cli: release 0.1.89 (#4173)

Release to pick up this: https://github.com/langchain-ai/langgraph/pull/4164

docs/cassettes/create-react-agent-manage-message-history_26c53429-90ba-4d0b-abb9-423d9120ad26.msgpack.zlib

@@ -0,0 +1 @@
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

docs/docs/cloud/deployment/cloud.md

@@ -1,6 +1,6 @@
-# How to Deploy to LangGraph Cloud
+# How to Deploy to Cloud SaaS
 
-LangGraph Cloud is available within <a href="https://www.langchain.com/langsmith" target="_blank">LangSmith</a>. To deploy a LangGraph Cloud API, navigate to the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>.
+Before deploying, review the [conceptual guide for the Cloud SaaS](../../concepts/langgraph_cloud.md) deployment option.
 
 ## Prerequisites
 

docs/docs/cloud/deployment/self_hosted_control_plane.md

@@ -0,0 +1,56 @@
+# How to Deploy Self-Hosted Control Plane
+
+Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.
+
+## Prerequisites
+
+1. You are using Kubernetes.
+1. You have self-hosted LangSmith deployed.
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`) and push it to a registry your Kubernetes cluster has access to.
+1. `KEDA` is installed on your cluster.
+
+         helm repo add kedacore https://kedacore.github.io/charts 
+         helm install keda kedacore/keda --namespace keda --create-namespace
+
+1. Ingress Configuration (recommended)
+    1. Install `Ingress Nginx` to serve as a reverse proxy for your deployment.
+
+            helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
+            helm repo update
+            helm install ingress-nginx ingress-nginx/ingress-nginx
+
+    1. Provision a root domain that will suffix all domains for your workloads (e.g. `us.langgraph.app`).
+    1. Provision wildcard certificates to terminate TLS for your deployments.
+    1. Note: If this step is skipped, you will need to provision domains/certs for each of your deployments.
+
+1. You have slack space in your cluster for multiple deployments. `Cluster-Autoscaler` is recommended to automatically provision new nodes.
+
+## Setup
+
+1. As part of configuring your Self-Hosted LangSmith instance, you enable the `langgraphPlatform` option. This will provision a few key resources.
+    1. `listener`: This is a service that listens to the [control plane](../../concepts/langgraph_control_plane.md) for changes to your deployments and creates/updates downstream CRDs.
+    1. `LangGraphPlatform CRD`: A CRD for LangGraph Platform deployments. This contains the spec for managing an instance of a LangGraph platform deployment.
+    1. `operator`: This operator handles changes to your LangGraph Platform CRDs.
+    1. `host-backend`: This is the [control plane](../../concepts/langgraph_control_plane.md).
+1. Two additional images will be used by the chart.
+
+        hostBackendImage:
+          repository: "docker.io/langchain/hosted-langserve-backend"
+          pullPolicy: IfNotPresent
+          tag: "0.9.80"
+        operatorImage:
+          repository: "docker.io/langchain/langgraph-operator"
+          pullPolicy: IfNotPresent
+          tag: "aa9dff4"
+
+1. In your `values.yaml` file, enable the `langgraphPlatform` option.
+
+        config:
+          langgraphPlatform:
+            enabled: true
+            langgraphPlatformLicenseKey: "YOUR_LANGGRAPH_PLATFORM_LICENSE_KEY"
+            rootDomain: "YOUR_ROOT_DOMAIN"
+
+1. You can also configure base templates for your agents by overriding the base templates [here](https://github.com/langchain-ai/helm/blob/main/charts/langsmith/values.yaml#L898).
+1. You create a deployment from the [Control Plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).

docs/docs/cloud/deployment/self_hosted_data_plane.md

@@ -0,0 +1,53 @@
+# How to Deploy Self-Hosted Data Plane
+
+Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.
+
+## Prerequisites
+
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`) and push it to a registry your Kubernetes cluster or Amazon ECS cluster has access to.
+
+## Kubernetes
+
+### Prerequisites
+1. `KEDA` is installed on your cluster.
+
+        helm repo add kedacore https://kedacore.github.io/charts 
+        helm install keda kedacore/keda --namespace keda --create-namespace
+
+1. A valid `Ingress` controller is install on your cluster.
+1. You have slack space in your cluster for multiple deployments. `Cluster-Autoscaler` is recommended to automatically provision new nodes.
+
+### Setup
+
+1. You give us your LangSmith organization ID. We will enable the Self-Hosted Data Plane for your organization.
+1. We provide you a [Helm chart](https://github.com/langchain-ai/helm/tree/main/charts/langgraph-dataplane) which you run to setup your Kubernetes cluster. This chart contains a few important components.
+    1. `langgraph-listener`: This is a service that listens to LangChain's [control plane](../../concepts/langgraph_control_plane.md) for changes to your deployments and creates/updates downstream CRDs.
+    1. `LangGraphPlatform CRD`: A CRD for LangGraph Platform deployments. This contains the spec for managing an instance of a LangGraph Platform deployment.
+    1. `langgraph-platform-operator`: This operator handles changes to your LangGraph Platform CRDs.
+1. Configure your `langgraph-dataplane-values.yaml` file.
+
+        config:
+          langgraphPlatformLicenseKey: "" # Your LangGraph Platform license key
+          langsmithApiKey: "" # API Key of your Workspace
+          langsmithWorkspaceId: "" # Workspace ID
+          hostBackendUrl: "https://api.host.langchain.com" # Only override this if on EU
+          smithBackendUrl: "https://api.smith.langchain.com" # Only override this if on EU
+
+1. Deploy `langgraph-dataplane` Helm chart.
+
+        helm repo add langchain https://langchain-ai.github.io/helm/
+        helm repo update
+        helm upgrade -i langgraph-dataplane langchain/langgraph-dataplane --values langgraph-dataplane-values.yaml
+
+1. If successful, you will see two services start up in your namespace.
+
+        NAME                                          READY   STATUS              RESTARTS   AGE
+        langgraph-dataplane-listener-7fccd788-wn2dx   0/1     Running             0          9s
+        langgraph-dataplane-redis-0                   0/1     ContainerCreating   0          9s
+
+1. You create a deployment from the [Control Plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
+
+## Amazon ECS
+
+Coming soon!

docs/docs/cloud/deployment/standalone_container.md

@@ -0,0 +1,110 @@
+# How to Deploy a Standalone Container
+
+Before deploying, review the [conceptual guide for the Standalone Container](../../concepts/langgraph_standalone_container.md) deployment option.
+
+## Prerequisites
+
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`).
+1. The following environment variables are needed for a standalone container deployment.
+    1. `REDIS_URI`: Connection details to a Redis instance. Redis will be used as a pub-sub broker to enable streaming real time output from background runs. The value of `REDIS_URI` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
+
+        !!! Note "Shared Redis Instance"
+            Multiple self-hosted deployments can share the same Redis instance. For example, for `Deployment A`, `REDIS_URI` can be set to `redis://<hostname_1>:<port>/1` and for `Deployment B`, `REDIS_URI` can be set to `redis://<hostname_1>:<port>/2`.
+
+            `1` and `2` are different database numbers within the same instance, but `<hostname_1>` is shared. **The same database number cannot be used for separate deployments**.
+
+    1. `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics. The value of `DATABASE_URI` must be a valid [Postgres connection URI](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING-URIS).
+
+        !!! Note "Shared Postgres Instance"
+            Multiple self-hosted deployments can share the same Postgres instance. For example, for `Deployment A`, `DATABASE_URI` can be set to `postgres://<user>:<password>@/<database_name_1>?host=<hostname_1>` and for `Deployment B`, `DATABASE_URI` can be set to `postgres://<user>:<password>@/<database_name_2>?host=<hostname_1>`.
+
+            `<database_name_1>` and `database_name_2` are different databases within the same instance, but `<hostname_1>` is shared. **The same database cannot be used for separate deployments**.
+
+    1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_data_plane.md#lite-vs-enterprise)) LangSmith API key. This will be used to authenticate ONCE at server start up.
+    1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#lite-vs-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
+    1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
+
+## Kubernetes (Helm)
+
+Use this [Helm chart](https://github.com/langchain-ai/helm/blob/main/charts/langgraph-cloud/README.md) to deploy a LangGraph Server to a Kubernetes cluster.
+
+## Docker
+
+Run the following `docker` command:
+```shell
+docker run \
+    --env-file .env \
+    -p 8123:8000 \
+    -e REDIS_URI="foo" \
+    -e DATABASE_URI="bar" \
+    -e LANGSMITH_API_KEY="baz" \
+    my-image
+```
+
+!!! note
+
+    * You need to replace `my-image` with the name of the image you built in the prerequisite steps (from `langgraph build`)
+    and you should provide appropriate values for `REDIS_URI`, `DATABASE_URI`, and `LANGSMITH_API_KEY`.
+    * If your application requires additional environment variables, you can pass them in a similar way.
+
+## Docker Compose
+
+Docker Compose YAML file:
+```yml
+volumes:
+    langgraph-data:
+        driver: local
+services:
+    langgraph-redis:
+        image: redis:6
+        healthcheck:
+            test: redis-cli ping
+            interval: 5s
+            timeout: 1s
+            retries: 5
+    langgraph-postgres:
+        image: postgres:16
+        ports:
+            - "5433:5432"
+        environment:
+            POSTGRES_DB: postgres
+            POSTGRES_USER: postgres
+            POSTGRES_PASSWORD: postgres
+        volumes:
+            - langgraph-data:/var/lib/postgresql/data
+        healthcheck:
+            test: pg_isready -U postgres
+            start_period: 10s
+            timeout: 1s
+            retries: 5
+            interval: 5s
+    langgraph-api:
+        image: ${IMAGE_NAME}
+        ports:
+            - "8123:8000"
+        depends_on:
+            langgraph-redis:
+                condition: service_healthy
+            langgraph-postgres:
+                condition: service_healthy
+        env_file:
+            - .env
+        environment:
+            REDIS_URI: redis://langgraph-redis:6379
+            LANGSMITH_API_KEY: ${LANGSMITH_API_KEY}
+            POSTGRES_URI: postgres://postgres:postgres@langgraph-postgres:5432/postgres?sslmode=disable
+```
+
+You can run the command `docker compose up` with this Docker Compose file in the same folder.
+
+This will launch a LangGraph Server on port `8123` (if you want to change this, you can change this by changing the ports in the `langgraph-api` volume). You can test if the application is healthy by running:
+
+```shell
+curl --request GET --url 0.0.0.0:8123/ok
+```
+Assuming everything is running correctly, you should see a response like:
+
+```shell
+{"ok":true}
+```

docs/docs/cloud/how-tos/generative_ui_react.md

@@ -12,10 +12,6 @@ Generative user interfaces (Generative UI) allows agents to go beyond text and g
 
 LangGraph Platform supports colocating your React components with your graph code. This allows you to focus on building specific UI components for your graph while easily plugging into existing chat interfaces such as [Agent Chat](https://agentchat.vercel.app) and loading the code only when actually needed.
 
-!!! warning "LangGraph.js only"
-
-    Currently only LangGraph.js supports Generative UI. Support for Python is coming soon.
-
 ## Tutorial
 
 ### 1. Define and configure UI components
@@ -74,58 +70,105 @@ CSS and Tailwind 4.x is also supported out of the box, so you can freely use Tai
 
 ### 2. Send the UI components in your graph
 
-Use the `typedUi` utility to emit UI elements from your agent nodes:
+=== "Python"
 
-```typescript title="src/agent/index.ts"
-import {
-  typedUi,
-  uiMessageReducer,
-} from "@langchain/langgraph-sdk/react-ui/server";
+    ```python title="src/agent.py"
+    import uuid
+    from typing import Annotated, Sequence, TypedDict
 
-import { ChatOpenAI } from "@langchain/openai";
-import { v4 as uuidv4 } from "uuid";
-import { z } from "zod";
+    from langchain_core.messages import AIMessage, BaseMessage
+    from langchain_openai import ChatOpenAI
+    from langgraph.graph import StateGraph
+    from langgraph.graph.message import add_messages
+    from langgraph.graph.ui import AnyUIMessage, ui_message_reducer, push_ui_message
 
-import type ComponentMap from "./ui.js";
 
-import {
-  Annotation,
-  MessagesAnnotation,
-  StateGraph,
-  type LangGraphRunnableConfig,
-} from "@langchain/langgraph";
+    class AgentState(TypedDict):  # noqa: D101
+        messages: Annotated[Sequence[BaseMessage], add_messages]
+        ui: Annotated[Sequence[AnyUIMessage], ui_message_reducer]
 
-const AgentState = Annotation.Root({
-  ...MessagesAnnotation.spec,
-  ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
-});
 
-export const graph = new StateGraph(AgentState)
-  .addNode("weather", async (state, config) => {
-    // Provide the type of the component map to ensure
-    // type safety of `ui.push()` calls as well as
-    // pushing the messages to the `ui` and sending a custom event as well.
-    const ui = typedUi<typeof ComponentMap>(config);
+    async def weather(state: AgentState):
+        class WeatherOutput(TypedDict):
+            city: str
 
-    const weather = await new ChatOpenAI({ model: "gpt-4o-mini" })
-      .withStructuredOutput(z.object({ city: z.string() }))
-      .withConfig({ tags: ["langsmith:nostream"] })
-      .invoke(state.messages);
+        weather: WeatherOutput = (
+            await ChatOpenAI(model="gpt-4o-mini")
+            .with_structured_output(WeatherOutput)
+            .with_config({"tags": ["nostream"]})
+            .ainvoke(state["messages"])
+        )
 
-    const response = {
-      id: uuidv4(),
-      type: "ai",
-      content: `Here's the weather for ${weather.city}`,
-    };
+        message = AIMessage(
+            id=str(uuid.uuid4()),
+            content=f"Here's the weather for {weather['city']}",
+        )
 
-    // Emit UI elements with associated AI message
-    ui.push({ name: "weather", props: weather }, { message: response });
+        # Emit UI elements associated with the message
+        push_ui_message("weather", weather, message=message)
+        return {"messages": [message]}
 
-    return { messages: [response] };
-  })
-  .addEdge("__start__", "weather")
-  .compile();
-```
+
+    workflow = StateGraph(AgentState)
+    workflow.add_node(weather)
+    workflow.add_edge("__start__", "weather")
+    graph = workflow.compile()
+    ```
+
+=== "JS"
+
+    Use the `typedUi` utility to emit UI elements from your agent nodes:
+
+    ```typescript title="src/agent/index.ts"
+    import {
+      typedUi,
+      uiMessageReducer,
+    } from "@langchain/langgraph-sdk/react-ui/server";
+
+    import { ChatOpenAI } from "@langchain/openai";
+    import { v4 as uuidv4 } from "uuid";
+    import { z } from "zod";
+
+    import type ComponentMap from "./ui.js";
+
+    import {
+      Annotation,
+      MessagesAnnotation,
+      StateGraph,
+      type LangGraphRunnableConfig,
+    } from "@langchain/langgraph";
+
+    const AgentState = Annotation.Root({
+      ...MessagesAnnotation.spec,
+      ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
+    });
+
+    export const graph = new StateGraph(AgentState)
+      .addNode("weather", async (state, config) => {
+        // Provide the type of the component map to ensure
+        // type safety of `ui.push()` calls as well as
+        // pushing the messages to the `ui` and sending a custom event as well.
+        const ui = typedUi<typeof ComponentMap>(config);
+
+        const weather = await new ChatOpenAI({ model: "gpt-4o-mini" })
+          .withStructuredOutput(z.object({ city: z.string() }))
+          .withConfig({ tags: ["nostream"] })
+          .invoke(state.messages);
+
+        const response = {
+          id: uuidv4(),
+          type: "ai",
+          content: `Here's the weather for ${weather.city}`,
+        };
+
+        // Emit UI elements associated with the AI message
+        ui.push({ name: "weather", props: weather }, { message: response });
+
+        return { messages: [response] };
+      })
+      .addEdge("__start__", "weather")
+      .compile();
+    ```
 
 ### 3. Handle UI elements in your React application
 
@@ -294,18 +337,29 @@ const { thread, submit } = useStream({
 
 ### Remove UI messages from state
 
-Similar to how messages can be removed from the state by appending a RemoveMessage you can remove an UI message from the state by calling `ui.delete` with the ID of the UI message.
+Similar to how messages can be removed from the state by appending a RemoveMessage you can remove an UI message from the state by calling `remove_ui_message` / `ui.delete` with the ID of the UI message.
 
