CLI: Ensure correct api version is used (#4237 )

Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
chore(docs,cli): remove python-only label for custom auth, add test for custom auth in CLI (#4233 )
2026-08-18 05:35:43 +02:00 · 2025-04-10 15:56:07 +00:00 · 2025-04-10 16:02:12 +02:00 · 2025-04-10 15:06:55 +02:00 · 2025-04-10 14:34:52 +02:00 · 2025-04-09 20:22:36 -04:00
140 changed files with 8432 additions and 2161 deletions
@@ -4,7 +4,7 @@ on:
  workflow_call:

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

 jobs:
  build:
@@ -71,4 +71,3 @@ jobs:
        working-directory: libs/cli/js-examples
        run: |
          langgraph build -t langgraph-test-e
-  
@@ -9,7 +9,7 @@ on:
        description: "From which folder this pipeline executes"

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

  # This env var allows us to get inline annotations when ruff has complaints.
  RUFF_OUTPUT_FORMAT: github
@@ -50,12 +50,6 @@ jobs:
        working-directory: ${{ inputs.working-directory }}
        run: poetry check

-      - name: Check lock file
-        if: steps.changed-files.outputs.all
-        shell: bash
-        working-directory: ${{ inputs.working-directory }}
-        run: poetry check --lock
-
      - name: Install dependencies
        if: steps.changed-files.outputs.all
        # Also installs dev/lint/test/typing dependencies, to ensure we have
@@ -9,7 +9,7 @@ on:
        description: "From which folder this pipeline executes"

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

 jobs:
  build:
@@ -39,12 +39,6 @@ jobs:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_RO_TOKEN }}

-      - name: Check Lock
-        shell: bash
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          poetry check --lock
-
      - name: Install dependencies
        shell: bash
        working-directory: ${{ inputs.working-directory }}
@@ -4,7 +4,7 @@ on:
  workflow_call:

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

 jobs:
  build:
@@ -9,7 +9,7 @@ on:
        description: "From which folder this pipeline executes"

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"
  PYTHON_VERSION: "3.10"

 jobs:
@@ -4,7 +4,7 @@ on:
  workflow_call:

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

 jobs:
  build:
@@ -8,7 +8,7 @@ on:
      - "libs/**"

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

 jobs:
  benchmark:
@@ -6,7 +6,7 @@ on:
      - "libs/**"

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

 jobs:
  benchmark:
@@ -17,7 +17,7 @@ concurrency:
  cancel-in-progress: true

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

 jobs:
  changes:
@@ -10,7 +10,7 @@ on:
  workflow_dispatch:

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

 permissions:
  contents: read
@@ -99,7 +99,7 @@ jobs:
        env:
          LANGCHAIN_API_KEY: test
        run: |
-          if [ "${{ github.event_name }}" == "schedule" ] || [ "${{ github.event_name }}" == "workflow_dispatch" ] || ([ "${{ github.event_name }}" == "push" ] && [ "${{ github.ref }}" == "refs/heads/main" ]); then
+          if [ "${{ github.event_name }}" == "schedule" ]; then
            echo "Running link check on all HTML files matching notebooks in docs directory..."
            poetry run pytest -v \
              --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
@@ -12,7 +12,7 @@ on:
  workflow_dispatch:

 env:
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

 jobs:
  markdown-link-check:
@@ -42,8 +42,8 @@ jobs:

      - name: Check README.md is in sync
        run: |
-            if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
-            echo "README.md is out of sync with libs/langgraph/README.md"
-            diff -C 3 README.md libs/langgraph/README.md
-            exit 1
-            fi
+          if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
+          echo "README.md is out of sync with libs/langgraph/README.md"
+          diff -C 3 README.md libs/langgraph/README.md
+          exit 1
+          fi
@@ -10,7 +10,7 @@ on:

 env:
  PYTHON_VERSION: "3.11"
-  POETRY_VERSION: "1.7.1"
+  POETRY_VERSION: "2.1.2"

 jobs:
  build:
@@ -9,7 +9,7 @@ on:
        type: string
        description: "JSON string of changed files"
  schedule:
-    - cron: '0 13 * * *'
+    - cron: "0 13 * * *"

 defaults:
  run:
@@ -30,12 +30,12 @@ jobs:
        uses: "./.github/actions/poetry_setup"
        with:
          python-version: 3.11
-          poetry-version: 1.7.1
+          poetry-version: 2.1.2
          cache-key: test-langgraph-notebooks

      - name: Install dependencies
        run: |
-          poetry install --with test
+          poetry install --with test --no-root
          poetry run pip install jupyter

      - name: Start services
@@ -36,3 +36,6 @@ packages:
  - name: "langgraph-reflection"
    repo: "langchain-ai/langgraph-reflection"
    description: "LangGraph agent that runs a reflection step."
+  - name: "langgraph-codeact"
+    repo: "langchain-ai/langgraph-codeact"
+    description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."
@@ -0,0 +1 @@
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@@ -10,14 +10,17 @@ This list of companies using LangGraph and their success stories is compiled fro
 | [Athena Intelligence](https://www.athenaintel.com/) | Software & Technology (GenAI Native) | Research & summarization | [Case study, 2024](https://blog.langchain.dev/customers-athena-intelligence/) |
 | [Captide](https://www.captide.co/) | Software & Technology (GenAI Native) | Data extraction | [Case study, 2025](https://blog.langchain.dev/how-captide-is-redefining-equity-research-with-agentic-workflows-built-on-langgraph-and-langsmith/) |
 | [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) |
+| [C.H. Robinson](https://www.chrobinson.com/en-us/) | Logistics | Automation | [Case study, 2025](https://blog.langchain.dev/customers-chrobinson/) |
 | [Elastic](https://www.elastic.co/) | Software & Technology | Copilot for domain-specific task | [Blog post, 2025](https://www.elastic.co/blog/elastic-security-generative-ai-features) |
 | [GitLab](https://about.gitlab.com/) | Software & Technology | Code generation | [Duo workflow docs](https://handbook.gitlab.com/handbook/engineering/architecture/design-documents/duo_workflow/) |
+| [Inconvo](https://inconvo.ai/?ref=blog.langchain.dev) | Software & Technology | Code generation | [Case study, 2025](https://blog.langchain.dev/customers-inconvo/) |
 | [Infor](https://infor.com/) | Software & Technology | GenAI embedded product experiences; customer support; copilot | [Case study, 2025](https://blog.langchain.dev/customers-infor/) |
 | [Klarna](https://www.klarna.com/) | Fintech | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/customers-klarna/) |
 | [Komodo Health](https://www.komodohealth.com/) | Healthcare | Copilot for domain-specific task | [Blog post](https://www.komodohealth.com/perspectives/new-gen-ai-assistant-empowers-the-enterprise/) |
 | [LinkedIn](https://www.linkedin.com/) | Social Media | Code generation; Search & discovery | [Blog post, 2025](https://www.linkedin.com/blog/engineering/ai/practical-text-to-sql-for-data-analytics); [Blog post, 2024](https://www.linkedin.com/blog/engineering/generative-ai/behind-the-platform-the-journey-to-create-the-linkedin-genai-application-tech-stack) |
 | [Minimal](https://gominimal.ai/) | E-commerce | Customer support | [Case study, 2025](https://blog.langchain.dev/how-minimal-built-a-multi-agent-customer-support-system-with-langgraph-langsmith/) |
 | [OpenRecovery](https://www.openrecovery.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-openrecovery/) |
+| [Qodo](https://www.qodo.ai/) | Software & Technology (GenAI Native) | Code generation | [Blog post, 2025](https://www.qodo.ai/blog/why-we-chose-langgraph-to-build-our-coding-agent/) |
 | [Rakuten](https://www.rakuten.com/) | E-commerce / Fintech | Copilot for domain-specific task | [Blog post, 2025](https://rakuten.today/blog/from-ai-hype-to-real-world-tools-rakuten-teams-up-with-langchain.html) |
 | [Replit](https://replit.com/) | Software & Technology | Code generation | [Blog post, 2024](https://blog.langchain.dev/customers-replit/); [Breakout agent story, 2024](https://www.langchain.com/breakoutagents/replit); [Fireside chat video, 2024](https://www.youtube.com/watch?v=ViykMqljjxU) |
 | [Rexera](https://www.rexera.com/) | Real Estate (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-rexera/) |
@@ -25,3 +28,4 @@ This list of companies using LangGraph and their success stories is compiled fro
 | [Uber](https://www.uber.com/) | Transportation | Developer productivity; Code generation | [Presentation, 2024](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/); [Video, 2024](https://www.youtube.com/watch?v=8rkA5vWUE4Y) |
 | [Unify](https://www.unifygtm.com/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Blog post, 2024](https://blog.langchain.dev/unify-launches-agents-for-account-qualification-using-langgraph-and-langsmith/) |
 | [Vizient](https://www.vizientinc.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/p/3d2cd58c-13a5-4df9-bd84-7d54ed0ed82c/) |
+| [Vodafone](https://www.vodafone.com/) | Telecommunications | Code generation; internal search | [Case study, 2025](https://blog.langchain.dev/customers-vodafone/) |
@@ -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

@@ -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).
@@ -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!
@@ -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}
+```
@@ -0,0 +1,31 @@
+# Testing local agents with remote traces
+
+## Overview
+
+A common workflow when debugging production-deployed agents is to test the same thread against a local version of the same agent, which may have modifications.
+
+To support this, LangGraph Studio, in combination with LangSmith, allows you to clone remote threads traced in LangSmith into your locally running agent. This cloned thread can then be used to re-run specific nodes within Studio.
+
+## Requirements
+
+!!! info "Prerequisites"
+
+    - langgraph>=0.3.18
+    - langgraph-api>=0.0.32
+
+- A thread traced in LangSmith.
+- A locally running agent. See [here](../../how-tos/local-studio.md) for setup instructions.
+  - Note that your local agent must be using the above specified `langgraph` and `langgraph-api` versions.
+  - The nodes present in the remote trace must exist in at least one of the graphs in your local agent.
+
+## Cloning Thread
+
+First navigate to the LangSmith trace. Here you should see a button to "Run in Studio".
+
+![Run in Studio](../img/run_in_studio.png){width=1200}
+
+This will prompt you to enter the url that your locally running agent is accessible at. Once provided, select "Clone thread locally". If you have multiple graphs in your agent, you will also be prompted to select a graph to clone this thread under.
+
+Once selected, a will a new thread in your local agent will be created and the thread history will be reconstruced to reflect the original trace.
+
+Alternatively, if your trace originates from an agent deployed on LangGraph Platform, you can "View original thread" to open Studio with the actual deployed thread.
@@ -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

@@ -1,7 +1,8 @@
 # How to integrate LangGraph into your React application

-!!! info "Prerequisites"
-    - [LangGraph Platform](../../concepts/langgraph_platform.md)
+!!! info "Prerequisites" 
+
+    - [LangGraph Platform](../../concepts/langgraph_platform.md) 
    - [LangGraph Server](../../concepts/langgraph_server.md)

 The `useStream()` React hook provides a seamless way to integrate LangGraph into your React applications. It handles all the complexities of streaming, state management, and branching logic, letting you focus on building great chat experiences.
@@ -169,10 +170,7 @@ The `useStream()` hook exposes the `interrupt` property, which will be filled wi
 Learn more about interrupts in the [How to handle interrupts](../../how-tos/human_in_the_loop/wait-user-input.ipynb) guide.

 ```tsx
-const thread = useStream<
-  { messages: Message[] },
-  { InterruptType: string }
->({
+const thread = useStream<{ messages: Message[] }, { InterruptType: string }>({
  apiUrl: "http://localhost:2024",
  assistantId: "agent",
  messagesKey: "messages",
@@ -182,7 +180,6 @@ if (thread.interrupt) {
  return (
    <div>
      Interrupted! {thread.interrupt.value}
-
      <button
        type="button"
        onClick={() => {
@@ -313,7 +310,7 @@ export default function App() {
                  onEdit={(message) =>
                    thread.submit(
                      { messages: [message] },
-                      { checkpoint: parentCheckpoint },
+                      { checkpoint: parentCheckpoint }
                    )
                  }
                />
@@ -370,6 +367,33 @@ export default function App() {

 For advanced use cases you can use the `experimental_branchTree` property to get the tree representation of the thread, which can be used to render branching controls for non-message based graphs.

+### Optimistic Updates
+
+You can optimistically update the client state before performing a network request to the agent, allowing you to provide immediate feedback to the user, such as showing the user message immediately before the agent has seen the request.
+
+```tsx
+const stream = useStream({
+  apiUrl: "http://localhost:2024",
+  assistantId: "agent",
+  messagesKey: "messages",
+});
+
+const handleSubmit = (text: string) => {
+  const newMessage = { type: "human" as const, content: text };
+
+  stream.submit(
+    { messages: [newMessage] },
+    {
+      optimisticValues(prev) {
+        const prevMessages = prev.messages ?? [];
+        const newMessages = [...prevMessages, newMessage];
+        return { ...prev, messages: newMessages };
+      },
+    }
+  );
+};
+```
+
 ### TypeScript

 The `useStream()` hook is friendly for apps written in TypeScript and you can specify types for the state to get better type safety and IDE support.
@@ -397,21 +421,23 @@ You can also optionally specify types for different scenarios, such as:
 - `UpdateType`: Type for the submit function (default: `Partial<State>`)

 ```tsx
-
-const thread = useStream<State, {
-  UpdateType: {
-    messages: Message[] | Message;
-    context?: Record<string, unknown>;
-  };
-  InterruptType: string;
-  CustomEventType: {
-    type: "progress" | "debug";
-    payload: unknown;
-  };
-  ConfigurableType: {
-    model: string;
-  };
-}>({
+const thread = useStream<
+  State,
+  {
+    UpdateType: {
+      messages: Message[] | Message;
+      context?: Record<string, unknown>;
+    };
+    InterruptType: string;
+    CustomEventType: {
+      type: "progress" | "debug";
+      payload: unknown;
+    };
+    ConfigurableType: {
+      model: string;
+    };
+  }
+>({
  apiUrl: "http://localhost:2024",
  assistantId: "agent",
  messagesKey: "messages",
@@ -22,7 +22,7 @@
      "description": "A run is an invocation of a graph / assistant, with no state or memory persistence."
    },
    {
-      "name": "Crons (Enterprise-only)",
+      "name": "Crons (Plus tier)",
      "description": "A cron is a periodic run that recurs on a given schedule. The repeats can be isolated, or share state in a thread"
    },
    {
@@ -805,6 +805,58 @@
        }
      }
    },
+    "/threads/state/bulk": {
+      "post": {
+        "tags": [
+          "Threads"
+        ],
+        "summary": "Bulk Update Thread State",
+        "description": "Create a new thread from a batch of state updates.",
+        "operationId": "bulk_update_thread_state_post",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/ThreadStateBulkUpdate"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Success",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/Thread"
+                }
+              }
+            }
+          },
+          "409": {
+            "description": "Conflict",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
    "/threads/{thread_id}/state": {
      "get": {
        "tags": [
@@ -1342,6 +1394,21 @@
            },
            "name": "offset",
            "in": "query"
+          },
+          {
+            "required": false,
+            "schema": {
+              "type": "string",
+              "enum": [
+                "pending",
+                "error",
+                "success",
+                "timeout",
+                "interrupted"
+              ]
+            },
+            "name": "status",
+            "in": "query"
          }
        ],
        "responses": {
@@ -1458,7 +1525,7 @@
    "/threads/{thread_id}/runs/crons": {
      "post": {
        "tags": [
-          "Crons (Enterprise-only)"
+          "Crons (Plus tier)"
        ],
        "summary": "Create Thread Cron",
        "description": "Create a cron to schedule runs on a thread.",
@@ -1836,6 +1903,17 @@
            },
            "name": "run_id",
            "in": "path"
+          },
+          {
+            "required": false,
+            "schema": {
+              "type": "boolean",
+              "title": "Cancel on Disconnect",
+              "description": "If true, the run will be cancelled if the client disconnects.",
+              "default": false
+            },
+            "name": "cancel_on_disconnect",
+            "in": "query"
          }
        ],
        "responses": {
@@ -2032,7 +2110,7 @@
    "/runs/crons": {
      "post": {
        "tags": [
-          "Crons (Enterprise-only)"
+          "Crons (Plus tier)"
        ],
        "summary": "Create Cron",
        "description": "Create a cron to schedule runs on new threads.",
@@ -2084,7 +2162,7 @@
    "/runs/crons/search": {
      "post": {
        "tags": [
-          "Crons (Enterprise-only)"
+          "Crons (Plus tier)"
        ],
        "summary": "Search Crons",
        "description": "Search all active crons",
@@ -2190,6 +2268,68 @@
        }
      }
    },
+    "/runs/cancel": {
+      "post": {
+        "tags": [
+          "Thread Runs"
+        ],
+        "summary": "Cancel Runs",
+        "description": "Cancel one or more runs. Can cancel runs by thread ID and run IDs, or by status filter.",
+        "operationId": "cancel_runs_post",
+        "parameters": [
+          {
+            "description": "Action to take when cancelling the run. Possible values are `interrupt` or `rollback`. `interrupt` will simply cancel the run. `rollback` will cancel the run and delete the run and associated checkpoints afterwards.",
+            "required": false,
+            "schema": {
+              "type": "string",
+              "enum": [
+                "interrupt",
+                "rollback"
+              ],
+              "title": "Action",
+              "default": "interrupt"
+            },
+            "name": "action",
+            "in": "query"
+          }
+        ],
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/RunsCancel"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "204": {
+            "description": "Success - Runs cancelled"
+          },
+          "404": {
+            "description": "Not Found",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
    "/runs/wait": {
      "post": {
        "tags": [
@@ -2373,7 +2513,7 @@
    "/runs/crons/{cron_id}": {
      "delete": {
        "tags": [
-          "Crons (Enterprise-only)"
+          "Crons (Plus tier)"
        ],
        "summary": "Delete Cron",
        "description": "Delete a cron by ID.",
@@ -2936,7 +3076,7 @@
            "type": "string",
            "maxLength": 65536,
            "minLength": 1,
-            "format": "uri",
+            "format": "uri-reference",
            "title": "Webhook",
            "description": "Webhook to call after LangGraph API call is done."
          },
@@ -3216,7 +3356,11 @@
        "description": "The command to run.",
        "properties": {
          "update": {
-            "type": "object",
+            "type": [
+              "object",
+              "array",
+              "null"
+            ],
            "title": "Update",
            "description": "An update to the state."
          },
@@ -3226,12 +3370,13 @@
              "array",
              "number",
              "string",
+              "boolean",
              "null"
            ],
            "title": "Resume",
            "description": "A value to pass to an interrupted node."
          },
-          "send": {
+          "goto": {
            "anyOf": [
              {
                "$ref": "#/components/schemas/Send"
@@ -3242,10 +3387,21 @@
                  "$ref": "#/components/schemas/Send"
                }
              },
+              {
+                "type": "string"
+              },
+              {
+                "type": "array",
+                "items": {
+                  "type": "string"
+                }
+              },
              {
                "type": "null"
              }
-            ]
+            ],
+            "title": "Goto",
+            "description": "Name of the node(s) to navigate to next or node(s) to be executed with a provided input."
          }
        }
      },
@@ -3276,6 +3432,18 @@
              {
                "type": "object"
              },
+              {
+                "type": "array"
+              },
+              {
+                "type": "string"
+              },
+              {
+                "type": "number"
+              },
+              {
+                "type": "boolean"
+              },
              {
                "type": "null"
              }
@@ -3326,7 +3494,7 @@
            "type": "string",
            "maxLength": 65536,
            "minLength": 1,
-            "format": "uri",
+            "format": "uri-reference",
            "title": "Webhook",
            "description": "Webhook to call after LangGraph API call is done."
          },
@@ -3491,6 +3659,18 @@
              {
                "type": "object"
              },
+              {
+                "type": "array"
+              },
+              {
+                "type": "string"
+              },
+              {
+                "type": "number"
+              },
+              {
+                "type": "boolean"
+              },
              {
                "type": "null"
              }
@@ -3541,7 +3721,7 @@
            "type": "string",
            "maxLength": 65536,
            "minLength": 1,
-            "format": "uri",
+            "format": "uri-reference",
            "title": "Webhook",
            "description": "Webhook to call after LangGraph API call is done."
          },
@@ -3840,6 +4020,36 @@
            "title": "If Exists",
            "description": "How to handle duplicate creation. Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing thread).",
            "default": "raise"
+          },
+          "ttl": {
+            "type": "object",
+            "title": "TTL",
+            "description": "The time-to-live for the thread.",
+            "properties": {
+              "strategy": {
+                "type": "string",
+                "enum": ["delete"],
+                "description": "The TTL strategy. 'delete' removes the entire thread.",
+                "default": "delete"
+              },
+              "ttl": {
+                "type": "number",
+                "description": "The time-to-live in minutes from now until thread should be swept."
+              }
+            }
+          },
+          "supersteps": {
+            "type": "array",
+            "items": {
+              "type": "object",
+              "properties": {
+                "updates": {
+                  "type": "array",
+                  "items": { "$ref": "#/components/schemas/ThreadSuperstepUpdate" }
+                }
+              },
+              "required": ["updates"]
+            }
          }
        },
        "type": "object",
@@ -4028,6 +4238,43 @@
        "title": "ThreadStateUpdate",
        "description": "Payload for updating the state of a thread."
      },
+      "ThreadSuperstepUpdate": {
+        "properties": {
+          "values": {
+            "anyOf": [
+              {
+                "type": "array",
+                "items": {
+                  "type": "object"
+                }
+              },
+              {
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ]
+          },
+          "command": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/Command"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "The command associated with the update."
+          },
+          "as_node": {
+            "type": "string",
+            "description": "Update the state as if this node had just executed."
+          }
+        },
+        "required": ["as_node"],
+        "type": "object"
+      },
      "ThreadStateUpdateResponse": {
        "properties": {
          "checkpoint": {
@@ -4230,6 +4477,42 @@
        },
        "description": "Represents a single document or data entry in the graph's Store. Items are used to store cross-thread memories."
      },
+      "RunsCancel": {
+        "type": "object",
+        "title": "RunsCancel",
+        "description": "Payload for cancelling runs.",
+        "properties": {
+          "status": {
+            "type": "string",
+            "enum": ["pending", "running", "all"],
+            "title": "Status",
+            "description": "Filter runs by status to cancel. Must be one of 'pending', 'running', or 'all'."
+          },
+          "thread_id": {
+            "type": "string",
+            "format": "uuid",
+            "title": "Thread Id",
+            "description": "The ID of the thread containing runs to cancel."
+          },
+          "run_ids": {
+            "type": "array",
+            "items": {
+              "type": "string",
+              "format": "uuid"
+            },
+            "title": "Run Ids",
+            "description": "List of run IDs to cancel."
+          }
+        },
+        "oneOf": [
+          {
+            "required": ["status"]
+          },
+          {
+            "required": ["thread_id", "run_ids"]
+          }
+        ]
+      },
      "SearchItemsResponse": {
        "type": "object",
        "required": [
@@ -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>
@@ -42,15 +42,16 @@ The LangGraph CLI requires a JSON configuration file with the following keys:

    | Key                                                          | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
    | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-    | <span style="white-space: nowrap;">`dependencies`</span>     | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.                                                                                                                                                                                                                                                                                     |
+    | <span style="white-space: nowrap;">`dependencies`</span>     | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: <ul><li>A single period (`"."`), which will look for local Python packages.</li><li>The directory path where `pyproject.toml`, `setup.py` or `requirements.txt` is located.</br></br>For example, if `requirements.txt` is located in the root of the project directory, specify `"./"`. If it's located in a subdirectory called `local_package`, specify `"./local_package"`. Do not specify the string `"requirements.txt"` itself.</li><li>A Python package name.</li></ul> |
    | <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;">`python_version`</span>   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+    | <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"
    
