docs: fix langgraph dataplane docs to remove license key

docs/docs/cloud/deployment/cloud.md

@@ -62,15 +62,6 @@ Starting from the `LangGraph Platform` view...
 1. In the panel, select the `Server` tab to view server logs for the revision. Server logs are only available after a revision has been deployed.
 1. Within the `Server` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 7 days`.
 
-## View Deployment Metrics
-
-Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
-
-1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
-1. Select an existing deployment to monitor.
-1. Select the `Monitoring` tab to view the deployment metrics. See a list of [all available metrics](../../concepts/langgraph_control_plane.md#monitoring).
-1. Within the `Monitoring` tab, use the date/time range picker as needed. By default, the date/time range picker is set to the `Last 15 minutes`.
-
 ## Interrupt Revision
 
 Interrupting a revision will stop deployment of the revision.

docs/docs/cloud/deployment/self_hosted_control_plane.md

@@ -30,17 +30,18 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
     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. Use the images that are specified in the latest release.
+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 config file for langsmith (usually `langsmith_config.yaml`, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
-
+1. In your `values.yaml` file, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
         config:
           langgraphPlatform:
             enabled: true

docs/docs/cloud/deployment/self_hosted_data_plane.md

@@ -31,7 +31,6 @@ Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](.
 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

docs/docs/cloud/how-tos/studio/manage_assistants.md

@@ -2,7 +2,7 @@
 
 !!! info "Prerequisites"
 
-    - [Assistants Overview](../../../concepts/assistants.md)
+    - [Assistants Overview](../../concepts/assistants.md)
 
 LangGraph Studio lets you view, edit, and update your assistants, and allows you to run your graph using these assistant configurations.
 

docs/docs/cloud/how-tos/studio/run_evals.md

@@ -1,57 +0,0 @@
-# Run experiments over a dataset
-
-LangGraph Studio supports evaluations by allowing you to run your assistant over a pre-defined LangSmith dataset. This enables you to understand how your application performs over a variety of inputs, compare the results to reference outputs, and score the results using [evaluators](../../../agents/evals.md).
-
-This guide shows you how to run an experiment end-to-end from Studio.
-
----
-
-## Prerequisites
-
-Before running an experiment, ensure you have the following:
-
-1.  **A LangSmith dataset**: Your dataset should contain the inputs you want to test and optionally, reference outputs for comparison.
-
-    - The schema for the inputs must match the required input schema for the assistant. For more information on schemas, see [here](../../../concepts/low_level.md#schema).
-    - For more on creating datasets, see [How to Manage Datasets](https://docs.smith.langchain.com/evaluation/how_to_guides/manage_datasets_in_application#set-up-your-dataset).
-
-2.  **(Optional) Evaluators**: You can attach evaluators (e.g., LLM-as-a-Judge, heuristics, or custom functions) to your dataset in LangSmith. These will run automatically after the graph has processed all inputs.
-
-    - To learn more, read about [Evaluation Concepts](https://docs.smith.langchain.com/evaluation/concepts#evaluators).
-
-3.  **A running application**: The experiment can be run against:
-    - An application deployed on [LangGraph Platform](../../quick_start.md).
-    - A locally running application started via the [langgraph-cli](../../../tutorials/langgraph-platform/local-server.md).
-
----
-
-## Step-by-step guide
-
-### 1. Launch the experiment
-
-Click the **Run experiment** button in the top right corner of the Studio page.
-
-### 2. Select your dataset
-
-In the modal that appears, select the dataset (or a specific dataset split) to use for the experiment and click **Start**.
-
-### 3. Monitor the progress
-
-All of the inputs in the dataset will now be run against the active assistant. Monitor the experiment's progress via the badge in the top right corner.
-
-You can continue to work in Studio while the experiment runs in the background. Click the arrow icon button at any time to navigate to LangSmith and view the detailed experiment results.
-
----
-
-## Troubleshooting
-
-### "Run experiment" button is disabled
-
-If the "Run experiment" button is disabled, check the following:
-
-- **Deployed application**: If your application is deployed on LangGraph Platform, you may need to create a new revision to enable this feature.
-- **Local development server**: If you are running your application locally, make sure you have upgraded to the latest version of the `langgraph-cli` (`pip install -U langgraph-cli`). Additionally, ensure you have tracing enabled by setting the `LANGSMITH_API_KEY` in your project's `.env` file.
-
-### Evaluator results are missing
-
-When you run an experiment, any attached evaluators are scheduled for execution in a queue. If you don't see results immediately, it likely means they are still pending.

docs/docs/cloud/reference/cli.md

@@ -50,7 +50,6 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | <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;">`pip_installer`</span> | _(Added in v0.3)_ Optional. Python package installer selector. It can be set to `"auto"`, `"pip"`, or `"uv"`. From version&nbsp;0.3 onward the default strategy is to run `uv pip`, which typically delivers faster builds while remaining a drop-in replacement. In the uncommon situation where `uv` cannot handle your dependency graph or the structure of your `pyproject.toml`, specify `"pip"` here to revert to the earlier behaviour. |
     | <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><li>`configurable_headers`: Define which request headers to exclude or include as a run's configurable values.</li></ul> |
@@ -129,7 +128,7 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
         - `cohere:embed-english-v3.0`: 1024 
         - `cohere:embed-english-light-v3.0`: 384 
         - `cohere:embed-multilingual-v3.0`: 1024 
-        - `cohere:embed-multilingual-light-v3.0`: 384 
+        - `cohere:embed-multilingual-light-v3.0`: 384
 