-```tsx
-// pushed message
-const message = ui.push({ name: "weather", props: { city: "London" } });
+=== "Python"
 
-// remove said message
-ui.delete(message.id);
+    ```python
+    from langgraph.graph.ui import push_ui_message, delete_ui_message
 
-// return new state to persist changes
-return { ui: ui.items };
-```
+    # push message
+    message = push_ui_message("weather", {"city": "London"})
+
+    # remove said message
+    delete_ui_message(message["id"])
+    ```
+
+=== "JS"
+
+    ```tsx
+    // push message
+    const message = ui.push({ name: "weather", props: { city: "London" } });
+
+    // remove said message
+    ui.delete(message.id);
+    ```
 
 ## Learn more
 

docs/docs/cloud/reference/cli.md

@@ -29,7 +29,7 @@ The LangGraph command line interface includes commands to build and run a LangGr
 
 ## Configuration File {#configuration-file}
 
-The LangGraph CLI requires a JSON configuration file with the following keys:
+The LangGraph CLI requires a JSON configuration file that follows this [schema](https://raw.githubusercontent.com/langchain-ai/langgraph/refs/heads/main/libs/cli/schemas/schema.json). It contains the following properties:
 
 <div class="admonition tip">
     <p class="admonition-title">Note</p>
@@ -46,11 +46,12 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
     | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul>                                    |
     | <span style="white-space: nowrap;">`auth`</span>             | _(Added in v0.0.11)_ Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See [authentication guide](../../concepts/auth.md) for details.                                                                                                                                                                                                                                                                                                                        |
     | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
-    | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, which means to index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul> |
+    | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search and/or time-to-live (TTL) to the BaseStore. Contains the following fields: <ul><li>`index` (optional): Configuration for semantic search indexing with fields `embed`, `dims`, and optional `fields`.</li><li>`ttl` (optional): Configuration for item expiration. An object with optional fields: `refresh_on_read` (boolean, defaults to `true`), `default_ttl` (float, lifespan in **minutes**, defaults to no expiration), and `sweep_interval_minutes` (integer, how often to check for expired items, defaults to no sweeping).</li></ul> |
     | <span style="white-space: nowrap;">`python_version`</span>   | `3.11`, `3.12`, or `3.13`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
     | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
     | <span style="white-space: nowrap;">`pip_config_file`</span>  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
     | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+    | <span style="white-space: nowrap;">`checkpointer`</span>   | Configuration for the checkpointer. Contains a `ttl` field which is an object with the following keys: <ul><li>`strategy`: How to handle expired checkpoints (e.g., `"delete"`).</li><li>`sweep_interval_minutes`: How often to check for expired checkpoints (integer).</li><li>`default_ttl`: Default time-to-live for checkpoints in **minutes** (integer). Defines how long checkpoints are kept before the specified strategy is applied.</li></ul> |
     | <span style="white-space: nowrap;">`http`</span>            | HTTP server configuration with the following fields: <ul><li>`app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See [custom routes guide](../../how-tos/http/custom_routes.md).</li><li>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</li><li>`cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.</li></ul> |
 
 === "JS"
@@ -59,9 +60,10 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
     | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
     | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./src/graph.ts:variable`, where `variable` is an instance of `CompiledStateGraph`</li><li>`./src/graph.ts:makeGraph`, where `makeGraph` is a function that takes a config dictionary (`LangGraphRunnableConfig`) and creates an instance of `StateGraph` / `CompiledStateGraph`.</li></ul>                                    |
     | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
-    | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, which means to index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul> |
+    | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search and/or time-to-live (TTL) to the BaseStore. Contains the following fields: <ul><li>`index` (optional): Configuration for semantic search indexing with fields `embed`, `dims`, and optional `fields`.</li><li>`ttl` (optional): Configuration for item expiration. An object with optional fields: `refresh_on_read` (boolean, defaults to `true`), `default_ttl` (float, lifespan in **minutes**, defaults to no expiration), and `sweep_interval_minutes` (integer, how often to check for expired items, defaults to no sweeping).</li></ul> |
     | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
     | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+    | <span style="white-space: nowrap;">`checkpointer`</span>   | Configuration for the checkpointer. Contains a `ttl` field which is an object with the following keys: <ul><li>`strategy`: How to handle expired checkpoints (e.g., `"delete"`).</li><li>`sweep_interval_minutes`: How often to check for expired checkpoints (integer).</li><li>`default_ttl`: Default time-to-live for checkpoints in **minutes** (integer). Defines how long checkpoints are kept before the specified strategy is applied.</li></ul> |
 
 ### Examples
 
@@ -82,7 +84,7 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
 
     All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
 
-    The `fields` configuration determines which parts of your documents to embed:
+    The `index.fields` configuration determines which parts of your documents to embed:
 
     - If omitted or set to `["$"]`, the entire document will be embedded
     - To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
@@ -171,6 +173,62 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
 
     See the [authentication conceptual guide](../../concepts/auth.md) for details, and the [setting up custom authentication](../../tutorials/auth/getting_started.md) guide for a practical walk through of the process.
 
+    #### Configuring Store Item Time-to-Live (TTL)
+
+    You can configure default data expiration for items/memories in the BaseStore using the `store.ttl` key. This determines how long items are retained after they are last accessed (with reads potentially refreshing the timer based on `refresh_on_read`). Note that these defaults can be overwritten on a per-call basis by modifying the corresponding arguments in `get`, `search`, etc.
+    
+    The `ttl` configuration is an object containing optional fields:
+
+    - `refresh_on_read`: If `true` (the default), accessing an item via `get` or `search` resets its expiration timer. Set to `false` to only refresh TTL on writes (`put`).
+    - `default_ttl`: The default lifespan of an item in **minutes**. If not set, items do not expire by default.
+    - `sweep_interval_minutes`: How frequently (in minutes) the system should run a background process to delete expired items. If not set, sweeping does not occur automatically.
+
+    Here is an example enabling a 7-day TTL (10080 minutes), refreshing on reads, and sweeping every hour:
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "memory_agent": "./agent/graph.py:graph"
+      },
+      "store": {
+        "ttl": {
+          "refresh_on_read": true,
+          "sweep_interval_minutes": 60,
+          "default_ttl": 10080 
+        }
+      }
+    }
+    ```
+
+    #### Configuring Checkpoint Time-to-Live (TTL)
+
+    You can configure the time-to-live (TTL) for checkpoints using the `checkpointer` key. This determines how long checkpoint data is retained before being automatically handled according to the specified strategy (e.g., deletion). The `ttl` configuration is an object containing:
+
+    - `strategy`: The action to take on expired checkpoints (currently `"delete"` is the only accepted option).
+    - `sweep_interval_minutes`: How frequently (in minutes) the system checks for expired checkpoints.
+    - `default_ttl`: The default lifespan of a checkpoint in **minutes**.
+
+    Here's an example setting a default TTL of 30 days (43200 minutes):
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "chat": "./chat/graph.py:graph"
+      },
+      "checkpointer": {
+        "ttl": {
+          "strategy": "delete",
+          "sweep_interval_minutes": 10,
+          "default_ttl": 43200
+        }
+      }
+    }
+    ```
+
+    In this example, checkpoints older than 30 days will be deleted, and the check runs every 10 minutes.
+
 
 === "JS"
     

docs/docs/concepts/deployment_options.md

@@ -10,90 +10,65 @@
 
 There are 4 main options for deploying with the LangGraph Platform:
 
-1. **[Self-Hosted Lite](#self-hosted-lite)**: Available for all plans.
+1. **[Cloud SaaS](#cloud-saas)**: Available for **Plus** and **Enterprise** plans.
 
-2. **[Self-Hosted Enterprise](#self-hosted-enterprise)**: Available for the **Enterprise** plan.
+1. **[Self-Hosted Data Plane](#self-hosted-data-plane)**: Available for the **Enterprise** plan.
 
-3. **[Cloud SaaS](#cloud-saas)**: Available for **Plus** and **Enterprise** plans.
+1. **[Self-Hosted Control Plane](#self-hosted-control-plane)**: Available for the **Enterprise** plan.
 
-4. **[Bring Your Own Cloud](#bring-your-own-cloud)**: Available only for **Enterprise** plans and **only on AWS**.
+1. **[Standalone Container](#standalone-container)**: Available for all plans.
 
 Please see the [LangGraph Platform Plans](./plans.md) for more information on the different plans.
 
 The guide below will explain the differences between the deployment options.
 
-## Self-Hosted Enterprise
-
-!!! important
-
-    The Self-Hosted Enterprise version is only available for the **Enterprise** plan.
-
-!!! warning "Note"
-
-    The LangGraph Platform Deployments view is optionally available for Self-Hosted Enterprise LangGraph deployments. With one click, self-hosted LangGraph deployments can be deployed in the same Kubernetes cluster where a self-hosted LangSmith instance is deployed.
-
-With a Self-Hosted Enterprise deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
-
-You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
-
-For more information, please see:
-
-* [Self-Hosted conceptual guide](./self_hosted.md)
-* [Self-Hosted Deployment how-to guide](../how-tos/deploy-self-hosted.md)
-
-## Self-Hosted Lite
-
-!!! important
-
-    The Self-Hosted Lite version is available for all plans.
-
-!!! warning "Note"
-
-    The LangGraph Platform Deployments view is optionally available for Self-Hosted Lite LangGraph deployments. With one click, self-hosted LangGraph deployments can be deployed in the same Kubernetes cluster where a self-hosted LangSmith instance is deployed.
-
-The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed per year), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.
-
-With a Self-Hosted Lite deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
-
-You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
-
-[Cron jobs](../cloud/how-tos/cron_jobs.md) are not available for Self-Hosted Lite deployments.
-
-For more information, please see:
-
-* [Self-Hosted conceptual guide](./self_hosted.md)
-* [Self-Hosted deployment how-to guide](../how-tos/deploy-self-hosted.md)
-
 ## Cloud SaaS
 
-!!! important
+The [Cloud SaaS](./langgraph_cloud.md) deployment option is a fully managed model for deployment where we manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in our cloud. This option provides a simple way to deploy and manage your LangGraph Servers.
 
-    The Cloud SaaS version of LangGraph Platform is only available for **Plus** and **Enterprise** plans.
-
-The [Cloud SaaS](./langgraph_cloud.md) version of LangGraph Platform is hosted as part of [LangSmith](https://smith.langchain.com/).
-
-The Cloud SaaS version of LangGraph Platform provides a simple way to deploy and manage your LangGraph applications.
-
-This deployment option provides access to the LangGraph Platform UI (within LangSmith) and an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.
+Connect your GitHub repositories to the platform and deploy your LangGraph Servers from the [Control Plane UI](./langgraph_control_plane.md#control-plane-ui). The build process (i.e. CI/CD) is managed internally by the platform.
 
 For more information, please see:
 
 * [Cloud SaaS Conceptual Guide](./langgraph_cloud.md)
 * [How to deploy to Cloud SaaS](../cloud/deployment/cloud.md)
 
+## Self-Hosted Data Plane
 
-## Bring Your Own Cloud
+The [Self-Hosted Data Plane](./langgraph_self_hosted_data_plane.md) deployment option is a "hybrid" model for deployemnt where we manage the [control plane](./langgraph_control_plane.md) in our cloud and you manage the [data plane](./langgraph_data_plane.md) in your cloud. This option provides a way to securely manage your data plane infrastructure, while offloading control plane management to us.
 
-!!! important
+Build a Docker image using the [LangGraph CLI](./langgraph_cli.md) and deploy your LangGraph Server from the [Control Plane UI](./langgraph_control_plane.md#control-plane-ui).
 
-    The Bring Your Own Cloud version of LangGraph Platform is only available for **Enterprise** plans.
+Supported Compute Platforms: [Kubernetes](https://kubernetes.io/), [Amazon ECS](https://aws.amazon.com/ecs/) (coming soon!)
 
+For more information, please see:
 
-This combines the best of both worlds for Cloud and Self-Hosted. Create your deployments through the LangGraph Platform UI (within LangSmith) and we manage the infrastructure so you don't have to. The infrastructure all runs within your cloud. This is currently only available on AWS.
+* [Self-Hosted Data Plane Conceptual Guide](./langgraph_self_hosted_data_plane.md)
+* [How to deploy the Self-Hosted Data Plane](../cloud/deployment/self_hosted_data_plane.md)
 
-For more information please see:
+## Self-Hosted Control Plane
 
-* [Bring Your Own Cloud Conceptual Guide](./bring_your_own_cloud.md)
+The [Self-Hosted Control Plane](./langgraph_self_hosted_control_plane.md) deployment option is a fully self-hosted model for deployment where you manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in your cloud. This option give you full control and responsibility of the control plane and data plane infrastructure.
+
+Build a Docker image using the [LangGraph CLI](./langgraph_cli.md) and deploy your LangGraph Server from the [Control Plane UI](./langgraph_control_plane.md#control-plane-ui).
+
+Supported Compute Platforms: [Kubernetes](https://kubernetes.io/)
+
+For more information, please see:
+
+* [Self-Hosted Control Plane Conceptual Guide](./langgraph_self_hosted_control_plane.md)
+* [How to deploy the Self-Hosted Control Plane](../cloud/deployment/self_hosted_control_plane.md)
+
+## Standalone Container
+
+The [Standalone Container](./langgraph_standalone_container.md) deployment option is the least restrictive model for deployment. Deploy standalone instances of a LangGraph Server in your cloud.
+
+Build a Docker image using the [LangGraph CLI](./langgraph_cli.md) and deploy your LangGraph Server using the container deployment tooling of your choice. Images can be deployed to any compute platform.
+
+For more information, please see:
+
+* [Sandalone Container Conceptual Guide](./langgraph_standalone_container.md)
+* [How to deploy a Standalone Container](../cloud/deployment/standalone_container.md)
 
