diff --git a/src/oss/langchain/short-term-memory.mdx b/src/oss/langchain/short-term-memory.mdx
index fb771648f9..9138212245 100644
--- a/src/oss/langchain/short-term-memory.mdx
+++ b/src/oss/langchain/short-term-memory.mdx
@@ -107,7 +107,40 @@ const checkpointer = PostgresSaver.fromConnString(DB_URI);
 ```
 :::
 
+You can also use Azure Cosmos DB as a checkpoint saver:
 
+:::python
+```shell
+pip install langgraph-checkpoint-cosmosdb
+```
+
+```python
+from langgraph.graph import StateGraph, MessagesState, START
+from langgraph_checkpoint_cosmosdb import CosmosDBSaver  # [!code highlight]
+import os
+
+# Set environment variables for authentication
+os.environ["COSMOSDB_ENDPOINT"] = "your_cosmosdb_endpoint"
+os.environ["COSMOSDB_KEY"] = "your_cosmosdb_key"
+
+# Database and Container are created if they don't exist
+checkpointer = CosmosDBSaver(  # [!code highlight]
+    database_name="your_database",  # [!code highlight]
+    container_name="your_container"  # [!code highlight]
+)  # [!code highlight]
+
+# Build your graph with the checkpointer
+builder = StateGraph(MessagesState)
+builder.add_node("call_model", call_model)
+builder.add_edge(START, "call_model")
+graph = builder.compile(checkpointer=checkpointer)  # [!code highlight]
+```
+
+:::
+
+<Note>
+    Azure Cosmos DB checkpointer supports both sync and async operations. If the database and container already exist, you can use default RBAC credentials (e.g., `az login`) instead of setting the endpoint and key.
+</Note>
 ## Customizing agent memory
 
 By default, agents use @[`AgentState`] to manage short term memory, specifically the conversation history via a `messages` key.
@@ -679,7 +712,7 @@ def update_user_info(
     runtime: ToolRuntime[CustomContext, CustomState],
 ) -> Command:
     """Look up and update user info."""
-    user_id = runtime.context.user_id 
+    user_id = runtime.context.user_id
     name = "John Smith" if user_id == "user_123" else "Unknown user"
     return Command(update={  # [!code highlight]
         "user_name": name,
@@ -704,7 +737,7 @@ agent = create_agent(
     model="gpt-5-nano",
     tools=[update_user_info, greet],
     state_schema=CustomState, # [!code highlight]
-    context_schema=CustomContext,  
+    context_schema=CustomContext,
 )
 
 agent.invoke(