@@ -2,6 +2,22 @@

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

+## `BG_JOB_ISOLATED_LOOPS`
+
+Set `BG_JOB_ISOLATED_LOOPS` to `True` to execute background runs in an isolated event loop separate from the serving API event loop.
+
+This environment variable should be set to `True` if the implementation of a graph/node contains synchronous code. In this situation, the synchronous code will block the serving API event loop, which may cause the API to be unavailable. A symptom of an unavailable API is continuous application restarts due to failing health checks.
+
+Defaults to `False`.
+
+## `BG_JOB_TIMEOUT_SECS`
+
+The timeout of a background run can be increased. However, the infrastructure for a Cloud SaaS deployment enforces a 1 hour timeout limit for API requests. This means the connection between client and server will timeout after 1 hour. This is not configurable.
+
+A background run can execute for longer than 1 hour, but a client must reconnect to the server (e.g. join stream via `POST /threads/{thread_id}/runs/{run_id}/stream`) to retrieve output from the run if the run is taking longer than 1 hour.
+
+Defaults to `3600`.
+
 ## `DD_API_KEY`

 Specify `DD_API_KEY` (your [Datadog API Key](https://docs.datadoghq.com/account_management/api-app-keys/)) to automatically enable Datadog tracing for the deployment. Specify other [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) to configure the tracing instrumentation.
@@ -28,6 +44,10 @@ Set this environment variable to have a BYOC deployment send traces to a self-ho

 `SELF_HOSTED_LANGSMITH_HOSTNAME` is the hostname of the self-hosted LangSmith instance. It must be accessible to the BYOC deployment. `LANGSMITH_API_KEY` is a LangSmith API generated from the self-hosted LangSmith instance.

+## `LOG_LEVEL`
+
+Configure [log level](https://docs.python.org/3/library/logging.html#logging-levels). Defaults to `INFO`.
+
 ## `N_JOBS_PER_WORKER`

 Number of jobs per worker for the LangGraph Cloud task queue. Defaults to `10`.
@@ -55,3 +75,9 @@ Database Connectivity:

 - The externally managed Postgres instance must be accessible by the LangGraph Server service in the ECS cluster. The BYOC user is responsible for ensuring connectivity.
 - For example, if an AWS RDS Postgres instance is provisioned, it can be provisioned in the same VPC (`langgraph-cloud-vpc`) as the ECS cluster with the `langgraph-cloud-service-sg` security group to ensure connectivity.
+
+## `REDIS_URI_CUSTOM`
+
+For [Bring Your Own Cloud (BYOC)](../../concepts/bring_your_own_cloud.md) deployments only.
+
+Specify `REDIS_URI_CUSTOM` to use an externally managed Redis instance. The value of `REDIS_URI_CUSTOM` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
@@ -2,10 +2,6 @@

 LangGraph Platform provides a flexible authentication and authorization system that can integrate with most authentication schemes.

-!!! note "Python only"
-
-    We currently only support custom authentication and authorization in Python deployments with `langgraph-api>=0.0.11`. Support for LangGraph.JS will be added soon.
-
 ## Core Concepts

 ### Authentication vs Authorization
@@ -146,7 +142,7 @@ The returned user information is available:

 After authentication, LangGraph calls your [`@auth.on`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.on) handlers to control access to specific resources (e.g., threads, assistants, crons). These handlers can:

-1. Add metadata to be saved during resource creation by mutating the `value["metadata"]` dictionary directly. See the [supported actions table](##supported-actions) for the list of types the value can take for each action.
+1. Add metadata to be saved during resource creation by mutating the `value["metadata"]` dictionary directly. See the [supported actions table](#supported-actions) for the list of types the value can take for each action.
 2. Filter resources by metadata during search/list or read operations by returning a [filter dictionary](#filter-operations).
 3. Raise an HTTP exception if access is denied.

@@ -289,7 +285,7 @@ async def on_assistant_create(
        )
 ```

-Notice that we are mixing global and resource-specific handlers in the above example. Since each request is handled by the most specific handler, a request to create a `thread` would match the `on_thread_create` handler but NOT the `reject_unhandled_requests` handler. A request to `update` a thread, however would be handled by the global handler, since we don't have a more specific handler for that resource and action. Requests to create, update, 
+Notice that we are mixing global and resource-specific handlers in the above example. Since each request is handled by the most specific handler, a request to create a `thread` would match the `on_thread_create` handler but NOT the `reject_unhandled_requests` handler. A request to `update` a thread, however would be handled by the global handler, since we don't have a more specific handler for that resource and action.

 ### Filter Operations {#filter-operations}

@@ -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

@@ -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."""
@@ -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.
@@ -1,101 +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 |
-
-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.
-
-## 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_V2` and `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)
@@ -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.
@@ -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   |
@@ -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)

@@ -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.
@@ -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...
@@ -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.
@@ -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"},
@@ -4,6 +4,10 @@ LangGraph has a built-in persistence layer, implemented through checkpointers. W

 ![Checkpoints](img/persistence/checkpoints.jpg)

+!!! info "LangGraph API handles checkpointing automatically"
+
+    When using the LangGraph API, you don't need to implement or configure checkpointers manually. The API handles all persistence infrastructure for you behind the scenes.
+
 ## Threads

 A thread is a unique ID or [thread identifier](#threads) assigned to each checkpoint saved by a checkpointer. When invoking graph with a checkpointer, you **must** specify a `thread_id` as part of the `configurable` portion of the config:
@@ -26,7 +30,7 @@ Let's see what checkpoints are saved when a simple graph is invoked as follows:

 ```python
 from langgraph.graph import StateGraph, START, END
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver
 from typing import Annotated
 from typing_extensions import TypedDict
 from operator import add
@@ -49,7 +53,7 @@ workflow.add_edge(START, "node_a")
 workflow.add_edge("node_a", "node_b")
 workflow.add_edge("node_b", END)

-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 graph = workflow.compile(checkpointer=checkpointer)

 config = {"configurable": {"thread_id": "1"}}
@@ -223,6 +227,10 @@ But, what if we want to retain some information *across threads*? Consider the c

 With checkpointers alone, we cannot share information across threads. This motivates the need for the [`Store`](../reference/store.md#langgraph.store.base.BaseStore) interface. As an illustration, we can define an `InMemoryStore` to store information about a user across threads. We simply compile our graph with a checkpointer, as before, and with our new `in_memory_store` variable.

+!!!  info "LangGraph API handles stores automatically"
+
+    When using the LangGraph API, you don't need to implement or configure stores manually. The API handles all storage infrastructure for you behind the scenes.
+
 ### Basic Usage