 ## Related
 

docs/docs/concepts/functional_api.md

@@ -23,9 +23,11 @@ This provides a minimal abstraction for building workflows with state management
 Below we demonstrate a simple application that writes an essay and [interrupts](human_in_the_loop.md) to request human review.
 
 ```python
+from langgraph.checkpoint.memory import MemorySaver
 from langgraph.func import entrypoint, task
 from langgraph.types import interrupt
 
+
 @task
 def write_essay(topic: str) -> str:
     """Write an essay about the given topic."""

docs/docs/concepts/index.md

@@ -49,7 +49,7 @@ The LangGraph Platform offers a few different deployment options described in th
 - [Why LangGraph Platform?](./langgraph_platform.md): The LangGraph platform is an opinionated way to deploy and manage LangGraph applications. This guide provides an overview of the key features and concepts behind LangGraph Platform.
 - [Platform Architecture](./platform_architecture.md): A high-level overview of the architecture of the LangGraph Platform.
 - [Scalability and Resilience](./scalability_and_resilience.md): LangGraph Platform is designed to be scalable and resilient. This document explains how the platform achieves this.
-- [Deployment Options](./deployment_options.md): LangGraph Platform offers four deployment options: [Self-Hosted Lite](./self_hosted.md#self-hosted-lite), [Self-Hosted Enterprise](./self_hosted.md#self-hosted-enterprise), [bring your own cloud (BYOC)](./bring_your_own_cloud.md), and [Cloud SaaS](./langgraph_cloud.md). This guide explains the differences between these options, and which Plans they are available on.
+- [Deployment Options](./deployment_options.md): LangGraph Platform offers four deployment options: [Cloud SaaS](./langgraph_cloud.md), [Self-Hosted Data Plane](./langgraph_self_hosted_data_plane.md), [Self-Hosted Control Plane](./langgraph_self_hosted_control_plane.md), and [Standalone Container](./langgraph_standalone_container.md). This guide explains the differences between these options, and which Plans they are available on.
 - [Plans](./plans.md): LangGraph Platforms offer three different plans: Developer, Plus, Enterprise. This guide explains the differences between these options, what deployment options are available for each, and how to sign up for each one.
 - [Template Applications](./template_applications.md): Reference applications designed to help you get started quickly when building with LangGraph.
 
@@ -62,6 +62,8 @@ The LangGraph Platform comprises several components that work together to suppor
 - [LangGraph CLI](./langgraph_cli.md): LangGraph CLI is a command-line interface that helps to interact with a local LangGraph
 - [Python/JS SDK](./sdk.md): The Python/JS SDK provides a programmatic way to interact with deployed LangGraph Applications.
 - [Remote Graph](../how-tos/use-remote-graph.md): A RemoteGraph allows you to interact with any deployed LangGraph application as though it were running locally.
+- [LangGraph Control Plane](./langgraph_control_plane.md): The LangGraph Control Plane refers to the Control Plane UI where users create and update LangGraph Servers and the Control Plane APIs that support the UI experience.
+- [LangGraph Data Plane](./langgraph_data_plane.md): The LangGraph Data Plane refers to LangGraph Servers, the corresponding infrastructure for each server, and the "listener" application that continuously polls for updates from the LangGraph Control Plane.
 
 ### LangGraph Server
 
@@ -74,7 +76,7 @@ The LangGraph Platform comprises several components that work together to suppor
 
 ### Deployment Options
 
-- [Self-Hosted Lite](./self_hosted.md): A free (up to 1 million nodes executed per year), limited version of LangGraph Platform that you can run locally or in a self-hosted manner
-- [Cloud SaaS](./langgraph_cloud.md): Hosted as part of LangSmith.
-- [Bring Your Own Cloud](./bring_your_own_cloud.md): We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud.
-- [Self-Hosted Enterprise](./self_hosted.md): Completely managed by you.
+- [Cloud SaaS](../concepts/langgraph_cloud.md): Connect to your GitHub repositories and deploy LangGraph Servers to LangChain's cloud. We manage everything.
+- [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md): Create deployments from the [Control Plane UI](../concepts/langgraph_control_plane.md#control-plane-ui) and deploy LangGraph Servers to your cloud. We manage the [control plane](../concepts/langgraph_control_plane.md), you manage the deployments.
+- [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md#control-plane-ui): Create deployments from a self-hosted [Control Plane UI](../concepts/langgraph_control_plane.md) and deploy LangGraph Servers to your cloud. You manage everything.
+- [Standalone Container](../concepts/langgraph_standalone_container.md): Deploy LangGraph Server Docker images however you like.

docs/docs/concepts/langgraph_cloud.md

@@ -1,107 +1,17 @@
 # Cloud SaaS
 
-!!! info "Prerequisites" 
-    - [LangGraph Platform](./langgraph_platform.md) 
-    - [LangGraph Server](./langgraph_server.md)
+To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy to Cloud SaaS](../cloud/deployment/cloud.md).
 
 ## Overview
 
-LangGraph's Cloud SaaS is a managed service for deploying LangGraph Servers, regardless of its definition or dependencies. The service offers managed implementations of checkpointers and stores, allowing you to focus on building the right cognitive architecture for your use case. By handling scalable & secure infrastructure, LangGraph Cloud SaaS offers the fastest path to getting your LangGraph Server deployed to production.
+The Cloud SaaS deployment option is a fully managed model for deployment where we manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in our cloud.
 
-## Deployment
-
-A **deployment** is an instance of a LangGraph Server. A single deployment can have many [revisions](#revision). When a deployment is created, all the necessary infrastructure (e.g. database, containers, secrets store) are automatically provisioned. See the [architecture diagram](#architecture) below for more details.
-
-Resource Allocation:
-
-| **Deployment Type** | **CPU** | **Memory** | **Scaling**         |
-|---------------------|---------|------------|---------------------|
-| Development         | 1 CPU   | 1 GB       | Up to 1 container   |
-| Production          | 2 CPU   | 2 GB       | Up to 10 containers |
-
-CPU and memory resources are per container.
-
-For **Production type** deployments, resources can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.
-
-See the [how-to guide](../cloud/deployment/cloud.md#create-new-deployment) for creating a new deployment.
-
-## Revision
-
-A revision is an iteration of a [deployment](#deployment). When a new deployment is created, an initial revision is automatically created. To deploy new code changes or update environment variable configurations for a deployment, a new revision must be created. When a revision is created, a new container image is built automatically.
-
-See the [how-to guide](../cloud/deployment/cloud.md#create-new-revision) for creating a new revision.
-
-## Persistence
-
-A dedicated database is automatically created for each deployment. The database serves as the [persistence layer](../concepts/persistence.md) for the deployment.
-
-When defining a graph to be deployed to LangGraph Cloud SaaS, a [checkpointer](../concepts/persistence.md#checkpointer-libraries) should not be configured by the user. Instead, a checkpointer is automatically configured for the graph.
-
-There is no direct access to the database. All access to the database occurs through the LangGraph Server APIs.
-
-The database is never deleted until the deployment itself is deleted. See [Automatic Deletion](#automatic-deletion) for additional details.
-
-## Autoscaling
-`Production` type deployments automatically scale up to 10 containers. Scaling is based on the current request load for a single container. Specifically, the autoscaling implementation scales the deployment so that each container is processing about 10 concurrent requests. For example... 
-
-- If the deployment is processing 20 concurrent requests, the deployment will scale up from 1 container to 2 containers (20 requests / 2 containers = 10 requests per container).
-- If a deployment of 2 containers is processing 10 requests, the deployment will scale down from 2 containers to 1 container (10 requests / 1 container = 10 requests per container).
-
-10 concurrent requests per container is the target threshold. However, 10 concurrent requests per container is not a hard limit. The number of concurrent requests can exceed 10 if there is a sudden burst of requests.
-
-Scale down actions are delayed for 30 minutes before any action is taken. In other words, if the autoscaling implementation decides to scale down a deployment, it will first wait for 30 minutes before scaling down. After 30 minutes, the concurrency metric is recomputed and the deployment will scale down if the concurrency metric has met the target threshold. Otherwise, the deployment remains scaled up. This "cool down" period ensures that deployments do not scale up and down too frequently.
-
-In the future, the autoscaling implementation may evolve to accommodate other metrics such as background run queue size.
-
-## Asynchronous Deployment
-
-Infrastructure for [deployments](#deployment) and [revisions](#revision) are provisioned and deployed asynchronously. They are not deployed immediately after submission. Currently, deployment can take up to several minutes.
-
-- When a new deployment is created, a new database is created for the deployment. Database creation is a one-time step. This step contributes to a longer deployment time for the initial revision of the deployment.
-- When a subsequent revision is created for a deployment, there is no database creation step. The deployment time for a subsequent revision is significantly faster compared to the deployment time of the initial revision.
-- The deployment process for each revision contains a build step, which can take up to a few minutes.
-
-## LangSmith Integration
-
-A [LangSmith](https://docs.smith.langchain.com/) tracing project is automatically created for each deployemnt. The tracing project has the same name as the deployment. When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set internally, automatically. Traces are created for each run and are emitted to the tracing project automatically.
-
-When a deployment is deleted, the traces and the tracing project are not deleted.
-
-## Automatic Deletion
-
-Deployments are automatically deleted after 28 consecutive days of non-use (it is in an unused state). A deployment is in an unused state if there are no traces emitted to LangSmith from the deployment after 28 consecutive days. On any given day, if a deployment emits a trace to LangSmith, the counter for consecutive days of non-use is reset.
-
-- An email notification is sent after 7 consecutive days of non-use.
-- A deployment is deleted after 28 consecutive days of non-use.
-
-!!! danger "Data Cannot Be Recovered"
-    After a deployment is deleted, the data (i.e. [persistence](#persistence)) from the deployment cannot be recovered.
+|                   | [Control Plane](../concepts/langgraph_control_plane.md) | [Data Plane](../concepts/langgraph_data_plane.md) |
+|-------------------|-------------------|------------|
+| **What is it?** | <ul><li>Control Plane UI for creating deployments and revisions</li><li>Control Plane APIs for creating deployments and revisions</li></ul> | <ul><li>Data plane "listener" for reconciling deployments with control plane state</li><li>LangGraph Servers</li><li>Postgres, Redis, etc</li></ul> |
+| **Where is it hosted?** | LangChain's cloud | LangChain's cloud |
+| **Who provisions and manages it?** | LangChain | LangChain |
 
 ## Architecture
 
-!!! warning "Subject to Change"
-    The Cloud SaaS deployment architecture may change in the future.
-
-A high-level diagram of a Cloud SaaS deployment.
-
-![diagram](img/langgraph_cloud_architecture.png)
-
-## Whitelisting IP Addresses
-
-All traffic from `LangGraph Platform` deployments created after January 6th 2025 will come through a NAT gateway.
-This NAT gateway will have several static ip addresses depending on the region you are deploying in. Refer to the table below for the list of IP addresses to whitelist:
-
-| US             | EU             |
-|----------------|----------------|
-| 35.197.29.146  | 34.13.192.67   |
-| 34.145.102.123 | 34.147.105.64  |
-| 34.169.45.153  | 34.90.22.166   |
-| 34.82.222.17   | 34.147.36.213  |
-| 35.227.171.135 | 34.32.137.113  | 
-| 34.169.88.30   | 34.91.238.184  |
-| 34.19.93.202   | 35.204.101.241 |
-| 34.19.34.50    | 35.204.48.32   |
-
-## Related
-
-- [Deployment Options](./deployment_options.md)
+![Cloud SaaS](./img/self_hosted_control_plane_architecture.png)

docs/docs/concepts/langgraph_control_plane.md

@@ -0,0 +1,98 @@
+# LangGraph Control Plane
+
+The term "control plane" is used broadly to refer to the Control Plane UI where users create and update [LangGraph Servers](./langgraph_server.md) (deployments) and the Control Plane APIs that support the UI experience.
+
+When a user makes an update through the Control Plane UI, the update is stored in the control plane state. The [LangGraph Data Plane](./langgraph_data_plane.md) "listener" application polls for these updates by calling the Control Plane APIs.
+
+## Control Plane UI
+
+From the Control Plane UI, you can:
+
+- View a list of outstanding deployments.
+- View details of an individual deployment.
+- Create a new deployment.
+- Update a deployment.
+- Update environment variables for a deployment.
+- View build and server logs of a deployment.
+- Delete a deployment.
+
+The Control Plane UI is embedded in [LangSmith](https://docs.smith.langchain.com/langgraph_cloud).
+
+## Control Plane API
+
+This section describes data model of the LangGraph Control Plane API. Control Plane API is used to create, update, and delete deployments. However, they are not publicly accessible.
+
+### Deployment
+
+A deployment is an instance of a LangGraph Server. A single deployment can have many revisions.
+
+### Revision
+
+A revision is an iteration of a deployment. When a new deployment is created, an initial revision is automatically created. To deploy code changes or update environment variables for a deployment, a new revision must be created.
+
+### Environment Variable
+
+Environment variables are set for a deployment. All environment variables are stored as secrets (i.e. saved in a secrets store).
+
+## Control Plane Features
+
+This section describes various features of the control plane.
+
+### Deployment Types
+
+For simplicity, the control plane offers two deployment types with different resource allocations: `Development` and `Production`.
+
+| **Deployment Type** | **CPU** | **Memory** | **Scaling**         |
+|---------------------|---------|------------|---------------------|
+| Development         | 1 CPU   | 1 GB       | Up to 1 container   |
+| Production          | 2 CPU   | 2 GB       | Up to 10 containers |
+
+CPU and memory resources are per container.
+
+!!! info "For [Cloud SaaS](../concepts/langgraph_cloud.md)"
+    For `Production` type deployments, resources can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.
+
+!!! info "For [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md)"
+    Resources for [Self-Hosted Data Plane](../concepts/langgraph_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_control_plane.md) deployments can be fully customized.
+
+### Database Provisioning
+
+The control plane and [LangGraph Data Plane](./langgraph_data_plane.md) "listener" application coordinate to automatically create a Postgres database for each deployment. The database serves as the [persistence layer](../concepts/persistence.md) for the deployment.
+
+When implementing a LangGraph application, a [checkpointer](../concepts/persistence.md#checkpointer-libraries) does not need to be configured by the developer. Instead, a checkpointer is automatically configured for the graph. Any checkpointer configured for a graph will be replaced by the one that is automatically configured.
+
+There is no direct access to the database. All access to the database occurs through the [LangGraph Server](../concepts/langgraph_server.md).
+
+The database is never deleted until the deployment itself is deleted. See [Automatic Deletion](#automatic-deletion) for additional details.
+
+!!! info "For [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md)"
+    A custom Postgres instance can be configured for [Self-Hosted Data Plane](../concepts/langgraph_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_control_plane.md) deployments.
+
+### Asynchronous Deployment
+
+Infrastructure for deployments and revisions are provisioned and deployed asynchronously. They are not deployed immediately after submission. Currently, deployment can take up to several minutes.
+
+- When a new deployment is created, a new database is created for the deployment. Database creation is a one-time step. This step contributes to a longer deployment time for the initial revision of the deployment.
+- When a subsequent revision is created for a deployment, there is no database creation step. The deployment time for a subsequent revision is significantly faster compared to the deployment time of the initial revision.
+- The deployment process for each revision contains a build step, which can take up to a few minutes.
+
+The control plane and [LangGraph Data Plane](./langgraph_data_plane.md) "listener" application coordinate to achieve asynchronous deployments.
+
+### Automatic Deletion
+
+!!! info "Only for [Cloud SaaS](../concepts/langgraph_cloud.md)"
+    Automatic deletion of deployments is only available for [Cloud SaaS](../concepts/langgraph_cloud.md).
+
+The control plane automatically deletes deployments after 28 consecutive days of non-use (it is in an unused state). A deployment is in an unused state if there are no traces emitted to LangSmith from the deployment after 28 consecutive days. On any given day, if a deployment emits a trace to LangSmith, the counter for consecutive days of non-use is reset.
+
+- An email notification is sent after 7 consecutive days of non-use.
+- A deployment is deleted after 28 consecutive days of non-use.
+
+!!! danger "Data Cannot Be Recovered"
+    After a deployment is deleted, the data (e.g. Postgres) from the deployment cannot be recovered.
+
+### LangSmith Integration
+
+A [LangSmith](https://docs.smith.langchain.com/) tracing project is automatically created for each deployment. The tracing project has the same name as the deployment. When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set automatically by the control plane.
+
+When a deployment is deleted, the traces and the tracing project are not deleted.

docs/docs/concepts/langgraph_data_plane.md

@@ -0,0 +1,74 @@
+# LangGraph Data Plane
+
+The term "data plane" is used broadly to refer to [LangGraph Servers](./langgraph_server.md) (deployments), the corresponding infrastructure for each server, and the "listener" application that continuously polls for updates from the [LangGraph Control Plane](./langgraph_control_plane.md).
+
+## Server Infrastructure
+
+In addition to the [LangGraph Server](./langgraph_server.md) itself, the following infrastructure for each server are also included in the broad definition of "data plane":
+
+- [Postgres](../concepts/platform_architecture.md#how-we-use-postgres)
+- [Redis](../concepts/platform_architecture.md#how-we-use-redis)
+- Secrets store
+- Autoscalers
+
+See [LangGraph Platform Architecture](../concepts/platform_architecture.md) for more details.
+
+## "Listener" Application
+
+The data plane "listener" application periodically calls [Control Plane APIs](../concepts/langgraph_control_plane.md#control-plane-api) to:
+
+- Determine if new deployments should be created.
+- Determine if existing deployments should be updated (i.e. new revisions).
+- Determine if existing deployments should be deleted.
+
+In other words, the data plane "listener" reads the latest state of the control plane (desired state) and takes action to reconcile outstanding deployments (current state) to match the latest state.
+
+## Data Plane Features
+
+This section describes various features of the data plane.
+
+### Lite vs Enterprise
+
+There are two versions of the LangGraph Server: `Lite` and `Enterprise`.
+
+The `Lite` version is a limited version of the LangGraph Server that you can run locally or in a self-hosted manner (up to 1 million nodes executed per year). `Lite` is only available for the [Standalone Container](../concepts/langgraph_standalone_container.md) deployment option.
+
+The `Enterprise` version is the full version of the LangGraph Server. To use the `Enterprise` version, you must acquire a license key that you will need to specify when running the Docker image. To acquire a license key, please email sales@langchain.dev. `Enterprise` is available for [Cloud SaaS](../concepts/langgraph_cloud.md), [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md), and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployment options.
+
+Feature Differences:
+
+|       | Lite       | Enterprise |
+|-------|------------|------------|
+| [Cron Jobs](../concepts/langgraph_server.md#cron-jobs) |❌|✅|
+| [Custom Authentication](../concepts/auth.md) |❌|✅|
+
+### Autoscaling
+
+[`Production` type](../concepts/langgraph_control_plane.md#deployment-types) deployments automatically scale up to 10 containers. Scaling is based on the current request load for a single container. Specifically, the autoscaling implementation scales the deployment so that each container is processing about 10 concurrent requests. For example... 
+
+- If the deployment is processing 20 concurrent requests, the deployment will scale up from 1 container to 2 containers (20 requests / 2 containers = 10 requests per container).
+- If a deployment of 2 containers is processing 10 requests, the deployment will scale down from 2 containers to 1 container (10 requests / 1 container = 10 requests per container).
+
+10 concurrent requests per container is the target threshold. However, 10 concurrent requests per container is not a hard limit. The number of concurrent requests can exceed 10 if there is a sudden burst of requests.
+
+Scale down actions are delayed for 30 minutes before any action is taken. In other words, if the autoscaling implementation decides to scale down a deployment, it will first wait for 30 minutes before scaling down. After 30 minutes, the concurrency metric is recomputed and the deployment will scale down if the concurrency metric has met the target threshold. Otherwise, the deployment remains scaled up. This "cool down" period ensures that deployments do not scale up and down too frequently.
+
+In the future, the autoscaling implementation may evolve to accommodate other metrics such as background run queue size.
+
+### Static IP Addresses
+
+!!! info "Only for Cloud SaaS"
+    Static IP addresses are only available for [Cloud SaaS](../concepts/langgraph_cloud.md).
+
+All traffic from deployments created after January 6th 2025 will come through a NAT gateway. This NAT gateway will have several static IP addresses depending on the data region. Refer to the table below for the list of static IP addresses:
+
+| US             | EU             |
+|----------------|----------------|
+| 35.197.29.146  | 34.13.192.67   |
+| 34.145.102.123 | 34.147.105.64  |
+| 34.169.45.153  | 34.90.22.166   |
+| 34.82.222.17   | 34.147.36.213  |
+| 35.227.171.135 | 34.32.137.113  | 
+| 34.169.88.30   | 34.91.238.184  |
+| 34.19.93.202   | 35.204.101.241 |
+| 34.19.34.50    | 35.204.48.32   |

docs/docs/concepts/langgraph_platform.md

@@ -16,6 +16,8 @@ The LangGraph Platform consists of several components that work together to supp
 - [LangGraph CLI](./langgraph_cli.md): LangGraph CLI is a command-line interface that helps to interact with a local LangGraph
 - [Python/JS SDK](./sdk.md): The Python/JS SDK provides a programmatic way to interact with deployed LangGraph Applications.
 - [Remote Graph](../how-tos/use-remote-graph.md): A RemoteGraph allows you to interact with any deployed LangGraph application as though it were running locally.
+- [LangGraph Control Plane](./langgraph_control_plane.md): The LangGraph Control Plane refers to the Control Plane UI where users create and update LangGraph Servers and the Control Plane APIs that support the UI experience.
+- [LangGraph Data Plane](./langgraph_data_plane.md): The LangGraph Data Plane refers to LangGraph Servers, the corresponding infrastructure for each server, and the "listener" application that continuously polls for updates from the LangGraph Control Plane.
 
 ![](img/lg_platform.png)
 

docs/docs/concepts/langgraph_self_hosted_control_plane.md

@@ -0,0 +1,23 @@
+# Self-Hosted Control Plane
+
+To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy the Self-Hosted Control Plane](../cloud/deployment/self_hosted_control_plane.md).
+
+## Overview
+
+The Self-Hosted Control Plane deployment option is a fully self-hosted model for deployment where you manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in your cloud (this option implies that the data plane is self-hosted).
+
+|                   | [Control Plane](../concepts/langgraph_control_plane.md) | [Data Plane](../concepts/langgraph_data_plane.md) |
+|-------------------|-------------------|------------|
+| **What is it?** | <ul><li>Control Plane UI for creating deployments and revisions</li><li>Control Plane APIs for creating deployments and revisions</li></ul> | <ul><li>Data plane "listener" for reconciling deployments with control plane state</li><li>LangGraph Servers</li><li>Postgres, Redis, etc</li></ul> |
+| **Where is it hosted?** | Your cloud | Your cloud |
+| **Who provisions and manages it?** | You | You |
+
+## Architecture
+
+![Self-Hosted Control Plane Architecture](./img/self_hosted_control_plane_architecture.png)
+
+## Compute Platforms
+
+### Kubernetes
+
+The Self-Hosted Control Plane deployment option supports deploying control plane and data plane infrastructure to any Kubernetes cluster.

docs/docs/concepts/langgraph_self_hosted_data_plane.md

@@ -0,0 +1,27 @@
+# Self-Hosted Data Plane
+
+To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy the Self-Hosted Data Plane](../cloud/deployment/self_hosted_data_plane.md).
+
+## Overview
+
+LangGraph Platform's Self-Hosted Data Plane deployment option is a "hybrid" model for deployemnt where we manage the [control plane](./langgraph_control_plane.md) in our cloud and you manage the [data plane](./langgraph_data_plane.md) in your cloud.
+
+|                   | [Control Plane](../concepts/langgraph_control_plane.md) | [Data Plane](../concepts/langgraph_data_plane.md) |
+|-------------------|-------------------|------------|
+| **What is it?** | <ul><li>Control Plane UI for creating deployments and revisions</li><li>Control Plane APIs for creating deployments and revisions</li></ul> | <ul><li>Data plane "listener" for reconciling deployments with control plane state</li><li>LangGraph Servers</li><li>Postgres, Redis, etc</li></ul> |
+| **Where is it hosted?** | LangChain's cloud | Your cloud |
+| **Who provisions and manages it?** | LangChain | You |
+
+## Architecture
+
+![Self-Hosted Data Plane Architecture](./img/self_hosted_data_plane_architecture.png)
+
+## Compute Platforms
+
+### Kubernetes
+
+The Self-Hosted Data Plane deployment option supports deploying data plane infrastructure to any Kubernetes cluster.
+
+### Amazon ECS
+
+Coming soon...

docs/docs/concepts/langgraph_standalone_container.md

@@ -0,0 +1,27 @@
+# Standalone Container
+
+To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy a Standalone Container](../cloud/deployment/standalone_container.md).
+
+## Overview
+
+The Standalone Container deployment option is the least restrictive model for deployment. There is no [control plane](./langgraph_control_plane.md). [Data plane](./langgraph_data_plane.md) infrastructure is managed by you.
+
+|                   | [Control Plane](../concepts/langgraph_control_plane.md) | [Data Plane](../concepts/langgraph_data_plane.md) |
+|-------------------|-------------------|------------|
+| **What is it?** | n/a | <ul><li>LangGraph Servers</li><li>Postgres, Redis, etc</li></ul> |
+| **Where is it hosted?** | n/a | Your cloud |
+| **Who provisions and manages it?** | n/a | You |
+
+## Architecture
+
+![Standalone Container](./img/langgraph_platform_deployment_architecture.png)
+
+## Compute Platforms
+
+### Kubernetes
+
+The Standalone Container deployment option supports deploying data plane infrastructure to a Kubernetes cluster.
+
+### Docker
+
+The Standalone Container deployment option supports deploying data plane infrastructure to any Docker-supported compute platform.

docs/docs/concepts/multi_agent.md

@@ -50,7 +50,7 @@ def agent(state) -> Command[Literal["agent", "another_agent"]]:
 In a more complex scenario where each agent node is itself a graph (i.e., a [subgraph](./low_level.md#subgraphs)), a node in one of the agent subgraphs might want to navigate to a different agent. For example, if you have two agents, `alice` and `bob` (subgraph nodes in a parent graph), and `alice` needs to navigate to `bob`, you can set `graph=Command.PARENT` in the `Command` object:
 
