first pass at data storage docs

* docs: fix assistants links

* fix another page

docs/docs/cloud/deployment/cloud.md

@@ -62,6 +62,15 @@ 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

@@ -30,18 +30,17 @@ 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.
+1. Two additional images will be used by the chart. Use the images that are specified in the latest release.
 
         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. Note that you must also have a valid ingress setup:
+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:
+
         config:
           langgraphPlatform:
             enabled: true

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

@@ -0,0 +1,57 @@
+# 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/concepts/data_storage_and_privacy.md

@@ -0,0 +1,127 @@
+# Data Storage and Privacy
+
+This document provides a comprehensive overview of what data is stored, collected, and processed when using LangGraph, particularly with the CLI tools like `langgraph dev`.
+
+## What Data is Stored
+
+### CLI Telemetry (Opt-out)
+
+By default, the LangGraph CLI collects minimal analytics data to help improve the tool:
+
+**Data Collected:**
+- CLI command used (e.g., `dev`, `up`, `build`)
+- CLI version
+- Operating system type and version
+- Python version
+- Anonymized parameter usage (boolean flags indicating non-default options were used)
+
+**Data NOT Collected:**
+- Actual parameter values
+- File contents or paths
+- Personal information
+- Code or graph implementations
+- API keys or sensitive data
+
+**How to Opt Out:**
+Set the environment variable `LANGGRAPH_CLI_NO_ANALYTICS=1` to disable all CLI analytics collection.
+
+### LangSmith Integration (Opt-in)
+
+When a `LANGSMITH_API_KEY` is provided (not required):
+- Metadata on number of runs executed
+- Current API version being run
+- Trace data (if tracing is enabled)
+
+This data is only sent when explicitly configured with LangSmith credentials.
+
+### Tracing Data (Opt-in)
+
+When tracing is enabled:
+- Execution traces are logged to the configured tracing backend
+- This requires explicit configuration and is not enabled by default
+
+## What Data is NOT Stored Remotely
+
+- **Checkpoints**: Stored locally in your development environment
+- **Memory store data**: Persisted locally, not transmitted
+- **Graph state**: Remains in your local environment
+- **Application data**: Your actual application logic and data stay local
+
+## Local Data Storage
+
+### Development Mode (`langgraph dev`)
+
+When using `langgraph dev`:
+- State is persisted to a local directory
+- Checkpoints are stored locally for debugging and development
+- No remote storage or transmission of your application data
+
+### Checkpoints and State Persistence
+
+LangGraph automatically persists:
+- **Checkpoints**: Snapshots of graph state at each execution step
+- **Thread data**: Conversation/execution history organized by thread IDs
+- **Graph state**: Node outputs, intermediate results, and execution metadata
+- **Memory/Store data**: Information that persists across multiple threads
+
+**Storage Locations:**
+- **Local development**: Local directory (configurable)
+- **Docker deployment**: Local Docker volumes
+- **LangGraph Platform**: Managed database infrastructure
+
+## Security and Encryption
+
+### Data Encryption
+
+- Checkpointers can optionally encrypt all persisted state
+- Encryption uses AES encryption via `EncryptedSerializer`
+- When `LANGGRAPH_AES_KEY` environment variable is present, encryption is automatically enabled on LangGraph Platform
+
+### Data Retention
+
+- **TTL (Time-to-Live)**: Configurable automatic cleanup of old data
+- **Default TTL**: Can be set in minutes for automatic expiration
+- **Automatic sweeping**: Expired data is automatically removed at configurable intervals
+
+## Privacy Controls
+
+### Environment Variables
+
+Key environment variables for controlling data collection and storage:
+
+- `LANGGRAPH_CLI_NO_ANALYTICS=1`: Disable CLI analytics collection
+- `LANGGRAPH_AES_KEY`: Enable automatic encryption of stored data
+- `LANGSMITH_TRACING=false`: Disable tracing to LangSmith (self-hosted deployments)
+- `LANGSMITH_API_KEY`: Enable LangSmith integration (opt-in)
+
+### Logging Controls
+
+- `LOG_LEVEL`: Control verbosity of logs
+- `LOG_JSON`: Format logs as JSON
+- Various other logging configuration options
+
+## Security Policy
+
+For security vulnerabilities:
+- Report through the huntr.com bounty program
+- LangGraph is in-scope for security bounties
+- Security contact: `security@langchain.dev`
+
+## Best Practices for Privacy
+
+1. **Review Analytics**: Set `LANGGRAPH_CLI_NO_ANALYTICS=1` if you prefer not to share usage analytics
+2. **Enable Encryption**: Use `LANGGRAPH_AES_KEY` for sensitive data
+3. **Configure TTL**: Set appropriate data retention policies
+4. **Monitor Tracing**: Only enable tracing when needed and review what data is being sent
+5. **Environment Variables**: Audit your environment variables to ensure proper privacy controls
+
+## Summary
+
+LangGraph is designed with privacy in mind:
+- Minimal data collection (analytics can be disabled)
+- Local storage by default for development
+- Optional encryption for sensitive data
+- Clear opt-in requirements for external services
+- Comprehensive privacy controls through environment variables
+
+Your application data, checkpoints, and state remain under your control and are not transmitted unless you explicitly configure external services like LangSmith.

docs/docs/concepts/langgraph_control_plane.md

@@ -19,6 +19,7 @@ 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).
@@ -88,6 +89,17 @@ 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_studio.md

@@ -21,9 +21,10 @@ 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.md)
+- [Manage assistants](../cloud/how-tos/studio/manage_assistants.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)
 
@@ -41,4 +42,4 @@ 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/sdk.md

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

docs/mkdocs.yml

@@ -179,10 +179,12 @@ 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
       - Data management:
+        - Data storage & privacy: concepts/data_storage_and_privacy.md
         - Add semantic search: cloud/deployment/semantic_search.md
         - Add TTLs: how-tos/ttl/configure_ttl.md
       - Authentication & access control:
@@ -364,16 +366,6 @@ 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/runner.py

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