 First, let's showcase this in isolation without using LangGraph.
@@ -324,10 +332,10 @@ store.put(
 With this all in place, we use the `in_memory_store` in LangGraph. The `in_memory_store` works hand-in-hand with the checkpointer: the checkpointer saves state to threads, as discussed above, and the `in_memory_store` allows us to store arbitrary information for access *across* threads. We compile the graph with both the checkpointer and the `in_memory_store` as follows. 

 ```python
-from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.memory import InMemorySaver

 # We need this because we want to enable threads (conversations)
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()

 # ... Define the graph ...

@@ -440,6 +448,7 @@ Under the hood, checkpointing is powered by checkpointer objects that conform to
 * `langgraph-checkpoint-sqlite`: An implementation of LangGraph checkpointer that uses SQLite database ([SqliteSaver][langgraph.checkpoint.sqlite.SqliteSaver] / [AsyncSqliteSaver][langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver]). Ideal for experimentation and local workflows. Needs to be installed separately.
 * `langgraph-checkpoint-postgres`: An advanced checkpointer that uses Postgres database ([PostgresSaver][langgraph.checkpoint.postgres.PostgresSaver] / [AsyncPostgresSaver][langgraph.checkpoint.postgres.aio.AsyncPostgresSaver]), used in LangGraph Cloud. Ideal for using in production. Needs to be installed separately.

+
 ### Checkpointer interface

 Each checkpointer conforms to [BaseCheckpointSaver][langgraph.checkpoint.base.BaseCheckpointSaver] interface and implements the following methods:
@@ -452,7 +461,7 @@ Each checkpointer conforms to [BaseCheckpointSaver][langgraph.checkpoint.base.Ba
 If the checkpointer is used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), asynchronous versions of the above methods will be used (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`).

 !!! note Note
-    For running your graph asynchronously, you can use `MemorySaver`, or async versions of Sqlite/Postgres checkpointers -- `AsyncSqliteSaver` / `AsyncPostgresSaver` checkpointers.
+    For running your graph asynchronously, you can use `InMemorySaver`, or async versions of Sqlite/Postgres checkpointers -- `AsyncSqliteSaver` / `AsyncPostgresSaver` checkpointers.

 ### Serializer

@@ -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

@@ -300,6 +304,7 @@ LangGraph Studio is a built-in UI for visualizing, testing, and debugging your a
 - [How to interact with threads in LangGraph Studio](../cloud/how-tos/threads_studio.md)
 - [How to add nodes as dataset examples in LangGraph Studio](../cloud/how-tos/datasets_studio.md)
 - [How to engineer prompts in LangGraph Studio](../cloud/how-tos/iterate_graph_studio.md)
+- [How to test your agent against remote traces](../cloud/how-tos/clone_traces_studio.md)

 ## Troubleshooting

@@ -16,6 +16,10 @@
    "    - [Memory](../../concepts/memory/)\n",
    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
    "\n",
+    "!!! info \"Not needed for LangGraph API users\"\n",
+    "\n",
+    "    If you're using the LangGraph API, you needn't manually implement a checkpointer. The API automatically handles checkpointing for you. This guide is relevant when implementing LangGraph in your own custom server.\n",
+    "\n",
    "Many AI applications need memory to share context across multiple interactions on the same [thread](../../concepts/persistence#threads) (e.g., multiple turns of a conversation). In LangGraph functional API, this kind of memory can be added to any [entrypoint()][langgraph.func.entrypoint] workflow using [thread-level persistence](https://langchain-ai.github.io/langgraph/concepts/persistence).\n",
    "\n",
    "When creating a LangGraph workflow, you can set it up to persist its results by using a [checkpointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#basecheckpointsaver):\n",
@@ -31,6 +31,10 @@
    "    </p>\n",
    "</div> \n",
    "\n",
+    "!!! info \"Not needed for LangGraph API users\"\n",
+    "\n",
+    "    If you're using the LangGraph API, you needn't manually implement a checkpointer. The API automatically handles checkpointing for you. This guide is relevant when implementing LangGraph in your own custom server.\n",
+    "\n",
    "Many AI applications need memory to share context across multiple interactions. In LangGraph, this kind of memory can be added to any [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph) using [thread-level persistence](https://langchain-ai.github.io/langgraph/concepts/persistence) .\n",
    "\n",
    "When creating any LangGraph graph, you can set it up to persist its state by adding a [checkpointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#basecheckpointsaver) when compiling the graph:\n",
@@ -26,6 +26,10 @@
    "    </p>\n",
    "</div> \n",
    "\n",
+    "!!! info \"Not needed for LangGraph API users\"\n",
+    "\n",
+    "    If you're using the LangGraph API, you needn't manually implement a checkpointer. The API automatically handles checkpointing for you. This guide is relevant when implementing LangGraph in your own custom server.\n",
+    "\n",
    "When creating LangGraph agents, you can also set them up so that they persist their state. This allows you to do things like interact with an agent multiple times and have it remember previous interactions.\n",
    "\n",
    "This how-to guide shows how to use `Postgres` as the backend for persisting checkpoint state using the [`langgraph-checkpoint-postgres`](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-postgres) library.\n",
@@ -44,7 +48,7 @@
    "...\n",
    "```\n",
    "\n",
-    "!!! info \"Setup\"",
+    "!!! info \"Setup\"\n",
    "\n",
    "    You need to run `.setup()` once on your checkpointer to initialize the database before you can use it."
   ]
@@ -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/persistence.md#memory-store) 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.
+
@@ -1,13 +1,20 @@
-# LLMs-txt for LangGraph
+# LLMs-txt Overview

 ## Overview

-LangGraph provides documentation files in the [`llms.txt`](https://llmstxt.org/) format, specifically `llms.txt` and `llms-full.txt`. These files allow large language models (LLMs) and agents to access programming documentation and APIs, particularly useful within integrated development environments (IDEs).
+Below you can find a list of documentation files in the [`llms.txt`](https://llmstxt.org/) format, specifically `llms.txt` and `llms-full.txt`. These files allow large language models (LLMs) and agents to access programming documentation and APIs, particularly useful within integrated development environments (IDEs).

 | Language Version | llms.txt                                                                                                   | llms-full.txt                                                                                                        |
 |------------------|------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|
 | LangGraph Python | [https://langchain-ai.github.io/langgraph/llms.txt](https://langchain-ai.github.io/langgraph/llms.txt)     | [https://langchain-ai.github.io/langgraph/llms-full.txt](https://langchain-ai.github.io/langgraph/llms-full.txt)     |
 | LangGraph JS     | [https://langchain-ai.github.io/langgraphjs/llms.txt](https://langchain-ai.github.io/langgraphjs/llms.txt) | [https://langchain-ai.github.io/langgraphjs/llms-full.txt](https://langchain-ai.github.io/langgraphjs/llms-full.txt) |
+| LangChain Python | [https://python.langchain.com/llms.txt](https://python.langchain.com/llms.txt)                             | N/A                                                                                                                  |
+| LangChain JS     | [https://js.langchain.com/llms.txt](https://js.langchain.com/llms.txt)                                     | N/A                                                                                                                  |
+
+!!! info "Review the output"
+
+    Even with access to up-to-date documentation, current state-of-the-art models may not always generate correct code. Treat the generated code as a starting point, and always review it before shipping
+    code to production.

 ## Differences Between `llms.txt` and `llms-full.txt`

@@ -19,9 +26,17 @@ A key consideration when using `llms-full.txt` is its size. For extensive docume

 ## Using `llms.txt` via an MCP Server

-As of March 9, 2025, IDEs [do not yet have robust native support for `llms.txt`](https://x.com/jeremyphoward/status/1902109312216129905?t=1eHFv2vdNdAckajnug0_Vw&s=19). However, you can utilize `llms.txt` effectively through an MCP server.
+As of March 9, 2025, IDEs [do not yet have robust native support for `llms.txt`](https://x.com/jeremyphoward/status/1902109312216129905?t=1eHFv2vdNdAckajnug0_Vw&s=19). However, you can still use `llms.txt` effectively through an MCP server.

-We provide an MCP server specifically designed to serve documentation, called [`mcpdoc`](https://github.com/langchain-ai/mcpdoc). This setup is compatible with IDEs and platforms such as Cursor, Windsurf, Claude, and Claude Code. Instructions for using `mcpdoc` with these tools are available in the repository.
+### 🚀 Use the `mcpdoc` Server
+
+We provide an **MCP server** that was designed to serve documentation for LLMs and IDEs:
+
+👉 **[langchain-ai/mcpdoc GitHub Repository](https://github.com/langchain-ai/mcpdoc)**
+
+This MCP server allows integrating `llms.txt` into tools like **Cursor**, **Windsurf**, **Claude**, and **Claude Code**.
+
+📘 **Setup instructions and usage examples** are available in the repository.

 ## Using `llms-full.txt`

@@ -0,0 +1,38 @@
+:root {
+  --md-admonition-icon--version-added: url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h4l3 3 3-3h4c1.1 0 2-.9 2-2V4c0-1.1-.9-2-2-2m0 16h-4.2l-.8.8-2 2-2-2-.8-.8H5V4h14z"/><path d="M11 15h2v2h-2v-2m0-10h2v8h-2V5"/></svg>');
+  --md-admonition-icon--version-changed: url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 2H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h4l3 3 3-3h4c1.1 0 2-.9 2-2V4c0-1.1-.9-2-2-2m0 16h-4.2l-.8.8-2 2-2-2-.8-.8H5V4h14z"/><path d="M15 11h-2V9h-2v2H9v2h2v2h2v-2h2v-2Z"/></svg>');
+}
+
+.md-typeset .admonition.version-added,
+.md-typeset details.version-added {
+  border-color: rgb(0, 191, 165);
+}
+
+.md-typeset .version-added > .admonition-title,
+.md-typeset .version-added > summary {
+  background-color: rgba(0, 191, 165, 0.1);
+}
+
+.md-typeset .version-added > .admonition-title::before,
+.md-typeset .version-added > summary::before {
+  background-color: rgb(0, 191, 165);
+  -webkit-mask-image: var(--md-admonition-icon--version-added);
+          mask-image: var(--md-admonition-icon--version-added);
+}
+
+.md-typeset .admonition.version-changed,
+.md-typeset details.version-changed {
+  border-color: rgb(100, 221, 23);
+}
+
+.md-typeset .version-changed > .admonition-title,
+.md-typeset .version-changed > summary {
+  background-color: rgba(100, 221, 23, 0.1);
+}
+
+.md-typeset .version-changed > .admonition-title::before,
+.md-typeset .version-changed > summary::before {
+  background-color: rgb(100, 221, 23);
+  -webkit-mask-image: var(--md-admonition-icon--version-changed);
+          mask-image: var(--md-admonition-icon--version-changed);
+} 
@@ -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 |
@@ -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",
@@ -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
@@ -256,6 +263,8 @@ nav:
                - cloud/how-tos/invoke_studio.md
                - cloud/how-tos/threads_studio.md
                - cloud/how-tos/datasets_studio.md
+                - cloud/how-tos/iterate_graph_studio.md
+                - cloud/how-tos/clone_traces_studio.md
      - Concepts:
        - concepts/index.md
        - LangGraph:
@@ -283,6 +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
@@ -296,9 +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:
@@ -503,3 +515,5 @@ validation:
  not_found: info
 copyright: >
  Copyright &copy; 2025 LangChain, Inc | <a href="#__consent">Consent Preferences</a>
+extra_css:
+  - stylesheets/version_admonitions.css
@@ -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 {
@@ -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"
@@ -1,5 +1,6 @@
 import asyncio
 import threading
+import warnings
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import asynccontextmanager, contextmanager
 from typing import Any, Optional
@@ -150,7 +151,7 @@ def _dump_blobs(
    checkpoint_ns: str,
    values: dict[str, Any],
    versions: ChannelVersions,
-) -> list[tuple[str, str, str, str, str, Optional[bytes]]]:
+) -> list[tuple[str, str, str, str, Optional[bytes]]]:
    if not versions:
        return []

@@ -188,6 +189,12 @@ class ShallowPostgresSaver(BasePostgresSaver):
        pipe: Optional[Pipeline] = None,
        serde: Optional[SerializerProtocol] = None,
    ) -> None:
+        warnings.warn(
+            "ShallowPostgresSaver is deprecated as of version 2.0.20 and will be removed in 3.0.0. "
+            "Use PostgresSaver instead, and invoke the graph with `graph.invoke(..., checkpoint_during=False)`.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
        super().__init__(serde=serde)
        if isinstance(conn, ConnectionPool) and pipe is not None:
            raise ValueError(
@@ -528,6 +535,12 @@ class AsyncShallowPostgresSaver(BasePostgresSaver):
        pipe: Optional[AsyncPipeline] = None,
        serde: Optional[SerializerProtocol] = None,
    ) -> None:
+        warnings.warn(
+            "AsyncShallowPostgresSaver is deprecated as of version 2.0.20 and will be removed in 3.0.0. "
+            "Use AsyncPostgresSaver instead, and invoke the graph with `await graph.ainvoke(..., checkpoint_during=False)`.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
        super().__init__(serde=serde)
        if isinstance(conn, AsyncConnectionPool) and pipe is not None:
            raise ValueError(
@@ -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)"]
@@ -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,
@@ -124,6 +126,7 @@ def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
    )


+# Kept for backwards compat, newer versions of LangGraph no longer use this.
 def create_checkpoint(
    checkpoint: Checkpoint,
    channels: Optional[Mapping[str, ChannelProtocol]],
@@ -38,6 +38,8 @@ class InMemorySaver(
        Only use `InMemorySaver` for debugging or testing purposes.
        For production use cases we recommend installing [langgraph-checkpoint-postgres](https://pypi.org/project/langgraph-checkpoint-postgres/) and using `PostgresSaver` / `AsyncPostgresSaver`.

+        If you are using the LangGraph Platform, no checkpointer needs to be specified. The correct managed checkpointer will be used automatically.
+
    Args:
        serde (Optional[SerializerProtocol]): The serializer to use for serializing and deserializing checkpoints. Defaults to None.

@@ -30,6 +30,7 @@ from langgraph.checkpoint.serde.types import SendProtocol
 from langgraph.store.base import Item

 LC_REVIVER = Reviver()
+EMPTY_BYTES = b""


 class JsonPlusSerializer(SerializerProtocol):
@@ -194,7 +195,9 @@ class JsonPlusSerializer(SerializerProtocol):
        )

    def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
-        if isinstance(obj, bytes):
+        if obj is None:
+            return "null", EMPTY_BYTES
+        elif isinstance(obj, bytes):
            return "bytes", obj
        elif isinstance(obj, bytearray):
            return "bytearray", obj
@@ -211,7 +214,9 @@ class JsonPlusSerializer(SerializerProtocol):

    def loads_typed(self, data: tuple[str, bytes]) -> Any:
        type_, data_ = data
-        if type_ == "bytes":
+        if type_ == "null":
+            return None
+        elif type_ == "bytes":
            return data_
        elif type_ == "bytearray":
            return bytearray(data_)
@@ -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"
@@ -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"
@@ -15,12 +15,15 @@ import msgspec

 from langgraph_cli.config import (
    AuthConfig,
+    CheckpointerConfig,
    Config,
    CorsConfig,
    HttpConfig,
    IndexConfig,
    SecurityConfig,
    StoreConfig,
+    ThreadTTLConfig,
+    TTLConfig,
 )


@@ -106,6 +109,9 @@ def add_descriptions_to_schema(schema, cls):
                SecurityConfig,
                HttpConfig,
                CorsConfig,
+                ThreadTTLConfig,
+                CheckpointerConfig,
+                TTLConfig,
            ]:
                if potential_cls.__name__ == def_name:
                    add_descriptions_to_schema(def_schema, potential_cls)
@@ -580,6 +580,12 @@ def dockerfile(save_path: str, config: pathlib.Path, add_docker_compose: bool) -
    default=None,
    help="URL of the LangGraph Studio instance to connect to. Defaults to https://smith.langchain.com",
 )
+@click.option(
+    "--allow-blocking",
+    is_flag=True,
+    help="Don't raise errors for synchronous I/O blocking operations in your code.",
+    default=False,
+)
@cli.command(
    "dev",
    help="🏃‍♀️‍➡️ Run LangGraph API server in development mode with hot reloading and debugging support",
@@ -595,6 +601,7 @@ def dev(
    debug_port: Optional[int],
    wait_for_client: bool,
    studio_url: Optional[str],
+    allow_blocking: bool,
 ):
    """CLI entrypoint for running the LangGraph API server."""
    try:
@@ -658,7 +665,10 @@ 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,
    )


@@ -3,7 +3,7 @@ import os
 import pathlib
 import textwrap
 from collections import Counter
-from typing import Any, NamedTuple, Optional, TypedDict, Union
+from typing import Any, Literal, NamedTuple, Optional, TypedDict, Union

 import click

@@ -111,6 +111,36 @@ class StoreConfig(TypedDict, total=False):
    """


+class ThreadTTLConfig(TypedDict, total=False):
+    """Configure a default TTL for checkpointed data within threads."""
+
+    strategy: Literal["delete"]
+    """Strategy to use for deleting checkpointed data.
+    
+    Choices:
+      - "delete": Delete all checkpoints for a thread after TTL expires.
+    """
+    default_ttl: Optional[float]
+    """Default TTL (time-to-live) in minutes for checkpointed data."""
+    sweep_interval_minutes: Optional[int]
+    """Interval in minutes between sweep iterations.
+    If omitted, a default interval will be used (typically ~ 5 minutes)."""
+
+
+class CheckpointerConfig(TypedDict, total=False):
+    """Configuration for the built-in checkpointer, which handles checkpointing of state.
+
+    If omitted, no checkpointer is set up (the object store will still be present, however).
+    """
+
+    ttl: Optional[ThreadTTLConfig]
+    """Optional. Defines the TTL (time-to-live) behavior configuration.
+    
+    If provided, the checkpointer will apply TTL settings according to the configuration.
+    If omitted, no TTL behavior is configured.
+    """
+
+
 class SecurityConfig(TypedDict, total=False):
    """Configuration for OpenAPI security definitions and requirements.

@@ -229,7 +259,7 @@ class CorsConfig(TypedDict, total=False):
    allow_origin_regex: str
    """Optional. A regex pattern for matching allowed origins, used if you have dynamic subdomains.
    
-    Example: "^https://.*\.mycompany\.com$"
+    Example: "^https://.*\\.mycompany\\.com$"
    """
    expose_headers: list[str]
    """Optional. List of headers that browsers are allowed to read from the response in cross-origin contexts."""
@@ -355,6 +385,12 @@ class Config(TypedDict, total=False):
    If omitted, no vector index is set up (the object store will still be present, however).
    """

+    checkpointer: Optional[CheckpointerConfig]
+    """Optional. Configuration for the built-in checkpointer, which handles checkpointing of state.
+    
+    If omitted, no checkpointer is set up (the object store will still be present, however).
+    """
+
    auth: Optional[AuthConfig]
    """Optional. Custom authentication config, including the path to your Python auth logic and 
    the OpenAPI security definitions it uses.
@@ -370,6 +406,18 @@ class Config(TypedDict, total=False):
    """


+PIP_CLEANUP_LINES = """# -- Ensure user deps didn't inadvertently overwrite langgraph-api
+RUN mkdir -p /api/langgraph_api /api/langgraph_runtime /api/langgraph_license && \
+    touch /api/langgraph_api/__init__.py /api/langgraph_runtime/__init__.py /api/langgraph_license/__init__.py
+RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir --no-deps -e /api
+# -- End of ensuring user deps didn't inadvertently overwrite langgraph-api --
+# -- Removing pip from the final image ~<:===~~~ --
+RUN pip uninstall -y pip setuptools wheel && \
+    rm -rf /usr/local/lib/python*/site-packages/pip* /usr/local/lib/python*/site-packages/setuptools* /usr/local/lib/python*/site-packages/wheel* && \
+    find /usr/local/bin -name "pip*" -delete
+# -- End of pip removal --"""
+
+
 def _parse_version(version_str: str) -> tuple[int, int]:
    """Parse a version string into a tuple of (major, minor)."""
    try:
@@ -404,7 +452,9 @@ def validate_config(config: Config) -> Config:
            "store": config.get("store"),
            "auth": config.get("auth"),
            "http": config.get("http"),
+            "checkpointer": config.get("checkpointer"),
            "ui": config.get("ui"),
+            "ui_config": config.get("ui_config"),
        }
        if config.get("node_version")
        else {
@@ -417,7 +467,9 @@ def validate_config(config: Config) -> Config:
            "store": config.get("store"),
            "auth": config.get("auth"),
            "http": config.get("http"),
+            "checkpointer": config.get("checkpointer"),
            "ui": config.get("ui"),
+            "ui_config": config.get("ui_config"),
        }
    )

@@ -738,7 +790,22 @@ 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 = (
@@ -778,7 +845,10 @@ 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(
@@ -875,6 +945,66 @@ 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]]:
@@ -955,10 +1085,32 @@ 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,
@@ -979,6 +1131,11 @@ ADD {relpath} /deps/{name}
    if (http_config := config.get("http")) is not None:
        env_vars.append(f"ENV LANGGRAPH_HTTP='{json.dumps(http_config)}'")

+    if (checkpointer_config := config.get("checkpointer")) is not None:
+        env_vars.append(
+            f"ENV LANGGRAPH_CHECKPOINTER='{json.dumps(checkpointer_config)}'"
+        )
+
    graphs = config["graphs"]
    env_vars.append(f"ENV LANGSERVE_GRAPHS='{json.dumps(graphs)}'")

@@ -994,6 +1151,10 @@ ADD {relpath} /deps/{name}
        "# -- End of local dependencies install --",
        os.linesep.join(env_vars),
        "",
+        ui_inst_str,
+        "",
+        PIP_CLEANUP_LINES,  # Add pip cleanup after all installations are complete
+        "",
        f"WORKDIR {local_deps.working_dir}" if local_deps.working_dir else "",
    ]

@@ -1014,62 +1175,7 @@ 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 = (
        ""
@@ -1085,6 +1191,10 @@ ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'
    if (http_config := config.get("http")) is not None:
        env_additional_config += f"""
 ENV LANGGRAPH_HTTP='{json.dumps(http_config)}'
+"""
+    if (checkpointer_config := config.get("checkpointer")) is not None:
+        env_additional_config += f"""
+ENV LANGGRAPH_CHECKPOINTER='{json.dumps(checkpointer_config)}'
 """

    return (
@@ -1098,6 +1208,7 @@ RUN cd {faux_path} && {install_cmd}
 {env_additional_config}
 ENV LANGSERVE_GRAPHS='{json.dumps(config["graphs"])}'
 {f"ENV LANGGRAPH_UI='{json.dumps(config['ui'])}'" if config.get("ui") else ""}
+{f"ENV LANGGRAPH_UI_CONFIG='{json.dumps(config['ui_config'])}'" if config.get("ui_config") else ""}

 WORKDIR {faux_path}

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.

 [[package]]
 name = "annotated-types"
@@ -6,6 +6,8 @@ version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -17,6 +19,8 @@ version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = true
 python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