 ```python
-def some_node_inside_alice(state)
+def some_node_inside_alice(state):
     return Command(
         goto="bob",
         update={"my_state_key": "my_state_value"},

docs/docs/how-tos/index.md

@@ -163,6 +163,7 @@ These guides show how to use the prebuilt ReAct agent:
 - [How to add human-in-the-loop processes to a ReAct agent](create-react-agent-hitl.ipynb)
 - [How to return structured output from a ReAct agent](create-react-agent-structured-output.ipynb)
 - [How to add semantic search for long-term memory to a ReAct agent](memory/semantic-search.ipynb#using-in-create-react-agent)
+- [How to manage message history in a ReAct agent](create-react-agent-manage-message-history.ipynb)
 
 Interested in further customizing the ReAct agent? This guide provides an
 overview of its underlying implementation to help you customize for your own needs:
@@ -201,11 +202,14 @@ Learn how to set up your app for deployment to LangGraph Platform:
 
 ### Deployment
 
-LangGraph applications can be deployed using LangGraph Cloud, which provides a range of services to help you deploy, manage, and scale your applications.
+LangGraph applications can be deployed using LangGraph Platform, which provides a range of services to help you deploy, manage, and scale your applications.
 
-- [How to deploy to LangGraph cloud](../cloud/deployment/cloud.md)
-- [How to deploy to a self-hosted environment](./deploy-self-hosted.md)
+- [How to deploy to Cloud SaaS](../cloud/deployment/cloud.md)
+- [How to deploy the Self-Hosted Data Plane](../cloud/deployment/self_hosted_data_plane.md)
+- [How to deploy the Self-Hosted Control Plane](../cloud/deployment/self_hosted_control_plane.md)
+- [How to deploy a Standalone Container](../cloud/deployment/standalone_container.md)
 - [How to interact with the deployment using RemoteGraph](./use-remote-graph.md)
+- [How to add TTLs to your LangGraph application](./ttl/configure_ttl.md)
 
 ### Authentication & Access Control
 

docs/docs/how-tos/ttl/configure_ttl.md

@@ -0,0 +1,102 @@
+# How to add TTLs to your LangGraph application
+
+!!! tip "Prerequisites"
+
+    This guide assumes familiarity with the [LangGraph Platform](../../concepts/index.md#langgraph-platform), [Persistence](../../concepts/persistence.md), and [Cross-thread persistence](../../concepts/store.md) concepts.
+
+???+ note "LangGraph platform only"
+    
+    TTLs are only supported for LangGraph platform deployments. This guide does not apply to LangGraph OSS.
+
+The LangGraph Platform persists both [checkpoints](../../concepts/persistence.md#checkpoints) (thread state) and [cross-thread memories](../../concepts/persistence.md#memory-store) (store items). Configure Time-to-Live (TTL) policies in `langgraph.json` to automatically manage the lifecycle of this data, preventing indefinite accumulation.
+
+## Configuring Checkpoint TTL
+
+Checkpoints capture the state of conversation threads. Setting a TTL ensures old checkpoints and threads are automatically deleted.
+
+Add a `checkpointer.ttl` configuration to your `langgraph.json` file:
+
+```json
+{
+  "dependencies": ["."],
+  "graphs": {
+    "agent": "./agent.py:graph"
+  },
+  "checkpointer": {
+    "ttl": {
+      "strategy": "delete",
+      "sweep_interval_minutes": 60,
+      "default_ttl": 43200 
+    }
+  }
+}
+```
+
+*   `strategy`: Specifies the action taken on expiration. Currently, only `"delete"` is supported, which deletes all checkpoints in the thread upon expiration.
+*   `sweep_interval_minutes`: Defines how often, in minutes, the system checks for expired checkpoints.
+*   `default_ttl`: Sets the default lifespan of checkpoints in minutes (e.g., 43200 minutes = 30 days).
+
+## Configuring Store Item TTL
+
+Store items allow cross-thread data persistence. Configuring TTL for store items helps manage memory by removing stale data.
+
+Add a `store.ttl` configuration to your `langgraph.json` file:
+
+```json
+{
+  "dependencies": ["."],
+  "graphs": {
+    "agent": "./agent.py:graph"
+  },
+  "store": {
+    "ttl": {
+      "refresh_on_read": true,
+      "sweep_interval_minutes": 120,
+      "default_ttl": 10080
+    }
+  }
+}
+```
+
+*   `refresh_on_read`: (Optional, default `true`) If `true`, accessing an item via `get` or `search` resets its expiration timer. If `false`, TTL only refreshes on `put`.
+*   `sweep_interval_minutes`: (Optional) Defines how often, in minutes, the system checks for expired items. If omitted, no sweeping occurs.
+*   `default_ttl`: (Optional) Sets the default lifespan of store items in minutes (e.g., 10080 minutes = 7 days). If omitted, items do not expire by default.
+
+## Combining TTL Configurations
+
+You can configure TTLs for both checkpoints and store items in the same `langgraph.json` file to set different policies for each data type. Here is an example:
+
+```json
+{
+  "dependencies": ["."],
+  "graphs": {
+    "agent": "./agent.py:graph"
+  },
+  "checkpointer": {
+    "ttl": {
+      "strategy": "delete",
+      "sweep_interval_minutes": 60,
+      "default_ttl": 43200
+    }
+  },
+  "store": {
+    "ttl": {
+      "refresh_on_read": true,
+      "sweep_interval_minutes": 120,
+      "default_ttl": 10080
+    }
+  }
+}
+```
+
+## Runtime Overrides
+
+The default `store.ttl` settings from `langgraph.json` can be overridden at runtime by providing specific TTL values in SDK method calls like `get`, `put`, and `search`.
+
+## Deployment Process
+
+After configuring TTLs in `langgraph.json`, deploy or restart your LangGraph application for the changes to take effect. Use `langgraph dev` for local development or `langgraph up` for Docker deployment.
+
+
+See the [langgraph.json CLI reference][configuration-file] for more details on the other configurable options.
+

docs/docs/tutorials/deployment.md

@@ -17,7 +17,18 @@ Get started deploying your LangGraph applications locally or on the cloud with
 
 ## Deployment Options
 
-- [Self-Hosted Lite](../concepts/self_hosted.md): A free (up to 1 million nodes executed per year), limited version of LangGraph Platform that you can run locally or in a self-hosted manner
-- [Cloud SaaS](../concepts/langgraph_cloud.md): Hosted as part of LangSmith.
-- [Bring Your Own Cloud](../concepts/bring_your_own_cloud.md): We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud.
-- [Self-Hosted Enterprise](../concepts/self_hosted.md): Completely managed by you.
+- [Cloud SaaS](../concepts/langgraph_cloud.md): Connect to your GitHub repositories and deploy LangGraph Servers to LangChain's cloud. We manage everything.
+- [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md): Create deployments from the [Control Plane UI](../concepts/langgraph_control_plane.md#control-plane-ui) and deploy LangGraph Servers to your cloud. We manage the [control plane](../concepts/langgraph_control_plane.md), you manage the deployments.
+- [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md#control-plane-ui): Create deployments from a self-hosted [Control Plane UI](../concepts/langgraph_control_plane.md) and deploy LangGraph Servers to your cloud. You manage everything.
+- [Standalone Container](../concepts/langgraph_standalone_container.md): Deploy LangGraph Server Docker images however you like.
+
+A quick comparison...
+
+|                      | **Cloud SaaS** | **Self-Hosted [Data Plane](../concepts/langgraph_data_plane.md)** | **Self-Hosted [Control Plane](../concepts/langgraph_control_plane.md)** | **Standalone Container** |
+|----------------------|----------------|----------------------------|-------------------------------|--------------------------|
+| **[Control Plane UI/API](../concepts/langgraph_control_plane.md)** | Yes | Yes | Yes | No |
+| **CI/CD** | Managed internally by platform | Managed externally by you | Managed externally by you | Managed externally by you |
+| **Data/Compute Residency** | LangChain’s cloud | Your cloud | Your cloud | Your cloud |
+| **Required Permissions** | None | See details [here](). | See details [here](). | None |
+| **LangSmith Compatibility** | Trace to LangSmith SaaS | Trace to LangSmith SaaS | Trace to Self-Hosted LangSmith | Optional tracing |
+| **[Pricing](https://www.langchain.com/pricing-langgraph-platform)** | Plus | Enterprise | Enterprise | Developer |

docs/docs/tutorials/introduction.ipynb

@@ -279,7 +279,6 @@
     "        if user_input.lower() in [\"quit\", \"exit\", \"q\"]:\n",
     "            print(\"Goodbye!\")\n",
     "            break\n",
-    "\n",
     "        stream_graph_updates(user_input)\n",
     "    except:\n",
     "        # fallback if input() is not available\n",

docs/mkdocs.yml

@@ -185,6 +185,7 @@ nav:
               - how-tos/create-react-agent-system-prompt.ipynb
               - how-tos/create-react-agent-hitl.ipynb
               - how-tos/create-react-agent-structured-output.ipynb
+              - how-tos/create-react-agent-manage-message-history.ipynb
               - how-tos/react-agent-from-scratch.ipynb
               - how-tos/react-agent-from-scratch-functional.ipynb
         - LangGraph Platform:
@@ -202,8 +203,14 @@ nav:
             - Deployment:
                 - Deployment: how-tos#deployment
                 - cloud/deployment/cloud.md
+                - cloud/deployment/self_hosted_data_plane.md
+                - cloud/deployment/self_hosted_control_plane.md
+                - cloud/deployment/standalone_container.md
                 - how-tos/deploy-self-hosted.md
                 - how-tos/use-remote-graph.md
+                - how-tos/ttl/configure_ttl.md
+            - Data Management:
+                - how-tos/ttl/configure_ttl.md
             - Authentication & Access Control:
                 - Authentication & Access Control: how-tos#authentication-access-control
                 - cloud/how-tos/auth/custom_auth_new.md
@@ -285,6 +292,8 @@ nav:
             - concepts/template_applications.md
           - Components:
             - Components: concepts#components
+            - concepts/langgraph_control_plane.md
+            - concepts/langgraph_data_plane.md
             - concepts/langgraph_server.md
             - concepts/langgraph_studio.md
             - concepts/langgraph_cli.md
@@ -298,9 +307,10 @@ nav:
             - concepts/auth.md
           - Deployment Options:
             - Deployment Options: concepts#deployment-options
-            - concepts/self_hosted.md
             - concepts/langgraph_cloud.md
-            - concepts/bring_your_own_cloud.md
+            - concepts/langgraph_self_hosted_data_plane.md
+            - concepts/langgraph_self_hosted_control_plane.md
+            - concepts/langgraph_standalone_container.md
       - Tutorials:
         - tutorials/index.md
         - Quick Start:

docs/overrides/main.html

@@ -1,6 +1,7 @@
 {% extends "base.html" %}
 
 {% block extrahead %}
+  <meta name="algolia-site-verification" content="165B7E7C89E49946" />
   <style>
     @import url("https://fonts.googleapis.com/css2?family=Public+Sans&display=swap");
     :root {

docs/pyproject.toml

@@ -50,7 +50,8 @@ langchain-community = "^0.3.0"
 langchain-experimental = "^0.3.2"
 langchain-mistralai = "^0.2.6"
 langgraph-checkpoint-mongodb = "^0.1.0"
-langsmith = "^0.2.0"
+langmem = "^0.0.19"
+langsmith = "^0.3.0"
 chromadb = "^0.5.5"
 gpt4all = "^2.8.2"
 scikit-learn = "^1.5.2"

libs/checkpoint-sqlite/poetry.lock

@@ -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 2.1.1 and should not be changed by hand.
 
