGraph
 from langgraph.graph.message import add_messages
 from langgraph.managed import IsLastStep, RemainingSteps
-from langgraph.prebuilt.tool_executor import ToolExecutor
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.store.base import BaseStore
 from langgraph.types import Checkpointer, Send
@@ -68,13 +67,6 @@ StateSchemaType = Type[StateSchema]

 PROMPT_RUNNABLE_NAME = "Prompt"

-MessagesModifier = Union[
-    SystemMessage,
-    str,
-    Callable[[Sequence[BaseMessage]], LanguageModelInput],
-    Runnable[Sequence[BaseMessage], LanguageModelInput],
-]
-
 Prompt = Union[
    SystemMessage,
    str,
@@ -119,43 +111,20 @@ def _get_prompt_runnable(prompt: Optional[Prompt]) -> Runnable:
    return prompt_runnable


-def _convert_messages_modifier_to_prompt(
-    messages_modifier: MessagesModifier,
-) -> Prompt:
-    prompt: Prompt
-    if isinstance(messages_modifier, (str, SystemMessage)):
-        return messages_modifier
-    elif callable(messages_modifier):
-
-        def prompt(state: AgentState) -> Sequence[BaseMessage]:
-            return messages_modifier(state["messages"])
-
-        return prompt
-    elif isinstance(messages_modifier, Runnable):
-        prompt = (lambda state: state["messages"]) | messages_modifier
-        return prompt
-    raise ValueError(
-        f"Got unexpected type for `messages_modifier`: {type(messages_modifier)}"
-    )
-
-
 def _convert_modifier_to_prompt(func: F) -> F:
-    """Decorator that converts state_modifier/messages_modifier kwargs to prompt kwarg."""
+    """Decorator that converts state_modifier kwarg to prompt kwarg."""

    @functools.wraps(func)
    def wrapper(*args: Any, **kwargs: Any) -> Any:
        prompt = kwargs.get("prompt")
        state_modifier = kwargs.pop("state_modifier", None)
-        messages_modifier = kwargs.pop("messages_modifier", None)
-        if sum(p is not None for p in (prompt, state_modifier, messages_modifier)) > 1:
+        if sum(p is not None for p in (prompt, state_modifier)) > 1:
            raise ValueError(
-                "Expected only one of prompt, state_modifier, or messages_modifier, got multiple values"
+                "Expected only one of (prompt, state_modifier), got multiple values"
            )

        if state_modifier is not None:
            prompt = state_modifier
-        elif messages_modifier is not None:
-            prompt = _convert_messages_modifier_to_prompt(messages_modifier)

        kwargs["prompt"] = prompt
        return func(*args, **kwargs)
@@ -244,7 +213,7 @@ def _validate_chat_history(
@_convert_modifier_to_prompt
 def create_react_agent(
    model: Union[str, LanguageModelLike],
-    tools: Union[ToolExecutor, Sequence[BaseTool], ToolNode],
+    tools: Union[Sequence[BaseTool], ToolNode],
    *,
    prompt: Optional[Prompt] = None,
    response_format: Optional[
@@ -264,7 +233,7 @@ def create_react_agent(

    Args:
        model: The `LangChain` chat model that supports tool calling.
-        tools: A list of tools, a ToolExecutor, or a ToolNode instance.
+        tools: A list of tools or a ToolNode instance.
            If an empty list is provided, the agent will consist of a single LLM node without tool calling.
        prompt: An optional prompt for the LLM. Can take a few different forms:

@@ -273,8 +242,6 @@ def create_react_agent(
            - Callable: This function should take in full graph state and the output is then passed to the language model.
            - Runnable: This runnable should take in full graph state and the output is then passed to the language model.

-            !!! Note
-                Prior to `v0.2.68`, the prompt was set using `state_modifier` / `messages_modifier` parameters.
        response_format: An optional schema for the final agent output.

            If provided, output will be formatted to match the given schema and returned in the 'structured_response' state key.