 files = [
    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
@@ -29,15 +33,33 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}

 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "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)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""]
 trio = ["trio (>=0.26.1)"]

+[[package]]
+name = "blockbuster"
+version = "1.5.24"
+description = "Utility to detect blocking calls in the async event loop"
+optional = true
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
+files = [
+    {file = "blockbuster-1.5.24-py3-none-any.whl", hash = "sha256:e703497b55bc72af09d60d1cd746c2f3ba7ce0c446fa256be6ccda5e7d403520"},
+    {file = "blockbuster-1.5.24.tar.gz", hash = "sha256:97645775761a5d425666ec0bc99629b65c7eccdc2f770d2439850682567af4ec"},
+]
+
+[package.dependencies]
+forbiddenfruit = {version = ">=0.1.4", markers = "implementation_name == \"cpython\""}
+
 [[package]]
 name = "certifi"
 version = "2025.1.31"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = true
 python-versions = ">=3.6"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
 files = [
    {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"},
    {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"},
@@ -49,6 +71,8 @@ version = "1.17.1"
 description = "Foreign Function Interface for Python calling C code."
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "cffi-1.17.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14"},
    {file = "cffi-1.17.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67"},
@@ -128,6 +152,8 @@ version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "charset_normalizer-3.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:91b36a978b5ae0ee86c394f5a54d6ef44db1de0815eb43de826d41d21e4af3de"},
    {file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7461baadb4dc00fd9e0acbe254e3d7d2112e7f92ced2adc96e54ef6501c5f176"},
@@ -229,6 +255,7 @@ version = "8.1.8"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
    {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"},
    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
@@ -237,12 +264,26 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}

+[[package]]
+name = "cloudpickle"
+version = "3.1.1"
+description = "Pickler class to extend the standard pickle.Pickler functionality"
+optional = true
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
+files = [
+    {file = "cloudpickle-3.1.1-py3-none-any.whl", hash = "sha256:c8c5a44295039331ee9dad40ba100a9c7297b6f988e50e87ccdf3765a668350e"},
+    {file = "cloudpickle-3.1.1.tar.gz", hash = "sha256:b216fa8ae4019d5482a8ac3c95d8f6346115d8835911fd4aefd1a445e4242c64"},
+]
+
 [[package]]
 name = "codespell"
 version = "2.4.1"
 description = "Fix common misspellings in text files"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
    {file = "codespell-2.4.1-py3-none-any.whl", hash = "sha256:3dadafa67df7e4a3dbf51e0d7315061b80d265f9552ebd699b3dd6834b47e425"},
    {file = "codespell-2.4.1.tar.gz", hash = "sha256:299fcdcb09d23e81e35a671bbe746d5ad7e8385972e65dbb833a2eaac33c01e5"},
@@ -251,7 +292,7 @@ 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]]
@@ -260,10 +301,12 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["main", "dev"]
 files = [
    {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
    {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
+markers = {main = "platform_system == \"Windows\""}

 [[package]]
 name = "cryptography"
@@ -271,6 +314,8 @@ version = "43.0.3"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "cryptography-43.0.3-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:bf7a1932ac4176486eab36a19ed4c0492da5d97123f1406cf15e41b05e787d2e"},
    {file = "cryptography-43.0.3-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:63efa177ff54aec6e1c0aefaa1a241232dcd37413835a9b674b6e3f0ae2bfd3e"},
@@ -320,6 +365,7 @@ version = "0.6.2"
 description = "Pythonic argument parser, that will make you smile"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
    {file = "docopt-0.6.2.tar.gz", hash = "sha256:49b3a825280bd66b3aa83585ef59c4a8c82f2c8a522dbe754a8bc8d08c85c491"},
 ]
@@ -330,6 +376,8 @@ version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version < \"3.11\""
 files = [
    {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
    {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
@@ -338,12 +386,26 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]

+[[package]]
+name = "forbiddenfruit"
+version = "0.1.4"
+description = "Patch python built-in objects"
+optional = true
+python-versions = "*"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\" and implementation_name == \"cpython\""
+files = [
+    {file = "forbiddenfruit-0.1.4.tar.gz", hash = "sha256:e3f7e66561a29ae129aac139a85d610dbf3dd896128187ed5454b6421f624253"},
+]
+
 [[package]]
 name = "h11"
 version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
 files = [
    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
@@ -355,6 +417,8 @@ version = "1.0.7"
 description = "A minimal low-level HTTP client."
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
 files = [
    {file = "httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd"},
    {file = "httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c"},
@@ -376,6 +440,8 @@ version = "0.28.1"
 description = "The next generation HTTP client."
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
 files = [
    {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"},
    {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
@@ -388,7 +454,7 @@ httpcore = "==1.*"
 idna = "*"

 [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.*)"]
@@ -400,6 +466,8 @@ version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = true
 python-versions = ">=3.6"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
 files = [
    {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
    {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
@@ -414,6 +482,7 @@ version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
    {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
    {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
@@ -425,6 +494,8 @@ version = "1.33"
 description = "Apply JSON-Patches (RFC 6902)"
 optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"},
    {file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"},
@@ -439,6 +510,8 @@ version = "3.0.0"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
    {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
@@ -450,6 +523,8 @@ version = "0.20.0"
 description = "A high-performance JSON Schema validator for Python"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "jsonschema_rs-0.20.0-cp310-cp310-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:d4b12f8aaec5037529fd11e5f71032cb53d44e8e2236bb7c3fb35e6efc7ce7f2"},
    {file = "jsonschema_rs-0.20.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:25d512c47c5c391020c9fc4223f270cf42fbdd39b2906dbc894fe0205168b8f8"},
@@ -499,6 +574,8 @@ version = "0.3.40"
 description = "Building applications with LLMs through composability"
 optional = true
 python-versions = "<4.0,>=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "langchain_core-0.3.40-py3-none-any.whl", hash = "sha256:9f31358741f10a13db8531e8288b8a5ae91904018c5c2e6f739d6645a98fca03"},
    {file = "langchain_core-0.3.40.tar.gz", hash = "sha256:893a238b38491967c804662c1ec7c3e6ebaf223d1125331249c3cf3862ff2746"},
@@ -522,6 +599,8 @@ version = "0.3.1"
 description = "Building stateful, multi-actor applications with LLMs"
 optional = true
 python-versions = "<4.0,>=3.9.0"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "langgraph-0.3.1-py3-none-any.whl", hash = "sha256:212e1220d6a2af27048109604c816ccfbceb53a9aa93721be874305d8e28b7f5"},
    {file = "langgraph-0.3.1.tar.gz", hash = "sha256:81cb89c381b089a20eac9a247f7ebcf3f41c922ac79e06dbcc4fc136c6f73dd5"},
@@ -535,23 +614,27 @@ langgraph-sdk = ">=0.1.42,<0.2.0"

 [[package]]
 name = "langgraph-api"
-version = "0.0.32"
+version = "0.1.0"
 description = ""
 optional = true
 python-versions = "<4.0,>=3.11.0"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
-    {file = "langgraph_api-0.0.32-py3-none-any.whl", hash = "sha256:7990cedc65f784813aba867c5bde3fdfae3fa4588baef1aa346cbeac7c3aebf1"},
-    {file = "langgraph_api-0.0.32.tar.gz", hash = "sha256:6f5b698ad8d136b73c2c53bcfa30670e9244a318b08b5e9cf00a707ea57c058c"},
+    {file = "langgraph_api-0.1.0-py3-none-any.whl", hash = "sha256:93eb369849d5ea0dd6076f0a36fe0a9669415c46e95fcb28ccf30c73e13e5e01"},
+    {file = "langgraph_api-0.1.0.tar.gz", hash = "sha256:d02201d34172f3020af8f21c4560142b302474cee4a27f4335fbd950ab22f22f"},
 ]

 [package.dependencies]
-cryptography = ">=43.0.3,<44.0.0"
+blockbuster = ">=1.5.24,<2.0.0"
+cloudpickle = ">=3.0.0,<4.0.0"
+cryptography = ">=42.0.0,<45.0"
 httpx = ">=0.25.0"
 jsonschema-rs = ">=0.20.0,<0.30"
 langchain-core = ">=0.2.38,<0.4.0"
 langgraph = ">=0.2.56,<0.4.0"
-langgraph-checkpoint = ">=2.0.21,<3.0"
-langgraph-sdk = ">=0.1.58,<0.2.0"
+langgraph-checkpoint = ">=2.0.23,<3.0"
+langgraph-sdk = ">=0.1.61,<0.2.0"
 langsmith = ">=0.1.63,<0.4.0"
 orjson = ">=3.9.7"
 pyjwt = ">=2.9.0,<3.0.0"
@@ -564,18 +647,20 @@ watchfiles = ">=0.13"

 [[package]]
 name = "langgraph-checkpoint"
-version = "2.0.21"
+version = "2.0.23"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = true
 python-versions = "<4.0.0,>=3.9.0"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
-    {file = "langgraph_checkpoint-2.0.21-py3-none-any.whl", hash = "sha256:ca89c2090cd9729f83f9782226935dc5ff9fe7756c24936f484ccb0ce367f87b"},
-    {file = "langgraph_checkpoint-2.0.21.tar.gz", hash = "sha256:52beeb6dc1bd8c487b8315466cab271093b65eb97f54a0942dfe105cd20b237f"},
+    {file = "langgraph_checkpoint-2.0.23-py3-none-any.whl", hash = "sha256:e54d070124f685eab095bd87e4df35dc5eca11d1e28553d5803c28c5f571b4e0"},
+    {file = "langgraph_checkpoint-2.0.23.tar.gz", hash = "sha256:38bd1fe451b569b773fef6e3daecdeb85f3deac2d94f7551bfd20f1818042c8a"},
 ]

 [package.dependencies]
 langchain-core = ">=0.2.38,<0.4"
-msgpack = ">=1.1.0,<2.0.0"
+ormsgpack = ">=1.8.0,<2.0.0"

 [[package]]
 name = "langgraph-prebuilt"
@@ -583,6 +668,8 @@ version = "0.1.1"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 optional = true
 python-versions = "<4.0.0,>=3.9.0"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "langgraph_prebuilt-0.1.1-py3-none-any.whl", hash = "sha256:148a9558a36ec7e83cc6512f3521425c862b0463251ae0242ade52a448c54e78"},
    {file = "langgraph_prebuilt-0.1.1.tar.gz", hash = "sha256:420a748ff93842f2b1a345a0c1ca3939d2bc7a2d46c20e9a9a0d8f148152cc47"},
@@ -592,15 +679,30 @@ files = [
 langchain-core = ">=0.2.43,<0.3.0 || >0.3.0,<0.3.1 || >0.3.1,<0.3.2 || >0.3.2,<0.3.3 || >0.3.3,<0.3.4 || >0.3.4,<0.3.5 || >0.3.5,<0.3.6 || >0.3.6,<0.3.7 || >0.3.7,<0.3.8 || >0.3.8,<0.3.9 || >0.3.9,<0.3.10 || >0.3.10,<0.3.11 || >0.3.11,<0.3.12 || >0.3.12,<0.3.13 || >0.3.13,<0.3.14 || >0.3.14,<0.3.15 || >0.3.15,<0.3.16 || >0.3.16,<0.3.17 || >0.3.17,<0.3.18 || >0.3.18,<0.3.19 || >0.3.19,<0.3.20 || >0.3.20,<0.3.21 || >0.3.21,<0.3.22 || >0.3.22,<0.4.0"
 langgraph-checkpoint = ">=2.0.10,<3.0.0"

+[[package]]
+name = "langgraph-runtime-inmem"
+version = "0.0.1"
+description = "Inmem implementation for the LangGraph API server."
+optional = true
+python-versions = ">=3.11"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
+files = [
+    {file = "langgraph_runtime_inmem-0.0.1-py3-none-any.whl", hash = "sha256:a25ec8e3219f2fd60450de38412d24c83fbf0b2521c13871cc26ce9a68ead496"},
+    {file = "langgraph_runtime_inmem-0.0.1.tar.gz", hash = "sha256:144bf5217efec4969f7f9c5e8279d6914cc5133d4b1a19e466b4966fa05f00c5"},
+]
+
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.58"
+version = "0.1.61"
 description = "SDK for interacting with LangGraph API"
 optional = true
 python-versions = "<4.0.0,>=3.9.0"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
 files = [
-    {file = "langgraph_sdk-0.1.58-py3-none-any.whl", hash = "sha256:65f88cf5582da0c316714dc475126fa03c5f74d72bc0b9221dd42649de8e23d4"},
-    {file = "langgraph_sdk-0.1.58.tar.gz", hash = "sha256:ef8b0e4c08af8c7efd3919497879c87a3627806b51e4ba5e8b06e0717e3d44cd"},
+    {file = "langgraph_sdk-0.1.61-py3-none-any.whl", hash = "sha256:f2d774b12497c428862993090622d51e0dbc3f53e0cee3d74a13c7495d835cc6"},
+    {file = "langgraph_sdk-0.1.61.tar.gz", hash = "sha256:87dd1f07ab82da8875ac343268ece8bf5414632017ebc9d1cef4b523962fd601"},
 ]

 [package.dependencies]
@@ -613,6 +715,8 @@ version = "0.3.11"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = true
 python-versions = "<4.0,>=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "langsmith-0.3.11-py3-none-any.whl", hash = "sha256:0cca22737ef07d3b038a437c141deda37e00add56022582680188b681bec095e"},
    {file = "langsmith-0.3.11.tar.gz", hash = "sha256:ddf29d24352e99de79c9618aaf95679214324e146c5d3d9475a7ddd2870018b1"},
@@ -634,85 +738,13 @@ zstandard = ">=0.23.0,<0.24.0"
 langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
 pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]

-[[package]]
-name = "msgpack"
-version = "1.1.0"
-description = "MessagePack serializer"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7ad442d527a7e358a469faf43fda45aaf4ac3249c8310a82f0ccff9164e5dccd"},
-    {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74bed8f63f8f14d75eec75cf3d04ad581da6b914001b474a5d3cd3372c8cc27d"},
-    {file = "msgpack-1.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:914571a2a5b4e7606997e169f64ce53a8b1e06f2cf2c3a7273aa106236d43dd5"},
-    {file = "msgpack-1.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c921af52214dcbb75e6bdf6a661b23c3e6417f00c603dd2070bccb5c3ef499f5"},
-    {file = "msgpack-1.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d8ce0b22b890be5d252de90d0e0d119f363012027cf256185fc3d474c44b1b9e"},
-    {file = "msgpack-1.1.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:73322a6cc57fcee3c0c57c4463d828e9428275fb85a27aa2aa1a92fdc42afd7b"},
-    {file = "msgpack-1.1.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:e1f3c3d21f7cf67bcf2da8e494d30a75e4cf60041d98b3f79875afb5b96f3a3f"},
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 version = "0.19.0"
 description = "A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML."
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
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 ]

 [package.extras]
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-test = ["attrs", "eval-type-backport", "msgpack", "pytest", "pyyaml", "tomli", "tomli_w"]
-toml = ["tomli", "tomli_w"]
+test = ["attrs", "eval-type-backport ; python_version < \"3.10\"", "msgpack", "pytest", "pyyaml", "tomli ; python_version < \"3.11\"", "tomli_w"]
+toml = ["tomli ; python_version < \"3.11\"", "tomli_w"]
 yaml = ["pyyaml"]

 [[package]]
@@ -765,6 +797,7 @@ version = "1.15.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
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 description = "Type system extensions for programs checked with the mypy type checker."
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
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 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
 files = [
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+optional = true
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
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+    {file = "ormsgpack-1.9.1.tar.gz", hash = "sha256:3da6e63d82565e590b98178545e64f0f8506137b92bd31a2d04fd7c82baf5794"},
+]
+
 [[package]]
 name = "packaging"
 version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
    {file = "packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759"},
    {file = "packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f"},
 ]
+markers = {main = "python_version >= \"3.11\" and extra == \"inmem\""}

 [[package]]
 name = "pluggy"
@@ -928,6 +1018,7 @@ version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
    {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
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@@ -943,6 +1034,8 @@ version = "2.22"
 description = "C parser in Python"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc"},
    {file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"},
@@ -954,6 +1047,8 @@ version = "2.10.6"
 description = "Data validation using Python type hints"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
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@@ -966,7 +1061,7 @@ typing-extensions = ">=4.12.2"

 [package.extras]
 email = ["email-validator (>=2.0.0)"]
-timezone = ["tzdata"]
+timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""]

 [[package]]
 name = "pydantic-core"
@@ -974,6 +1069,8 @@ version = "2.27.2"
 description = "Core functionality for Pydantic validation and serialization"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "pydantic_core-2.27.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:2d367ca20b2f14095a8f4fa1210f5a7b78b8a20009ecced6b12818f455b1e9fa"},
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@@ -1086,6 +1183,8 @@ version = "2.10.1"
 description = "JSON Web Token implementation in Python"
 optional = true
 python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "PyJWT-2.10.1-py3-none-any.whl", hash = "sha256:dcdd193e30abefd5debf142f9adfcdd2b58004e644f25406ffaebd50bd98dacb"},
    {file = "pyjwt-2.10.1.tar.gz", hash = "sha256:3cc5772eb20009233caf06e9d8a0577824723b44e6648ee0a2aedb6cf9381953"},
@@ -1103,6 +1202,7 @@ version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
    {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
    {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
@@ -1125,6 +1225,7 @@ version = "0.21.2"
 description = "Pytest support for asyncio"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
    {file = "pytest_asyncio-0.21.2-py3-none-any.whl", hash = "sha256:ab664c88bb7998f711d8039cacd4884da6430886ae8bbd4eded552ed2004f16b"},
    {file = "pytest_asyncio-0.21.2.tar.gz", hash = "sha256:d67738fc232b94b326b9d060750beb16e0074210b98dd8b58a5239fa2a154f45"},
@@ -1143,6 +1244,7 @@ version = "3.14.0"
 description = "Thin-wrapper around the mock package for easier use with pytest"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
    {file = "pytest-mock-3.14.0.tar.gz", hash = "sha256:2719255a1efeceadbc056d6bf3df3d1c5015530fb40cf347c0f9afac88410bd0"},
    {file = "pytest_mock-3.14.0-py3-none-any.whl", hash = "sha256:0b72c38033392a5f4621342fe11e9219ac11ec9d375f8e2a0c164539e0d70f6f"},
@@ -1160,6 +1262,7 @@ version = "4.2.0"
 description = "Local continuous test runner with pytest and watchdog."
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
    {file = "pytest-watch-4.2.0.tar.gz", hash = "sha256:06136f03d5b361718b8d0d234042f7b2f203910d8568f63df2f866b547b3d4b9"},
 ]
@@ -1176,6 +1279,8 @@ version = "1.0.1"
 description = "Read key-value pairs from a .env file and set them as environment variables"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "extra == \"inmem\""
 files = [
    {file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"},
    {file = "python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a"},
@@ -1190,6 +1295,8 @@ version = "6.0.2"
 description = "YAML parser and emitter for Python"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "PyYAML-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086"},
    {file = "PyYAML-6.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf"},
@@ -1252,6 +1359,8 @@ version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
    {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
@@ -1273,6 +1382,8 @@ version = "1.0.0"
 description = "A utility belt for advanced users of python-requests"
 optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 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"},
@@ -1287,6 +1398,7 @@ version = "0.6.9"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
    {file = "ruff-0.6.9-py3-none-linux_armv6l.whl", hash = "sha256:064df58d84ccc0ac0fcd63bc3090b251d90e2a372558c0f057c3f75ed73e1ccd"},
    {file = "ruff-0.6.9-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:140d4b5c9f5fc7a7b074908a78ab8d384dd7f6510402267bc76c37195c02a7ec"},
@@ -1314,6 +1426,8 @@ version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = true
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
 files = [
    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
@@ -1325,6 +1439,8 @@ version = "2.1.3"
 description = "SSE plugin for Starlette"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "sse_starlette-2.1.3-py3-none-any.whl", hash = "sha256:8ec846438b4665b9e8c560fcdea6bc8081a3abf7942faa95e5a744999d219772"},
    {file = "sse_starlette-2.1.3.tar.gz", hash = "sha256:9cd27eb35319e1414e3d2558ee7414487f9529ce3b3cf9b21434fd110e017169"},
@@ -1344,6 +1460,8 @@ version = "0.46.0"
 description = "The little ASGI library that shines."
 optional = true
 python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "starlette-0.46.0-py3-none-any.whl", hash = "sha256:913f0798bd90ba90a9156383bcf1350a17d6259451d0d8ee27fc0cf2db609038"},
    {file = "starlette-0.46.0.tar.gz", hash = "sha256:b359e4567456b28d473d0193f34c0de0ed49710d75ef183a74a5ce0499324f50"},
@@ -1361,6 +1479,8 @@ version = "25.2.0"
 description = "Structured Logging for Python"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "structlog-25.2.0-py3-none-any.whl", hash = "sha256:0fecea2e345d5d491b72f3db2e5fcd6393abfc8cd06a4851f21fcd4d1a99f437"},
    {file = "structlog-25.2.0.tar.gz", hash = "sha256:d9f9776944207d1035b8b26072b9b140c63702fd7aa57c2f85d28ab701bd8e92"},
@@ -1378,6 +1498,8 @@ version = "9.0.0"
 description = "Retry code until it succeeds"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
    {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
@@ -1393,6 +1515,8 @@ version = "2.2.1"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version < \"3.11\""
 files = [
    {file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
    {file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
@@ -1434,10 +1558,12 @@ version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
+markers = {main = "python_version >= \"3.11\" and (python_version < \"3.13\" or extra == \"inmem\")"}

 [[package]]
 name = "urllib3"
@@ -1445,13 +1571,15 @@ version = "2.3.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = true
 python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
    {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
 ]

 [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)"]
@@ -1462,6 +1590,8 @@ version = "0.34.0"
 description = "The lightning-fast ASGI server."
 optional = true
 python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "uvicorn-0.34.0-py3-none-any.whl", hash = "sha256:023dc038422502fa28a09c7a30bf2b6991512da7dcdb8fd35fe57cfc154126f4"},
    {file = "uvicorn-0.34.0.tar.gz", hash = "sha256:404051050cd7e905de2c9a7e61790943440b3416f49cb409f965d9dcd0fa73e9"},
@@ -1472,7 +1602,7 @@ click = ">=7.0"
 h11 = ">=0.8"

 [package.extras]
-standard = ["colorama (>=0.4)", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]
+standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"]

 [[package]]
 name = "watchdog"
@@ -1480,6 +1610,7 @@ version = "6.0.0"
 description = "Filesystem events monitoring"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
    {file = "watchdog-6.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d1cdb490583ebd691c012b3d6dae011000fe42edb7a82ece80965b42abd61f26"},
    {file = "watchdog-6.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bc64ab3bdb6a04d69d4023b29422170b74681784ffb9463ed4870cf2f3e66112"},
@@ -1522,6 +1653,8 @@ version = "1.0.4"
 description = "Simple, modern and high performance file watching and code reload in python."
 optional = true
 python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "watchfiles-1.0.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:ba5bb3073d9db37c64520681dd2650f8bd40902d991e7b4cfaeece3e32561d08"},
    {file = "watchfiles-1.0.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9f25d0ba0fe2b6d2c921cf587b2bf4c451860086534f40c384329fb96e2044d1"},
@@ -1605,6 +1738,8 @@ version = "0.23.0"
 description = "Zstandard bindings for Python"
 optional = true
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
    {file = "zstandard-0.23.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bf0a05b6059c0528477fba9054d09179beb63744355cab9f38059548fedd46a9"},
    {file = "zstandard-0.23.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fc9ca1c9718cb3b06634c7c8dec57d24e9438b2aa9a0f02b8bb36bf478538880"},
@@ -1712,9 +1847,9 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
 cffi = ["cffi (>=1.11)"]

 [extras]
-inmem = ["langgraph-api", "python-dotenv"]
+inmem = ["langgraph-api", "langgraph-runtime-inmem", "python-dotenv"]

 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = "^3.9.0,<4.0"
-content-hash = "f5aa4d66f9c0b98b8321a70a82387dc6e5f3a3a7ecedd87ac00d6415199038f9"
+content-hash = "afc2f8776b4b6144bd1197df49ba34089889e2a1110b8470d8f1b212e0b08380"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.80"
+version = "0.2.1"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"
@@ -14,7 +14,9 @@ langgraph = "langgraph_cli.cli:cli"
 [tool.poetry.dependencies]
 python = "^3.9.0,<4.0"
 click = "^8.1.7"
-langgraph-api = { version = ">=0.0.32,<0.1.0", optional = true, python = ">=3.11,<4.0" }
+langgraph-api = { version = ">=0.1.0,<0.2.0", optional = true, python = ">=3.11,<4.0" }
+langgraph-runtime-inmem = { version = ">=0.0.1,<0.1.0", optional = true, python = ">=3.11,<4.0" }
+langgraph-sdk = { version = ">=0.1.0,<0.2.0", optional = true, python = ">=3.11,<4.0" }
 python-dotenv = { version = ">=0.8.0", optional = true }

 [tool.poetry.group.dev.dependencies]
@@ -28,7 +30,7 @@ mypy = "^1.10.0"
 msgspec = "^0.19.0"

 [tool.poetry.extras]
-inmem = ["langgraph-api", "python-dotenv"]
+inmem = ["langgraph-api", "langgraph-runtime-inmem", "python-dotenv"]

 [tool.pytest.ini_options]
 # --strict-markers will raise errors on unknown marks.
@@ -40,6 +40,17 @@
              ],
              "description": "Optional. Custom authentication config, including the path to your Python auth logic and\nthe OpenAPI security definitions it uses.\n"
            },
+            "checkpointer": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/CheckpointerConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Configuration for the built-in checkpointer, which handles checkpointing of state.\n\nIf omitted, no checkpointer is set up (the object store will still be present, however).\n"
+            },
            "dependencies": {
              "type": "array",
              "items": {
@@ -145,6 +156,17 @@
              ],
              "description": "Optional. Custom authentication config, including the path to your Python auth logic and\nthe OpenAPI security definitions it uses.\n"
            },
+            "checkpointer": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/CheckpointerConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Configuration for the built-in checkpointer, which handles checkpointing of state.\n\nIf omitted, no checkpointer is set up (the object store will still be present, however).\n"
+            },
            "dependencies": {
              "type": "array",
              "items": {
@@ -291,6 +313,61 @@
      },
      "required": []
    },
+    "CheckpointerConfig": {
+      "title": "CheckpointerConfig",
+      "description": "Configuration for the built-in checkpointer, which handles checkpointing of state.\n\nIf omitted, no checkpointer is set up (the object store will still be present, however).",
+      "type": "object",
+      "properties": {
+        "ttl": {
+          "anyOf": [
+            {
+              "$ref": "#/$defs/ThreadTTLConfig"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Optional. Defines the TTL (time-to-live) behavior configuration.\n\nIf provided, the checkpointer will apply TTL settings according to the configuration.\nIf omitted, no TTL behavior is configured.\n"
+        }
+      },
+      "required": []
+    },
+    "ThreadTTLConfig": {
+      "title": "ThreadTTLConfig",
+      "description": "Configure a default TTL for checkpointed data within threads.",
+      "type": "object",
+      "properties": {
+        "default_ttl": {
+          "anyOf": [
+            {
+              "type": "number"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Default TTL (time-to-live) in minutes for checkpointed data."
+        },
+        "strategy": {
+          "enum": [
+            "delete"
+          ],
+          "description": "Strategy to use for deleting checkpointed data.\n"
+        },
+        "sweep_interval_minutes": {
+          "anyOf": [
+            {
+              "type": "integer"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Interval in minutes between sweep iterations.\nIf omitted, a default interval will be used (typically ~ 5 minutes)."
+        }
+      },
+      "required": []
+    },
    "HttpConfig": {
      "title": "HttpConfig",
      "description": "Configuration for the built-in HTTP server that powers your deployment's routes and endpoints.",
@@ -455,10 +532,12 @@
            {
              "type": "null"
            }
-          ]
+          ],
+          "description": "Optional. Default TTL (time-to-live) in minutes for new items.\n\nIf provided, all new items will have this TTL unless explicitly overridden.\nIf omitted, items will have no TTL by default.\n"
        },
        "refresh_on_read": {
-          "type": "boolean"
+          "type": "boolean",
+          "description": "Default behavior for refreshing TTLs on read operations (GET and SEARCH).\n\nIf True, TTLs will be refreshed on read operations (get/search) by default.\nThis can be overridden per-operation by explicitly setting refresh_ttl.\nDefaults to True if not configured.\n"
        },
        "sweep_interval_minutes": {
          "anyOf": [
@@ -468,7 +547,8 @@
            {
              "type": "null"
            }
-          ]
+          ],
+          "description": "Optional. Interval in minutes between TTL sweep iterations.\n\nIf provided, the store will periodically delete expired items based on the TTL.\nIf omitted, no automatic sweeping will occur.\n"
        }
      },
      "required": []
@@ -40,6 +40,17 @@
              ],
              "description": "Optional. Custom authentication config, including the path to your Python auth logic and\nthe OpenAPI security definitions it uses.\n"
            },
+            "checkpointer": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/CheckpointerConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Configuration for the built-in checkpointer, which handles checkpointing of state.\n\nIf omitted, no checkpointer is set up (the object store will still be present, however).\n"
+            },
            "dependencies": {
              "type": "array",
              "items": {
@@ -145,6 +156,17 @@
              ],
              "description": "Optional. Custom authentication config, including the path to your Python auth logic and\nthe OpenAPI security definitions it uses.\n"
            },
+            "checkpointer": {
+              "anyOf": [
+                {
+                  "$ref": "#/$defs/CheckpointerConfig"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Configuration for the built-in checkpointer, which handles checkpointing of state.\n\nIf omitted, no checkpointer is set up (the object store will still be present, however).\n"
+            },
            "dependencies": {
              "type": "array",
              "items": {
@@ -291,6 +313,61 @@
      },
      "required": []
    },
+    "CheckpointerConfig": {
+      "title": "CheckpointerConfig",
+      "description": "Configuration for the built-in checkpointer, which handles checkpointing of state.\n\nIf omitted, no checkpointer is set up (the object store will still be present, however).",
+      "type": "object",
+      "properties": {
+        "ttl": {
+          "anyOf": [
+            {
+              "$ref": "#/$defs/ThreadTTLConfig"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Optional. Defines the TTL (time-to-live) behavior configuration.\n\nIf provided, the checkpointer will apply TTL settings according to the configuration.\nIf omitted, no TTL behavior is configured.\n"
+        }
+      },
+      "required": []
+    },
+    "ThreadTTLConfig": {
+      "title": "ThreadTTLConfig",
+      "description": "Configure a default TTL for checkpointed data within threads.",
+      "type": "object",
+      "properties": {
+        "default_ttl": {
+          "anyOf": [
+            {
+              "type": "number"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Default TTL (time-to-live) in minutes for checkpointed data."
+        },
+        "strategy": {
+          "enum": [
+            "delete"
+          ],
+          "description": "Strategy to use for deleting checkpointed data.\n"
+        },
+        "sweep_interval_minutes": {
+          "anyOf": [
+            {
+              "type": "integer"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "description": "Interval in minutes between sweep iterations.\nIf omitted, a default interval will be used (typically ~ 5 minutes)."
+        }
+      },
+      "required": []
+    },
    "HttpConfig": {
      "title": "HttpConfig",
      "description": "Configuration for the built-in HTTP server that powers your deployment's routes and endpoints.",
@@ -455,10 +532,12 @@
            {
              "type": "null"
            }
-          ]
+          ],
+          "description": "Optional. Default TTL (time-to-live) in minutes for new items.\n\nIf provided, all new items will have this TTL unless explicitly overridden.\nIf omitted, items will have no TTL by default.\n"
        },
        "refresh_on_read": {
-          "type": "boolean"
+          "type": "boolean",
+          "description": "Default behavior for refreshing TTLs on read operations (GET and SEARCH).\n\nIf True, TTLs will be refreshed on read operations (get/search) by default.\nThis can be overridden per-operation by explicitly setting refresh_ttl.\nDefaults to True if not configured.\n"
        },
        "sweep_interval_minutes": {
          "anyOf": [
@@ -468,7 +547,8 @@
            {
              "type": "null"
            }
-          ]
+          ],
+          "description": "Optional. Interval in minutes between TTL sweep iterations.\n\nIf provided, the store will periodically delete expired items based on the TTL.\nIf omitted, no automatic sweeping will occur.\n"
        }
      },
      "required": []
@@ -2,13 +2,14 @@ import json
 import pathlib
 import shutil
 import tempfile
+import textwrap
 from contextlib import contextmanager
 from pathlib import Path

 from click.testing import CliRunner

 from langgraph_cli.cli import cli, prepare_args_and_stdin
-from langgraph_cli.config import Config, validate_config
+from langgraph_cli.config import PIP_CLEANUP_LINES, Config, validate_config
 from langgraph_cli.docker import DEFAULT_POSTGRES_URI, DockerCapabilities, Version
 from langgraph_cli.util import clean_empty_lines

@@ -143,6 +144,7 @@ services:
                RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
                # -- End of local dependencies install --
                ENV LANGSERVE_GRAPHS='{{"agent": "agent.py:graph"}}'
+{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
                WORKDIR /deps/cli
        
        develop:
@@ -196,6 +198,50 @@ 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()
@@ -2,11 +2,13 @@ import json
 import os
 import pathlib
 import tempfile
+import textwrap

 import click
 import pytest

 from langgraph_cli.config import (
+    PIP_CLEANUP_LINES,
    config_to_compose,
    config_to_docker,
    validate_config,
@@ -32,8 +34,10 @@ def test_validate_config():
        "env": {},
        "store": None,
        "auth": None,
+        "checkpointer": None,
        "http": None,
        "ui": None,
+        "ui_config": None,
        **expected_config,
    }
    actual_config = validate_config(expected_config)
@@ -52,8 +56,10 @@ def test_validate_config():
        "env": env,
        "store": None,
        "auth": None,
+        "checkpointer": None,
        "http": None,
        "ui": None,
+        "ui_config": None,
    }
    actual_config = validate_config(expected_config)
    assert actual_config == expected_config
@@ -204,7 +210,7 @@ def test_config_to_docker_simple():
        ),
        "langchain/langgraph-api",
    )
-    expected_docker_stdin = """\
+    expected_docker_stdin = f"""\
 FROM langchain/langgraph-api:3.11
 # -- Installing local requirements --
 COPY --from=__outer_requirements.txt requirements.txt /deps/__outer_graphs_reqs_a/graphs_reqs_a/requirements.txt
@@ -238,8 +244,9 @@ RUN set -ex && \\
 # -- Installing all local dependencies --
 RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
-ENV LANGGRAPH_HTTP='{"app": "/deps/examples/my_app.py:app"}'
-ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}'
+ENV LANGGRAPH_HTTP='{{"app": "/deps/examples/my_app.py:app"}}'
+ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
+{PIP_CLEANUP_LINES}
 WORKDIR /deps/__outer_unit_tests/unit_tests\
 """
    assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
@@ -259,7 +266,8 @@ def test_config_to_docker_outside_path():
        validate_config({"dependencies": [".", ".."], "graphs": graphs}),
        "langchain/langgraph-api",
    )
-    expected_docker_stdin = """\
+    expected_docker_stdin = (
+        """\
 FROM langchain/langgraph-api:3.11
 # -- Adding non-package dependency unit_tests --
 ADD . /deps/__outer_unit_tests/unit_tests
@@ -287,8 +295,12 @@ RUN set -ex && \\
 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"}'
+"""
+        + PIP_CLEANUP_LINES
+        + """
 WORKDIR /deps/__outer_unit_tests/unit_tests\
 """
+    )
    assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
    assert additional_contexts == {
        "__outer_tests": str(pathlib.Path(__file__).parent.parent.absolute()),
@@ -308,7 +320,8 @@ def test_config_to_docker_pipconfig():
        ),
        "langchain/langgraph-api",
    )
-    expected_docker_stdin = """\
+    expected_docker_stdin = (
+        """\
 FROM langchain/langgraph-api:3.11
 ADD pipconfig.txt /pipconfig.txt
 # -- Adding non-package dependency unit_tests --
@@ -326,8 +339,12 @@ RUN set -ex && \\
 RUN PIP_CONFIG_FILE=/pipconfig.txt 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"}'
+"""
+        + PIP_CLEANUP_LINES
+        + """
 WORKDIR /deps/__outer_unit_tests/unit_tests\
 """
+    )
    assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
    assert additional_contexts == {}

@@ -364,7 +381,7 @@ def test_config_to_docker_local_deps():
        ),
        "langchain/langgraph-api-custom",
    )
-    expected_docker_stdin = """\
+    expected_docker_stdin = f"""\
 FROM langchain/langgraph-api-custom:3.11
 # -- Adding non-package dependency graphs --
 ADD ./graphs /deps/__outer_graphs/src
@@ -380,7 +397,8 @@ RUN set -ex && \\
 # -- 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_graphs/src/agent.py:graph"}'\
+ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_graphs/src/agent.py:graph"}}'
+{PIP_CLEANUP_LINES}\
 """
    assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
    assert additional_contexts == {}
@@ -407,7 +425,8 @@ dependencies = ["langchain"]"""
        "langchain/langgraph-api",
    )
    os.remove(pyproject_path)
-    expected_docker_stdin = """FROM langchain/langgraph-api:3.11
+    expected_docker_stdin = (
+        """FROM langchain/langgraph-api:3.11
 # -- Adding local package . --
 ADD . /deps/unit_tests
 # -- End of local package . --
@@ -415,7 +434,12 @@ ADD . /deps/unit_tests
 RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
 ENV LANGSERVE_GRAPHS='{"agent": "/deps/unit_tests/graphs/agent.py:graph"}'
-WORKDIR /deps/unit_tests"""
+"""
+        + PIP_CLEANUP_LINES
+        + "\n"
+        + "WORKDIR /deps/unit_tests"
+        ""
+    )
    assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
    assert additional_contexts == {}

@@ -435,7 +459,7 @@ def test_config_to_docker_end_to_end():
        ),
        "langchain/langgraph-api",
    )
-    expected_docker_stdin = """FROM langchain/langgraph-api:3.12
+    expected_docker_stdin = f"""FROM langchain/langgraph-api:3.12
 ARG meow
 ARG foo
 ADD pipconfig.txt /pipconfig.txt
@@ -454,7 +478,8 @@ RUN set -ex && \\
 # -- Installing all local dependencies --
 RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
-ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph"}'"""
+ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_graphs/src/agent.py:graph"}}'
+{PIP_CLEANUP_LINES}"""
    assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
    assert additional_contexts == {}

@@ -469,7 +494,9 @@ def test_config_to_docker_nodejs():
                "node_version": "20",
                "graphs": graphs,
                "dockerfile_lines": ["ARG meow", "ARG foo"],
+                "auth": {"path": "./graphs/auth.mts:auth"},
                "ui": {"agent": "./graphs/agent.ui.jsx"},
+                "ui_config": {"shared": ["nuqs"]},
            }
        ),
        "langchain/langgraphjs-api",
@@ -479,8 +506,10 @@ ARG meow
 ARG foo
 ADD . /deps/unit_tests
 RUN cd /deps/unit_tests && npm i
+ENV LANGGRAPH_AUTH='{"path": "./graphs/auth.mts:auth"}'
 ENV LANGSERVE_GRAPHS='{"agent": "./graphs/agent.js:graph"}'
 ENV LANGGRAPH_UI='{"agent": "./graphs/agent.ui.jsx"}'
+ENV LANGGRAPH_UI_CONFIG='{"shared": ["nuqs"]}'
 WORKDIR /deps/unit_tests
 RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not found, skipping") || tsx /api/langgraph_api/js/build.mts"""

@@ -488,11 +517,55 @@ 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 = f"""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 --
+{PIP_CLEANUP_LINES}
+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"}
-    expected_compose_stdin = """\
-        
+    # Create a properly indented version of PIP_CLEANUP_LINES for compose files
+    expected_compose_stdin = f"""
        pull_policy: build
        build:
            context: .
@@ -512,7 +585,8 @@ def test_config_to_compose_simple_config():
                # -- 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"}'
+                ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
+{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
                WORKDIR /deps/__outer_unit_tests/unit_tests
        """
    actual_compose_stdin = config_to_compose(
@@ -520,12 +594,15 @@ def test_config_to_compose_simple_config():
        validate_config({"dependencies": ["."], "graphs": graphs}),
        "langchain/langgraph-api",
    )
-    assert clean_empty_lines(actual_compose_stdin) == expected_compose_stdin
+    assert (
+        clean_empty_lines(actual_compose_stdin).strip()
+        == expected_compose_stdin.strip()
+    )


 def test_config_to_compose_env_vars():
    graphs = {"agent": "./agent.py:graph"}
-    expected_compose_stdin = """                        OPENAI_API_KEY: "key"
+    expected_compose_stdin = f"""                        OPENAI_API_KEY: "key"
        
        pull_policy: build
        build:
@@ -546,7 +623,8 @@ def test_config_to_compose_env_vars():
                # -- 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"}'
+                ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
+{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
                WORKDIR /deps/__outer_unit_tests/unit_tests
        """
    openai_api_key = "key"
@@ -566,7 +644,7 @@ def test_config_to_compose_env_vars():

 def test_config_to_compose_env_file():
    graphs = {"agent": "./agent.py:graph"}
-    expected_compose_stdin = """\
+    expected_compose_stdin = f"""\
        env_file: .env
        pull_policy: build
        build:
@@ -587,7 +665,8 @@ def test_config_to_compose_env_file():
                # -- 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"}'
+                ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
+{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
                WORKDIR /deps/__outer_unit_tests/unit_tests
        """
    actual_compose_stdin = config_to_compose(
@@ -600,7 +679,7 @@ def test_config_to_compose_env_file():

 def test_config_to_compose_watch():
    graphs = {"agent": "./agent.py:graph"}
-    expected_compose_stdin = """\
+    expected_compose_stdin = f"""\
        
        pull_policy: build
        build:
@@ -621,7 +700,8 @@ def test_config_to_compose_watch():
                # -- 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"}'
+                ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
+{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
                WORKDIR /deps/__outer_unit_tests/unit_tests
        
        develop:
@@ -643,7 +723,7 @@ def test_config_to_compose_watch():
 def test_config_to_compose_end_to_end():
    # test all of the above + langgraph API path
    graphs = {"agent": "./agent.py:graph"}
-    expected_compose_stdin = """\
+    expected_compose_stdin = f"""\
        env_file: .env
        pull_policy: build
        build:
@@ -664,7 +744,8 @@ def test_config_to_compose_end_to_end():
                # -- 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"}'
+                ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
+{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
                WORKDIR /deps/__outer_unit_tests/unit_tests
        
        develop:
@@ -9,6 +9,7 @@ from bench.fanout_to_subgraph import fanout_to_subgraph, fanout_to_subgraph_sync
 from bench.pydantic_state import pydantic_state
 from bench.react_agent import react_agent
 from bench.sequential import create_sequential
+from bench.wide_dict import wide_dict
 from bench.wide_state import wide_state
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import StateGraph
@@ -25,6 +26,7 @@ async def arun(graph: Pregel, input: dict):
                    "configurable": {"thread_id": str(uuid4())},
                    "recursion_limit": 1000000000,
                },
+                checkpoint_during=False,
            )
        ]
    )
@@ -41,6 +43,7 @@ async def arun_first_event_latency(graph: Pregel, input: dict) -> None:
            "configurable": {"thread_id": str(uuid4())},
            "recursion_limit": 1000000000,
        },
+        checkpoint_during=False,
    )

    try:
@@ -60,6 +63,7 @@ def run(graph: Pregel, input: dict):
                    "configurable": {"thread_id": str(uuid4())},
                    "recursion_limit": 1000000000,
                },
+                checkpoint_during=False,
            )
        ]
    )
@@ -76,6 +80,7 @@ def run_first_event_latency(graph: Pregel, input: dict) -> None:
            "configurable": {"thread_id": str(uuid4())},
            "recursion_limit": 1000000000,
        },
+        checkpoint_during=False,
    )

    try:
@@ -252,27 +257,111 @@ benchmarks = (
        },
    ),
    (
-        "sequential_20",
-        create_sequential(20).compile(),
-        create_sequential(20).compile(),
+        "wide_dict_25x300",
+        wide_dict(300).compile(checkpointer=None),
+        wide_dict(300).compile(checkpointer=None),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(5)
+                }
+            ]
+        },
+    ),
+    (
+        "wide_dict_25x300_checkpoint",
+        wide_dict(300).compile(checkpointer=MemorySaver()),
+        wide_dict(300).compile(checkpointer=MemorySaver()),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(5)
+                }
+            ]
+        },
+    ),
+    (
+        "wide_dict_15x600",
+        wide_dict(600).compile(checkpointer=None),
+        wide_dict(600).compile(checkpointer=None),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
+    (
+        "wide_dict_15x600_checkpoint",
+        wide_dict(600).compile(checkpointer=MemorySaver()),
+        wide_dict(600).compile(checkpointer=MemorySaver()),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(5)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
+    (
+        "wide_dict_9x1200",
+        wide_dict(1200).compile(checkpointer=None),
+        wide_dict(1200).compile(checkpointer=None),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(3)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
+    (
+        "wide_dict_9x1200_checkpoint",