 [[package]]
 name = "aiosqlite"
@@ -51,7 +51,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\""]
 trio = ["trio (>=0.23)"]
 
 [[package]]
@@ -181,7 +181,7 @@ files = [
 [package.extras]
 dev = ["Pygments", "build", "chardet", "pre-commit", "pytest", "pytest-cov", "pytest-dependency", "ruff", "tomli", "twine"]
 hard-encoding-detection = ["chardet"]
-toml = ["tomli"]
+toml = ["tomli ; python_version < \"3.11\""]
 types = ["chardet (>=5.1.0)", "mypy", "pytest", "pytest-cov", "pytest-dependency"]
 
 [[package]]
@@ -267,7 +267,7 @@ idna = "*"
 sniffio = "*"
 
 [package.extras]
-brotli = ["brotli", "brotlicffi"]
+brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
 cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
@@ -326,26 +326,26 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.0"
+version = "0.3.15"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main", "dev"]
 files = [
-    {file = "langchain_core-0.3.0-py3-none-any.whl", hash = "sha256:bee6dae2366d037ef0c5b87401fed14b5497cad26f97724e8c9ca7bc9239e847"},
-    {file = "langchain_core-0.3.0.tar.gz", hash = "sha256:1249149ea3ba24c9c761011483c14091573a5eb1a773aa0db9c8ad155dd4a69d"},
+    {file = "langchain_core-0.3.15-py3-none-any.whl", hash = "sha256:3d4ca6dbb8ed396a6ee061063832a2451b0ce8c345570f7b086ffa7288e4fa29"},
+    {file = "langchain_core-0.3.15.tar.gz", hash = "sha256:b1a29787a4ffb7ec2103b4e97d435287201da7809b369740dd1e32f176325aba"},
 ]
 
 [package.dependencies]
 jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.1.117,<0.2.0"
+langsmith = ">=0.1.125,<0.2.0"
 packaging = ">=23.2,<25"
 pydantic = [
     {version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 PyYAML = ">=5.3"
-tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
+tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10.0.0"
 typing-extensions = ">=4.7"
 
 [[package]]
@@ -368,24 +368,28 @@ url = "../checkpoint"
 
 [[package]]
 name = "langsmith"
-version = "0.1.120"
+version = "0.1.147"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 groups = ["main", "dev"]
 files = [
-    {file = "langsmith-0.1.120-py3-none-any.whl", hash = "sha256:54d2785e301646c0988e0a69ebe4d976488c87b41928b358cb153b6ddd8db62b"},
-    {file = "langsmith-0.1.120.tar.gz", hash = "sha256:25499ca187b41bd89d784b272b97a8d76f60e0e21bdf20336e8a2aa6a9b23ac9"},
+    {file = "langsmith-0.1.147-py3-none-any.whl", hash = "sha256:7166fc23b965ccf839d64945a78e9f1157757add228b086141eb03a60d699a15"},
+    {file = "langsmith-0.1.147.tar.gz", hash = "sha256:2e933220318a4e73034657103b3b1a3a6109cc5db3566a7e8e03be8d6d7def7a"},
 ]
 
 [package.dependencies]
 httpx = ">=0.23.0,<1"
-orjson = ">=3.9.14,<4.0.0"
+orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
 pydantic = [
     {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 requests = ">=2,<3"
+requests-toolbelt = ">=1.0.0,<2.0.0"
+
+[package.extras]
+langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
 
 [[package]]
 name = "mypy"
@@ -454,6 +458,7 @@ description = "Fast, correct Python JSON library supporting dataclasses, datetim
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
+markers = "platform_python_implementation != \"PyPy\""
 files = [
     {file = "orjson-3.10.6-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:fb0ee33124db6eaa517d00890fc1a55c3bfe1cf78ba4a8899d71a06f2d6ff5c7"},
     {file = "orjson-3.10.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c1c4b53b24a4c06547ce43e5fee6ec4e0d8fe2d597f4647fc033fd205707365"},
@@ -858,6 +863,21 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "requests-toolbelt"
+version = "1.0.0"
+description = "A utility belt for advanced users of python-requests"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["main", "dev"]
+files = [
+    {file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"},
+    {file = "requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06"},
+]
+
+[package.dependencies]
+requests = ">=2.0.1,<3.0.0"
+
 [[package]]
 name = "ruff"
 version = "0.6.2"
@@ -952,7 +972,7 @@ files = [
 ]
 
 [package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
 h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]

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

@@ -30,6 +30,7 @@ from langgraph.checkpoint.serde.types import (
 
 V = TypeVar("V", int, float, str)
 PendingWrite = Tuple[str, str, Any]
+# Kept for backwards compat, newer versions of LangGraph no longer use this.
 LATEST_VERSION = 2
 
 
@@ -100,6 +101,7 @@ class Checkpoint(TypedDict):
     Cleared by the next checkpoint."""
 
 
+# Kept for backwards compat, newer versions of LangGraph no longer use this.
 def empty_checkpoint() -> Checkpoint:
     return Checkpoint(
         v=LATEST_VERSION,

libs/checkpoint/poetry.lock

@@ -12,6 +12,29 @@ files = [
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
 ]
 
+[[package]]
+name = "anyio"
+version = "4.9.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
+    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
+
+[package.extras]
+doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+trio = ["trio (>=0.26.1)"]
+
 [[package]]
 name = "certifi"
 version = "2024.7.4"
@@ -177,7 +200,7 @@ version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
-groups = ["dev"]
+groups = ["main", "dev"]
 markers = "python_version < \"3.11\""
 files = [
     {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
@@ -187,6 +210,65 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
+name = "h11"
+version = "0.14.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
+    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.7"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd"},
+    {file = "httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c"},
+]
+
+[package.dependencies]
+certifi = "*"
+h11 = ">=0.13,<0.15"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<1.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.28.1"
+description = "The next generation HTTP client."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"},
+    {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
+]
+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+httpcore = "==1.*"
+idna = "*"
+
+[package.extras]
+brotli = ["brotli", "brotlicffi"]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+zstd = ["zstandard (>=0.18.0)"]
+
 [[package]]
 name = "idna"
 version = "3.7"
@@ -240,19 +322,19 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.2.38"
+version = "0.2.43"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 groups = ["main"]
 files = [
-    {file = "langchain_core-0.2.38-py3-none-any.whl", hash = "sha256:8a5729bc7e68b4af089af20eff44fe4e7ca21d0e0c87ec21cef7621981fd1a4a"},
-    {file = "langchain_core-0.2.38.tar.gz", hash = "sha256:eb69dbedd344f2ee1f15bcea6c71a05884b867588fadc42d04632e727c1238f3"},
+    {file = "langchain_core-0.2.43-py3-none-any.whl", hash = "sha256:619601235113298ebf8252a349754b7c28d3cf7166c7c922da24944b78a9363a"},
+    {file = "langchain_core-0.2.43.tar.gz", hash = "sha256:42c2ef6adedb911f4254068b6adc9eb4c4075f6c8cb3d83590d3539a815695f5"},
 ]
 
 [package.dependencies]
 jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.1.75,<0.2.0"
+langsmith = ">=0.1.112,<0.2.0"
 packaging = ">=23.2,<25"
 pydantic = [
     {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
@@ -264,23 +346,28 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langsmith"
-version = "0.1.93"
+version = "0.1.147"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 groups = ["main"]
 files = [
-    {file = "langsmith-0.1.93-py3-none-any.whl", hash = "sha256:811210b9d5f108f36431bd7b997eb9476a9ecf5a2abd7ddbb606c1cdcf0f43ce"},
-    {file = "langsmith-0.1.93.tar.gz", hash = "sha256:285b6ad3a54f50fa8eb97b5f600acc57d0e37e139dd8cf2111a117d0435ba9b4"},
+    {file = "langsmith-0.1.147-py3-none-any.whl", hash = "sha256:7166fc23b965ccf839d64945a78e9f1157757add228b086141eb03a60d699a15"},
+    {file = "langsmith-0.1.147.tar.gz", hash = "sha256:2e933220318a4e73034657103b3b1a3a6109cc5db3566a7e8e03be8d6d7def7a"},
 ]
 
 [package.dependencies]
-orjson = ">=3.9.14,<4.0.0"
+httpx = ">=0.23.0,<1"
+orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
 pydantic = [
     {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
     {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
 ]
 requests = ">=2,<3"
+requests-toolbelt = ">=1.0.0,<2.0.0"
+
+[package.extras]
+langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
 
 [[package]]
 name = "marshmallow"
@@ -369,6 +456,7 @@ description = "Fast, correct Python JSON library supporting dataclasses, datetim
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "platform_python_implementation != \"PyPy\""
 files = [
     {file = "orjson-3.10.6-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:fb0ee33124db6eaa517d00890fc1a55c3bfe1cf78ba4a8899d71a06f2d6ff5c7"},
     {file = "orjson-3.10.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c1c4b53b24a4c06547ce43e5fee6ec4e0d8fe2d597f4647fc033fd205707365"},
@@ -773,6 +861,21 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "requests-toolbelt"
+version = "1.0.0"
+description = "A utility belt for advanced users of python-requests"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["main"]
+files = [
+    {file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"},
+    {file = "requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06"},
+]
+
+[package.dependencies]
+requests = ">=2.0.1,<3.0.0"
+
 [[package]]
 name = "ruff"
 version = "0.6.2"
@@ -801,6 +904,18 @@ files = [
     {file = "ruff-0.6.2.tar.gz", hash = "sha256:239ee6beb9e91feb8e0ec384204a763f36cb53fb895a1a364618c6abb076b3be"},
 ]
 
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
+]
+
 [[package]]
 name = "tenacity"
 version = "8.5.0"

libs/checkpoint/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint"
-version = "2.0.23"
+version = "2.0.24"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 license = "MIT"

libs/cli/langgraph_cli/cli.py

@@ -665,6 +665,8 @@ def dev(
         wait_for_client=wait_for_client,
         auth=config_json.get("auth"),
         http=config_json.get("http"),
+        ui=config_json.get("ui"),
+        ui_config=config_json.get("ui_config"),
         studio_url=studio_url,
         allow_blocking=allow_blocking,
     )

libs/cli/langgraph_cli/config.py

@@ -778,7 +778,22 @@ def _update_graph_paths(
         FileNotFoundError: If the local file (module) does not actually exist on disk.
         IsADirectoryError: If `module_str` points to a directory instead of a file.
     """
-    for graph_id, import_str in config["graphs"].items():
+    for graph_id, data in config["graphs"].items():
+        if isinstance(data, dict):
+            # Then we're looking for a 'path' key
+            if "path" not in data:
+                raise ValueError(
+                    f"Graph '{graph_id}' must contain a 'path' key if "
+                    f" it is a dictionary."
+                )
+            import_str = data["path"]
+        elif isinstance(data, str):
+            import_str = data
+        else:
+            raise ValueError(
+                f"Graph '{graph_id}' must be a string or a dictionary with a 'path' key."
+            )
+
         module_str, _, attr_str = import_str.partition(":")
         if not module_str or not attr_str:
             message = (
@@ -818,7 +833,10 @@ def _update_graph_paths(
                             "Add its containing package to 'dependencies' list."
                         )
             # update the config
-            config["graphs"][graph_id] = f"{module_str}:{attr_str}"
+            if isinstance(data, dict):
+                config["graphs"][graph_id]["path"] = f"{module_str}:{attr_str}"
+            else:
+                config["graphs"][graph_id] = f"{module_str}:{attr_str}"
 
 
 def _update_auth_path(
@@ -915,6 +933,66 @@ def _update_http_app_path(
         http_config["app"] = f"{module_str}:{attr_str}"
 
 
+def _get_node_pm_install_cmd(config_path: pathlib.Path, config: Config) -> str:
+    def test_file(file_name):
+        full_path = config_path.parent / file_name
+        try:
+            return full_path.is_file()
+        except OSError:
+            return False
+
+    # inspired by `package-manager-detector`
+    def get_pkg_manager_name():
+        try:
+            with open(config_path.parent / "package.json") as f:
+                pkg = json.load(f)
+
+                if (pkg_manager_name := pkg.get("packageManager")) and isinstance(
+                    pkg_manager_name, str
+                ):
+                    return pkg_manager_name.lstrip("^").split("@")[0]
+
+                if (
+                    dev_engine_name := (
+                        (pkg.get("devEngines") or {}).get("packageManager") or {}
+                    ).get("name")
+                ) and isinstance(dev_engine_name, str):
+                    return dev_engine_name
+
+                return None
+        except Exception:
+            return None
+
+    npm, yarn, pnpm, bun = [
+        test_file("package-lock.json"),
+        test_file("yarn.lock"),
+        test_file("pnpm-lock.yaml"),
+        test_file("bun.lockb"),
+    ]
+
+    if yarn:
+        install_cmd = "yarn install --frozen-lockfile"
+    elif pnpm:
+        install_cmd = "pnpm i --frozen-lockfile"
+    elif npm:
+        install_cmd = "npm ci"
+    elif bun:
+        install_cmd = "bun i"
+    else:
+        pkg_manager_name = get_pkg_manager_name()
+
+        if pkg_manager_name == "yarn":
+            install_cmd = "yarn install"
+        elif pkg_manager_name == "pnpm":
+            install_cmd = "pnpm i"
+        elif pkg_manager_name == "bun":
+            install_cmd = "bun i"
+        else:
+            install_cmd = "npm i"
+
+    return install_cmd
+
+
 def python_config_to_docker(
     config_path: pathlib.Path, config: Config, base_image: str
 ) -> tuple[str, dict[str, str]]:
@@ -995,10 +1073,32 @@ ADD {relpath} /deps/{name}
         for fullpath, (relpath, name) in local_deps.real_pkgs.items()
     )
 
+    ui_inst_str: str = ""
+    install_node_str: str = ""
+
+    if config.get("ui") and local_deps.working_dir:
+        install_node_str = "RUN /storage/install-node.sh"
+
+        ui_inst: list[str] = []
+        ui_inst.append(f"ENV LANGGRAPH_UI='{json.dumps(config['ui'])}'")
+        if config.get("ui_config"):
+            ui_inst.append(
+                f"ENV LANGGRAPH_UI_CONFIG='{json.dumps(config['ui_config'])}'"
+            )
+
+        ui_inst.append(
+            f"RUN cd {local_deps.working_dir} && {_get_node_pm_install_cmd(config_path, config)} && tsx /api/langgraph_api/js/build.mts",
+        )
+
+        ui_inst_str = f"""# -- Installing UI dependencies --
+{os.linesep.join(ui_inst)}
+# -- End of UI dependencies install --"""
+
     installs = f"{os.linesep}{os.linesep}".join(
         filter(
             None,
             [
+                install_node_str,
                 pip_config_file_str,
                 pip_pkgs_str,
                 pip_reqs_str,
@@ -1039,6 +1139,8 @@ ADD {relpath} /deps/{name}
         "# -- End of local dependencies install --",
         os.linesep.join(env_vars),
         "",
+        ui_inst_str,
+        "",
         f"WORKDIR {local_deps.working_dir}" if local_deps.working_dir else "",
     ]
 
@@ -1059,62 +1161,7 @@ def node_config_to_docker(
     config_path: pathlib.Path, config: Config, base_image: str
 ) -> tuple[str, dict[str, str]]:
     faux_path = f"/deps/{config_path.parent.name}"
-
-    def test_file(file_name):
-        full_path = config_path.parent / file_name
-        try:
-            return full_path.is_file()
-        except OSError:
-            return False
-
-    # inspired by `package-manager-detector`
-    def get_pkg_manager_name():
-        try:
-            with open(config_path.parent / "package.json") as f:
-                pkg = json.load(f)
-
-                if (pkg_manager_name := pkg.get("packageManager")) and isinstance(
-                    pkg_manager_name, str
-                ):
-                    return pkg_manager_name.lstrip("^").split("@")[0]
-
-                if (
-                    dev_engine_name := (
-                        (pkg.get("devEngines") or {}).get("packageManager") or {}
-                    ).get("name")
-                ) and isinstance(dev_engine_name, str):
-                    return dev_engine_name
-
-                return None
-        except Exception:
-            return None
-
-    npm, yarn, pnpm, bun = [
-        test_file("package-lock.json"),
-        test_file("yarn.lock"),
-        test_file("pnpm-lock.yaml"),
-        test_file("bun.lockb"),
-    ]
-
-    if yarn:
-        install_cmd = "yarn install --frozen-lockfile"
-    elif pnpm:
-        install_cmd = "pnpm i --frozen-lockfile"
-    elif npm:
-        install_cmd = "npm ci"
-    elif bun:
-        install_cmd = "bun i"
-    else:
-        pkg_manager_name = get_pkg_manager_name()
-
-        if pkg_manager_name == "yarn":
-            install_cmd = "yarn install"
-        elif pkg_manager_name == "pnpm":
-            install_cmd = "pnpm i"
-        elif pkg_manager_name == "bun":
-            install_cmd = "bun i"
-        else:
-            install_cmd = "npm i"
+    install_cmd = _get_node_pm_install_cmd(config_path, config)
     store_config = config.get("store")
     env_additional_config = (
         ""

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.83"
+version = "0.1.89"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"

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

@@ -196,6 +196,50 @@ def test_dockerfile_command_basic() -> None:
         assert save_path.exists()
 
 
+def test_dockerfile_command_new_style_config() -> None:
+    """Test `dockerfile` command with a new style config.
+
+    This config format allows specifying agent data as a dictionary.
+    {
+        "graphs": {
+            "agent1": {
+                "path": ... # path to graph definition,
+                ... # other fields
+            }
+        }
+    }
+    """
+    runner = CliRunner()
+    config_content = {
+        "dependencies": ["./my_agent"],
+        "graphs": {
+            "agent": {
+                "path": "./my_agent/agent.py:graph",
+                "description": "This is a test agent",
+            }
+        },
+        "env": ".env",
+    }
+    with temporary_config_folder(config_content) as temp_dir:
+        save_path = temp_dir / "Dockerfile"
+        # Add agent.py file
+        agent_path = temp_dir / "my_agent" / "agent.py"
+        agent_path.parent.mkdir(parents=True, exist_ok=True)
+        agent_path.touch()
+
+        result = runner.invoke(
+            cli,
+            ["dockerfile", str(save_path), "--config", str(temp_dir / "config.json")],
+        )
+
+        # Assert command was successful
+        assert result.exit_code == 0, result.output
+        assert "✅ Created: Dockerfile" in result.output
+
+        # Check if Dockerfile was created
+        assert save_path.exists()
+
+
 def test_dockerfile_command_with_docker_compose() -> None:
     """Test the 'dockerfile' command with Docker Compose configuration."""
     runner = CliRunner()

libs/cli/tests/unit_tests/test_config.py

@@ -494,6 +494,49 @@ RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not foun
     assert additional_contexts == {}
 
