feat(sdk-py): Show is_studio_user (#5505)

docs/docs/how-tos/auth/custom_auth.md

@@ -31,12 +31,12 @@ To leverage custom authentication and access user-level metadata in your deploym
         api_key = headers.get("x-api-key")
         if not api_key or not is_valid_key(api_key):
             raise Auth.exceptions.HTTPException(status_code=401, detail="Invalid API key")
-        
-        # Fetch user-specific tokens from your secret store  
+
+        # Fetch user-specific tokens from your secret store
         user_tokens = await fetch_user_tokens(api_key)
 
         return { # (2)!
-            "identity": api_key,  #  fetch user ID from LangSmith 
+            "identity": api_key,  #  fetch user ID from LangSmith
             "github_token" : user_tokens.github_token
             "jira_token" : user_tokens.jira_token
             # ... custom fields/secrets here
@@ -50,14 +50,14 @@ To leverage custom authentication and access user-level metadata in your deploym
 
     ```json hl_lines="7-9"
     {
-    "dependencies": ["."],
-    "graphs": {
+      "dependencies": ["."],
+      "graphs": {
         "agent": "./agent.py:graph"
-    },
-    "env": ".env",
-    "auth": {
+      },
+      "env": ".env",
+      "auth": {
         "path": "./auth.py:my_auth"
-    }
+      }
     }
     ```
 
@@ -80,7 +80,7 @@ To leverage custom authentication and access user-level metadata in your deploym
 
         ```python
         from langgraph.pregel.remote import RemoteGraph
-        
+
         my_token = "your-token" # In practice, you would generate a signed token with your auth provider
         remote_graph = RemoteGraph(
             "agent",
@@ -133,15 +133,44 @@ To allow an agent to perform authenticated actions on behalf of the user, access
 def my_node(state, config):
     user_config = config["configurable"].get("langgraph_auth_user")
     # token was resolved during the @auth.authenticate function
-    token = user_config.get("github_token","") 
+    token = user_config.get("github_token","")
     ...
 ```
 
 !!! note
     Fetch user credentials from a secure secret store. Storing secrets in graph state is not recommended.
 
+### Authorizing a Studio user
+
+By default, if you add custom authorization on your resources, this will also apply to interactions made from the Studio. If you want, you can handle logged-in Studio users differently by checking [is_studio_user()](../../reference/functions/sdk_auth.isStudioUser.html).
+
+!!! note
+    `is_studio_user` was added in version 0.1.73 of the langgraph-sdk. If you're on an older version, you can still check whether `isinstance(ctx.user, StudioUser)`.
+
+```python
+from langgraph_sdk.auth import is_studio_user, Auth
+auth = Auth()
+
+# ... Setup authenticate, etc.
+
+@auth.on
+async def add_owner(
+    ctx: Auth.types.AuthContext,
+    value: dict  # The payload being sent to this access method
+) -> dict:  # Returns a filter dict that restricts access to resources
+    if is_studio_user(ctx.user):
+        return {}
+
+    filters = {"owner": ctx.user.identity}
+    metadata = value.setdefault("metadata", {})
+    metadata.update(filters)
+    return filters
+```
+
+Only use this if you want to permit developer access to a graph deployed on the managed LangGraph Platform SaaS.
+
 ## Learn more
 
-* [Authentication & Access Control](../../concepts/auth.md)
-* [LangGraph Platform](../../concepts/langgraph_platform.md)
-* [Setting up custom authentication tutorial](../../tutorials/auth/getting_started.md)
+- [Authentication & Access Control](../../concepts/auth.md)
+- [LangGraph Platform](../../concepts/langgraph_platform.md)
+- [Setting up custom authentication tutorial](../../tutorials/auth/getting_started.md)