+        wide_dict(1200).compile(checkpointer=MemorySaver()),
+        wide_dict(1200).compile(checkpointer=MemorySaver()),
+        {
+            "messages": [
+                {
+                    str(i) * 10: {
+                        str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                        for j in range(3)
+                    }
+                    for i in range(3)
+                }
+            ]
+        },
+    ),
+    (
+        "sequential_10",
+        create_sequential(10).compile(),
+        create_sequential(10).compile(),
        {"messages": []},  # Empty list of messages
    ),
    (
-        "sequential_50",
-        create_sequential(50).compile(),
-        create_sequential(50).compile(),
-        {"messages": []},  # Empty list of messages
-    ),
-    (
-        "sequential_100",
-        create_sequential(100).compile(),
-        create_sequential(100).compile(),
-        {"messages": []},  # Empty list of messages
-    ),
-    (
-        "sequential_200",
-        create_sequential(200).compile(),
-        create_sequential(200).compile(),
+        "sequential_1000",
+        create_sequential(1000).compile(),
+        create_sequential(1000).compile(),
        {"messages": []},  # Empty list of messages
    ),
    (
@@ -383,8 +472,17 @@ for name, agraph, graph, input in benchmarks:
        r.bench_func(name + "_sync", run, graph, input)


+# Pick a handful of graphs to measure the first event latency.
+# At the moment, limiting just due to the size of the annotation on github.
+GRAPHS_FOR_1st_EVENT_LATENCY = (
+    "sequential_1000",
+    "pydantic_state_25x300",
+)
+
 # First event latency
 for name, agraph, graph, input in benchmarks:
+    if graph not in GRAPHS_FOR_1st_EVENT_LATENCY:
+        continue
    r.bench_async_func(
        name + "_first_event_latency",
        arun_first_event_latency,
@@ -403,30 +501,14 @@ compilation_benchmarks = (
        "sequential_1000",
        create_sequential(1_000),
    ),
-    (
-        "sequential_10000",
-        create_sequential(10_000),
-    ),
    (
        "pydantic_state_25x300",
        pydantic_state(300),
    ),
-    (
-        "pydantic_state_15x600",
-        pydantic_state(600),
-    ),
-    (
-        "pydantic_state_9x1200",
-        pydantic_state(1200),
-    ),
    (
        "wide_state_15x600",
        wide_state(600),
    ),
-    (
-        "wide_state_9x1200",
-        wide_state(1200),
-    ),
 )

 for name, graph in compilation_benchmarks:
@@ -34,7 +34,7 @@ if __name__ == "__main__":

    import uvloop

-    graph = create_sequential(2000).compile()
+    graph = create_sequential(3000).compile()
    input = {"messages": []}  # Empty list of messages
    config = {"recursion_limit": 20000000000}

@@ -0,0 +1,153 @@
+import operator
+from functools import partial
+from random import choice
+from typing import Annotated, Optional, Sequence
+
+from typing_extensions import TypedDict
+
+from langgraph.constants import END, START
+from langgraph.graph.state import StateGraph
+
+
+def wide_dict(n: int) -> StateGraph:
+    class State(TypedDict):
+        messages: Annotated[list, operator.add]
+        trigger_events: Annotated[list, operator.add]
+        """The external events that are converted by the graph."""
+        primary_issue_medium: Annotated[str, lambda x, y: y or x]
+        autoresponse: Annotated[Optional[dict], lambda _, y: y]  # Always overwrite
+        issue: Annotated[dict | None, lambda x, y: y if y else x]
+        relevant_rules: Optional[list[dict]]
+        """SOPs fetched from the rulebook that are relevant to the current conversation."""
+        memory_docs: Optional[list[dict]]
+        """Memory docs fetched from the memory service that are relevant to the current conversation."""
+        categorizations: Annotated[list[dict], operator.add]
+        """The issue categorizations auto-generated by the AI."""
+        responses: Annotated[list[dict], operator.add]
+        """The draft responses recommended by the AI."""
+
+        user_info: Annotated[Optional[dict], lambda x, y: y if y is not None else x]
+        """The current user state (by email)."""
+        crm_info: Annotated[Optional[dict], lambda x, y: y if y is not None else x]
+        """The CRM information for organization the current user is from."""
+        email_thread_id: Annotated[
+            Optional[str], lambda x, y: y if y is not None else x
+        ]
+        """The current email thread ID."""
+        slack_participants: Annotated[dict, operator.or_]
+        """The growing list of current slack participants."""
+        bot_id: Optional[str]
+        """The ID of the bot user in the slack channel."""
+        notified_assignees: Annotated[dict, operator.or_]
+
+    list_fields = {
+        "messages",
+        "trigger_events",
+        "categorizations",
+        "responses",
+        "memory_docs",
+        "relevant_rules",
+    }
+    dict_fields = {
+        "user_info",
+        "crm_info",
+        "slack_participants",
+        "notified_assignees",
+        "autoresponse",
+        "issue",
+    }
+
+    def read_write(read: str, write: Sequence[str], input: State) -> dict:
+        val = input.get(read)
+        val = {val: val} if isinstance(val, str) else val
+        val_single = val[-1] if isinstance(val, list) else val
+        val_list = val if isinstance(val, list) else [val]
+        return {
+            k: val_list
+            if k in list_fields
+            else val_single
+            if k in dict_fields
+            else "".join(choice("abcdefghijklmnopqrstuvwxyz") for _ in range(n))
+            for k in write
+        }
+
+    builder = StateGraph(State)
+    builder.add_edge(START, "one")
+    builder.add_node(
+        "one",
+        partial(read_write, "messages", ["trigger_events", "primary_issue_medium"]),
+    )
+    builder.add_edge("one", "two")
+    builder.add_node(
+        "two",
+        partial(read_write, "trigger_events", ["autoresponse", "issue"]),
+    )
+    builder.add_edge("two", "three")
+    builder.add_edge("two", "four")
+    builder.add_node(
+        "three",
+        partial(read_write, "autoresponse", ["relevant_rules"]),
+    )
+    builder.add_node(
+        "four",
+        partial(
+            read_write,
+            "trigger_events",
+            ["categorizations", "responses", "memory_docs"],
+        ),
+    )
+    builder.add_node(
+        "five",
+        partial(
+            read_write,
+            "categorizations",
+            [
+                "user_info",
+                "crm_info",
+                "email_thread_id",
+                "slack_participants",
+                "bot_id",
+                "notified_assignees",
+            ],
+        ),
+    )
+    builder.add_edge(["three", "four"], "five")
+    builder.add_edge("five", "six")
+    builder.add_node(
+        "six",
+        partial(read_write, "responses", ["messages"]),
+    )
+    builder.add_conditional_edges(
+        "six", lambda state: END if len(state["messages"]) > n else "one"
+    )
+
+    return builder
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    import uvloop
+
+    from langgraph.checkpoint.memory import MemorySaver
+
+    graph = wide_dict(1000).compile(checkpointer=MemorySaver())
+    input = {
+        "messages": [
+            {
+                str(i) * 10: {
+                    str(j) * 10: ["hi?" * 10, True, 1, 6327816386138, None] * 5
+                    for j in range(50)
+                }
+                for i in range(50)
+            }
+        ]
+    }
+    config = {"configurable": {"thread_id": "1"}, "recursion_limit": 20000000000}
+
+    async def run():
+        async for c in graph.astream(input, config=config):
+            print(c.keys())
+
+    uvloop.install()
+    asyncio.run(run())
@@ -1,6 +1,7 @@
 import operator
 from dataclasses import dataclass, field
 from functools import partial
+from random import choice
 from typing import Annotated, Optional, Sequence

 from langgraph.constants import END, START
@@ -49,12 +50,34 @@ def wide_state(n: int) -> StateGraph:
        """The ID of the bot user in the slack channel."""
        notified_assignees: Annotated[dict, operator.or_] = field(default_factory=dict)

+    list_fields = {
+        "messages",
+        "trigger_events",
+        "categorizations",
+        "responses",
+        "memory_docs",
+        "relevant_rules",
+    }
+    dict_fields = {
+        "user_info",
+        "crm_info",
+        "slack_participants",
+        "notified_assignees",
+        "autoresponse",
+        "issue",
+    }
+
    def read_write(read: str, write: Sequence[str], input: State) -> dict:
        val = getattr(input, read)
+        val = {val: val} if isinstance(val, str) else val
        val_single = val[-1] if isinstance(val, list) else val
        val_list = val if isinstance(val, list) else [val]
        return {
-            k: val_list if isinstance(getattr(input, k), list) else val_single
+            k: val_list
+            if k in list_fields
+            else val_single
+            if k in dict_fields
+            else "".join(choice("abcdefghijklmnopqrstuvwxyz") for _ in range(n))
            for k in write
        }

@@ -1,4 +1,4 @@
-from typing import Any, Generic, Optional, Sequence, Type
+from typing import Any, Generic, Sequence, Type

 from typing_extensions import Self

@@ -30,10 +30,15 @@ class AnyValue(Generic[Value], BaseChannel[Value, Value, Value]):
        """The type of the update received by the channel."""
        return self.typ

-    def from_checkpoint(self, checkpoint: Optional[Value]) -> Self:
-        empty = self.__class__(self.typ)
-        empty.key = self.key
-        if checkpoint is not None:
+    def copy(self) -> Self:
+        """Return a copy of the channel."""
+        empty = self.__class__(self.typ, self.key)
+        empty.value = self.value
+        return empty
+
+    def from_checkpoint(self, checkpoint: Value) -> Self:
+        empty = self.__class__(self.typ, self.key)
+        if checkpoint is not MISSING:
            empty.value = checkpoint
        return empty

@@ -55,3 +60,6 @@ class AnyValue(Generic[Value], BaseChannel[Value, Value, Value]):

    def is_available(self) -> bool:
        return self.value is not MISSING
+
+    def checkpoint(self) -> Value:
+        return self.value
@@ -1,8 +1,9 @@
 from abc import ABC, abstractmethod
-from typing import Any, Generic, Optional, Sequence, TypeVar
+from typing import Any, Generic, Sequence, TypeVar

 from typing_extensions import Self

+from langgraph.constants import MISSING
 from langgraph.errors import EmptyChannelError, InvalidUpdateError

 Value = TypeVar("Value")
@@ -29,14 +30,23 @@ class BaseChannel(Generic[Value, Update, C], ABC):

    # serialize/deserialize methods

-    def checkpoint(self) -> Optional[C]:
+    def copy(self) -> Self:
+        """Return a copy of the channel.
+        By default, delegates to checkpoint() and from_checkpoint().
+        Subclasses can override this method with a more efficient implementation."""
+        return self.from_checkpoint(self.checkpoint())
+
+    def checkpoint(self) -> C:
        """Return a serializable representation of the channel's current state.
        Raises EmptyChannelError if the channel is empty (never updated yet),
        or doesn't support checkpoints."""
-        return self.get()
+        try:
+            return self.get()
+        except EmptyChannelError:
+            return MISSING

    @abstractmethod
-    def from_checkpoint(self, checkpoint: Optional[C]) -> Self:
+    def from_checkpoint(self, checkpoint: C) -> Self:
        """Return a new identical channel, optionally initialized from a checkpoint.
        If the checkpoint contains complex data structures, they should be copied."""

@@ -1,11 +1,5 @@
 import collections.abc
-from typing import (
-    Callable,
-    Generic,
-    Optional,
-    Sequence,
-    Type,
-)
+from typing import Callable, Generic, Sequence, Type

 from typing_extensions import NotRequired, Required, Self

@@ -72,10 +66,17 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
        """The type of the update received by the channel."""
        return self.typ

-    def from_checkpoint(self, checkpoint: Optional[Value]) -> Self:
+    def copy(self) -> Self:
+        """Return a copy of the channel."""
        empty = self.__class__(self.typ, self.operator)
        empty.key = self.key
-        if checkpoint is not None:
+        empty.value = self.value
+        return empty
+
+    def from_checkpoint(self, checkpoint: Value) -> Self:
+        empty = self.__class__(self.typ, self.operator)
+        empty.key = self.key
+        if checkpoint is not MISSING:
            empty.value = checkpoint
        return empty

@@ -96,3 +97,6 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):

    def is_available(self) -> bool:
        return self.value is not MISSING
+
+    def checkpoint(self) -> Value:
+        return self.value
@@ -3,6 +3,7 @@ from typing import Any, Generic, NamedTuple, Optional, Sequence, Type, Union
 from typing_extensions import Self

 from langgraph.channels.base import BaseChannel, Value
+from langgraph.constants import MISSING
 from langgraph.errors import EmptyChannelError, InvalidUpdateError


@@ -45,16 +46,23 @@ class DynamicBarrierValue(
        """The type of the update received by the channel."""
        return self.typ

+    def copy(self) -> Self:
+        """Return a copy of the channel."""
+        empty = self.__class__(self.typ)
+        empty.key = self.key
+        empty.names = self.names
+        empty.seen = self.seen.copy()
+        return empty
+
    def checkpoint(self) -> tuple[Optional[set[Value]], set[Value]]:
        return (self.names, self.seen)

    def from_checkpoint(
-        self,
-        checkpoint: Optional[tuple[Optional[set[Value]], set[Value]]],
+        self, checkpoint: tuple[Optional[set[Value]], set[Value]]
    ) -> Self:
        empty = self.__class__(self.typ)
        empty.key = self.key
-        if checkpoint is not None:
+        if checkpoint is not MISSING:
            names, seen = checkpoint
            empty.names = names if names is not None else None
            empty.seen = seen
@@ -1,4 +1,4 @@
-from typing import Any, Generic, Optional, Sequence, Type
+from typing import Any, Generic, Sequence, Type

 from typing_extensions import Self

@@ -30,10 +30,17 @@ class EphemeralValue(Generic[Value], BaseChannel[Value, Value, Value]):
        """The type of the update received by the channel."""
        return self.typ

-    def from_checkpoint(self, checkpoint: Optional[Value]) -> Self:
+    def copy(self) -> Self:
+        """Return a copy of the channel."""
        empty = self.__class__(self.typ, self.guard)
        empty.key = self.key
-        if checkpoint is not None:
+        empty.value = self.value
+        return empty
+
+    def from_checkpoint(self, checkpoint: Value) -> Self:
+        empty = self.__class__(self.typ, self.guard)
+        empty.key = self.key
+        if checkpoint is not MISSING:
            empty.value = checkpoint
        return empty

@@ -59,3 +66,6 @@ class EphemeralValue(Generic[Value], BaseChannel[Value, Value, Value]):

    def is_available(self) -> bool:
        return self.value is not MISSING
+
+    def checkpoint(self) -> Value:
+        return self.value
@@ -1,4 +1,4 @@
-from typing import Any, Generic, Optional, Sequence, Type
+from typing import Any, Generic, Sequence, Type

 from typing_extensions import Self

@@ -34,10 +34,15 @@ class LastValue(Generic[Value], BaseChannel[Value, Value, Value]):
        """The type of the update received by the channel."""
        return self.typ

-    def from_checkpoint(self, checkpoint: Optional[Value]) -> Self:
-        empty = self.__class__(self.typ)
-        empty.key = self.key
-        if checkpoint is not None:
+    def copy(self) -> Self:
+        """Return a copy of the channel."""
+        empty = self.__class__(self.typ, self.key)
+        empty.value = self.value
+        return empty
+
+    def from_checkpoint(self, checkpoint: Value) -> Self:
+        empty = self.__class__(self.typ, self.key)
+        if checkpoint is not MISSING:
            empty.value = checkpoint
        return empty

@@ -61,3 +66,6 @@ class LastValue(Generic[Value], BaseChannel[Value, Value, Value]):

    def is_available(self) -> bool:
        return self.value is not MISSING
+
+    def checkpoint(self) -> Value:
+        return self.value
@@ -1,8 +1,9 @@
-from typing import Generic, Optional, Sequence, Type
+from typing import Generic, Sequence, Type

 from typing_extensions import Self

 from langgraph.channels.base import BaseChannel, Value
+from langgraph.constants import MISSING
 from langgraph.errors import EmptyChannelError, InvalidUpdateError


@@ -32,13 +33,20 @@ class NamedBarrierValue(Generic[Value], BaseChannel[Value, Value, set[Value]]):
        """The type of the update received by the channel."""
        return self.typ

+    def copy(self) -> Self:
+        """Return a copy of the channel."""
+        empty = self.__class__(self.typ, self.names)
+        empty.key = self.key
+        empty.seen = self.seen.copy()
+        return empty
+
    def checkpoint(self) -> set[Value]:
        return self.seen

-    def from_checkpoint(self, checkpoint: Optional[set[Value]]) -> Self:
+    def from_checkpoint(self, checkpoint: set[Value]) -> Self:
        empty = self.__class__(self.typ, self.names)
        empty.key = self.key
-        if checkpoint is not None:
+        if checkpoint is not MISSING:
            empty.seen = checkpoint
        return empty

@@ -1,8 +1,9 @@
-from typing import Any, Generic, Iterator, Optional, Sequence, Type, Union
+from typing import Any, Generic, Iterator, Sequence, Type, Union

 from typing_extensions import Self

 from langgraph.channels.base import BaseChannel, Value
+from langgraph.constants import MISSING
 from langgraph.errors import EmptyChannelError


@@ -16,9 +17,7 @@ def flatten(values: Sequence[Union[Value, list[Value]]]) -> Iterator[Value]:

 class Topic(
    Generic[Value],
-    BaseChannel[
-        Sequence[Value], Union[Value, list[Value]], tuple[set[Value], list[Value]]
-    ],
+    BaseChannel[Sequence[Value], Union[Value, list[Value]], list[Value]],
 ):
    """A configurable PubSub Topic.

@@ -49,20 +48,28 @@ class Topic(
        """The type of the update received by the channel."""
        return Union[self.typ, list[self.typ]]  # type: ignore[name-defined]

-    def checkpoint(self) -> tuple[set[Value], list[Value]]:
-        return self.values
-
-    def from_checkpoint(self, checkpoint: Optional[list[Value]]) -> Self:
+    def copy(self) -> Self:
+        """Return a copy of the channel."""
        empty = self.__class__(self.typ, self.accumulate)
        empty.key = self.key
-        if checkpoint is not None:
+        empty.values = self.values.copy()
+        return empty
+
+    def checkpoint(self) -> list[Value]:
+        return self.values
+
+    def from_checkpoint(self, checkpoint: list[Value]) -> Self:
+        empty = self.__class__(self.typ, self.accumulate)
+        empty.key = self.key
+        if checkpoint is not MISSING:
            if isinstance(checkpoint, tuple):
+                # backwards compatibility
                empty.values = checkpoint[1]
            else:
                empty.values = checkpoint
        return empty

-    def update(self, values: Sequence[Union[Value, list[Value]]]) -> None:
+    def update(self, values: Sequence[Union[Value, list[Value]]]) -> bool:
        current = list(self.values)
        if not self.accumulate:
            self.values = list[Value]()
@@ -1,4 +1,4 @@
-from typing import Generic, Optional, Sequence, Type
+from typing import Generic, Sequence, Type

 from typing_extensions import Self

@@ -30,10 +30,17 @@ class UntrackedValue(Generic[Value], BaseChannel[Value, Value, Value]):
        """The type of the update received by the channel."""
        return self.typ

-    def checkpoint(self) -> Value:
-        raise EmptyChannelError()
+    def copy(self) -> Self:
+        """Return a copy of the channel."""
+        empty = self.__class__(self.typ, self.guard)
+        empty.key = self.key
+        empty.value = self.value
+        return empty

-    def from_checkpoint(self, checkpoint: Optional[Value]) -> Self:
+    def checkpoint(self) -> Value:
+        return MISSING
+
+    def from_checkpoint(self, checkpoint: Value) -> Self:
        empty = self.__class__(self.typ, self.guard)
        empty.key = self.key
        return empty
@@ -83,6 +83,8 @@ CONFIG_KEY_PREVIOUS = sys.intern("__pregel_previous")
 # holds the previous return value from a stateful Pregel graph.
 CONFIG_KEY_RUNNER_SUBMIT = sys.intern("__pregel_runner_submit")
 # holds a function that receives tasks from runner, executes them and returns results
+CONFIG_KEY_CHECKPOINT_DURING = sys.intern("__pregel_checkpoint_during")
+# holds a boolean indicating whether to checkpoint during the run (or only at the end)

 # --- Other constants ---
 PUSH = sys.intern("__pregel_push")
@@ -27,6 +27,8 @@ from langgraph.graph.state import StateGraph

 Messages = Union[list[MessageLikeRepresentation], MessageLikeRepresentation]

+REMOVE_ALL_MESSAGES = "__remove_all__"
+

 def _add_messages_wrapper(func: Callable) -> Callable[[Messages, Messages], Messages]:
    def _add_messages(
@@ -158,6 +160,7 @@ def add_messages(

        Support for 'format="langchain-openai"' flag added.
    """
+    remove_all_idx = None
    # coerce to list
    if not isinstance(left, list):
        left = [left]  # type: ignore[assignment]
@@ -176,9 +179,15 @@ def add_messages(
    for m in left:
        if m.id is None:
            m.id = str(uuid.uuid4())
-    for m in right:
+    for idx, m in enumerate(right):
        if m.id is None:
            m.id = str(uuid.uuid4())
+        if isinstance(m, RemoveMessage) and m.id == REMOVE_ALL_MESSAGES:
+            remove_all_idx = idx
+
+    if remove_all_idx is not None:
+        return right[remove_all_idx + 1 :]
+
    # merge
    merged = left.copy()
    merged_by_id = {m.id: i for i, m in enumerate(merged)}
@@ -1,3 +1,4 @@
+import functools
 import logging
 import weakref
 from inspect import isclass
@@ -16,6 +17,8 @@ from pydantic import BaseModel
 from pydantic.v1 import BaseModel as BaseModelV1
 from typing_extensions import Annotated

+__all__ = ["SchemaCoercionMapper"]
+
 logger = logging.getLogger(__name__)


@@ -25,54 +28,60 @@ _cache: weakref.WeakKeyDictionary[Type[Any], dict[int, "SchemaCoercionMapper"]]


 class SchemaCoercionMapper:
+    """Lightweight coercion of *dict* → *BaseModel* instances."""
+
    def __new__(
        cls,
        schema: Type[Any],
        type_hints: Optional[dict[str, Any]] = None,
+        *,
        max_depth: int = 12,
    ) -> "SchemaCoercionMapper":
-        if schema not in _cache:
-            _cache[schema] = {}
-        if max_depth in _cache[schema]:
-            return _cache[schema][max_depth]
-
+        by_depth = _cache.setdefault(schema, {})
+        if max_depth in by_depth:
+            return by_depth[max_depth]
        inst = super().__new__(cls)
-        _cache[schema][max_depth] = inst
+        by_depth[max_depth] = inst
        return inst

    def __init__(
        self,
        schema: Type[Any],
        type_hints: Optional[dict[str, Any]] = None,
+        *,
        max_depth: int = 12,
-    ):
-        if hasattr(self, "_inited"):
+    ) -> None:
+        if hasattr(self, "_initialised"):
            return
-        self._inited = True
+        self._initialised = True
+
        self.schema = schema
+        self.max_depth = max_depth
+
        self.type_hints = (
            type_hints
            if type_hints is not None
            else get_type_hints(schema, localns={schema.__name__: schema})
        )
-        self.max_depth = max_depth

-        if issubclass(schema, BaseModel):
-            self._fields = {
-                n: self.type_hints.get(n, f.annotation)
-                for n, f in schema.model_fields.items()
-            }
-            self._construct: Callable[..., Any] = schema.model_construct
-
-        elif issubclass(schema, BaseModelV1):
+        if issubclass(schema, BaseModelV1):
            self._fields = {
                n: self.type_hints.get(n, f.annotation)
                for n, f in schema.__fields__.items()
            }
            self._construct = schema.construct
+
+        elif issubclass(schema, BaseModel):
+            self._fields = {
+                n: self.type_hints.get(n, f.annotation)
+                for n, f in schema.model_fields.items()
+            }
+            self._construct: Callable[..., Any] = schema.model_construct  # type: ignore
+
        else:
-            raise TypeError("Schema is neither valid Pydantic v1 nor v2 model.")
-        self._field_coercers: Optional[dict[str, Callable[[Any, Any], Any]]] = None
+            raise TypeError("Schema is neither a Pydantic v1 nor v2 model.")
+
+        self._field_coercers: Optional[dict[str, Callable[[Any, int], Any]]] = None

    def __call__(self, input_data: Any, depth: Optional[int] = None) -> Any:
        return self.coerce(input_data, depth)
@@ -82,45 +91,51 @@ class SchemaCoercionMapper:
            depth = self.max_depth
        if not isinstance(input_data, dict) or depth <= 0:
            return input_data
-        processed = {}
+
        if self._field_coercers is None:
            self._field_coercers = {
                n: self._build_coercer(t, depth - 1) for n, t in self._fields.items()
            }
+
+        processed: dict[str, Any] = {}
        for k, v in input_data.items():
            fn = self._field_coercers.get(k)
            processed[k] = fn(v, depth - 1) if fn else v
        return self._construct(**processed)

    def _build_coercer(
-        self, field_type: Any, depth: int, throw: bool = False
+        self, field_type: Any, depth: int, *, throw: bool = False
    ) -> Callable[[Any, Any], Any]:
        if depth == 0:
            return self._passthrough
+
        origin = get_origin(field_type)

+        if (field_type in _IDENTITY_TYPES) or (origin in _IDENTITY_TYPES):
+            return self._passthrough
+
        if origin is Annotated:
            real_type, *_ = get_args(field_type)
            sub = self._build_coercer(real_type, depth - 1)
            return lambda v, d: sub(v, d)
+
        if isclass(field_type):
+            # This is needed bcs. of issubclass issues on older versions of python
            is_class_ = True
            try:
-                is_base_model = issubclass(field_type, BaseModel)
+                is_bm_v2 = issubclass(field_type, BaseModel)
            except TypeError:
+                # python < 3.11 issue.
                is_class_ = False
-                is_base_model = False
+                is_bm_v2 = False
+            if is_bm_v2 or (is_class_ and issubclass(field_type, BaseModelV1)):
+                mapper = SchemaCoercionMapper(field_type, max_depth=depth - 1)
+                return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v

-            if is_base_model:
-                mapper = SchemaCoercionMapper(field_type, max_depth=depth - 1)
-                return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v
-            if is_class_ and issubclass(field_type, BaseModelV1):
-                mapper = SchemaCoercionMapper(field_type, max_depth=depth - 1)
-                return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v
-        if origin is list or field_type is list:
+        if origin is list:
            args = get_args(field_type)
            if len(args) != 1:
-                return lambda v, d: v
+                return self._passthrough
            sub = self._build_coercer(args[0], depth - 1)

            def list_coercer(v: Any, d: Any) -> Any:
@@ -129,15 +144,21 @@ class SchemaCoercionMapper:
                return [sub(x, d - 1) for x in v]

            return list_coercer
+
        if origin is set or field_type is set:
            args = get_args(field_type)
-            if len(args) != 1:
-                return lambda v, d: v
-            sub = self._build_coercer(args[0], depth - 1)
+            if len(args) > 1:
+                return self._passthrough
+            elif len(args) == 1:
+                sub = self._build_coercer(args[0], depth - 1)
+            else:
+                sub = None  # type: ignore

            def set_coercer(v: Any, d: Any) -> Any:
                if not isinstance(v, (list, tuple, set)):
                    return v
+                if sub is None:
+                    return set(v)
                return {sub(x, d - 1) for x in v}

            return set_coercer
@@ -165,20 +186,19 @@ class SchemaCoercionMapper:
            return dict_coercer

        if origin is tuple:
-            targs = get_args(field_type)
-            if not targs:
-                return lambda v, d: v
-            subs = [self._build_coercer(a, depth - 1) for a in targs]
+            elem_types = get_args(field_type)
+            if not elem_types:
+                return self._passthrough
+            subs = [self._build_coercer(t, depth - 1) for t in elem_types]
+            return lambda v, d: (
+                tuple(
+                    subs[i](v[i] if i < len(v) else None, d - 1)
+                    for i in range(len(subs))
+                )
+                if isinstance(v, (list, tuple))
+                else v
+            )

-            def tuple_coercer(v: Any, d: Any) -> Any:
-                if not isinstance(v, (list, tuple)):
-                    return v
-                out = []
-                for i, sp in enumerate(subs):
-                    out.append(sp(v[i] if i < len(v) else None, d - 1))
-                return tuple(out)
-
-            return tuple_coercer
        if origin is Union:
            uargs = get_args(field_type)
            subs, none_in_union = [], False
@@ -204,7 +224,97 @@ class SchemaCoercionMapper:
                return v

            return union_coercer
-        return self._passthrough

-    def _passthrough(self, v: Any, d: Any) -> Any:
+        adapter_fn = _get_adapter(field_type)
+        return lambda v, _d: adapter_fn(v)
+
+    @staticmethod
+    def _passthrough(v: Any, _d: Any) -> Any:  # noqa: D401
        return v
+
+
+_adapter_cache: dict[Any, Callable[[Any], Any]] = {}
+
+
+_IDENTITY_TYPES: tuple[type[Any], ...] = (
+    int,
+    float,
+    str,
+    bool,
+    bytes,
+    bytearray,
+    complex,
+    memoryview,
+    type(None),
+)
+
+try:
+    # Pydantic v2.
+    from pydantic import TypeAdapter
+
+    try:
+        import pydantic.v1.types as v1_types_
+        from pydantic.v1 import parse_obj_as
+
+        v1_types = tuple(
+            v for k, v in vars(v1_types_).items() if k in v1_types_.__all__
+        )
+    except ImportError:
+        v1_types = ()
+
+        def parse_obj_as(tp: Any, v: Any) -> Any:  # type: ignore
+            return v
+
+    try:
+        from pydantic.v1 import parse_obj_as
+        from pydantic.v1.main import create_model
+    except ImportError:
+        create_model = None  # type: ignore
+
+    def _get_v1_parser(tp: Any) -> Any:
+        if create_model is not None:
+            try:
+                parser = create_model(
+                    f"ParsingModel[{tp}]",
+                    __root__=(tp, ...),
+                )
+                return lambda v: parser(__root__=v).__root__  # type: ignore
+            except RuntimeError:
+                return lambda v: v
+        return lambda v: parse_obj_as(tp, v)
+
+    @functools.lru_cache(maxsize=2048)
+    def _adapter_for(tp: Any) -> Callable[[Any], Any]:  # noqa: D401
+        if tp in v1_types:
+            return _get_v1_parser(tp)
+        try:
+            return TypeAdapter(
+                tp, config={"arbitrary_types_allowed": True}
+            ).validate_python
+        except TypeError:
+            # Delayed classes like ConstrainedList
+            return _get_v1_parser(tp)
+
+except ImportError:
+    # Pydantic V1
+    from pydantic.v1.main import create_model
+
+    @functools.lru_cache(maxsize=2048)
+    def _adapter_for(tp: Any) -> Callable[[Any], Any]:  # noqa: D401
+        try:
+            parser = create_model(
+                f"ParsingModel[{tp}]",
+                __root__=(tp, ...),
+            )
+            return lambda v: parser(__root__=v).__root__  # type: ignore
+        except RuntimeError:
+            return lambda v: v
+
+
+def _get_adapter(tp: Any) -> Callable[[Any], Any]:
+    try:
+        return _adapter_cache[tp]
+    except KeyError:
+        fn = _adapter_for(tp)
+        _adapter_cache[tp] = fn
+        return fn
@@ -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,
@@ -242,7 +252,7 @@ class StateGraph(Graph):
        metadata: Optional[dict[str, Any]] = None,
        input: Optional[Type[Any]] = None,
        retry: Optional[RetryPolicy] = None,
-        destinations: Optional[Union[dict[str, str], tuple[str]]] = None,
+        destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
    ) -> Self:
        """Adds a new node to the state graph.
        Will take the name of the function/runnable as the node name.
@@ -267,7 +277,7 @@ class StateGraph(Graph):
        metadata: Optional[dict[str, Any]] = None,
        input: Optional[Type[Any]] = None,
        retry: Optional[RetryPolicy] = None,
-        destinations: Optional[Union[dict[str, str], tuple[str]]] = None,
+        destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
    ) -> Self:
        """Adds a new node to the state graph.

@@ -291,7 +301,7 @@ class StateGraph(Graph):
        metadata: Optional[dict[str, Any]] = None,
        input: Optional[Type[Any]] = None,
        retry: Optional[RetryPolicy] = None,
-        destinations: Optional[Union[dict[str, str], tuple[str]]] = None,
+        destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
    ) -> Self:
        """Adds a new node to the state graph.

@@ -303,7 +313,7 @@ class StateGraph(Graph):
            metadata (Optional[dict[str, Any]]): The metadata associated with the node. (default: None)
            input (Optional[Type[Any]]): The input schema for the node. (default: the graph's input schema)
            retry (Optional[RetryPolicy]): The policy for retrying the node. (default: None)
-            destinations (Optional[Union[dict[str, str], tuple[str]]]): Destinations that indicate where a node can route to.
+            destinations (Optional[Union[dict[str, str], tuple[str, ...]]]): Destinations that indicate where a node can route to.
                This is useful for edgeless graphs with nodes that return `Command` objects.
                If a dict is provided, the keys will be used as the target node names and the values will be used as the labels for the edges.
                If a tuple is provided, the values will be used as the target node names.
@@ -766,13 +776,13 @@ class CompiledStateGraph(CompiledGraph):
                return updates
            elif (t := type(input)) and get_type_hints(t):
                # Pydantic v2
-                if isinstance(input, BaseModel):
-                    keep: Optional[set[str]] = input.model_fields_set
+                if isinstance(input, BaseModelV1):
+                    keep: Optional[set[str]] = input.__fields_set__
+                    defaults = {k: v.default for k, v in t.__fields__.items()}
+                elif isinstance(input, BaseModel):
+                    keep = input.model_fields_set
                    defaults = {k: v.default for k, v in input.model_fields.items()}
                # Pydantic v1
-                elif isinstance(input, BaseModelV1):
-                    keep = input.__fields_set__
-                    defaults = {k: v.default for k, v in t.__fields__.items()}
                else:
                    keep = None
                    defaults = {}
@@ -799,11 +809,11 @@ class CompiledStateGraph(CompiledGraph):
                raise InvalidUpdateError(msg)

        # state updaters
-        write_entries: list[Union[ChannelWriteEntry, ChannelWriteTupleEntry]] = [
+        write_entries: tuple[Union[ChannelWriteEntry, ChannelWriteTupleEntry], ...] = (
            ChannelWriteTupleEntry(
                mapper=_get_root if output_keys == ["__root__"] else _get_updates
-            )
-        ]
+            ),
+        )

        # add node and output channel
        if key == START:
@@ -811,20 +821,14 @@ class CompiledStateGraph(CompiledGraph):
                tags=[TAG_HIDDEN],
                triggers=[START],
                channels=[START],
-                writers=[
-                    ChannelWrite(
-                        write_entries,
-                        tags=[TAG_HIDDEN],
-                    ),
-                ],
+                writers=[ChannelWrite(write_entries, tags=[TAG_HIDDEN])],
            )
        elif node is not None:
            input_schema = node.input if node else self.builder.schema
            input_values = {k: k for k in self.builder.schemas[input_schema]}
            is_single_input = len(input_values) == 1 and "__root__" in input_values

-            branch_channel = f"branch:to:{key}"
-            self.channels[key] = EphemeralValue(Any, guard=False)
+            branch_channel = CHANNEL_BRANCH_TO.format(key)
            self.channels[branch_channel] = EphemeralValue(Any, guard=False)
            self.nodes[key] = PregelNode(
                triggers=[branch_channel],
@@ -836,13 +840,8 @@ class CompiledStateGraph(CompiledGraph):
                    input_schema,
                    self.builder.type_hints[input_schema],
                ),
-                writers=[
-                    # publish to this channel and state keys
-                    ChannelWrite(
-                        write_entries + [ChannelWriteEntry(key, key)],
-                        tags=[TAG_HIDDEN],
-                    ),
-                ],
+                # publish to state keys
+                writers=[ChannelWrite(write_entries, tags=[TAG_HIDDEN])],
                metadata=node.metadata,
                retry_policy=node.retry_policy,
                bound=node.runnable,
@@ -852,21 +851,13 @@ class CompiledStateGraph(CompiledGraph):

    def attach_edge(self, starts: Union[str, Sequence[str]], end: str) -> None:
        if isinstance(starts, str):
-            if starts == START:
-                channel_name = f"start:{end}"
-                # register channel
-                self.channels[channel_name] = EphemeralValue(Any)
-                # subscribe to channel
-                self.nodes[end].triggers.append(channel_name)
-                # publish to channel
-                self.nodes[START].writers.append(
+            # subscribe to start channel
+            if end != END:
+                self.nodes[starts].writers.append(
                    ChannelWrite(
-                        [ChannelWriteEntry(channel_name, START)], tags=[TAG_HIDDEN]
+                        (ChannelWriteEntry(CHANNEL_BRANCH_TO.format(end), None),)
                    )
                )
-            elif end != END:
-                # subscribe to start channel
-                self.nodes[end].triggers.append(starts)
        elif end != END:
            channel_name = f"join:{'+'.join(starts)}:{end}"
            # register channel
@@ -877,7 +868,7 @@ class CompiledStateGraph(CompiledGraph):
            for start in starts:
                self.nodes[start].writers.append(
                    ChannelWrite(
-                        [ChannelWriteEntry(channel_name, start)], tags=[TAG_HIDDEN]
+                        (ChannelWriteEntry(channel_name, start),), tags=[TAG_HIDDEN]
                    )
                )

@@ -890,7 +881,7 @@ class CompiledStateGraph(CompiledGraph):
            if filtered := [p for p in packets if p != END]:
                writes = [
                    (
-                        ChannelWriteEntry(f"branch:to:{p}", start)
+                        ChannelWriteEntry(CHANNEL_BRANCH_TO.format(p), None)
                        if not isinstance(p, Send)
                        else p
                    )
@@ -941,6 +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]
@@ -965,7 +1060,7 @@ def _pick_mapper(
        if issubclass(schema, dict):
            return None
        if issubclass(schema, (BaseModel, BaseModelV1)):
-            return SchemaCoercionMapper(schema, type_hints)
+            return SchemaCoercionMapper(schema, type_hints=type_hints)
    return partial(_coerce_state, schema)


@@ -1166,3 +1261,6 @@ def _get_schema(
                    if k in channels and isinstance(channels[k], BaseChannel)
                },
            )
+
+
+CHANNEL_BRANCH_TO = "branch:to:{}"
@@ -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
@@ -39,7 +39,6 @@ from langchain_core.runnables.utils import (
    ConfigurableFieldSpec,
    get_unique_config_specs,
 )
-from langchain_core.tracers._streaming import _StreamingCallbackHandler
 from pydantic import BaseModel
 from typing_extensions import Self

@@ -48,13 +47,13 @@ from langgraph.channels.base import (
 )
 from langgraph.checkpoint.base import (
    BaseCheckpointSaver,
+    Checkpoint,
    CheckpointTuple,
    copy_checkpoint,
-    create_checkpoint,
-    empty_checkpoint,
 )
 from langgraph.constants import (
    CONF,
+    CONFIG_KEY_CHECKPOINT_DURING,
    CONFIG_KEY_CHECKPOINT_ID,
    CONFIG_KEY_CHECKPOINT_NS,
    CONFIG_KEY_CHECKPOINTER,
@@ -91,6 +90,7 @@ from langgraph.pregel.algo import (
    local_write,
    prepare_next_tasks,
 )
+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
@@ -125,6 +125,11 @@ from langgraph.utils.fields import get_enhanced_type_hints
 from langgraph.utils.pydantic import create_model, is_supported_by_pydantic
 from langgraph.utils.queue import AsyncQueue, SyncQueue  # type: ignore[attr-defined]

+try:
+    from langchain_core.tracers._streaming import _StreamingCallbackHandler
+except ImportError:
+    _StreamingCallbackHandler = None  # type: ignore
+
 WriteValue = Union[Callable[[Input], Output], Any]


@@ -767,6 +772,10 @@ 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 +794,9 @@ class Pregel(PregelProtocol):
                tasks=(),
            )

+        # migrate checkpoint if needed
+        self._migrate_checkpoint(saved.checkpoint)
+
        with ChannelsManager(
            self.channels,
            saved.checkpoint,
@@ -898,6 +910,9 @@ class Pregel(PregelProtocol):
                tasks=(),
            )

+        # migrate checkpoint if needed
+        self._migrate_checkpoint(saved.checkpoint)
+
        async with AsyncChannelsManager(
            self.channels,
            saved.checkpoint,
@@ -1223,6 +1238,8 @@ 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()
            )
@@ -1535,12 +1552,9 @@ class Pregel(PregelProtocol):
                            ),
                            CONFIG_KEY_READ: partial(
                                local_read,
-                                step + 1,
-                                checkpoint,
                                channels,
                                managed,
                                task,
-                                config,
                            ),
                        },
                    ),
@@ -1636,6 +1650,8 @@ 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()
            )
@@ -1944,12 +1960,9 @@ class Pregel(PregelProtocol):
                            ),
                            CONFIG_KEY_READ: partial(
                                local_read,
-                                step + 1,
-                                checkpoint,
                                channels,
                                managed,
                                task,
-                                config,
                            ),
                        },
                    ),
@@ -2086,6 +2099,7 @@ class Pregel(PregelProtocol):
        output_keys: Optional[Union[str, Sequence[str]]] = None,
        interrupt_before: Optional[Union[All, Sequence[str]]] = None,
        interrupt_after: Optional[Union[All, Sequence[str]]] = None,
+        checkpoint_during: Optional[bool] = None,
        debug: Optional[bool] = None,
        subgraphs: bool = False,
    ) -> Iterator[Union[dict[str, Any], Any]]:
@@ -2107,6 +2121,7 @@ class Pregel(PregelProtocol):
            output_keys: The keys to stream, defaults to all non-context channels.
            interrupt_before: Nodes to interrupt before, defaults to all nodes in the graph.
            interrupt_after: Nodes to interrupt after, defaults to all nodes in the graph.
+            checkpoint_during: Whether to checkpoint intermediate steps, defaults to True. If False, only the final checkpoint is saved.
            debug: Whether to print debug information during execution, defaults to False.
            subgraphs: Whether to stream subgraphs, defaults to False.

@@ -2268,6 +2283,9 @@ class Pregel(PregelProtocol):
                config[CONF][CONFIG_KEY_STREAM_WRITER] = lambda c: stream.put(
                    ((), "custom", c)
                )
+            # set checkpointing mode for subgraphs
+            if checkpoint_during is not None:
+                config[CONF][CONFIG_KEY_CHECKPOINT_DURING] = checkpoint_during
            with SyncPregelLoop(
                input,
                input_model=self.input_model,
@@ -2283,7 +2301,11 @@ class Pregel(PregelProtocol):
                interrupt_after=interrupt_after_,
                manager=run_manager,
                debug=debug,
+                checkpoint_during=checkpoint_during
+                if checkpoint_during is not None
+                else config[CONF].get(CONFIG_KEY_CHECKPOINT_DURING, True),
                trigger_to_nodes=self.trigger_to_nodes,
+                migrate_checkpoint=self._migrate_checkpoint,
            ) as loop:
                # create runner
                runner = PregelRunner(
@@ -2364,6 +2386,7 @@ class Pregel(PregelProtocol):
        output_keys: Optional[Union[str, Sequence[str]]] = None,
        interrupt_before: Optional[Union[All, Sequence[str]]] = None,
        interrupt_after: Optional[Union[All, Sequence[str]]] = None,
+        checkpoint_during: Optional[bool] = None,
        debug: Optional[bool] = None,
        subgraphs: bool = False,
    ) -> AsyncIterator[Union[dict[str, Any], Any]]:
@@ -2385,6 +2408,7 @@ class Pregel(PregelProtocol):
            output_keys: The keys to stream, defaults to all non-context channels.
            interrupt_before: Nodes to interrupt before, defaults to all nodes in the graph.
            interrupt_after: Nodes to interrupt after, defaults to all nodes in the graph.
+            checkpoint_during: Whether to checkpoint intermediate steps, defaults to True. If False, only the final checkpoint is saved.
            debug: Whether to print debug information during execution, defaults to False.
            subgraphs: Whether to stream subgraphs, defaults to False.

@@ -2520,13 +2544,17 @@ class Pregel(PregelProtocol):
            run_id=config.get("run_id"),
        )
        # if running from astream_log() run each proc with streaming
-        do_stream = next(
-            (
-                cast(_StreamingCallbackHandler, h)
-                for h in run_manager.handlers
-                if isinstance(h, _StreamingCallbackHandler)
-            ),
-            None,
+        do_stream = (
+            next(
+                (
+                    cast(_StreamingCallbackHandler, h)
+                    for h in run_manager.handlers
+                    if isinstance(h, _StreamingCallbackHandler)
+                ),
+                None,
+            )
+            if _StreamingCallbackHandler is not None
+            else False
        )
        try:
            # assign defaults
@@ -2562,6 +2590,9 @@ class Pregel(PregelProtocol):
                        stream.put_nowait, ((), "custom", c)
                    )
                )
+            # set checkpointing mode for subgraphs
+            if checkpoint_during is not None:
+                config[CONF][CONFIG_KEY_CHECKPOINT_DURING] = checkpoint_during
            async with AsyncPregelLoop(
                input,
                input_model=self.input_model,
@@ -2577,12 +2608,11 @@ 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),