@@ -617,10 +584,7 @@ def create_react_agent(
            else AgentState
        )

-    if isinstance(tools, ToolExecutor):
-        tool_classes: Sequence[BaseTool] = tools.tools
-        tool_node = ToolNode(tool_classes)
-    elif isinstance(tools, ToolNode):
+    if isinstance(tools, ToolNode):
        tool_classes = list(tools.tools_by_name.values())
        tool_node = tools
    else:
@@ -29,6 +29,7 @@ from langchain_core.runnables import (
 )
 from langchain_core.runnables.config import get_executor_for_config
 from langchain_core.tools import BaseTool, create_schema_from_function
+from langchain_core.utils.pydantic import is_basemodel_subclass
 from pydantic import BaseModel, ValidationError
 from pydantic.v1 import BaseModel as BaseModelV1
 from pydantic.v1 import ValidationError as ValidationErrorV1
@@ -78,7 +79,7 @@ class ValidationNode(RunnableCallable):
        >>> from typing_extensions import TypedDict
        ...
        >>> from langchain_anthropic import ChatAnthropic
-        >>> from pydantic import BaseModel, validator
+        >>> from pydantic import BaseModel, field_validator
        ...
        >>> from langgraph.graph import END, START, StateGraph
        >>> from langgraph.prebuilt import ValidationNode
@@ -88,18 +89,15 @@ class ValidationNode(RunnableCallable):
        >>> class SelectNumber(BaseModel):
        ...     a: int
        ...
-        ...     @validator("a")
+        ...     @field_validator("a")
        ...     def a_must_be_meaningful(cls, v):
        ...         if v != 37:
        ...             raise ValueError("Only 37 is allowed")
        ...         return v
        ...
        ...
-        >>> class State(TypedDict):
-        ...     messages: Annotated[list, add_messages]
-        ...
-        >>> builder = StateGraph(State)
-        >>> llm = ChatAnthropic(model="claude-3-haiku-20240307").bind_tools([SelectNumber])
+        >>> builder = StateGraph(Annotated[list, add_messages])
+        >>> llm = ChatAnthropic(model="claude-3-5-haiku-latest").bind_tools([SelectNumber])
        >>> builder.add_node("model", llm)
        >>> builder.add_node("validation", ValidationNode([SelectNumber]))
        >>> builder.add_edge(START, "model")
@@ -177,6 +175,13 @@ class ValidationNode(RunnableCallable):
                    raise ValueError(
                        f"Tool {schema.name} does not have an args_schema defined."
                    )
+                elif not isinstance(
+                    schema.args_schema, type
+                ) or not is_basemodel_subclass(schema.args_schema):
+                    raise ValueError(
+                        "Validation node only works with tools that have a pydantic BaseModel args_schema. "
+                        f"Got {schema.name} with args_schema: {schema.args_schema}."
+                    )
                self.schemas_by_name[schema.name] = schema.args_schema
            elif isinstance(schema, type) and issubclass(
                schema, (BaseModel, BaseModelV1)
@@ -0,0 +1,62 @@
+[tool.poetry]
+name = "langgraph-prebuilt"
+version = "0.1.1"
+description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
+authors = []
+license = "MIT"
+readme = "README.md"
+repository = "https://www.github.com/langchain-ai/langgraph"
+packages = [{ include = "langgraph" }]
+
+[tool.poetry.dependencies]
+python = "^3.9.0,<4.0"
+langgraph-checkpoint = "^2.0.10"
+langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.20,!=0.3.21,!=0.3.22"
+
+[tool.poetry.group.dev.dependencies]
+ruff = "^0.6.2"
+codespell = "^2.2.0"
+pytest = "^7.2.1"
+pytest-asyncio = "^0.21.1"
+pytest-mock = "^3.11.1"
+pytest-watcher = "^0.4.1"
+mypy = "^1.10.0"
+langgraph = {path = "../langgraph", develop = true}
+langgraph-checkpoint = {path = "../checkpoint", develop = true}
+langgraph-checkpoint-sqlite = {path = "../checkpoint-sqlite", develop = true}
+langgraph-checkpoint-postgres = {path = "../checkpoint-postgres", develop = true}
+
+[tool.pytest.ini_options]
+# --strict-markers will raise errors on unknown marks.
+# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
+#
+# https://docs.pytest.org/en/7.1.x/reference/reference.html
+# --strict-config       any warnings encountered while parsing the `pytest`
+#                       section of the configuration file raise errors.
+addopts = "--strict-markers --strict-config --durations=5 -vv"
+asyncio_mode = "auto"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.ruff]
+lint.select = [ "E", "F", "I", "TID251" ]
+lint.ignore = [ "E501" ]
+
+[tool.pytest-watcher]
+now = true
+delay = 0.1
+runner_args = ["--ff", "-v", "--tb", "short"]
+patterns = ["*.py"]
+
+[tool.mypy]
+# https://mypy.readthedocs.io/en/stable/config_file.html
+disallow_untyped_defs = "True"
+explicit_package_bases = "True"
+warn_no_return = "False"
+warn_unused_ignores = "True"
+warn_redundant_casts = "True"
+allow_redefinition = "True"
+disable_error_code = "typeddict-item, return-value"
@@ -0,0 +1,86 @@
+import re
+from typing import Any, Sequence, Union
+
+from typing_extensions import Self
+
+
+class FloatBetween(float):
+    def __new__(cls, min_value: float, max_value: float) -> Self:
+        return super().__new__(cls, min_value)
+
+    def __init__(self, min_value: float, max_value: float) -> None:
+        super().__init__()
+        self.min_value = min_value
+        self.max_value = max_value
+
+    def __eq__(self, other: object) -> bool:
+        return (
+            isinstance(other, float)
+            and other >= self.min_value
+            and other <= self.max_value
+        )
+
+    def __hash__(self) -> int:
+        return hash((float(self), self.min_value, self.max_value))
+
+
+class AnyStr(str):
+    def __init__(self, prefix: Union[str, re.Pattern] = "") -> None:
+        super().__init__()
+        self.prefix = prefix
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, str) and (
+            other.startswith(self.prefix)
+            if isinstance(self.prefix, str)
+            else self.prefix.match(other)
+        )
+
+    def __hash__(self) -> int:
+        return hash((str(self), self.prefix))
+
+
+class AnyDict(dict):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, dict) or len(self) != len(other):
+            return False
+        for k, v in self.items():
+            if kk := next((kk for kk in other if kk == k), None):
+                if v == other[kk]:
+                    continue
+                else:
+                    return False
+        else:
+            return True
+
+
+class AnyVersion:
+    def __init__(self) -> None:
+        super().__init__()
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, (str, int, float))
+
+    def __hash__(self) -> int:
+        return hash(str(self))
+
+
+class UnsortedSequence:
+    def __init__(self, *values: Any) -> None:
+        self.seq = values
+
+    def __eq__(self, value: object) -> bool:
+        return (
+            isinstance(value, Sequence)
+            and len(self.seq) == len(value)
+            and all(a in value for a in self.seq)
+        )
+
+    def __hash__(self) -> int:
+        return hash(frozenset(self.seq))
+
+    def __repr__(self) -> str:
+        return repr(self.seq)
@@ -0,0 +1,17 @@
+name: langgraph-tests
+services:
+  postgres-test:
+    image: postgres:16
+    ports:
+      - "5442:5432"
+    environment:
+      POSTGRES_DB: postgres
+      POSTGRES_USER: postgres
+      POSTGRES_PASSWORD: postgres
+    healthcheck:
+      test: pg_isready -U postgres
+      start_period: 10s
+      timeout: 1s
+      retries: 5
+      interval: 60s
+      start_interval: 1s
@@ -0,0 +1,448 @@
+import sys
+from contextlib import asynccontextmanager
+from typing import AsyncIterator, Optional
+from uuid import UUID, uuid4
+
+import pytest
+from langchain_core import __version__ as core_version
+from packaging import version
+from psycopg import AsyncConnection, Connection
+from psycopg_pool import AsyncConnectionPool, ConnectionPool
+from pytest_mock import MockerFixture
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+from langgraph.checkpoint.postgres import PostgresSaver, ShallowPostgresSaver
+from langgraph.checkpoint.postgres.aio import (
+    AsyncPostgresSaver,
+    AsyncShallowPostgresSaver,
+)
+from langgraph.checkpoint.sqlite import SqliteSaver
+from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
+from langgraph.store.base import BaseStore
+from langgraph.store.memory import InMemoryStore
+from langgraph.store.postgres import AsyncPostgresStore, PostgresStore
+
+pytest.register_assert_rewrite("tests.memory_assert")
+
+DEFAULT_POSTGRES_URI = "postgres://postgres:postgres@localhost:5442/"
+# TODO: fix this once core is released
+IS_LANGCHAIN_CORE_030_OR_GREATER = version.parse(core_version) >= version.parse(
+    "0.3.0.dev0"
+)
+
+
+@pytest.fixture
+def anyio_backend():
+    return "asyncio"
+
+
+@pytest.fixture()
+def deterministic_uuids(mocker: MockerFixture) -> MockerFixture:
+    side_effect = (
+        UUID(f"00000000-0000-4000-8000-{i:012}", version=4) for i in range(10000)
+    )
+    return mocker.patch("uuid.uuid4", side_effect=side_effect)
+
+
+# checkpointer fixtures
+
+
+@pytest.fixture(scope="function")
+def checkpointer_memory():
+    from tests.memory_assert import MemorySaverAssertImmutable
+
+    yield MemorySaverAssertImmutable()
+
+
