fix(docs): squash js docs build errors (#5723)

docs/docs/concepts/assistants.md

@@ -14,7 +14,10 @@ The LangGraph Cloud API provides several endpoints for creating and managing ass
 
 ## Configuration
 
+:::python
 Assistants build on the LangGraph open source concepts of configuration and [runtime context](low_level.md#runtime-context).
+:::
+
 While these features are available in the open source LangGraph library, assistants are only present in [LangGraph Platform](langgraph_platform.md). This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default context and configuration settings.
 
 In practice, an assistant is just an _instance_ of a graph with a specific configuration. Therefore, multiple assistants can reference the same graph but can contain different configurations (e.g. prompts, models, tools). The LangGraph Server API provides several endpoints for creating and managing assistants. See the [API reference](../cloud/reference/api/api_ref.html) and [this how-to](../cloud/how-tos/configuration_cloud.md) for more details on how to create assistants.

docs/docs/concepts/persistence.md

@@ -1154,15 +1154,15 @@ Under the hood, checkpointing is powered by checkpointer objects that conform to
 - `langgraph-checkpoint`: The base interface for checkpointer savers (@[BaseCheckpointSaver]) and serialization/deserialization interface (@[SerializerProtocol][SerializerProtocol]). Includes in-memory checkpointer implementation (@[InMemorySaver][InMemorySaver]) for experimentation. LangGraph comes with `langgraph-checkpoint` included.
 - `langgraph-checkpoint-sqlite`: An implementation of LangGraph checkpointer that uses SQLite database (@[SqliteSaver][SqliteSaver] / @[AsyncSqliteSaver]). Ideal for experimentation and local workflows. Needs to be installed separately.
 - `langgraph-checkpoint-postgres`: An advanced checkpointer that uses Postgres database (@[PostgresSaver][PostgresSaver] / @[AsyncPostgresSaver]), used in LangGraph Platform. Ideal for using in production. Needs to be installed separately.
- 
+
 :::
 
 :::js
 
-- `@langchain/langgraph-checkpoint`: The base interface for checkpointer savers (@[BaseCheckpointSaver][BaseCheckpointSaver]) and serialization/deserialization interface (@[SerializerProtocol][SerializerProtocol]). Includes in-memory checkpointer implementation (@[InMemorySaver) for experimentation. LangGraph comes with `@langchain/langgraph-checkpoint` included.
+- `@langchain/langgraph-checkpoint`: The base interface for checkpointer savers (@[BaseCheckpointSaver][BaseCheckpointSaver]) and serialization/deserialization interface (@[SerializerProtocol][SerializerProtocol]). Includes in-memory checkpointer implementation (@[MemorySaver]) for experimentation. LangGraph comes with `@langchain/langgraph-checkpoint` included.
 - `@langchain/langgraph-checkpoint-sqlite`: An implementation of LangGraph checkpointer that uses SQLite database (@[SqliteSaver]). Ideal for experimentation and local workflows. Needs to be installed separately.
 - `@langchain/langgraph-checkpoint-postgres`: An advanced checkpointer that uses Postgres database (@[PostgresSaver]), used in LangGraph Platform. Ideal for using in production. Needs to be installed separately.
- 
+
 :::
 
 ### Checkpointer interface
@@ -1177,7 +1177,7 @@ Each checkpointer conforms to @[BaseCheckpointSaver] interface and implements th
 
 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 `InMemorySaver`, or async versions of Sqlite/Postgres checkpointers -- `AsyncSqliteSaver` / `AsyncPostgresSaver` checkpointers.
 
@@ -1190,7 +1190,7 @@ Each checkpointer conforms to the @[BaseCheckpointSaver][BaseCheckpointSaver] in
 - `.putWrites` - Store intermediate writes linked to a checkpoint (i.e. [pending writes](#pending-writes)).
 - `.getTuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`). This is used to populate `StateSnapshot` in `graph.getState()`.
 - `.list` - List checkpoints that match a given configuration and filter criteria. This is used to populate state history in `graph.getStateHistory()`
-:::
+  :::
 
 ### Serializer
 

docs/docs/concepts/server-mcp.md

@@ -89,7 +89,9 @@ After deployment, you can update the name and description using the LangGraph SD
 
 Define clear, minimal input and output schemas to avoid exposing unnecessary internal complexity to the LLM.
 
+:::python
 The default [MessagesState](./low_level.md#messagesstate) uses `AnyMessage`, which supports many message types but is too general for direct LLM exposure.
+:::
 
 Instead, define **custom agents or workflows** that use explicitly typed input and output structures.