 
+def test_config_to_docker_gen_ui_python():
+    graphs = {"agent": "./agent.py:graph"}
+    actual_docker_stdin, additional_contexts = config_to_docker(
+        PATH_TO_CONFIG,
+        validate_config(
+            {
+                "dependencies": ["."],
+                "graphs": graphs,
+                "ui": {"agent": "./graphs/agent.ui.jsx"},
+                "ui_config": {"shared": ["nuqs"]},
+            }
+        ),
+        "langchain/langgraph-api",
+    )
+
+    expected_docker_stdin = """FROM langchain/langgraph-api:3.11
+RUN /storage/install-node.sh
+# -- Adding non-package dependency unit_tests --
+ADD . /deps/__outer_unit_tests/unit_tests
+RUN set -ex && \\
+    for line in '[project]' \\
+                'name = "unit_tests"' \\
+                'version = "0.1"' \\
+                '[tool.setuptools.package-data]' \\
+                '"*" = ["**/*"]'; do \\
+        echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
+    done
+# -- End of non-package dependency unit_tests --
+# -- Installing all local dependencies --
+RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+# -- End of local dependencies install --
+ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}'
+# -- Installing UI dependencies --
+ENV LANGGRAPH_UI='{"agent": "./graphs/agent.ui.jsx"}'
+ENV LANGGRAPH_UI_CONFIG='{"shared": ["nuqs"]}'
+RUN cd /deps/__outer_unit_tests/unit_tests && npm i && tsx /api/langgraph_api/js/build.mts
+# -- End of UI dependencies install --
+WORKDIR /deps/__outer_unit_tests/unit_tests"""
+
+    assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
+    assert additional_contexts == {}
+
+
 # config_to_compose
 def test_config_to_compose_simple_config():
     graphs = {"agent": "./agent.py:graph"}

libs/langgraph/langgraph/graph/state.py

@@ -2,6 +2,7 @@ import inspect
 import logging
 import typing
 import warnings
+from collections import defaultdict
 from functools import partial
 from inspect import isclass, isfunction, ismethod, signature
 from types import FunctionType
@@ -35,7 +36,16 @@ from langgraph.channels.dynamic_barrier_value import DynamicBarrierValue, WaitFo
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.named_barrier_value import NamedBarrierValue
-from langgraph.constants import EMPTY_SEQ, MISSING, NS_END, NS_SEP, SELF, TAG_HIDDEN
+from langgraph.checkpoint.base import Checkpoint
+from langgraph.constants import (
+    EMPTY_SEQ,
+    INTERRUPT,
+    MISSING,
+    NS_END,
+    NS_SEP,
+    SELF,
+    TAG_HIDDEN,
+)
 from langgraph.errors import (
     ErrorCode,
     InvalidUpdateError,
@@ -922,6 +932,110 @@ class CompiledStateGraph(CompiledGraph):
                         )
                     )
 
+    def _migrate_checkpoint(self, checkpoint: Checkpoint) -> None:
+        """Migrate a checkpoint to new channel layout."""
+
+        values = checkpoint["channel_values"]
+        versions = checkpoint["channel_versions"]
+        seen = checkpoint["versions_seen"]
+
+        # empty checkpoints do not need migration
+        if not versions:
+            return
+
+        # current version
+        if checkpoint["v"] >= 3:
+            return
+
+        # Migrate from start:node to branch:to:node
+        for k in list(versions):
+            if k.startswith("start:"):
+                # confirm node is present
+                node = k.split(":")[1]
+                if node not in self.nodes:
+                    continue
+                # get next version
+                new_k = f"branch:to:{node}"
+                new_v = (
+                    max(versions[new_k], versions.pop(k))
+                    if new_k in versions
+                    else versions.pop(k)
+                )
+                # update seen
+                for ss in (seen.get(node, {}), seen.get(INTERRUPT, {})):
+                    if k in ss:
+                        s = ss.pop(k)
+                        if new_k in ss:
+                            ss[new_k] = max(s, ss[new_k])
+                        else:
+                            ss[new_k] = s
+                # update value
+                if new_k not in values and k in values:
+                    values[new_k] = values.pop(k)
+                # update version
+                versions[new_k] = new_v
+
+        # Migrate from branch:source:condition:node to branch:to:node
+        for k in list(versions):
+            if k.startswith("branch:") and k.count(":") == 3:
+                # confirm node is present
+                node = k.split(":")[-1]
+                if node not in self.nodes:
+                    continue
+                # get next version
+                new_k = f"branch:to:{node}"
+                new_v = (
+                    max(versions[new_k], versions.pop(k))
+                    if new_k in versions
+                    else versions.pop(k)
+                )
+                # update seen
+                for ss in (seen.get(node, {}), seen.get(INTERRUPT, {})):
+                    if k in ss:
+                        s = ss.pop(k)
+                        if new_k in ss:
+                            ss[new_k] = max(s, ss[new_k])
+                        else:
+                            ss[new_k] = s
+                # update value
+                if new_k not in values and k in values:
+                    values[new_k] = values.pop(k)
+                # update version
+                versions[new_k] = new_v
+
+        if not set(self.nodes).isdisjoint(versions):
+            # Migrate from "node" to "branch:to:node"
+            source_to_target = defaultdict(list)
+            for start, end in self.builder.edges:
+                if start != START and end != END:
+                    source_to_target[start].append(end)
+            for k in list(versions):
+                if k == START:
+                    continue
+                if k in self.nodes:
+                    v = versions.pop(k)
+                    c = values.pop(k, MISSING)
+                    for end in source_to_target[k]:
+                        # get next version
+                        new_k = f"branch:to:{end}"
+                        new_v = max(versions[new_k], v) if new_k in versions else v
+                        # update seen
+                        for ss in (seen.get(end, {}), seen.get(INTERRUPT, {})):
+                            if k in ss:
+                                s = ss.pop(k)
+                                if new_k in ss:
+                                    ss[new_k] = max(s, ss[new_k])
+                                else:
+                                    ss[new_k] = s
+                        # update value
+                        if new_k not in values and c is not MISSING:
+                            values[new_k] = c
+                        # update version
+                        versions[new_k] = new_v
+                    # pop interrupt seen
+                    if INTERRUPT in seen:
+                        seen[INTERRUPT].pop(k, MISSING)
+
 
 def _get_state_reader(
     builder: StateGraph, schema: Type[Any]

libs/langgraph/langgraph/graph/ui.py

@@ -0,0 +1,206 @@
+from typing import Any, Literal, Optional, Union
+from uuid import uuid4
+
+from langchain_core.messages import AnyMessage
+from typing_extensions import TypedDict
+
+from langgraph.constants import CONF, CONFIG_KEY_SEND
+from langgraph.utils.config import get_config, get_stream_writer
+
+
+class UIMessage(TypedDict):
+    """A message type for UI updates in LangGraph.
+
+    This TypedDict represents a UI message that can be sent to update the UI state.
+    It contains information about the UI component to render and its properties.
+
+    Attributes:
+        type: Literal type indicating this is a UI message.
+        id: Unique identifier for the UI message.
+        name: Name of the UI component to render.
+        props: Properties to pass to the UI component.
+        metadata: Additional metadata about the UI message.
+    """
+
+    type: Literal["ui"]
+    id: str
+    name: str
+    props: dict[str, Any]
+    metadata: dict[str, Any]
+
+
+class RemoveUIMessage(TypedDict):
+    """A message type for removing UI components in LangGraph.
+
+    This TypedDict represents a message that can be sent to remove a UI component
+    from the current state.
+
+    Attributes:
+        type: Literal type indicating this is a remove-ui message.
+        id: Unique identifier of the UI message to remove.
+    """
+
+    type: Literal["remove-ui"]
+    id: str
+
+
+AnyUIMessage = Union[UIMessage, RemoveUIMessage]
+
+
+def push_ui_message(
+    name: str,
+    props: dict[str, Any],
+    *,
+    id: Optional[str] = None,
+    metadata: Optional[dict[str, Any]] = None,
+    message: Optional[AnyMessage] = None,
+    state_key: str = "ui",
+) -> UIMessage:
+    """Push a new UI message to update the UI state.
+
+    This function creates and sends a UI message that will be rendered in the UI.
+    It also updates the graph state with the new UI message.
+
+    Args:
+        name: Name of the UI component to render.
+        props: Properties to pass to the UI component.
+        id: Optional unique identifier for the UI message.
+            If not provided, a random UUID will be generated.
+        metadata: Optional additional metadata about the UI message.
+        message: Optional message object to associate with the UI message.
+        state_key: Key in the graph state where the UI messages are stored.
+            Defaults to "ui".
+
+    Returns:
+        The created UI message.
+
+    Example:
+
+    .. code-block:: python
+
+        push_ui_message(
+            name="component-name",
+            props={"content": "Hello world"},
+        )
+
+    """
+    writer = get_stream_writer()
+    config = get_config()
+
+    message_id = None
+    if message:
+        if isinstance(message, dict) and "id" in message:
+            message_id = message.get("id")
+        elif hasattr(message, "id"):
+            message_id = message.id
+
+    evt: UIMessage = {
+        "type": "ui",
+        "id": id or str(uuid4()),
+        "name": name,
+        "props": props,
+        "metadata": {
+            **(config.get("metadata") or {}),
+            "tags": config.get("tags", None),
+            "name": config.get("run_name", None),
+            "run_id": config.get("run_id", None),
+            **(metadata or {}),
+            **({"message_id": message_id} if message_id else {}),
+        },
+    }
+
+    writer(evt)
+    config[CONF][CONFIG_KEY_SEND]([(state_key, evt)])
+
+    return evt
+
+
+def delete_ui_message(id: str, *, state_key: str = "ui") -> RemoveUIMessage:
+    """Delete a UI message by ID from the UI state.
+
+    This function creates and sends a message to remove a UI component from the current state.
+    It also updates the graph state to remove the UI message.
+
+    Args:
+        id: Unique identifier of the UI component to remove.
+        state_key: Key in the graph state where the UI messages are stored. Defaults to "ui".
+
+    Returns:
+        The remove UI message.
+
+    Example:
+
+    .. code-block:: python
+
+        delete_ui_message("message-123")
+
+    """
+    writer = get_stream_writer()
+    config = get_config()
+
+    evt: RemoveUIMessage = {"type": "remove-ui", "id": id}
+
+    writer(evt)
+    config[CONF][CONFIG_KEY_SEND]([(state_key, evt)])
+
+    return evt
+
+
+def ui_message_reducer(
+    left: Union[list[AnyUIMessage], AnyUIMessage],
+    right: Union[list[AnyUIMessage], AnyUIMessage],
+) -> list[AnyUIMessage]:
+    """Merge two lists of UI messages, supporting removing UI messages.
+
+    This function combines two lists of UI messages, handling both regular UI messages
+    and `remove-ui` messages. When a `remove-ui` message is encountered, it removes any
+    UI message with the matching ID from the current state.
+
+    Args:
+        left: First list of UI messages or single UI message.
+        right: Second list of UI messages or single UI message.
+
+    Returns:
+        Combined list of UI messages with removals applied.
+
+    Example:
+
+    .. code-block:: python
+
+        messages = ui_message_reducer(
+            [{"type": "ui", "id": "1", "name": "Chat", "props": {}}],
+            {"type": "remove-ui", "id": "1"}
+        )
+
+    """
+    if not isinstance(left, list):
+        left = [left]
+
+    if not isinstance(right, list):
+        right = [right]
+
+    # merge messages
+    merged = left.copy()
+    merged_by_id = {m.get("id"): i for i, m in enumerate(merged)}
+    ids_to_remove = set()
+
+    for msg in right:
+        msg_id = msg.get("id")
+
+        if (existing_idx := merged_by_id.get(msg_id)) is not None:
+            if msg.get("type") == "remove-ui":
+                ids_to_remove.add(msg_id)
+            else:
+                ids_to_remove.discard(msg_id)
+                merged[existing_idx] = msg
+        else:
+            if msg.get("type") == "remove-ui":
+                raise ValueError(
+                    f"Attempting to delete an UI message with an ID that doesn't exist ('{msg_id}')"
+                )
+
+            merged_by_id[msg_id] = len(merged)
+            merged.append(msg)
+
+    merged = [m for m in merged if m.get("id") not in ids_to_remove]
+    return merged

libs/langgraph/langgraph/pregel/__init__.py

@@ -48,9 +48,9 @@ from langgraph.channels.base import (
 )
 from langgraph.checkpoint.base import (
     BaseCheckpointSaver,
+    Checkpoint,
     CheckpointTuple,
     copy_checkpoint,
-    empty_checkpoint,
 )
 from langgraph.constants import (
     CONF,
@@ -90,7 +90,7 @@ from langgraph.pregel.algo import (
     local_write,
     prepare_next_tasks,
 )
-from langgraph.pregel.checkpoint import create_checkpoint
+from langgraph.pregel.checkpoint import create_checkpoint, empty_checkpoint
 from langgraph.pregel.debug import tasks_w_writes
 from langgraph.pregel.io import map_input, read_channels
 from langgraph.pregel.loop import AsyncPregelLoop, StreamProtocol, SyncPregelLoop
@@ -767,6 +767,10 @@ class Pregel(PregelProtocol):
         for name, node in self.get_subgraphs(namespace=namespace, recurse=recurse):
             yield name, node
 
+    def _migrate_checkpoint(self, checkpoint: Checkpoint) -> None:
+        """Migrate a saved checkpoint to new channel layout."""
+        pass
+
     def _prepare_state_snapshot(
         self,
         config: RunnableConfig,
@@ -785,6 +789,9 @@ class Pregel(PregelProtocol):
                 tasks=(),
             )
 
+        # migrate checkpoint if needed
+        self._migrate_checkpoint(saved.checkpoint)
+
         with ChannelsManager(
             self.channels,
             saved.checkpoint,
@@ -898,6 +905,9 @@ class Pregel(PregelProtocol):
                 tasks=(),
             )
 
+        # migrate checkpoint if needed
+        self._migrate_checkpoint(saved.checkpoint)
+
         async with AsyncChannelsManager(
             self.channels,
             saved.checkpoint,
@@ -1223,6 +1233,8 @@ class Pregel(PregelProtocol):
             # get last checkpoint
             config = ensure_config(self.config, input_config)
             saved = checkpointer.get_tuple(config)
+            if saved is not None:
+                self._migrate_checkpoint(saved.checkpoint)
             checkpoint = (
                 copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint()
             )
@@ -1633,6 +1645,8 @@ class Pregel(PregelProtocol):
             # get last checkpoint
             config = ensure_config(self.config, input_config)
             saved = await checkpointer.aget_tuple(config)
+            if saved is not None:
+                self._migrate_checkpoint(saved.checkpoint)
             checkpoint = (
                 copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint()
             )
@@ -2278,6 +2292,7 @@ class Pregel(PregelProtocol):
                 manager=run_manager,
                 debug=debug,
                 trigger_to_nodes=self.trigger_to_nodes,
+                migrate_checkpoint=self._migrate_checkpoint,
             ) as loop:
                 # create runner
                 runner = PregelRunner(
@@ -2571,12 +2586,8 @@ class Pregel(PregelProtocol):
                 interrupt_after=interrupt_after_,
                 manager=run_manager,
                 debug=debug,
-                # `self.nodes` can be modified after creation of `Pregel`. For example,
-                # that's how StateGraph compilation currently works.
-                # For now, we recompute the trigger_to_nodes mapping every time the
-                # loop is created. We could potentially memoize this if it becomes a
-                # performance issue.
-                trigger_to_nodes=_trigger_to_nodes(self.nodes),
+                trigger_to_nodes=self.trigger_to_nodes,
+                migrate_checkpoint=self._migrate_checkpoint,
             ) as loop:
                 # create runner
                 runner = PregelRunner(

libs/langgraph/langgraph/pregel/checkpoint.py

@@ -2,10 +2,24 @@ from datetime import datetime, timezone
 from typing import Mapping, Optional
 
 from langgraph.channels.base import BaseChannel
-from langgraph.checkpoint.base import LATEST_VERSION, Checkpoint
+from langgraph.checkpoint.base import Checkpoint
 from langgraph.checkpoint.base.id import uuid6
 from langgraph.constants import MISSING
 
+LATEST_VERSION = 3
+
+
+def empty_checkpoint() -> Checkpoint:
+    return Checkpoint(
+        v=LATEST_VERSION,
+        id=str(uuid6(clock_seq=-2)),
+        ts=datetime.now(timezone.utc).isoformat(),
+        channel_values={},
+        channel_versions={},
+        versions_seen={},
+        pending_sends=[],
+    )
+
 
 def create_checkpoint(
     checkpoint: Checkpoint,

libs/langgraph/langgraph/pregel/loop.py

@@ -30,6 +30,7 @@ from typing_extensions import ParamSpec, Self
 
 from langgraph.channels.base import BaseChannel
 from langgraph.checkpoint.base import (
+    EXCLUDED_METADATA_KEYS,
     WRITES_IDX_MAP,
     BaseCheckpointSaver,
     ChannelVersions,
@@ -38,7 +39,6 @@ from langgraph.checkpoint.base import (
     CheckpointTuple,
     PendingWrite,
     copy_checkpoint,
-    empty_checkpoint,
 )
 from langgraph.constants import (
     CONF,
@@ -87,7 +87,7 @@ from langgraph.pregel.algo import (
     should_interrupt,
     task_path_str,
 )
-from langgraph.pregel.checkpoint import create_checkpoint
+from langgraph.pregel.checkpoint import create_checkpoint, empty_checkpoint
 from langgraph.pregel.debug import (
     map_debug_checkpoint,
     map_debug_task_results,
@@ -175,6 +175,7 @@ class PregelLoop(LoopProtocol):
             Any,
         ]
     ]
+    _migrate_checkpoint: Optional[Callable[[Checkpoint], None]]
     submit: Submit
     channels: Mapping[str, BaseChannel]
     managed: ManagedValueMapping
@@ -212,6 +213,7 @@ class PregelLoop(LoopProtocol):
         manager: Union[None, AsyncParentRunManager, ParentRunManager] = None,
         input_model: Optional[Type[BaseModel]] = None,
         debug: bool = False,
+        migrate_checkpoint: Optional[Callable[[Checkpoint], None]] = None,
         trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None,
         checkpoint_every_step: bool = True,
     ) -> None:
@@ -237,6 +239,7 @@ class PregelLoop(LoopProtocol):
             CONFIG_KEY_CHECKPOINT_ID not in config[CONF]
             or CONFIG_KEY_DEDUPE_TASKS in config[CONF]
         )
+        self._migrate_checkpoint = migrate_checkpoint
         self.trigger_to_nodes = trigger_to_nodes
         self.checkpoint_every_step = checkpoint_every_step
         self.debug = debug
@@ -724,6 +727,8 @@ class PregelLoop(LoopProtocol):
         # bail if no checkpointer
         if self._checkpointer_put_after_previous is not None:
             for k, v in self.config["metadata"].items():
+                if k in EXCLUDED_METADATA_KEYS:
+                    continue
                 metadata.setdefault(k, v)  # type: ignore
 
             # create new checkpoint
@@ -900,6 +905,7 @@ class SyncPregelLoop(PregelLoop, ContextManager):
         stream_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
         input_model: Optional[Type[BaseModel]] = None,
         debug: bool = False,
+        migrate_checkpoint: Optional[Callable[[Checkpoint], None]] = None,
         trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None,
     ) -> None:
         super().__init__(
@@ -917,6 +923,7 @@ class SyncPregelLoop(PregelLoop, ContextManager):
             interrupt_before=interrupt_before,
             manager=manager,
             debug=debug,
+            migrate_checkpoint=migrate_checkpoint,
             trigger_to_nodes=trigger_to_nodes,
         )
         self.stack = ExitStack()
@@ -985,6 +992,8 @@ class SyncPregelLoop(PregelLoop, ContextManager):
             saved = CheckpointTuple(
                 self.config, empty_checkpoint(), {"step": -2}, None, []
             )
+        elif self._migrate_checkpoint is not None:
+            self._migrate_checkpoint(saved.checkpoint)
         self.checkpoint_config = {
             **self.config,
             **saved.config,
@@ -1043,6 +1052,7 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
         stream_keys: Union[str, Sequence[str]] = EMPTY_SEQ,
         input_model: Optional[Type[BaseModel]] = None,
         debug: bool = False,
+        migrate_checkpoint: Optional[Callable[[Checkpoint], None]] = None,
         trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None,
     ) -> None:
         super().__init__(
@@ -1060,6 +1070,7 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
             interrupt_before=interrupt_before,
             manager=manager,
             debug=debug,
+            migrate_checkpoint=migrate_checkpoint,
             trigger_to_nodes=trigger_to_nodes,
         )
         self.stack = AsyncExitStack()
@@ -1128,6 +1139,8 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
             saved = CheckpointTuple(
                 self.config, empty_checkpoint(), {"step": -2}, None, []
             )
+        elif self._migrate_checkpoint is not None:
+            self._migrate_checkpoint(saved.checkpoint)
         self.checkpoint_config = {
             **self.config,
             **saved.config,

libs/langgraph/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.
 
 [[package]]
 name = "aiosqlite"
@@ -946,14 +946,14 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.5"
+version = "3.1.6"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 groups = ["dev"]
 files = [
-    {file = "jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb"},
-    {file = "jinja2-3.1.5.tar.gz", hash = "sha256:8fefff8dc3034e27bb80d67c671eb8a9bc424c0ef4c0826edbff304cceff43bb"},
+    {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"},
+    {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"},
 ]
 
 [package.dependencies]

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.3.22"
+version = "0.3.25"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"

libs/langgraph/tests/any_str.py

@@ -4,6 +4,11 @@ from typing import Any, Sequence, Union
 from typing_extensions import Self
 
 
+class AnyObject:
+    def __eq__(self, value):
+        return True
+
+
 class FloatBetween(float):
     def __new__(cls, min_value: float, max_value: float) -> Self:
         return super().__new__(cls, min_value)

libs/langgraph/tests/test_large_cases.py

@@ -7782,7 +7782,6 @@ def test_nested_graph_state(
                         },
                         "step": 1,
                         "thread_id": "1",
-                        "checkpoint_ns": AnyStr("inner:"),
                         "langgraph_node": "inner",
                         "langgraph_path": [PULL, "inner"],
                         "langgraph_step": 2,
@@ -7977,7 +7976,6 @@ def test_nested_graph_state(
                 "step": 1,
                 "parents": {"": AnyStr()},
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("inner:"),
                 "langgraph_node": "inner",
                 "langgraph_path": [PULL, "inner"],
                 "langgraph_step": 2,
@@ -8020,7 +8018,6 @@ def test_nested_graph_state(
                 "step": 0,
                 "parents": {"": AnyStr()},
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("inner:"),
                 "langgraph_node": "inner",
                 "langgraph_path": [PULL, "inner"],
                 "langgraph_step": 2,
@@ -8069,7 +8066,6 @@ def test_nested_graph_state(
                 "step": -1,
                 "parents": {"": AnyStr()},
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("inner:"),
                 "langgraph_node": "inner",
                 "langgraph_path": [PULL, "inner"],
                 "langgraph_step": 2,
@@ -8420,51 +8416,65 @@ def test_doubly_nested_graph_state(
         ),
     )
     child_state = app.get_state(outer_state.tasks[0].state)
-    assert (
-        child_state.tasks[0]
-        == StateSnapshot(
-            values={"my_key": "hi my value"},
-            tasks=(
-                PregelTask(
-                    AnyStr(),
-                    "child_1",
-                    (PULL, "child_1"),
-                    state={
-                        "configurable": {
-                            "thread_id": "1",
-                            "checkpoint_ns": AnyStr(),
-                        }
-                    },
-                ),
+    assert child_state == StateSnapshot(
+        values={"my_key": "hi my value"},
+        tasks=(
+            PregelTask(
+                AnyStr(),
+                "child_1",
+                (PULL, "child_1"),
+                state={
+                    "configurable": {
+                        "thread_id": "1",
+                        "checkpoint_ns": AnyStr(),
+                    }
+                },
             ),
-            next=("child_1",),
-            config={
+        ),
+        next=("child_1",),
+        config={
+            "configurable": {
+                "thread_id": "1",
+                "checkpoint_ns": AnyStr("child:"),
+                "checkpoint_id": AnyStr(),
+                "checkpoint_map": AnyDict(
+                    {
+                        "": AnyStr(),
+                        AnyStr("child:"): AnyStr(),
+                    }
+                ),
+            }
+        },
+        metadata={
+            "langgraph_checkpoint_ns": AnyStr("child:"),
+            "langgraph_node": "child",
+            "langgraph_path": ["__pregel_pull", "child"],
+            "langgraph_step": 2,
+            "langgraph_triggers": ["branch:to:child"],
+            "parents": {"": AnyStr()},
+            "source": "loop",
+            "writes": None,
+            "step": 0,
+            "thread_id": "1",
+        },
+        created_at=AnyStr(),
+        parent_config=(
+            None
+            if "shallow" in checkpointer_name
+            else {
                 "configurable": {
                     "thread_id": "1",
                     "checkpoint_ns": AnyStr("child:"),
                     "checkpoint_id": AnyStr(),
+                    "checkpoint_map": AnyDict(
+                        {
+                            "": AnyStr(),
+                            AnyStr("child:"): AnyStr(),
+                        }
+                    ),
                 }
-            },
-            metadata={
-                "parents": {"": AnyStr()},
-                "source": "loop",
-                "writes": None,
-                "step": 0,
-                "thread_id": "1",
-            },
-            created_at=AnyStr(),
-            parent_config=(
-                None
-                if "shallow" in checkpointer_name
-                else {
-                    "configurable": {
-                        "thread_id": "1",
-                        "checkpoint_ns": AnyStr("child:"),
-                        "checkpoint_id": AnyStr(),
-                    }
-                }
-            ),
-        ).tasks[0]
+            }
+        ),
     )
     grandchild_state = app.get_state(child_state.tasks[0].state)
     assert grandchild_state == StateSnapshot(
@@ -8502,7 +8512,6 @@ def test_doubly_nested_graph_state(
             "writes": {"grandchild_1": {"my_key": "hi my value here"}},
             "step": 1,
             "thread_id": "1",
-            "checkpoint_ns": AnyStr("child:"),
             "langgraph_checkpoint_ns": AnyStr("child:"),
             "langgraph_node": "child_1",
             "langgraph_path": [PULL, AnyStr("child_1")],
@@ -8583,7 +8592,6 @@ def test_doubly_nested_graph_state(
                                     },
                                     "step": 1,
                                     "thread_id": "1",
-                                    "checkpoint_ns": AnyStr("child:"),
                                     "langgraph_checkpoint_ns": AnyStr("child:"),
                                     "langgraph_node": "child_1",
                                     "langgraph_path": [
@@ -8636,7 +8644,6 @@ def test_doubly_nested_graph_state(
                         "writes": None,
                         "step": 0,
                         "thread_id": "1",
-                        "checkpoint_ns": AnyStr("child:"),
                         "langgraph_node": "child",
                         "langgraph_path": [PULL, AnyStr("child")],
                         "langgraph_step": 2,
@@ -8932,7 +8939,6 @@ def test_doubly_nested_graph_state(
                 "step": 1,
                 "parents": {"": AnyStr()},
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("child:"),
                 "langgraph_node": "child",
                 "langgraph_path": [PULL, AnyStr("child")],
                 "langgraph_step": 2,
@@ -8971,7 +8977,6 @@ def test_doubly_nested_graph_state(
                 "step": 0,
                 "parents": {"": AnyStr()},
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("child:"),
                 "langgraph_node": "child",
                 "langgraph_path": [PULL, AnyStr("child")],
                 "langgraph_step": 2,
@@ -9023,7 +9028,6 @@ def test_doubly_nested_graph_state(
                 "step": -1,
                 "parents": {"": AnyStr()},
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("child:"),
                 "langgraph_node": "child",
                 "langgraph_path": [PULL, AnyStr("child")],
                 "langgraph_step": 2,
@@ -9073,7 +9077,6 @@ def test_doubly_nested_graph_state(
                     }
                 ),
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("child:"),
                 "langgraph_checkpoint_ns": AnyStr("child:"),
                 "langgraph_node": "child_1",
                 "langgraph_path": [
@@ -9128,7 +9131,6 @@ def test_doubly_nested_graph_state(
                     }
                 ),
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("child:"),
                 "langgraph_checkpoint_ns": AnyStr("child:"),
                 "langgraph_node": "child_1",
                 "langgraph_path": [
@@ -9190,7 +9192,6 @@ def test_doubly_nested_graph_state(
                     }
                 ),
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("child:"),
                 "langgraph_checkpoint_ns": AnyStr("child:"),
                 "langgraph_node": "child_1",
                 "langgraph_path": [
@@ -9252,7 +9253,6 @@ def test_doubly_nested_graph_state(
                     }
                 ),
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("child:"),
                 "langgraph_checkpoint_ns": AnyStr("child:"),
                 "langgraph_node": "child_1",
                 "langgraph_path": [
@@ -10379,7 +10379,6 @@ def test_weather_subgraph(
                         "step": 1,
                         "parents": {"": AnyStr()},
                         "thread_id": "14",
-                        "checkpoint_ns": AnyStr("weather_graph:"),
                         "langgraph_node": "weather_graph",
                         "langgraph_path": [PULL, "weather_graph"],
                         "langgraph_step": 2,

libs/langgraph/tests/test_large_cases_async.py

@@ -5332,7 +5332,6 @@ async def test_nested_graph_state(checkpointer_name: str) -> None:
                             },
                             "step": 1,
                             "thread_id": "1",
-                            "checkpoint_ns": AnyStr("inner:"),
                             "langgraph_node": "inner",
                             "langgraph_path": [PULL, "inner"],
                             "langgraph_step": 2,
@@ -5529,7 +5528,6 @@ async def test_nested_graph_state(checkpointer_name: str) -> None:
                     "step": 1,
                     "parents": {"": AnyStr()},
                     "thread_id": "1",
-                    "checkpoint_ns": AnyStr("inner:"),
                     "langgraph_node": "inner",
                     "langgraph_path": [PULL, "inner"],
                     "langgraph_step": 2,
@@ -5572,7 +5570,6 @@ async def test_nested_graph_state(checkpointer_name: str) -> None:
                     "step": 0,
                     "parents": {"": AnyStr()},
                     "thread_id": "1",
-                    "checkpoint_ns": AnyStr("inner:"),
                     "langgraph_node": "inner",
                     "langgraph_path": [PULL, "inner"],
                     "langgraph_step": 2,
@@ -5621,7 +5618,6 @@ async def test_nested_graph_state(checkpointer_name: str) -> None:
                     "step": -1,
                     "parents": {"": AnyStr()},
                     "thread_id": "1",
-                    "checkpoint_ns": AnyStr("inner:"),
                     "langgraph_node": "inner",
                     "langgraph_path": [PULL, "inner"],
                     "langgraph_step": 2,
@@ -5976,51 +5972,65 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
             ),
         )
         child_state = await app.aget_state(outer_state.tasks[0].state)
-        assert (
-            child_state.tasks[0]
-            == StateSnapshot(
-                values={"my_key": "hi my value"},
-                tasks=(
-                    PregelTask(
-                        AnyStr(),
-                        "child_1",
-                        (PULL, "child_1"),
-                        state={
-                            "configurable": {
-                                "thread_id": "1",
-                                "checkpoint_ns": AnyStr(),
-                            }
-                        },
-                    ),
+        assert child_state == StateSnapshot(
+            values={"my_key": "hi my value"},
+            tasks=(
+                PregelTask(
+                    AnyStr(),
+                    "child_1",
+                    (PULL, "child_1"),
+                    state={
+                        "configurable": {
+                            "thread_id": "1",
+                            "checkpoint_ns": AnyStr(),
+                        }
+                    },
                 ),
-                next=("child_1",),
-                config={
+            ),
+            next=("child_1",),
+            config={
+                "configurable": {
+                    "thread_id": "1",
+                    "checkpoint_ns": AnyStr("child:"),
+                    "checkpoint_id": AnyStr(),
+                    "checkpoint_map": AnyDict(
+                        {
+                            "": AnyStr(),
+                            AnyStr("child:"): AnyStr(),
+                        }
+                    ),
+                }
+            },
+            metadata={
+                "langgraph_checkpoint_ns": AnyStr("child:"),
+                "langgraph_node": "child",
+                "langgraph_path": ["__pregel_pull", "child"],
+                "langgraph_step": 2,
+                "langgraph_triggers": ["branch:to:child"],
+                "parents": {"": AnyStr()},
+                "source": "loop",
+                "writes": None,
+                "step": 0,
+                "thread_id": "1",
+            },
+            created_at=AnyStr(),
+            parent_config=(
+                None
+                if "shallow" in checkpointer_name
+                else {
                     "configurable": {
                         "thread_id": "1",
                         "checkpoint_ns": AnyStr("child:"),
                         "checkpoint_id": AnyStr(),
+                        "checkpoint_map": AnyDict(
+                            {
+                                "": AnyStr(),
+                                AnyStr("child:"): AnyStr(),
+                            }
+                        ),
                     }
-                },
-                metadata={
-                    "parents": {"": AnyStr()},
-                    "source": "loop",
-                    "writes": None,
-                    "step": 0,
-                    "thread_id": "1",
-                },
-                created_at=AnyStr(),
-                parent_config=(
-                    None
-                    if "shallow" in checkpointer_name
-                    else {
-                        "configurable": {
-                            "thread_id": "1",
-                            "checkpoint_ns": AnyStr("child:"),
-                            "checkpoint_id": AnyStr(),
-                        }
-                    }
-                ),
-            ).tasks[0]
+                }
+            ),
         )
         grandchild_state = await app.aget_state(child_state.tasks[0].state)
         assert grandchild_state == StateSnapshot(
@@ -6058,7 +6068,6 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                 "writes": {"grandchild_1": {"my_key": "hi my value here"}},
                 "step": 1,
                 "thread_id": "1",
-                "checkpoint_ns": AnyStr("child:"),
                 "langgraph_checkpoint_ns": AnyStr("child:"),
                 "langgraph_node": "child_1",
                 "langgraph_path": [PULL, AnyStr("child_1")],
@@ -6143,7 +6152,6 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                                         },
                                         "step": 1,
                                         "thread_id": "1",
-                                        "checkpoint_ns": AnyStr("child:"),
                                         "langgraph_checkpoint_ns": AnyStr("child:"),
                                         "langgraph_node": "child_1",
                                         "langgraph_path": [
@@ -6198,7 +6206,6 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                             "writes": None,
                             "step": 0,
                             "thread_id": "1",
-                            "checkpoint_ns": AnyStr("child:"),
                             "langgraph_node": "child",
                             "langgraph_path": [PULL, AnyStr("child")],
                             "langgraph_step": 2,
@@ -6498,7 +6505,6 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                     "step": 1,
                     "parents": {"": AnyStr()},
                     "thread_id": "1",
-                    "checkpoint_ns": AnyStr("child:"),
                     "langgraph_node": "child",
                     "langgraph_path": [PULL, AnyStr("child")],
                     "langgraph_step": 2,
@@ -6537,7 +6543,6 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                     "step": 0,
                     "parents": {"": AnyStr()},
                     "thread_id": "1",
-                    "checkpoint_ns": AnyStr("child:"),
                     "langgraph_node": "child",
                     "langgraph_path": [PULL, AnyStr("child")],
                     "langgraph_step": 2,
@@ -6589,7 +6594,6 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                     "step": -1,
                     "parents": {"": AnyStr()},
                     "thread_id": "1",
-                    "checkpoint_ns": AnyStr("child:"),
                     "langgraph_node": "child",
                     "langgraph_path": [PULL, AnyStr("child")],
                     "langgraph_step": 2,
@@ -6643,7 +6647,6 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                         }
                     ),
                     "thread_id": "1",
-                    "checkpoint_ns": AnyStr("child:"),
                     "langgraph_checkpoint_ns": AnyStr("child:"),
                     "langgraph_node": "child_1",
                     "langgraph_path": [
@@ -6700,7 +6703,6 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                         }
                     ),
                     "thread_id": "1",
-                    "checkpoint_ns": AnyStr("child:"),
                     "langgraph_checkpoint_ns": AnyStr("child:"),
                     "langgraph_node": "child_1",
                     "langgraph_path": [
@@ -6764,7 +6766,6 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                         }
                     ),
                     "thread_id": "1",
-                    "checkpoint_ns": AnyStr("child:"),
                     "langgraph_checkpoint_ns": AnyStr("child:"),
                     "langgraph_node": "child_1",
                     "langgraph_path": [
@@ -6828,7 +6829,6 @@ async def test_doubly_nested_graph_state(checkpointer_name: str) -> None:
                         }
                     ),
                     "thread_id": "1",
-                    "checkpoint_ns": AnyStr("child:"),
                     "langgraph_checkpoint_ns": AnyStr("child:"),
                     "langgraph_node": "child_1",
                     "langgraph_path": [
@@ -7244,7 +7244,6 @@ async def test_weather_subgraph(
                             "step": 1,
                             "parents": {"": AnyStr()},
                             "thread_id": "14",
-                            "checkpoint_ns": AnyStr("weather_graph:"),
                             "langgraph_node": "weather_graph",
                             "langgraph_path": [PULL, "weather_graph"],
                             "langgraph_step": 2,