+@pytest.fixture(scope="function")
+def checkpointer_sqlite():
+    with SqliteSaver.from_conn_string(":memory:") as checkpointer:
+        yield checkpointer
+
+
+@asynccontextmanager
+async def _checkpointer_sqlite_aio():
+    async with AsyncSqliteSaver.from_conn_string(":memory:") as checkpointer:
+        yield checkpointer
+
+
+@pytest.fixture(scope="function")
+def checkpointer_postgres():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        with PostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def checkpointer_postgres_shallow():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        with ShallowPostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def checkpointer_postgres_pipe():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        with PostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            checkpointer.setup()
+            # setup can't run inside pipeline because of implicit transaction
+            with checkpointer.conn.pipeline() as pipe:
+                checkpointer.pipe = pipe
+                yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def checkpointer_postgres_pool():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        with ConnectionPool(
+            DEFAULT_POSTGRES_URI + database, max_size=10, kwargs={"autocommit": True}
+        ) as pool:
+            checkpointer = PostgresSaver(pool)
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _checkpointer_postgres_aio():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        async with AsyncPostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _checkpointer_postgres_aio_shallow():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        async with AsyncShallowPostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _checkpointer_postgres_aio_pipe():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        async with AsyncPostgresSaver.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as checkpointer:
+            await checkpointer.setup()
+            # setup can't run inside pipeline because of implicit transaction
+            async with checkpointer.conn.pipeline() as pipe:
+                checkpointer.pipe = pipe
+                yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _checkpointer_postgres_aio_pool():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield checkpointer
+        async with AsyncConnectionPool(
+            DEFAULT_POSTGRES_URI + database, max_size=10, kwargs={"autocommit": True}
+        ) as pool:
+            checkpointer = AsyncPostgresSaver(pool)
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def awith_checkpointer(
+    checkpointer_name: Optional[str],
+) -> AsyncIterator[BaseCheckpointSaver]:
+    if checkpointer_name is None:
+        yield None
+    elif checkpointer_name == "memory":
+        from tests.memory_assert import MemorySaverAssertImmutable
+
+        yield MemorySaverAssertImmutable()
+    elif checkpointer_name == "sqlite_aio":
+        async with _checkpointer_sqlite_aio() as checkpointer:
+            yield checkpointer
+    elif checkpointer_name == "postgres_aio":
+        async with _checkpointer_postgres_aio() as checkpointer:
+            yield checkpointer
+    elif checkpointer_name == "postgres_aio_shallow":
+        async with _checkpointer_postgres_aio_shallow() as checkpointer:
+            yield checkpointer
+    elif checkpointer_name == "postgres_aio_pipe":
+        async with _checkpointer_postgres_aio_pipe() as checkpointer:
+            yield checkpointer
+    elif checkpointer_name == "postgres_aio_pool":
+        async with _checkpointer_postgres_aio_pool() as checkpointer:
+            yield checkpointer
+    else:
+        raise NotImplementedError(f"Unknown checkpointer: {checkpointer_name}")
+
+
+@asynccontextmanager
+async def _store_postgres_aio():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as store:
+            await store.setup()
+            yield store
+    finally:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _store_postgres_aio_pipe():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database
+        ) as store:
+            await store.setup()  # Run in its own transaction
+        async with AsyncPostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database, pipeline=True
+        ) as store:
+            yield store
+    finally:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@asynccontextmanager
+async def _store_postgres_aio_pool():
+    if sys.version_info < (3, 10):
+        pytest.skip("Async Postgres tests require Python 3.10+")
+    database = f"test_{uuid4().hex[:16]}"
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with AsyncPostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database,
+            pool_config={"max_size": 10},
+        ) as store:
+            await store.setup()
+            yield store
+    finally:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def store_postgres():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield store
+        with PostgresStore.from_conn_string(DEFAULT_POSTGRES_URI + database) as store:
+            store.setup()
+            yield store
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def store_postgres_pipe():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield store
+        with PostgresStore.from_conn_string(DEFAULT_POSTGRES_URI + database) as store:
+            store.setup()  # Run in its own transaction
+        with PostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database, pipeline=True
+        ) as store:
+            yield store
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def store_postgres_pool():
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        # yield store
+        with PostgresStore.from_conn_string(
+            DEFAULT_POSTGRES_URI + database, pool_config={"max_size": 10}
+        ) as store:
+            store.setup()
+            yield store
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
+@pytest.fixture(scope="function")
+def store_in_memory():
+    yield InMemoryStore()
+
+
+@asynccontextmanager
+async def awith_store(store_name: Optional[str]) -> AsyncIterator[BaseStore]:
+    if store_name is None:
+        yield None
+    elif store_name == "in_memory":
+        yield InMemoryStore()
+    elif store_name == "postgres_aio":
+        async with _store_postgres_aio() as store:
+            yield store
+    elif store_name == "postgres_aio_pipe":
+        async with _store_postgres_aio_pipe() as store:
+            yield store
+    elif store_name == "postgres_aio_pool":
+        async with _store_postgres_aio_pool() as store:
+            yield store
+    else:
+        raise NotImplementedError(f"Unknown store {store_name}")
+
+
+ALL_CHECKPOINTERS_SYNC = [
+    "memory",
+    "sqlite",
+    "postgres",
+    "postgres_pipe",
+    "postgres_pool",
+    "postgres_shallow",
+]
+
+ALL_CHECKPOINTERS_ASYNC = [
+    "memory",
+    "sqlite_aio",
+    "postgres_aio",
+    "postgres_aio_pipe",
+    "postgres_aio_pool",
+    "postgres_aio_shallow",
+]
@@ -0,0 +1,134 @@
+import asyncio
+import os
+import tempfile
+from collections import defaultdict
+from functools import partial
+from typing import Any, Optional
+
+from langchain_core.runnables import RunnableConfig
+
+from langgraph.checkpoint.base import (
+    ChannelVersions,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+    SerializerProtocol,
+    copy_checkpoint,
+)
+from langgraph.checkpoint.memory import InMemorySaver, PersistentDict
+
+
+class NoopSerializer(SerializerProtocol):
+    def loads_typed(self, data: tuple[str, bytes]) -> Any:
+        return data[1]
+
+    def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
+        return "type", obj
+
+
+class MemorySaverAssertImmutable(InMemorySaver):
+    storage_for_copies: defaultdict[str, dict[str, dict[str, Checkpoint]]]
+
+    def __init__(
+        self,
+        *,
+        serde: Optional[SerializerProtocol] = None,
+        put_sleep: Optional[float] = None,
+    ) -> None:
+        _, filename = tempfile.mkstemp()
+        super().__init__(
+            serde=serde, factory=partial(PersistentDict, filename=filename)
+        )
+        self.storage_for_copies = defaultdict(lambda: defaultdict(dict))
+        self.put_sleep = put_sleep
+        self.stack.callback(os.remove, filename)
+
+    def put(
+        self,
+        config: dict,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> None:
+        if self.put_sleep:
+            import time
+
+            time.sleep(self.put_sleep)
+        # assert checkpoint hasn't been modified since last written
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        if saved := super().get(config):
+            assert (
+                self.serde.loads_typed(
+                    self.storage_for_copies[thread_id][checkpoint_ns][saved["id"]]
+                )
+                == saved
+            )
+        self.storage_for_copies[thread_id][checkpoint_ns][checkpoint["id"]] = (
+            self.serde.dumps_typed(copy_checkpoint(checkpoint))
+        )
+        # call super to write checkpoint
+        return super().put(config, checkpoint, metadata, new_versions)
+
+
+class MemorySaverAssertCheckpointMetadata(InMemorySaver):
+    """This custom checkpointer is for verifying that a run's configurable
+    fields are merged with the previous checkpoint config for each step in
+    the run. This is the desired behavior. Because the checkpointer's (a)put()
+    method is called for each step, the implementation of this checkpointer
+    should produce a side effect that can be asserted.
+    """
+
+    def put(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> None:
+        """The implementation of put() merges config["configurable"] (a run's
+        configurable fields) with the metadata field. The state of the
+        checkpoint metadata can be asserted to confirm that the run's
+        configurable fields were merged with the previous checkpoint config.
+        """
+        configurable = config["configurable"].copy()
+
+        # remove checkpoint_id to make testing simpler
+        checkpoint_id = configurable.pop("checkpoint_id", None)
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"]["checkpoint_ns"]
+        self.storage[thread_id][checkpoint_ns].update(
+            {
+                checkpoint["id"]: (
+                    self.serde.dumps_typed(checkpoint),
+                    # merge configurable fields and metadata
+                    self.serde.dumps_typed({**configurable, **metadata}),
+                    checkpoint_id,
+                )
+            }
+        )
+        return {
+            "configurable": {
+                "thread_id": config["configurable"]["thread_id"],
+                "checkpoint_id": checkpoint["id"],
+            }
+        }
+
+    async def aput(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        return await asyncio.get_running_loop().run_in_executor(
+            None, self.put, config, checkpoint, metadata, new_versions
+        )
+
+
+class MemorySaverNoPending(InMemorySaver):
+    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+        result = super().get_tuple(config)
+        if result:
+            return CheckpointTuple(result.config, result.checkpoint, result.metadata)
+        return result
@@ -0,0 +1,50 @@
+"""Redefined messages as a work-around for pydantic issue with AnyStr.
+
+The code below creates version of pydantic models
+that will work in unit tests with AnyStr as id field
+Please note that the `id` field is assigned AFTER the model is created
+to workaround an issue with pydantic ignoring the __eq__ method on
+subclassed strings.
+"""
+
+from typing import Any
+
+from langchain_core.documents import Document
+from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage, ToolMessage
+
+from tests.any_str import AnyStr
+
+
+def _AnyIdDocument(**kwargs: Any) -> Document:
+    """Create a document with an id field."""
+    message = Document(**kwargs)
+    message.id = AnyStr()
+    return message
+
+
+def _AnyIdAIMessage(**kwargs: Any) -> AIMessage:
+    """Create ai message with an any id field."""
+    message = AIMessage(**kwargs)
+    message.id = AnyStr()
+    return message
+
+
+def _AnyIdAIMessageChunk(**kwargs: Any) -> AIMessageChunk:
+    """Create ai message with an any id field."""
+    message = AIMessageChunk(**kwargs)
+    message.id = AnyStr()
+    return message
+
+
+def _AnyIdHumanMessage(**kwargs: Any) -> HumanMessage:
+    """Create a human message with an any id field."""
+    message = HumanMessage(**kwargs)
+    message.id = AnyStr()
+    return message
+
+
+def _AnyIdToolMessage(**kwargs: Any) -> ToolMessage:
+    """Create a tool message with an any id field."""
+    message = ToolMessage(**kwargs)
+    message.id = AnyStr()
+    return message
@@ -0,0 +1,98 @@
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Type,
+    Union,
+)
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models import BaseChatModel, LanguageModelInput
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    ToolCall,
+)
+from langchain_core.outputs import ChatGeneration, ChatResult
+from langchain_core.runnables import Runnable, RunnableLambda
+from langchain_core.tools import BaseTool
+from pydantic import BaseModel
+
+from langgraph.prebuilt.chat_agent_executor import StructuredResponse
+
+
+class FakeToolCallingModel(BaseChatModel):
+    tool_calls: Optional[list[list[ToolCall]]] = None
+    structured_response: Optional[StructuredResponse] = None
+    index: int = 0
+    tool_style: Literal["openai", "anthropic"] = "openai"
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Top Level call"""
+        messages_string = "-".join([m.content for m in messages])
+        tool_calls = (
+            self.tool_calls[self.index % len(self.tool_calls)]
+            if self.tool_calls
+            else []
+        )
+        message = AIMessage(
+            content=messages_string, id=str(self.index), tool_calls=tool_calls.copy()
+        )
+        self.index += 1
+        return ChatResult(generations=[ChatGeneration(message=message)])
+
+    @property
+    def _llm_type(self) -> str:
+        return "fake-tool-call-model"
+
+    def with_structured_output(
+        self, schema: Type[BaseModel]
+    ) -> Runnable[LanguageModelInput, StructuredResponse]:
+        if self.structured_response is None:
+            raise ValueError("Structured response is not set")
+
+        return RunnableLambda(lambda x: self.structured_response)
+
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        if len(tools) == 0:
+            raise ValueError("Must provide at least one tool")
+
+        tool_dicts = []
+        for tool in tools:
+            if not isinstance(tool, BaseTool):
+                raise TypeError(
+                    "Only BaseTool is supported by FakeToolCallingModel.bind_tools"
+                )
+
+            # NOTE: this is a simplified tool spec for testing purposes only
+            if self.tool_style == "openai":
+                tool_dicts.append(
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": tool.name,
+                        },
+                    }
+                )
+            elif self.tool_style == "anthropic":
+                tool_dicts.append(
+                    {
+                        "name": tool.name,
+                    }
+                )
+
+        return self.bind(tools=tool_dicts)
@@ -0,0 +1,81 @@
+from typing import Any
+
+import pytest
+from langchain_core.messages import AIMessage
+from langchain_core.tools import tool as dec_tool
+from pydantic import BaseModel
+from pydantic.v1 import BaseModel as BaseModelV1
+
+from langgraph.prebuilt import ValidationNode
+
+pytestmark = pytest.mark.anyio
+
+
+def my_function(some_val: int, some_other_val: str) -> str:
+    return f"{some_val} - {some_other_val}"
+
+
+class MyModel(BaseModel):
+    some_val: int
+    some_other_val: str
+
+
+class MyModelV1(BaseModelV1):
+    some_val: int
+    some_other_val: str
+
+
+@dec_tool
+def my_tool(some_val: int, some_other_val: str) -> str:
+    """Cool."""
+    return f"{some_val} - {some_other_val}"
+
+
+@pytest.mark.parametrize(
+    "tool_schema",
+    [
+        my_function,
+        MyModel,
+        MyModelV1,
+        my_tool,
+    ],
+)
+@pytest.mark.parametrize("use_message_key", [True, False])
+async def test_validation_node(tool_schema: Any, use_message_key: bool):
+    validation_node = ValidationNode([tool_schema])
+    tool_name = getattr(tool_schema, "name", getattr(tool_schema, "__name__", None))
+    inputs = [
+        AIMessage(
+            "hi?",
+            tool_calls=[
+                {
+                    "name": tool_name,
+                    "args": {"some_val": 1, "some_other_val": "foo"},
+                    "id": "some 0",
+                },
+                {
+                    "name": tool_name,
+                    # Wrong type for some_val
+                    "args": {"some_val": "bar", "some_other_val": "foo"},
+                    "id": "some 1",
+                },
+            ],
+        ),
+    ]
+    if use_message_key:
+        inputs = {"messages": inputs}
+    result = await validation_node.ainvoke(inputs)
+    if use_message_key:
+        result = result["messages"]
+
+    def check_results(messages: list):
+        assert len(messages) == 2
+        assert all(m.type == "tool" for m in messages)
+        assert not messages[0].additional_kwargs.get("is_error")
+        assert messages[1].additional_kwargs.get("is_error")
+
+    check_results(result)
+    result_sync = validation_node.invoke(inputs)
+    if use_message_key:
+        result_sync = result_sync["messages"]
+    check_results(result_sync)
@@ -1,6 +1,6 @@
 {
  "name": "@langchain/langgraph-sdk",
-  "version": "0.0.46",
+  "version": "0.0.45",
  "description": "Client library for interacting with the LangGraph API",
  "type": "module",
  "packageManager": "yarn@1.22.19",
Author	SHA1	Message	Date
Vadym BardaandGitHub	fe0de3e07e	ci: update release workflow for prebuilt (#3621 )	2025-02-27 09:49:15 -05:00
David DuongandGitHub	f51831f48e	docs: update getting started docs for studio (#3542 )	2025-02-27 15:41:52 +01:00
Vadym BardaandGitHub	745eb90a6d	prebuilt: remove langgraph dependency (#3620 )	2025-02-27 09:41:43 -05:00
Arjun Natarajan	f136e40065	fix link	2025-02-26 21:47:32 -05:00
Arjun Natarajan	de888b4032	make reference to langsmith a bit cleareR	2025-02-26 21:33:45 -05:00
Arjun Natarajan	7cb0bd52e8	add link to cli	2025-02-26 21:31:33 -05:00
Vadym BardaandGitHub	3778f6113c	prebuilt: release 0.1.0 (#3610 )	2025-02-26 18:48:03 -05:00
Vadym BardaandGitHub	24c13c211e	langgraph: separate prebuilt into a standalone package (#3589 )	2025-02-26 18:33:07 -05:00
Vadym BardaandGitHub	2e36189c16	langgraph: release 0.2.76 (#3609 )	2025-02-26 16:41:28 -05:00
Arjun Natarajan	302aa8b9cb	clean up docs	2025-02-26 15:57:05 -05:00
Vadym BardaandGitHub	dac11f875c	langgraph: fix get_state(subgraphs=True) for checkpointer=True (#3607 )	2025-02-26 20:43:46 +00:00
andrestorres123andGitHub	264be423f9	docs: Add Delve Taxonomy Generator package to the Prebuilt Agents (#3606 )	2025-02-26 19:03:51 +00:00
HackHuangandGitHub	9a05600ff9	docs(concepts) : Add a tutorial link for map-reduce (#3577 ) Update `low_level.md`: Add a tutorial link for map-reduce, now it's perfect !!!	2025-02-26 12:23:02 -05:00
Eugene YurtsevandGitHub	56dd728975	docs: remove one more beta (#3601 )	2025-02-26 12:22:22 -05:00
Eugene YurtsevandGitHub	c157c956f4	docs: prebuilt add github stars (#3603 )	2025-02-26 12:22:08 -05:00
Vadym BardaandGitHub	dd293dad30	langgraph: release 0.2.75 (#3602 )	2025-02-26 11:47:16 -05:00
Vadym BardaandGitHub	30811d7841	langgraph: add py.typed files to modules (#3600 )	2025-02-26 16:41:51 +00:00
Vadym BardaandGitHub	28a705b71a	docs: update requirements (#3598 )	2025-02-26 10:35:16 -05:00
Vadym BardaandGitHub	3b8130b96f	docs: add swarm (#3597 )	2025-02-26 09:59:29 -05:00
Harrison ChaseandGitHub	94fc0adb05	faq about no langsmith (#3596 )	2025-02-26 09:43:28 -05:00
Nuno CamposandGitHub	162e96262f	langgraph: stream_mode=messages should not emit input or state messages (#3591 ) - any messages seen in inputs in on_chain_start should not be emitted	2025-02-25 19:09:57 -08:00
Nuno Campos	1bb0037450	langgraph: stream_mode=messages should not emit input or state messages - any messages seen in inputs in on_chain_start should not be emitted	2025-02-25 19:00:33 -08:00
Nuno CamposandGitHub	50c53d3120	checkpoint-sqlite: update aiosqlite bounds (#3540 ) aiosqlite 0.21.0 was released on Feb 2 It seems to be a maintenance release: https://github.com/omnilib/aiosqlite/blob/main/CHANGELOG.md#v0210	2025-02-25 16:56:10 -08:00
Nuno CamposandGitHub	0b7b849633	Update adopters.md to include Cisco Outshift (#3573 )	2025-02-25 16:47:52 -08:00
Nuno CamposandGitHub	678eb5cdbe	pregel: update validation error messages (#3517 ) OK not including as well, but this was useful for troubleshooting as a new user (especially before there were any examples)	2025-02-25 16:45:57 -08:00
Arjun Natarajan	a0969b61a3	add back desktop docs in dedicated section w deprecation warning	2025-02-25 16:08:21 -05:00
Arjun Natarajan	e679ab73c4	fix broken docs part	2025-02-25 15:53:23 -05:00
Andrew NguonlyandGitHub	bdf1215ced	docs: Add docs for `DD_API_KEY` env var (#3585 )	2025-02-25 11:24:59 -08:00
William FHandGitHub	cba1852720	Fix typo (#3583 )	2025-02-25 18:19:48 +00:00
jessicaouandGitHub	b8fafa2795	Update adopters.md to include Cisco Outshift	2025-02-24 18:34:38 -08:00
Theodore NiandGitHub	e6681bc175	Merge branch 'main' into update-aiosqlite-bounds	2025-02-22 06:16:58 -06:00
Arjun Natarajan	6615c6bb0d	remove other references to desktop :	2025-02-20 18:16:49 -08:00
Arjun Natarajan	32df0016ee	update getting started docs	2025-02-20 18:00:26 -08:00
Theodore Ni	a9cdb9c948	checkpoint-sqlite: update aiosqlite bounds	2025-02-20 14:51:51 -08:00
Eugene Yurtsev	4dc8f813e7	x	2025-02-19 16:16:14 -05:00