     #### Semantic search with a custom embedding function
 
@@ -362,8 +361,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
 
     **Options**
 
-    | Option               | Default          | Description                                                                                                     |
-    | -------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
+    | Option               | Default          | Description                                                                                                                  |
+    | -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
     | `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
     | `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
     | `--pull / --no-pull` | `--pull`         | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Platform API server with locally built images. |
@@ -382,8 +381,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
 
     **Options**
 
-    | Option               | Default          | Description                                                                                                     |
-    | -------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
+    | Option               | Default          | Description                                                                                                                  |
+    | -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
     | `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
     | `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
     | `--no-pull`          |                  | Use locally built images. Defaults to `false` to build with latest remote Docker image.                                      |

docs/docs/concepts/faq.md

@@ -63,8 +63,4 @@ Yes! LangGraph is totally ambivalent to what LLMs are used under the hood. The m
 
 Yes! You can use the [development version of LangGraph Server](../tutorials/langgraph-platform/local-server.md) to run the backend locally.
 This will connect to the studio frontend hosted as part of LangSmith.
-If you set an environment variable of `LANGSMITH_TRACING=false`, then no traces will be sent to LangSmith.
-
-## What does "nodes executed" mean for LangGraph Platform usage?
-
-**Nodes Executed** is the aggregate number of nodes in a LangGraph application that are called and completed successfully during an invocation of the application. If a node in the graph is not called during execution or ends in an error state, these nodes will not be counted. If a node is called and completes successfully multiple times, each occurrence will be counted.
+If you set an environment variable of `LANGSMITH_TRACING=false` then no traces will be sent to LangSmith.

docs/docs/concepts/langgraph_control_plane.md

@@ -19,7 +19,6 @@ From the control plane UI, you can:
 - Update a deployment.
 - Update environment variables for a deployment.
 - View build and server logs of a deployment.
-- View deployment metrics such as CPU and memory usage.
 - Delete a deployment.
 
 The Control Plane UI is embedded in [LangSmith](https://docs.smith.langchain.com/langgraph_cloud).
@@ -89,17 +88,6 @@ Infrastructure for deployments and revisions are provisioned and deployed asynch
 
 The control plane and [LangGraph Data Plane](./langgraph_data_plane.md) "listener" application coordinate to achieve asynchronous deployments.
 
-### Monitoring
-
-After a deployment is ready, the control plane monitors the deployment and records various metrics, such as:
-
-- CPU and memory usage of the deployment.
-- Number of container restarts.
-- Number of replicas (this will increase with [autoscaling](../concepts/langgraph_data_plane.md#autoscaling)).
-- [Postgres](../concepts/langgraph_data_plane.md#postgres) CPU, memory usage, and disk usage.
-
-These metrics are displayed as charts in the Control Plane UI.
-
 ### 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.

docs/docs/concepts/langgraph_server.md

@@ -17,7 +17,7 @@ Use LangGraph Server to create and manage [assistants](assistants.md), [threads]
 
 There are two versions of LangGraph Server:
 
-- `Lite` 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](../concepts/faq.md#what-does-nodes-executed-mean-for-langgraph-platform-usage) per year).
+- `Lite` 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).
 - `Enterprise` 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.
 
 Feature Differences:

docs/docs/concepts/langgraph_studio.md

@@ -21,10 +21,9 @@ Key features of LangGraph Studio:
 
 - Visualize your graph architecture
 - [Run and interact with your agent](../cloud/how-tos/invoke_studio.md)
-- [Manage assistants](../cloud/how-tos/studio/manage_assistants.md)
+- [Manage assistants](../cloud/how-tos/studio/manage_assistants.md.md)
 - [Manage threads](../cloud/how-tos/threads_studio.md)
 - [Iterate on prompts](../cloud/how-tos/iterate_graph_studio.md)
-- [Run experiments over a dataset](../cloud/how-tos/studio/run_evals.md)
 - Manage [long term memory](memory.md)
 - Debug agent state via [time travel](time-travel.md)
 
@@ -42,4 +41,4 @@ Chat mode is a simpler UI for iterating on and testing chat-specific agents. It
 
 ## Learn more
 
-- See this guide on how to [get started](../cloud/how-tos/studio/quick_start.md) with LangGraph Studio.
+- See this guide on how to [get started](../cloud/how-tos/studio/quick_start.md) with LangGraph Studio.