libs/langgraph/tests/test_pregel.py

@@ -1234,7 +1234,7 @@ def test_pending_writes_resume(
             }
         },
         checkpoint={
-            "v": 2,
+            "v": 3,
             "id": AnyStr(),
             "ts": AnyStr(),
             "pending_sends": [],
@@ -1292,7 +1292,7 @@ def test_pending_writes_resume(
             }
         },
         checkpoint={
-            "v": 2,
+            "v": 3,
             "id": AnyStr(),
             "ts": AnyStr(),
             "pending_sends": [],
@@ -1343,7 +1343,7 @@ def test_pending_writes_resume(
             }
         },
         checkpoint={
-            "v": 2,
+            "v": 3,
             "id": AnyStr(),
             "ts": AnyStr(),
             "pending_sends": [],

libs/langgraph/tests/test_pregel_async.py

@@ -2068,7 +2068,7 @@ async def test_pending_writes_resume(
                 }
             },
             checkpoint={
-                "v": 2,
+                "v": 3,
                 "id": AnyStr(),
                 "ts": AnyStr(),
                 "pending_sends": [],
@@ -2128,7 +2128,7 @@ async def test_pending_writes_resume(
                 }
             },
             checkpoint={
-                "v": 2,
+                "v": 3,
                 "id": AnyStr(),
                 "ts": AnyStr(),
                 "pending_sends": [],
@@ -2181,7 +2181,7 @@ async def test_pending_writes_resume(
                 }
             },
             checkpoint={
-                "v": 2,
+                "v": 3,
                 "id": AnyStr(),
                 "ts": AnyStr(),
                 "pending_sends": [],

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -18,7 +18,13 @@ from langchain_core.language_models import (
     LanguageModelInput,
     LanguageModelLike,
 )
-from langchain_core.messages import AIMessage, BaseMessage, SystemMessage, ToolMessage
+from langchain_core.messages import (
+    AIMessage,
+    AnyMessage,
+    BaseMessage,
+    SystemMessage,
+    ToolMessage,
+)
 from langchain_core.runnables import (
     Runnable,
     RunnableBinding,
@@ -37,7 +43,7 @@ from langgraph.managed import IsLastStep, RemainingSteps
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.store.base import BaseStore
 from langgraph.types import Checkpointer, Send
-from langgraph.utils.runnable import RunnableCallable
+from langgraph.utils.runnable import RunnableCallable, RunnableLike
 
 StructuredResponse = Union[dict, BaseModel]
 StructuredResponseSchema = Union[dict, type[BaseModel]]
@@ -263,6 +269,7 @@ def create_react_agent(
     response_format: Optional[
         Union[StructuredResponseSchema, tuple[str, StructuredResponseSchema]]
     ] = None,
+    pre_model_hook: Optional[RunnableLike] = None,
     state_schema: Optional[StateSchemaType] = None,
     config_schema: Optional[Type[Any]] = None,
     checkpointer: Optional[Checkpointer] = None,
@@ -305,6 +312,36 @@ def create_react_agent(
             !!! Note
                 The graph will make a separate call to the LLM to generate the structured response after the agent loop is finished.
                 This is not the only strategy to get structured responses, see more options in [this guide](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/).
+
+        pre_model_hook: An optional node to add before the `agent` node (i.e., the node that calls the LLM).
+            Useful for managing long message histories (e.g., message trimming, summarization, etc.).
+            Pre-model hook must be a callable or a runnable that takes in current graph state and returns a state update in the form of
+                ```python
+                # At least one of `messages` or `llm_input_messages` MUST be provided
+                {
+                    # If provided, will UPDATE the `messages` in the state
+                    "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), ...],
+                    # If provided, will be used as the input to the LLM,
+                    # and will NOT UPDATE `messages` in the state
+                    "llm_input_messages": [...],
+                    # Any other state keys that need to be propagated
+                    ...
+                }
+                ```
+
+            !!! Important
+                At least one of `messages` or `llm_input_messages` MUST be provided and will be used as an input to the `agent` node.
+                The rest of the keys will be added to the graph state.
+
+            !!! Warning
+                If you are returning `messages` in the pre-model hook, you should OVERWRITE the `messages` key by doing the following:
+
+                ```python
+                {
+                    "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), *new_messages]
+                    ...
+                }
+                ```
         state_schema: An optional state schema that defines graph state.
             Must have `messages` and `remaining_steps` keys.
             Defaults to `AgentState` that defines those two keys.
@@ -678,10 +715,33 @@ def create_react_agent(
             or (remaining_steps is not None and remaining_steps < 2 and has_tool_calls)
         )
 
+    def _get_model_input_state(state: StateSchema) -> StateSchema:
+        if pre_model_hook is not None:
+            messages = (
+                _get_state_value(state, "llm_input_messages")
+            ) or _get_state_value(state, "messages")
+            error_msg = f"Expected input to call_model to have 'llm_input_messages' or 'messages' key, but got {state}"
+        else:
+            messages = _get_state_value(state, "messages")
+            error_msg = (
+                f"Expected input to call_model to have 'messages' key, but got {state}"
+            )
+
+        if messages is None:
+            raise ValueError(error_msg)
+
+        _validate_chat_history(messages)
+        # we're passing messages under `messages` key, as this is expected by the prompt
+        if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):
+            state.messages = messages  # type: ignore
+        else:
+            state["messages"] = messages  # type: ignore
+
+        return state
+
     # Define the function that calls the model
     def call_model(state: StateSchema, config: RunnableConfig) -> StateSchema:
-        messages = _get_state_value(state, "messages")
-        _validate_chat_history(messages)
+        state = _get_model_input_state(state)
         response = cast(AIMessage, model_runnable.invoke(state, config))
         # add agent name to the AIMessage
         response.name = name
@@ -699,8 +759,7 @@ def create_react_agent(
         return {"messages": [response]}
 
     async def acall_model(state: StateSchema, config: RunnableConfig) -> StateSchema:
-        messages = _get_state_value(state, "messages")
-        _validate_chat_history(messages)
+        state = _get_model_input_state(state)
         response = cast(AIMessage, await model_runnable.ainvoke(state, config))
         # add agent name to the AIMessage
         response.name = name
@@ -716,6 +775,27 @@ def create_react_agent(
         # We return a list, because this will get added to the existing list
         return {"messages": [response]}
 
+    input_schema: StateSchemaType
+    if pre_model_hook is not None:
+        # Dynamically create a schema that inherits from state_schema and adds 'llm_input_messages'
+        if isinstance(state_schema, type) and issubclass(state_schema, BaseModel):
+            # For Pydantic schemas
+            from pydantic import create_model
+
+            input_schema = create_model(
+                "CallModelInputSchema",
+                llm_input_messages=(list[AnyMessage], ...),
+                __base__=state_schema,
+            )
+        else:
+            # For TypedDict schemas
+            class CallModelInputSchema(state_schema):  # type: ignore
+                llm_input_messages: list[AnyMessage]
+
+            input_schema = CallModelInputSchema
+    else:
+        input_schema = state_schema
+
     def generate_structured_response(
         state: StateSchema, config: RunnableConfig
     ) -> StateSchema:
@@ -749,8 +829,20 @@ def create_react_agent(
     if not tool_calling_enabled:
         # Define a new graph
         workflow = StateGraph(state_schema, config_schema=config_schema)
-        workflow.add_node("agent", RunnableCallable(call_model, acall_model))
-        workflow.set_entry_point("agent")
+        workflow.add_node(
+            "agent",
+            RunnableCallable(call_model, acall_model),
+            input=input_schema,
+        )
+        if pre_model_hook is not None:
+            workflow.add_node("pre_model_hook", pre_model_hook)
+            workflow.add_edge("pre_model_hook", "agent")
+            entrypoint = "pre_model_hook"
+        else:
+            entrypoint = "agent"
+
+        workflow.set_entry_point(entrypoint)
+
         if response_format is not None:
             workflow.add_node(
                 "generate_structured_response",
@@ -791,12 +883,23 @@ def create_react_agent(
     workflow = StateGraph(state_schema or AgentState, config_schema=config_schema)
 
     # Define the two nodes we will cycle between
-    workflow.add_node("agent", RunnableCallable(call_model, acall_model))
+    workflow.add_node(
+        "agent", RunnableCallable(call_model, acall_model), input=input_schema
+    )
     workflow.add_node("tools", tool_node)
 
+    # Optionally add a pre-model hook node that will be called
+    # every time before the "agent" (LLM-calling node)
+    if pre_model_hook is not None:
+        workflow.add_node("pre_model_hook", pre_model_hook)
+        workflow.add_edge("pre_model_hook", "agent")
+        entrypoint = "pre_model_hook"
+    else:
+        entrypoint = "agent"
+
     # Set the entrypoint as `agent`
     # This means that this node is the first one called
-    workflow.set_entry_point("agent")
+    workflow.set_entry_point(entrypoint)
 
     # Add a structured output node if response_format is provided
     if response_format is not None:
@@ -821,18 +924,20 @@ def create_react_agent(
         path_map=should_continue_destinations,
     )
 
-    def route_tool_responses(state: StateSchema) -> Literal["agent", "__end__"]:
+    def route_tool_responses(state: StateSchema) -> str:
         for m in reversed(_get_state_value(state, "messages")):
             if not isinstance(m, ToolMessage):
                 break
             if m.name in should_return_direct:
                 return END
-        return "agent"
+        return entrypoint
 
     if should_return_direct:
-        workflow.add_conditional_edges("tools", route_tool_responses)
+        workflow.add_conditional_edges(
+            "tools", route_tool_responses, path_map=[entrypoint, END]
+        )
     else:
-        workflow.add_edge("tools", "agent")
+        workflow.add_edge("tools", entrypoint)
 
     # Finally, we compile it!
     # This compiles it into a LangChain Runnable,

libs/prebuilt/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-prebuilt"
-version = "0.1.7"
+version = "0.1.8"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 license = "MIT"

libs/prebuilt/tests/test_react_agent.py

@@ -16,6 +16,7 @@ from langchain_core.messages import (
     AIMessage,
     AnyMessage,
     HumanMessage,
+    RemoveMessage,
     SystemMessage,
     ToolCall,
     ToolMessage,
@@ -29,6 +30,7 @@ from typing_extensions import TypedDict
 
 from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.graph import START, MessagesState, StateGraph, add_messages
+from langgraph.graph.message import REMOVE_ALL_MESSAGES
 from langgraph.prebuilt import (
     ToolNode,
     create_react_agent,
@@ -1432,3 +1434,36 @@ def test_get_model() -> None:
 
     with pytest.raises(TypeError):
         _get_model(RunnableLambda(lambda message: message))
+
+
+def test_pre_model_hook() -> None:
+    model = FakeToolCallingModel(tool_calls=[])
+
+    # Test `llm_input_messages`
+    def pre_model_hook(state: AgentState):
+        return {"llm_input_messages": [HumanMessage("Hello!")]}
+
+    agent = create_react_agent(model, [], pre_model_hook=pre_model_hook)
+    assert "pre_model_hook" in agent.nodes
+    result = agent.invoke({"messages": [HumanMessage("hi?")]})
+    assert result == {
+        "messages": [
+            _AnyIdHumanMessage(content="hi?"),
+            AIMessage(content="Hello!", id="0"),
+        ]
+    }
+
+    # Test `messages`
+    def pre_model_hook(state: AgentState):
+        return {
+            "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), HumanMessage("Hello!")]
+        }
+
+    agent = create_react_agent(model, [], pre_model_hook=pre_model_hook)
+    result = agent.invoke({"messages": [HumanMessage("hi?")]})
+    assert result == {
+        "messages": [
+            _AnyIdHumanMessage(content="Hello!"),
+            AIMessage(content="Hello!", id="1"),
+        ]
+    }

libs/sdk-js/src/types.ts

@@ -19,7 +19,7 @@ export type StreamEvent =
 
 export interface Send {
   node: string;
-  input: Record<string, unknown> | null;
+  input: unknown | null;
 }
 
 export interface Command {

libs/sdk-py/langgraph_sdk/client.py

@@ -631,6 +631,7 @@ class AssistantsClient:
         if_exists: Optional[OnConflictBehavior] = None,
         name: Optional[str] = None,
         headers: Optional[dict[str, str]] = None,
+        description: Optional[str] = None,
     ) -> Assistant:
         """Create a new assistant.
 
@@ -645,6 +646,8 @@ class AssistantsClient:
                 Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing assistant).
             name: The name of the assistant. Defaults to 'Untitled' under the hood.
             headers: Optional custom headers to include with the request.
+            description: Optional description of the assistant.
+                The description field is available for langgraph-api server version>=0.0.45
 
         Returns:
             Assistant: The created assistant.
@@ -673,6 +676,8 @@ class AssistantsClient:
             payload["if_exists"] = if_exists
         if name:
             payload["name"] = name
+        if description:
+            payload["description"] = description
         return await self.http.post("/assistants", json=payload, headers=headers)
 
     async def update(
@@ -684,6 +689,7 @@ class AssistantsClient:
         metadata: Json = None,
         name: Optional[str] = None,
         headers: Optional[dict[str, str]] = None,
+        description: Optional[str] = None,
     ) -> Assistant:
         """Update an assistant.
 
@@ -697,6 +703,8 @@ class AssistantsClient:
             metadata: Metadata to merge with existing assistant metadata.
             name: The new name for the assistant.
             headers: Optional custom headers to include with the request.
+            description: Optional description of the assistant.
+                The description field is available for langgraph-api server version>=0.0.45
 
         Returns:
             Assistant: The updated assistant.
@@ -720,6 +728,8 @@ class AssistantsClient:
             payload["metadata"] = metadata
         if name:
             payload["name"] = name
+        if description:
+            payload["description"] = description
         return await self.http.patch(
             f"/assistants/{assistant_id}",
             json=payload,
@@ -3055,6 +3065,7 @@ class SyncAssistantsClient:
         if_exists: Optional[OnConflictBehavior] = None,
         name: Optional[str] = None,
         headers: Optional[dict[str, str]] = None,
+        description: Optional[str] = None,
     ) -> Assistant:
         """Create a new assistant.
 
@@ -3069,6 +3080,8 @@ class SyncAssistantsClient:
                 Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing assistant).
             name: The name of the assistant. Defaults to 'Untitled' under the hood.
             headers: Optional custom headers to include with the request.
+            description: Optional description of the assistant.
+                The description field is available for langgraph-api server version>=0.0.45
 
         Returns:
             Assistant: The created assistant.
@@ -3097,6 +3110,8 @@ class SyncAssistantsClient:
             payload["if_exists"] = if_exists
         if name:
             payload["name"] = name
+        if description:
+            payload["description"] = description
         return self.http.post("/assistants", json=payload, headers=headers)
 
     def update(
@@ -3108,6 +3123,7 @@ class SyncAssistantsClient:
         metadata: Json = None,
         name: Optional[str] = None,
         headers: Optional[dict[str, str]] = None,
+        description: Optional[str] = None,
     ) -> Assistant:
         """Update an assistant.
 
@@ -3121,6 +3137,8 @@ class SyncAssistantsClient:
             metadata: Metadata to merge with existing assistant metadata.
             name: The new name for the assistant.
             headers: Optional custom headers to include with the request.
+            description: Optional description of the assistant.
+                The description field is available for langgraph-api server version>=0.0.45
 
         Returns:
             Assistant: The updated assistant.
@@ -3144,6 +3162,8 @@ class SyncAssistantsClient:
             payload["metadata"] = metadata
         if name:
             payload["name"] = name
+        if description:
+            payload["description"] = description
         return self.http.patch(
             f"/assistants/{assistant_id}",
             json=payload,

libs/sdk-py/langgraph_sdk/schema.py

@@ -171,6 +171,8 @@ class AssistantBase(TypedDict):
     """The version of the assistant"""
     name: str
     """The name of the assistant"""
+    description: Optional[str]
+    """The description of the assistant"""
 
 
 class AssistantVersion(AssistantBase):

libs/sdk-py/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-sdk"
-version = "0.1.60"
+version = "0.1.61"
 description = "SDK for interacting with LangGraph API"
 authors = []
 license = "MIT"