+                checkpoint_during=checkpoint_during
+                if checkpoint_during is not None
+                else config[CONF].get(CONFIG_KEY_CHECKPOINT_DURING, True),
+                trigger_to_nodes=self.trigger_to_nodes,
+                migrate_checkpoint=self._migrate_checkpoint,
            ) as loop:
                # create runner
                runner = PregelRunner(
@@ -2656,6 +2686,7 @@ class Pregel(PregelProtocol):
        output_keys: Optional[Union[str, Sequence[str]]] = None,
        interrupt_before: Optional[Union[All, Sequence[str]]] = None,
        interrupt_after: Optional[Union[All, Sequence[str]]] = None,
+        checkpoint_during: Optional[bool] = None,
        debug: Optional[bool] = None,
        **kwargs: Any,
    ) -> Union[dict[str, Any], Any]:
@@ -2687,6 +2718,7 @@ class Pregel(PregelProtocol):
            output_keys=output_keys,
            interrupt_before=interrupt_before,
            interrupt_after=interrupt_after,
+            checkpoint_during=checkpoint_during,
            debug=debug,
            **kwargs,
        ):
@@ -2708,6 +2740,7 @@ class Pregel(PregelProtocol):
        output_keys: Optional[Union[str, Sequence[str]]] = None,
        interrupt_before: Optional[Union[All, Sequence[str]]] = None,
        interrupt_after: Optional[Union[All, Sequence[str]]] = None,
+        checkpoint_during: Optional[bool] = None,
        debug: Optional[bool] = None,
        **kwargs: Any,
    ) -> Union[dict[str, Any], Any]:
@@ -2740,6 +2773,7 @@ class Pregel(PregelProtocol):
            output_keys=output_keys,
            interrupt_before=interrupt_before,
            interrupt_after=interrupt_after,
+            checkpoint_during=checkpoint_during,
            debug=debug,
            **kwargs,
        ):
@@ -1,6 +1,7 @@
 import binascii
 import itertools
 import sys
+import threading
 from collections import defaultdict, deque
 from functools import partial
 from hashlib import sha1
@@ -32,7 +33,6 @@ from langgraph.checkpoint.base import (
    Checkpoint,
    PendingWrite,
    V,
-    copy_checkpoint,
 )
 from langgraph.constants import (
    CONF,
@@ -68,12 +68,10 @@ from langgraph.managed.base import ManagedValueMapping
 from langgraph.pregel.call import get_runnable_for_task
 from langgraph.pregel.io import read_channel, read_channels
 from langgraph.pregel.log import logger
-from langgraph.pregel.manager import ChannelsManager
 from langgraph.pregel.read import PregelNode
 from langgraph.store.base import BaseStore
 from langgraph.types import (
    All,
-    LoopProtocol,
    PregelExecutableTask,
    PregelScratchpad,
    PregelTask,
@@ -168,39 +166,39 @@ def should_interrupt(


 def local_read(
-    step: int,
-    checkpoint: Checkpoint,
    channels: Mapping[str, BaseChannel],
    managed: ManagedValueMapping,
    task: WritesProtocol,
-    config: RunnableConfig,
    select: Union[list[str], str],
    fresh: bool = False,
 ) -> Union[dict[str, Any], Any]:
    """Function injected under CONFIG_KEY_READ in task config, to read current state.
    Used by conditional edges to read a copy of the state with reflecting the writes
    from that node only."""
+    updated: dict[str, list[Any]] = defaultdict(list)
    if isinstance(select, str):
        managed_keys = []
-        for c, _ in task.writes:
+        for c, v in task.writes:
            if c == select:
-                updated = {c}
-                break
-        else:
-            updated = set()
+                updated[c].append(v)
    else:
        managed_keys = [k for k in select if k in managed]
        select = [k for k in select if k not in managed]
-        updated = set(select).intersection(c for c, _ in task.writes)
+        for c, v in task.writes:
+            if c in select:
+                updated[c].append(v)
    if fresh and updated:
-        with ChannelsManager(
-            {k: v for k, v in channels.items() if k in updated},
-            checkpoint,
-            LoopProtocol(config=config, step=step, stop=step + 1),
-            skip_context=True,
-        ) as (local_channels, _):
-            apply_writes(copy_checkpoint(checkpoint), local_channels, [task], None)
-            values = read_channels({**channels, **local_channels}, select)
+        # apply writes
+        local_channels: dict[str, BaseChannel] = {}
+        for k in channels:
+            if k in updated:
+                cc = channels[k].copy()
+                cc.update(updated[k])
+            else:
+                cc = channels[k]
+            local_channels[k] = cc
+        # read fresh values
+        values = read_channels(local_channels, select)
    else:
        values = read_channels(channels, select)
    if managed_keys:
@@ -334,6 +332,17 @@ def apply_writes(
    return pending_writes_by_managed, updated_channels


+def has_next_tasks(
+    trigger_to_nodes: Mapping[str, Sequence[str]],
+    updated_channels: set[str],
+    checkpoint: Checkpoint,
+) -> bool:
+    """Check if there are any tasks that should be run in the next step."""
+    return bool(checkpoint["pending_sends"]) or not updated_channels.isdisjoint(
+        trigger_to_nodes
+    )
+
+
@overload
 def prepare_next_tasks(
    checkpoint: Checkpoint,
@@ -561,12 +570,9 @@ def prepare_single_task(
                        ),
                        CONFIG_KEY_READ: partial(
                            local_read,
-                            step,
-                            checkpoint,
                            channels,
                            managed,
                            PregelTaskWrites(task_path[:3], name, writes, triggers),
-                            config,
                        ),
                        CONFIG_KEY_STORE: (store or configurable.get(CONFIG_KEY_STORE)),
                        CONFIG_KEY_CHECKPOINTER: (
@@ -666,14 +672,11 @@ def prepare_single_task(
                            ),
                            CONFIG_KEY_READ: partial(
                                local_read,
-                                step,
-                                checkpoint,
                                channels,
                                managed,
                                PregelTaskWrites(
                                    task_path[:3], packet.node, writes, triggers
                                ),
-                                config,
                            ),
                            CONFIG_KEY_STORE: (
                                store or configurable.get(CONFIG_KEY_STORE)
@@ -788,8 +791,6 @@ def prepare_single_task(
                                ),
                                CONFIG_KEY_READ: partial(
                                    local_read,
-                                    step,
-                                    checkpoint,
                                    channels,
                                    managed,
                                    PregelTaskWrites(
@@ -798,7 +799,6 @@ def prepare_single_task(
                                        writes,
                                        triggers,
                                    ),
-                                    config,
                                ),
                                CONFIG_KEY_STORE: (
                                    store or configurable.get(CONFIG_KEY_STORE)
@@ -869,11 +869,30 @@ def _scratchpad(
    pending_writes: list[PendingWrite],
    task_id: str,
 ) -> PregelScratchpad:
-    # None cannot be used as a resume value, because it would be difficult to
-    # distinguish from missing when used over http
-    null_resume_write = next(
-        (w for w in pending_writes if w[0] == NULL_TASK_ID and w[1] == RESUME), None
-    )
+    if len(pending_writes) > 0:
+        # find global resume value
+        for w in pending_writes:
+            if w[0] == NULL_TASK_ID and w[1] == RESUME:
+                null_resume_write = w
+                break
+        else:
+            # None cannot be used as a resume value, because it would be difficult to
+            # distinguish from missing when used over http
+            null_resume_write = None
+        # find task-specific resume value
+        for w in pending_writes:
+            if w[0] == task_id and w[1] == RESUME:
+                task_resume_write = w[2]
+                if not isinstance(task_resume_write, list):
+                    task_resume_write = [task_resume_write]
+                break
+        else:
+            task_resume_write = []
+        # clear var
+        del w
+    else:
+        null_resume_write = None
+        task_resume_write = []

    def get_null_resume(consume: bool = False) -> Any:
        if null_resume_write is None:
@@ -891,15 +910,13 @@ def _scratchpad(
    # using itertools.count as an atomic counter (+= 1 is not thread-safe)
    return PregelScratchpad(
        # call
-        call_counter=itertools.count(0).__next__,
+        call_counter=LazyAtomicCounter(),
        # interrupt
-        interrupt_counter=itertools.count(0).__next__,
-        resume=next(
-            (w[2] for w in pending_writes if w[0] == task_id and w[1] == RESUME), []
-        ),
+        interrupt_counter=LazyAtomicCounter(),
+        resume=task_resume_write,
        get_null_resume=get_null_resume,
        # subgraph
-        subgraph_counter=itertools.count(0).__next__,
+        subgraph_counter=LazyAtomicCounter(),
    )


@@ -973,3 +990,22 @@ def task_path_str(tup: Union[str, int, tuple]) -> str:
        if isinstance(tup, int)
        else str(tup)
    )
+
+
+LAZY_ATOMIC_COUNTER_LOCK = threading.Lock()
+
+
+class LazyAtomicCounter:
+    __slots__ = ("_counter",)
+
+    _counter: Optional[Callable[[], int]]
+
+    def __init__(self) -> None:
+        self._counter = None
+
+    def __call__(self) -> int:
+        if self._counter is None:
+            with LAZY_ATOMIC_COUNTER_LOCK:
+                if self._counter is None:
+                    self._counter = itertools.count(0).__next__
+        return self._counter()
@@ -0,0 +1,51 @@
+from datetime import datetime, timezone
+from typing import Mapping, Optional
+
+from langgraph.channels.base import BaseChannel
+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,
+    channels: Optional[Mapping[str, BaseChannel]],
+    step: int,
+    *,
+    id: Optional[str] = None,
+) -> Checkpoint:
+    """Create a checkpoint for the given channels."""
+    ts = datetime.now(timezone.utc).isoformat()
+    if channels is None:
+        values = checkpoint["channel_values"]
+    else:
+        values = {}
+        for k in channels:
+            if k not in checkpoint["channel_versions"]:
+                continue
+            v = channels[k].checkpoint()
+            if v is not MISSING:
+                values[k] = v
+    return Checkpoint(
+        v=LATEST_VERSION,
+        ts=ts,
+        id=id or str(uuid6(clock_seq=step)),
+        channel_values=values,
+        channel_versions=checkpoint["channel_versions"],
+        versions_seen=checkpoint["versions_seen"],
+        pending_sends=checkpoint.get("pending_sends", []),
+    )
@@ -156,10 +156,16 @@ class AsyncBackgroundExecutor(AsyncContextManager):
        if self.semaphore:
            coro = gated(self.semaphore, coro)
        if CONTEXT_NOT_SUPPORTED:
-            task = run_coroutine_threadsafe(coro, self.loop, name=__name__)
+            task = run_coroutine_threadsafe(
+                coro, self.loop, name=__name__, lazy=__next_tick__
+            )
        else:
            task = run_coroutine_threadsafe(
-                coro, self.loop, name=__name__, context=copy_context()
+                coro,
+                self.loop,
+                name=__name__,
+                context=copy_context(),
+                lazy=__next_tick__,
            )
        self.tasks[task] = (__cancel_on_exit__, __reraise_on_exit__)
        task.add_done_callback(self.done)
@@ -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,
@@ -38,8 +39,6 @@ from langgraph.checkpoint.base import (
    CheckpointTuple,
    PendingWrite,
    copy_checkpoint,
-    create_checkpoint,
-    empty_checkpoint,
 )
 from langgraph.constants import (
    CONF,
@@ -64,6 +63,7 @@ from langgraph.constants import (
    RESUME,
    SCHEDULED,
    TAG_HIDDEN,
+    TASKS,
 )
 from langgraph.errors import (
    CheckpointNotLatest,
@@ -88,6 +88,7 @@ from langgraph.pregel.algo import (
    should_interrupt,
    task_path_str,
 )
+from langgraph.pregel.checkpoint import create_checkpoint, empty_checkpoint
 from langgraph.pregel.debug import (
    map_debug_checkpoint,
    map_debug_task_results,
@@ -155,6 +156,8 @@ class PregelLoop(LoopProtocol):
    manager: Union[None, AsyncParentRunManager, ParentRunManager]
    interrupt_after: Union[All, Sequence[str]]
    interrupt_before: Union[All, Sequence[str]]
+    checkpoint_during: bool
+    debug: bool

    checkpointer_get_next_version: GetNextVersion
    checkpointer_put_writes: Optional[
@@ -173,10 +176,12 @@ class PregelLoop(LoopProtocol):
            Any,
        ]
    ]
+    _migrate_checkpoint: Optional[Callable[[Checkpoint], None]]
    submit: Submit
    channels: Mapping[str, BaseChannel]
    managed: ManagedValueMapping
    checkpoint: Checkpoint
+    checkpoint_id_saved: str
    checkpoint_ns: tuple[str, ...]
    checkpoint_config: RunnableConfig
    checkpoint_metadata: CheckpointMetadata
@@ -210,7 +215,9 @@ 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_during: bool = True,
    ) -> None:
        super().__init__(
            step=0,
@@ -234,7 +241,9 @@ 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_during = checkpoint_during
        self.debug = debug
        if self.stream is not None and CONFIG_KEY_STREAM in config[CONF]:
            self.stream = DuplexStream(self.stream, config[CONF][CONFIG_KEY_STREAM])
@@ -287,29 +296,19 @@ class PregelLoop(LoopProtocol):
        """Put writes for a task, to be read by the next tick."""
        if not writes:
            return
+        # always checkpoint writes containing Send, as they are fetched from the
+        # parent checkpoint, not the current one
+        checkpoint_during = self.checkpoint_during or any(w[0] == TASKS for w in writes)
        # deduplicate writes to special channels, last write wins
        if all(w[0] in WRITES_IDX_MAP for w in writes):
            writes = list({w[0]: w for w in writes}.values())
+        # remove existing writes for this task
+        self.checkpoint_pending_writes = [
+            w for w in self.checkpoint_pending_writes if w[0] != task_id
+        ]
        # save writes
-        for c, v in writes:
-            if (
-                c in WRITES_IDX_MAP
-                and (
-                    idx := next(
-                        (
-                            i
-                            for i, w in enumerate(self.checkpoint_pending_writes)
-                            if w[0] == task_id and w[1] == c
-                        ),
-                        None,
-                    )
-                )
-                is not None
-            ):
-                self.checkpoint_pending_writes[idx] = (task_id, c, v)
-            else:
-                self.checkpoint_pending_writes.append((task_id, c, v))
-        if self.checkpointer_put_writes is not None:
+        self.checkpoint_pending_writes.extend((task_id, c, v) for c, v in writes)
+        if checkpoint_during and self.checkpointer_put_writes is not None:
            config = patch_configurable(
                self.checkpoint_config,
                {
@@ -342,6 +341,46 @@ class PregelLoop(LoopProtocol):
        if hasattr(self, "tasks"):
            self._output_writes(task_id, writes)

+    def _put_pending_writes(self) -> None:
+        if self.checkpointer_put_writes is None:
+            return
+        if not self.checkpoint_pending_writes:
+            return
+        # patch config
+        config = patch_configurable(
+            self.checkpoint_config,
+            {
+                CONFIG_KEY_CHECKPOINT_NS: self.config[CONF].get(
+                    CONFIG_KEY_CHECKPOINT_NS, ""
+                ),
+                CONFIG_KEY_CHECKPOINT_ID: self.checkpoint["id"],
+            },
+        )
+        # group by task id
+        by_task = defaultdict(list)
+        for task_id, channel, value in self.checkpoint_pending_writes:
+            by_task[task_id].append((channel, value))
+        # submit writes to checkpointer
+        for task_id, writes in by_task.items():
+            if self.checkpointer_put_writes_accepts_task_path and hasattr(
+                self, "tasks"
+            ):
+                task = self.tasks.get(task_id)
+                self.submit(
+                    self.checkpointer_put_writes,
+                    config,
+                    writes,
+                    task_id,
+                    task_path_str(task.path) if task else "",
+                )
+            else:
+                self.submit(
+                    self.checkpointer_put_writes,
+                    config,
+                    writes,
+                    task_id,
+                )
+
    def accept_push(
        self, task: PregelExecutableTask, write_idx: int, call: Optional[Call] = None
    ) -> Optional[PregelExecutableTask]:
@@ -703,27 +742,44 @@ class PregelLoop(LoopProtocol):
        return updated_channels

    def _put_checkpoint(self, metadata: CheckpointMetadata) -> None:
-        for k, v in self.config["metadata"].items():
-            metadata.setdefault(k, v)  # type: ignore
        # assign step and parents
-        metadata["step"] = self.step
-        metadata["parents"] = self.config[CONF].get(CONFIG_KEY_CHECKPOINT_MAP, {})
-        # debug flag
-        if self.debug:
-            print_step_checkpoint(
-                metadata,
-                self.channels,
-                (
-                    [self.stream_keys]
-                    if isinstance(self.stream_keys, str)
-                    else self.stream_keys
-                ),
-            )
-        # create new checkpoint
-        self.checkpoint = create_checkpoint(self.checkpoint, self.channels, self.step)
-        # bail if no checkpointer
-        if self._checkpointer_put_after_previous is not None:
+        exiting = metadata is self.checkpoint_metadata
+        if exiting and self.checkpoint["id"] == self.checkpoint_id_saved:
+            # checkpoint already saved
+            return
+        if not exiting:
+            metadata["step"] = self.step
+            metadata["parents"] = self.config[CONF].get(CONFIG_KEY_CHECKPOINT_MAP, {})
            self.checkpoint_metadata = metadata
+            # debug flag
+            if self.debug:
+                print_step_checkpoint(
+                    metadata,
+                    self.channels,
+                    (
+                        [self.stream_keys]
+                        if isinstance(self.stream_keys, str)
+                        else self.stream_keys
+                    ),
+                )
+            self.checkpoint_id_prev = self.checkpoint["id"] if self.step > -1 else None
+        # do checkpoint?
+        do_checkpoint = self._checkpointer_put_after_previous is not None and (
+            exiting or self.checkpoint_during
+        )
+        # create new checkpoint
+        self.checkpoint = create_checkpoint(
+            self.checkpoint,
+            self.channels if do_checkpoint else None,
+            self.step,
+            id=self.checkpoint["id"] if exiting else None,
+        )
+        # bail if no checkpointer
+        if do_checkpoint and 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

            self.prev_checkpoint_config = (
                self.checkpoint_config
@@ -735,6 +791,8 @@ class PregelLoop(LoopProtocol):
                **self.checkpoint_config,
                CONF: {
                    **self.checkpoint_config[CONF],
+                    # this is guaranteed to be set by code above
+                    CONFIG_KEY_CHECKPOINT_ID: self.checkpoint_id_prev,
                    CONFIG_KEY_CHECKPOINT_NS: self.config[CONF].get(
                        CONFIG_KEY_CHECKPOINT_NS, ""
                    ),
@@ -765,8 +823,9 @@ class PregelLoop(LoopProtocol):
                    CONFIG_KEY_CHECKPOINT_ID: self.checkpoint["id"],
                },
            }
-        # increment step
-        self.step += 1
+        if not exiting:
+            # increment step
+            self.step += 1

    def _update_mv(self, key: str, values: Sequence[Any]) -> None:
        raise NotImplementedError
@@ -777,6 +836,10 @@ class PregelLoop(LoopProtocol):
        exc_value: Optional[BaseException],
        traceback: Optional[TracebackType],
    ) -> Optional[bool]:
+        # persist current checkpoint and writes
+        if not self.checkpoint_during:
+            self._put_checkpoint(self.checkpoint_metadata)
+            self._put_pending_writes()
        # suppress interrupt
        suppress = isinstance(exc_value, GraphInterrupt) and not self.is_nested
        if suppress:
@@ -893,7 +956,9 @@ 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,
+        checkpoint_during: bool = True,
    ) -> None:
        super().__init__(
            input,
@@ -910,7 +975,9 @@ class SyncPregelLoop(PregelLoop, ContextManager):
            interrupt_before=interrupt_before,
            manager=manager,
            debug=debug,
+            migrate_checkpoint=migrate_checkpoint,
            trigger_to_nodes=trigger_to_nodes,
+            checkpoint_during=checkpoint_during,
        )
        self.stack = ExitStack()
        if checkpointer:
@@ -978,6 +1045,8 @@ 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,
@@ -988,6 +1057,7 @@ class SyncPregelLoop(PregelLoop, ContextManager):
            },
        }
        self.prev_checkpoint_config = saved.parent_config
+        self.checkpoint_id_saved = saved.checkpoint["id"]
        self.checkpoint = saved.checkpoint
        self.checkpoint_metadata = saved.metadata
        self.checkpoint_pending_writes = (
@@ -1036,7 +1106,9 @@ 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,
+        checkpoint_during: bool = True,
    ) -> None:
        super().__init__(
            input,
@@ -1053,7 +1125,9 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
            interrupt_before=interrupt_before,
            manager=manager,
            debug=debug,
+            migrate_checkpoint=migrate_checkpoint,
            trigger_to_nodes=trigger_to_nodes,
+            checkpoint_during=checkpoint_during,
        )
        self.stack = AsyncExitStack()
        if checkpointer:
@@ -1121,6 +1195,8 @@ 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,
@@ -1131,6 +1207,7 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager):
            },
        }
        self.prev_checkpoint_config = saved.parent_config
+        self.checkpoint_id_saved = saved.checkpoint["id"]
        self.checkpoint = saved.checkpoint
        self.checkpoint_metadata = saved.metadata
        self.checkpoint_pending_writes = (
@@ -4,6 +4,7 @@ from typing import AsyncIterator, Iterator, Mapping, Union

 from langgraph.channels.base import BaseChannel
 from langgraph.checkpoint.base import Checkpoint
+from langgraph.constants import MISSING
 from langgraph.managed.base import (
    ConfiguredManagedValue,
    ManagedValueMapping,
@@ -36,7 +37,7 @@ def ChannelsManager(
    with ExitStack() as stack:
        yield (
            {
-                k: v.from_checkpoint(checkpoint["channel_values"].get(k))
+                k: v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
                for k, v in channel_specs.items()
            },
            ManagedValueMapping(
@@ -90,7 +91,7 @@ async def AsyncChannelsManager(
        yield (
            # channels: enter each channel with checkpoint
            {
-                k: v.from_checkpoint(checkpoint["channel_values"].get(k))
+                k: v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
                for k, v in channel_specs.items()
            },
            # managed: build mapping from spec to result
--- a/Show More
+++ b/Show More
				`@@ -0,0 +1 @@`
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