docs/docs/concepts/plans.md

@@ -20,7 +20,7 @@ There are three different plans for using it.
 |                                                                  | Developer                                   | Plus                                                  | Enterprise                                          |
 |------------------------------------------------------------------|---------------------------------------------|-------------------------------------------------------|-----------------------------------------------------|
 | Deployment Options                                               | Standalone Container (Lite)                          | Cloud SaaS                                         | <ul><li>Cloud SaaS</li><li>Self-Hosted Data Plane</li><li>Self-Hosted Control Plane</li><li>Standalone Container (Enterprise)</li></ul> |
-| Usage                                                            | Free, limited to 1M [nodes executed](../concepts/faq.md#what-does-nodes-executed-mean-for-langgraph-platform-usage) per year | See [Pricing](https://www.langchain.com/langgraph-platform-pricing) | Custom                                              |
+| Usage                                                            | Free, limited to 1M nodes executed per year | Free while in Beta, will be charged per node executed | Custom                                              |
 | APIs for retrieving and updating state and conversational history | ✅                                           | ✅                                                     | ✅                                                   |
 | APIs for retrieving and updating long-term memory                | ✅                                           | ✅                                                     | ✅                                                   |
 | Horizontally scalable task queues and servers                    | ✅                                           | ✅                                                     | ✅                                                   |

docs/docs/concepts/sdk.md

@@ -5,7 +5,7 @@ search:
 
 # LangGraph SDK
 
-LangGraph Platform provides both a Python and JS SDK for interacting with [LangGraph Server](./langgraph_server.md).
+LangGraph Platform provides both a Python SDK for interacting with [LangGraph Server](./langgraph_server.md).
 
 !!! tip "Python SDK reference"
   

docs/mkdocs.yml

@@ -179,7 +179,6 @@ nav:
           - cloud/how-tos/studio/manage_assistants.md
           - cloud/how-tos/threads_studio.md
           - cloud/how-tos/iterate_graph_studio.md
-          - cloud/how-tos/studio/run_evals.md
           - cloud/how-tos/clone_traces_studio.md
           - cloud/how-tos/datasets_studio.md
         - LangGraph SDK: concepts/sdk.md
@@ -365,6 +364,16 @@ markdown_extensions:
 hooks:
   - _scripts/notebook_hooks.py
 extra:
+  consent:
+    title: Cookie consent
+    actions:
+      - accept
+      - reject
+    description: >- 
+      We use cookies to recognize your repeated visits and preferences, as well
+      as to measure the effectiveness of our documentation and whether users
+      find what they're searching for. <strong>Clicking "Accept" makes our
+      documentation better. Thank you!</strong> ❤️
   social:
     - icon: fontawesome/brands/js
       link: https://langchain-ai.github.io/langgraphjs/

libs/langgraph/langgraph/pregel/algo.py

@@ -275,7 +275,7 @@ def apply_writes(
             )
 
     # clear pending sends
-    if checkpoint.get("pending_sends") and bump_step:
+    if checkpoint["pending_sends"] and bump_step:
         checkpoint["pending_sends"].clear()
 
     # Group writes by channel
@@ -286,7 +286,7 @@ def apply_writes(
             if chan in (NO_WRITES, PUSH, RESUME, INTERRUPT, RETURN, ERROR):
                 pass
             elif chan == TASKS:
-                checkpoint.setdefault("pending_sends", []).append(val)
+                checkpoint["pending_sends"].append(val)
             elif chan in channels:
                 pending_writes_by_channel[chan].append(val)
             else:
@@ -327,7 +327,7 @@ def apply_writes(
     # If this is (tentatively) the last superstep, notify all channels of finish
     if (
         bump_step
-        and not checkpoint.get("pending_sends")
+        and not checkpoint["pending_sends"]
         and updated_channels.isdisjoint(trigger_to_nodes)
     ):
         for chan in channels:
@@ -344,6 +344,17 @@ 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,
@@ -435,7 +446,7 @@ def prepare_next_tasks(
     null_version = checkpoint_null_version(checkpoint)
     tasks: list[Union[PregelTask, PregelExecutableTask]] = []
     # Consume pending_sends from previous step
-    for idx, _ in enumerate(checkpoint.get("pending_sends", ())):
+    for idx, _ in enumerate(checkpoint["pending_sends"]):
         if task := prepare_single_task(
             (PUSH, idx),
             None,
@@ -637,7 +648,7 @@ def prepare_single_task(
             # SEND tasks, executed in superstep n+1
             # (PUSH, idx of pending send)
             idx = cast(int, task_path[1])
-            if idx >= len(checkpoint.get("pending_sends", ())):
+            if idx >= len(checkpoint["pending_sends"]):
                 return
             packet = checkpoint["pending_sends"][idx]
             if not isinstance(packet, Send):

libs/langgraph/langgraph/pregel/runner.py

@@ -104,8 +104,7 @@ class FuturesDict(Generic[F, E], dict[F, Optional[PregelExecutableTask]]):
         fut: F,
     ) -> None:
         try:
-            if cb := self.callback():
-                cb(task, _exception(fut))
+            self.callback()(task, _exception(fut))  # type: ignore[misc]
         finally:
             with self.lock:
                 self.done.add(fut)

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "0.4.10"
+version = "0.4.8"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.9"

libs/langgraph/uv.lock

@@ -1197,7 +1197,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.4.10"
+version = "0.4.8"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },