Update changelog via LangGraph Server Changelog Bot

docs/docs/cloud/reference/langgraph_server_changelog.md

@@ -5,7 +5,7 @@
 ---
 
 ## v0.2.87 (2025-07-14)
-- Added more detailed logs for Redis worker signaling to improve debugging.
+- Enhanced logging for Redis worker signaling to provide more helpful insights into worker activities.
 
 ## v0.2.86 (2025-07-11)
 - Honored tool descriptions in the `/mcp` endpoint to align with expected functionality.

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",
@@ -133,44 +133,15 @@ 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)

docs/docs/how-tos/graph-api.md

@@ -1194,7 +1194,7 @@ from IPython.display import Image, display
 display(Image(graph.get_graph().draw_mermaid_png()))
 ```
 
-![Map-reduce graph with fanout](assets/graph_api_image_6.png)
+![Map-reduce graph with fanout](assets/graph_api_image_2.png)
 
 ```python
 # Call the graph: here we call it to generate a list of jokes
@@ -1446,7 +1446,7 @@ Recursion Error
     display(Image(graph.get_graph().draw_mermaid_png()))
     ```
 
-    ![Complex loop graph with branches](assets/graph_api_image_8.png)
+    ![Complex loop graph with branches](assets/graph_api_image_4.png)
 
     This graph looks complex, but can be conceptualized as loop of [supersteps](../concepts/low_level.md#graphs):
 

libs/langgraph/uv.lock

@@ -1463,7 +1463,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.73"
+version = "0.1.72"
 source = { editable = "../sdk-py" }
 dependencies = [
     { name = "httpx" },

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -1,36 +1,3 @@
-"""Tool execution node for LangGraph workflows.
-
-This module provides prebuilt functionality for executing tools in LangGraph.
-
-Tools are functions that models can call to interact with external systems,
-APIs, databases, or perform computations.
-
-The module implements several key design patterns:
-- Parallel execution of multiple tool calls for efficiency
-- Robust error handling with customizable error messages
-- State injection for tools that need access to graph state
-- Store injection for tools that need persistent storage
-- Command-based state updates for advanced control flow
-
-Key Components:
-    ToolNode: Main class for executing tools in LangGraph workflows
-    InjectedState: Annotation for injecting graph state into tools
-    InjectedStore: Annotation for injecting persistent store into tools
-    tools_condition: Utility function for conditional routing based on tool calls
-
-Typical Usage:
-    ```python
-    from langchain_core.tools import tool
-    from langgraph.prebuilt import ToolNode
-
-    @tool
-    def my_tool(x: int) -> str:
-        return f"Result: {x}"
-
-    tool_node = ToolNode([my_tool])
-    ```
-"""
-
 import asyncio
 import inspect
 import json
@@ -82,24 +49,6 @@ TOOL_CALL_ERROR_TEMPLATE = "Error: {error}\n Please fix your mistakes."
 
 
 def msg_content_output(output: Any) -> Union[str, list[dict]]:
-    """Convert tool output to valid message content format.
-
-    LangChain ToolMessages accept either string content or a list of content blocks.
-    This function ensures tool outputs are properly formatted for message consumption
-    by attempting to preserve structured data when possible, falling back to JSON
-    serialization or string conversion.
-
-    Args:
-        output: The raw output from a tool execution. Can be any type.
-
-    Returns:
-        Either a string representation of the output or a list of content blocks
-        if the output is already in the correct format for structured content.
-
-    Note:
-        This function prioritizes backward compatibility by defaulting to JSON
-        serialization rather than supporting all possible message content formats.
-    """
     if isinstance(output, str):
         return output
     elif isinstance(output, list) and all(
@@ -109,10 +58,9 @@ def msg_content_output(output: Any) -> Union[str, list[dict]]:
         ]
     ):
         return output
-    # Technically a list of strings is also valid message content, but it's
-    # not currently well tested that all chat models support this.
-    # And for backwards compatibility we want to make sure we don't break
-    # any existing ToolNode usage.
+    # Technically a list of strings is also valid message content but it's not currently
+    # well tested that all chat models support this. And for backwards compatibility
+    # we want to make sure we don't break any existing ToolNode usage.
     else:
         try:
             return json.dumps(output, ensure_ascii=False)
@@ -130,30 +78,6 @@ def _handle_tool_error(
         tuple[type[Exception], ...],
     ],
 ) -> str:
-    """Generate error message content based on exception handling configuration.
-
-    This function centralizes error message generation logic, supporting different
-    error handling strategies configured via the ToolNode's handle_tool_errors
-    parameter.
-
-    Args:
-        e: The exception that occurred during tool execution.
-        flag: Configuration for how to handle the error. Can be:
-            - bool: If True, use default error template
-            - str: Use this string as the error message
-            - Callable: Call this function with the exception to get error message
-            - tuple: Not used in this context (handled by caller)
-
-    Returns:
-        A string containing the error message to include in the ToolMessage.
-
-    Raises:
-        ValueError: If flag is not one of the supported types.
-
-    Note:
-        The tuple case is handled by the caller through exception type checking,
-        not by this function directly.
-    """
     if isinstance(flag, (bool, tuple)):
         content = TOOL_CALL_ERROR_TEMPLATE.format(error=repr(e))
     elif isinstance(flag, str):
@@ -169,29 +93,6 @@ def _handle_tool_error(
 
 
 def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception], ...]:
-    """Infer exception types handled by a custom error handler function.
-
-    This function analyzes the type annotations of a custom error handler to determine
-    which exception types it's designed to handle. This enables type-safe error handling
-    where only specific exceptions are caught and processed by the handler.
-
-    Args:
-        handler: A callable that takes an exception and returns an error message string.
-                The first parameter (after self/cls if present) should be type-annotated
-                with the exception type(s) to handle.
-
-    Returns:
-        A tuple of exception types that the handler can process. Returns (Exception,)
-        if no specific type information is available for backward compatibility.
-
-    Raises:
-        ValueError: If the handler's annotation contains non-Exception types or
-                   if Union types contain non-Exception types.
-
-    Note:
-        This function supports both single exception types and Union types for
-        handlers that need to handle multiple exception types differently.
-    """
     sig = inspect.signature(handler)
     params = list(sig.parameters.values())
     if params:
@@ -210,9 +111,8 @@ def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception],
                     return tuple(args)
                 else:
                     raise ValueError(
-                        "All types in the error handler error annotation must be "
-                        "Exception types. For example, "
-                        "`def custom_handler(e: Union[ValueError, TypeError])`. "
+                        "All types in the error handler error annotation must be Exception types. "
+                        "For example, `def custom_handler(e: Union[ValueError, TypeError])`. "
                         f"Got '{first_param.annotation}' instead."
                     )
 
@@ -221,16 +121,13 @@ def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception],
                 return (exception_type,)
             else:
                 raise ValueError(
-                    f"Arbitrary types are not supported in the error handler "
-                    f"signature. Please annotate the error with either a "
-                    f"specific Exception type or a union of Exception types. "
-                    "For example, `def custom_handler(e: ValueError)` or "
-                    "`def custom_handler(e: Union[ValueError, TypeError])`. "
+                    f"Arbitrary types are not supported in the error handler signature. "
+                    "Please annotate the error with either a specific Exception type or a union of Exception types. "
+                    "For example, `def custom_handler(e: ValueError)` or `def custom_handler(e: Union[ValueError, TypeError])`. "
                     f"Got '{exception_type}' instead."
                 )
 
-    # If no type information is available, return (Exception,)
-    # for backwards compatibility.
+    # If no type information is available, return (Exception,) for backwards compatibility.
     return (Exception,)
 
 
@@ -244,72 +141,60 @@ class ToolNode(RunnableCallable):
     Tool calls can also be passed directly as a list of `ToolCall` dicts.
 
     Args:
-        tools: A sequence of tools that can be invoked by this node. Tools can be
-            BaseTool instances or plain functions that will be converted to tools.
-        name: The name identifier for this node in the graph. Used for debugging
-            and visualization. Defaults to "tools".
-        tags: Optional metadata tags to associate with the node for filtering
-            and organization. Defaults to None.
-        handle_tool_errors: Configuration for error handling during tool execution.
-            Defaults to True. Supports multiple strategies:
+        tools: A sequence of tools that can be invoked by the ToolNode.
+        name: The name of the ToolNode in the graph. Defaults to "tools".
+        tags: Optional tags to associate with the node. Defaults to None.
+        handle_tool_errors: How to handle tool errors raised by tools inside the node. Defaults to True.
+            Must be one of the following:
 
-            - True: Catch all errors and return a ToolMessage with the default
-              error template containing the exception details.
-            - str: Catch all errors and return a ToolMessage with this custom
-              error message string.
-            - tuple[type[Exception], ...]: Only catch exceptions of the specified
-              types and return default error messages for them.
-            - Callable[..., str]: Catch exceptions matching the callable's signature
-              and return the string result of calling it with the exception.
-            - False: Disable error handling entirely, allowing exceptions to propagate.
+            - True: all errors will be caught and
+                a ToolMessage with a default error message (TOOL_CALL_ERROR_TEMPLATE) will be returned.
+            - str: all errors will be caught and
+                a ToolMessage with the string value of 'handle_tool_errors' will be returned.
+            - tuple[type[Exception], ...]: exceptions in the tuple will be caught and
+                a ToolMessage with a default error message (TOOL_CALL_ERROR_TEMPLATE) will be returned.
+            - Callable[..., str]: exceptions from the signature of the callable will be caught and
+                a ToolMessage with the string value of the result of the 'handle_tool_errors' callable will be returned.
+            - False: none of the errors raised by the tools will be caught
+        messages_key: The state key in the input that contains the list of messages.
+            The same key will be used for the output from the ToolNode.
+            Defaults to "messages".
 
-        messages_key: The key in the state dictionary that contains the message list.
-            This same key will be used for the output ToolMessages. Defaults to "messages".
+    The `ToolNode` is roughly analogous to:
 
-    Example:
-        Basic usage with simple tools:
+    ```python
+    tools_by_name = {tool.name: tool for tool in tools}
+    def tool_node(state: dict):
+        result = []
+        for tool_call in state["messages"][-1].tool_calls:
+            tool = tools_by_name[tool_call["name"]]
+            observation = tool.invoke(tool_call["args"])
+            result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
+        return {"messages": result}
+    ```
 
-        ```python
-        from langgraph.prebuilt import ToolNode
-        from langchain_core.tools import tool
+    Tool calls can also be passed directly to a ToolNode. This can be useful when using
+    the Send API, e.g., in a conditional edge:
 
-        @tool
-        def calculator(a: int, b: int) -> int:
-            \"\"\"Add two numbers.\"\"\"
-            return a + b
+    ```python
+    def example_conditional_edge(state: dict) -> List[Send]:
+        tool_calls = state["messages"][-1].tool_calls
+        # If tools rely on state or store variables (whose values are not generated
+        # directly by a model), you can inject them into the tool calls.
+        tool_calls = [
+            tool_node.inject_tool_args(call, state, store)
+            for call in last_message.tool_calls
+        ]
+        return [Send("tools", [tool_call]) for tool_call in tool_calls]
+    ```
 
-        tool_node = ToolNode([calculator])
-        ```
-
-        Custom error handling:
-
-        ```python
-        def handle_math_errors(e: ZeroDivisionError) -> str:
-            return "Cannot divide by zero!"
-
-        tool_node = ToolNode([calculator], handle_tool_errors=handle_math_errors)
-        ```
-
-        Direct tool call execution:
-
-        ```python
-        tool_calls = [{"name": "calculator", "args": {"a": 5, "b": 3}, "id": "1", "type": "tool_call"}]
-        result = tool_node.invoke(tool_calls)
-        ```
-
-    Note:
-        The ToolNode expects input in one of three formats:
-        1. A dictionary with a messages key containing a list of messages
-        2. A list of messages directly
-        3. A list of tool call dictionaries
-
-        When using message formats, the last message must be an AIMessage with
-        tool_calls populated. The node automatically extracts and processes these
-        tool calls concurrently.
-
-        For advanced use cases involving state injection or store access, tools
-        can be annotated with InjectedState or InjectedStore to receive graph
-        context automatically.
+    Important:
+        - The input state can be one of the following:
+            - A dict with a messages key containing a list of messages.
+            - A list of messages.
+            - A list of tool calls.
+        - If operating on a message list, the last message must be an `AIMessage` with
+            `tool_calls` populated.
     """
 
     name: str = "ToolNode"
@@ -325,15 +210,6 @@ class ToolNode(RunnableCallable):
         ] = True,
         messages_key: str = "messages",
     ) -> None:
-        """Initialize the ToolNode with the provided tools and configuration.
-
-        Args:
-            tools: Sequence of tools to make available for execution.
-            name: Node name for graph identification.
-            tags: Optional metadata tags.
-            handle_tool_errors: Error handling configuration.
-            messages_key: State key containing messages.
-        """
         super().__init__(self._func, self._afunc, name=name, tags=tags, trace=False)
         self.tools_by_name: dict[str, BaseTool] = {}
         self.tool_to_state_args: dict[str, dict[str, Optional[str]]] = {}
@@ -665,38 +541,20 @@ class ToolNode(RunnableCallable):
         ],
         store: Optional[BaseStore],
     ) -> ToolCall:
-        """Inject graph state and store into tool call arguments.
+        """Injects the state and store into the tool call.
 
-        This method enables tools to access graph context that should not be controlled
-        by the model. Tools can declare dependencies on graph state or persistent storage
-        using InjectedState and InjectedStore annotations. This method automatically
-        identifies these dependencies and injects the appropriate values.
-
-        The injection process preserves the original tool call structure while adding
-        the necessary context arguments. This allows tools to be both model-callable
-        and context-aware without exposing internal state management to the model.
+        Tool arguments with types annotated as `InjectedState` and `InjectedStore` are
+        ignored in tool schemas for generation purposes. This method injects them into
+        tool calls for tool invocation.
 
         Args:
-            tool_call: The tool call dictionary to augment with injected arguments.
-                Must contain 'name', 'args', 'id', and 'type' fields.
-            input: The current graph state to inject into tools requiring state access.
-                Can be a message list, state dictionary, or BaseModel instance.
-            store: The persistent store instance to inject into tools requiring storage.
-                Will be None if no store is configured for the graph.
+            tool_call: The tool call to inject state and store into.
+            input: The input state
+                to inject.
+            store: The store to inject.
 
         Returns:
-            A new ToolCall dictionary with the same structure as the input but with
-            additional arguments injected based on the tool's annotation requirements.
-
-        Raises:
-            ValueError: If a tool requires store injection but no store is provided,
-                       or if state injection requirements cannot be satisfied.
-
-        Note:
-            This method is automatically called during tool execution but can also
-            be used manually when working with the Send API or custom routing logic.
-            The injection is performed on a copy of the tool call to avoid mutating
-            the original.
+            ToolCall: The tool call with injected state and store.
         """
         if tool_call["name"] not in self.tools_by_name:
             return tool_call
@@ -767,66 +625,55 @@ def tools_condition(
     state: Union[list[AnyMessage], dict[str, Any], BaseModel],
     messages_key: str = "messages",
 ) -> Literal["tools", "__end__"]:
-    """Conditional routing function for tool-calling workflows.
+    """Use in the conditional_edge to route to the ToolNode if the last message
 
-    This utility function implements the standard conditional logic for ReAct-style
-    agents: if the last AI message contains tool calls, route to the tool execution
-    node; otherwise, end the workflow. This pattern is fundamental to most tool-calling
-    agent architectures.
-
-    The function handles multiple state formats commonly used in LangGraph applications,
-    making it flexible for different graph designs while maintaining consistent behavior.
+    has tool calls. Otherwise, route to the end.
 
     Args:
-        state: The current graph state to examine for tool calls. Supported formats:
-            - List of messages (for MessageGraph)
-            - Dictionary containing a messages key (for StateGraph)
-            - BaseModel instance with a messages attribute
-        messages_key: The key or attribute name containing the message list in the state.
-            This allows customization for graphs using different state schemas.
-            Defaults to "messages".
+        state: The state to check for
+            tool calls. Must have a list of messages (MessageGraph) or have the
+            "messages" key (StateGraph).
 
     Returns:
-        Either "tools" if tool calls are present in the last AI message, or "__end__"
-        to terminate the workflow. These are the standard routing destinations for
-        tool-calling conditional edges.
+        The next node to route to.
 
-    Raises:
-        ValueError: If no messages can be found in the provided state format.
 
-    Example:
-        Basic usage in a ReAct agent:
+    Examples:
+        Create a custom ReAct-style agent with tools.
 
-        ```python
-        from langgraph.graph import StateGraph
-        from langgraph.prebuilt import ToolNode, tools_condition
-        from typing_extensions import TypedDict
-
-        class State(TypedDict):
-            messages: list
-
-        graph = StateGraph(State)
-        graph.add_node("llm", call_model)
-        graph.add_node("tools", ToolNode([my_tool]))
-        graph.add_conditional_edges(
-            "llm",
-            tools_condition,  # Routes to "tools" or "__end__"
-            {"tools": "tools", "__end__": "__end__"}
-        )
+        ```pycon
+        >>> from langchain_anthropic import ChatAnthropic
+        >>> from langchain_core.tools import tool
+        ...
+        >>> from langgraph.graph import StateGraph
+        >>> from langgraph.prebuilt import ToolNode, tools_condition
+        >>> from langgraph.graph.message import add_messages
+        ...
+        >>> from typing import Annotated
+        >>> from typing_extensions import TypedDict
+        ...
+        >>> @tool
+        >>> def divide(a: float, b: float) -> int:
+        ...     \"\"\"Return a / b.\"\"\"
+        ...     return a / b
+        ...
+        >>> llm = ChatAnthropic(model="claude-3-haiku-20240307")
+        >>> tools = [divide]
+        ...
+        >>> class State(TypedDict):
+        ...     messages: Annotated[list, add_messages]
+        >>>
+        >>> graph_builder = StateGraph(State)
+        >>> graph_builder.add_node("tools", ToolNode(tools))
+        >>> graph_builder.add_node("chatbot", lambda state: {"messages":llm.bind_tools(tools).invoke(state['messages'])})
+        >>> graph_builder.add_edge("tools", "chatbot")
+        >>> graph_builder.add_conditional_edges(
+        ...     "chatbot", tools_condition
+        ... )
+        >>> graph_builder.set_entry_point("chatbot")
+        >>> graph = graph_builder.compile()
+        >>> graph.invoke({"messages": {"role": "user", "content": "What's 329993 divided by 13662?"}})
         ```
-
-        Custom messages key:
-
-        ```python
-        def custom_condition(state):
-            return tools_condition(state, messages_key="chat_history")
-        ```
-
-    Note:
-        This function is designed to work seamlessly with ToolNode and standard
-        LangGraph patterns. It expects the last message to be an AIMessage when
-        tool calls are present, which is the standard output format for tool-calling
-        language models.
     """
     if isinstance(state, list):
         ai_message = state[-1]
@@ -842,18 +689,16 @@ def tools_condition(
 
 
 class InjectedState(InjectedToolArg):
-    """Annotation for injecting graph state into tool arguments.
+    """Annotation for a Tool arg that is meant to be populated with the graph state.
 
-    This annotation enables tools to access graph state without exposing state
-    management details to the language model. Tools annotated with InjectedState
-    receive state data automatically during execution while remaining invisible
-    to the model's tool-calling interface.
+    Any Tool argument annotated with InjectedState will be hidden from a tool-calling
+    model, so that the model doesn't attempt to generate the argument. If using
+    ToolNode, the appropriate graph state field will be automatically injected into
+    the model-generated tool args.
 
     Args:
-        field: Optional key to extract from the state dictionary. If None, the entire
-            state is injected. If specified, only that field's value is injected.
-            This allows tools to request specific state components rather than
-            processing the full state structure.
+        field: The key from state to insert. If None, the entire state is expected to
+            be passed in.
 
     Example:
         ```python
@@ -900,15 +745,6 @@ class InjectedState(InjectedToolArg):
             ToolMessage(content='bar2', name='foo_tool', tool_call_id='2')
         ]
         ```
-
-    Note:
-        - InjectedState arguments are automatically excluded from tool schemas
-          presented to language models
-        - ToolNode handles the injection process during execution
-        - Tools can mix regular arguments (controlled by the model) with injected
-          arguments (controlled by the system)
-        - State injection occurs after the model generates tool calls but before
-          tool execution
     """  # noqa: E501
 
     def __init__(self, field: Optional[str] = None) -> None:
@@ -916,97 +752,61 @@ class InjectedState(InjectedToolArg):
 
 
 class InjectedStore(InjectedToolArg):
-    """Annotation for injecting persistent store into tool arguments.
+    """Annotation for a Tool arg that is meant to be populated with LangGraph store.
 
-    This annotation enables tools to access LangGraph's persistent storage system
-    without exposing storage details to the language model. Tools annotated with
-    InjectedStore receive the store instance automatically during execution while
-    remaining invisible to the model's tool-calling interface.
-
-    The store provides persistent, cross-session data storage that tools can use
-    for maintaining context, user preferences, or any other data that needs to
-    persist beyond individual workflow executions.
+    Any Tool argument annotated with InjectedStore will be hidden from a tool-calling
+    model, so that the model doesn't attempt to generate the argument. If using
+    ToolNode, the appropriate store field will be automatically injected into
+    the model-generated tool args. Note: if a graph is compiled with a store object,
+    the store will be automatically propagated to the tools with InjectedStore args
+    when using ToolNode.
 
     !!! Warning
         `InjectedStore` annotation requires `langchain-core >= 0.3.8`
 
     Example:
         ```python
+        from typing import Any
         from typing_extensions import Annotated
+
+        from langchain_core.messages import AIMessage
         from langchain_core.tools import tool
+
         from langgraph.store.memory import InMemoryStore
         from langgraph.prebuilt import InjectedStore, ToolNode
 
-        @tool
-        def save_preference(
-            key: str,
-            value: str,
-            store: Annotated[Any, InjectedStore()]
-        ) -> str:
-            \"\"\"Save user preference to persistent storage.\"\"\"
-            store.put(("preferences",), key, value)
-            return f"Saved {key} = {value}"
-
-        @tool
-        def get_preference(
-            key: str,
-            store: Annotated[Any, InjectedStore()]
-        ) -> str:
-            \"\"\"Retrieve user preference from persistent storage.\"\"\"
-            result = store.get(("preferences",), key)
-            return result.value if result else "Not found"
-        ```
-
-        Usage with ToolNode and graph compilation:
-
-        ```python
-        from langgraph.graph import StateGraph
-        from langgraph.store.memory import InMemoryStore
-
         store = InMemoryStore()
-        tool_node = ToolNode([save_preference, get_preference])
+        store.put(("values",), "foo", {"bar": 2})
 
-        graph = StateGraph(State)
-        graph.add_node("tools", tool_node)
-        compiled_graph = graph.compile(store=store)  # Store is injected automatically
+        @tool
+        def store_tool(x: int, my_store: Annotated[Any, InjectedStore()]) -> str:
+            '''Do something with store.'''
+            stored_value = my_store.get(("values",), "foo").value["bar"]
+            return stored_value + x
+
+        node = ToolNode([store_tool])
+
+        tool_call = {"name": "store_tool", "args": {"x": 1}, "id": "1", "type": "tool_call"}
+        state = {
+            "messages": [AIMessage("", tool_calls=[tool_call])],
+        }
+
+        node.invoke(state, store=store)
         ```
 
-        Cross-session persistence:
-
-        ```python
-        # First session
-        result1 = graph.invoke({"messages": [HumanMessage("Save my favorite color as blue")]})
-
-        # Later session - data persists
-        result2 = graph.invoke({"messages": [HumanMessage("What's my favorite color?")]})
+        ```pycon
+        {
+            "messages": [
+                ToolMessage(content='3', name='store_tool', tool_call_id='1'),
+            ]
+        }
         ```
-
-    Note:
-        - InjectedStore arguments are automatically excluded from tool schemas
-          presented to language models
-        - The store instance is automatically injected by ToolNode during execution
-        - Tools can access namespaced storage using the store's get/put methods
-        - Store injection requires the graph to be compiled with a store instance
-        - Multiple tools can share the same store instance for data consistency
     """  # noqa: E501
 
 
 def _is_injection(
     type_arg: Any, injection_type: Union[Type[InjectedState], Type[InjectedStore]]
 ) -> bool:
-    """Check if a type argument represents an injection annotation.
-
-    This utility function determines whether a type annotation indicates that
-    an argument should be injected with state or store data. It handles both
-    direct annotations and nested annotations within Union or Annotated types.
-
-    Args:
-        type_arg: The type argument to check for injection annotations.
-        injection_type: The injection type to look for (InjectedState or InjectedStore).
-
-    Returns:
-        True if the type argument contains the specified injection annotation.
-    """
     if isinstance(type_arg, injection_type) or (
         isinstance(type_arg, type) and issubclass(type_arg, injection_type)
     ):
@@ -1018,19 +818,6 @@ def _is_injection(
 
 
 def _get_state_args(tool: BaseTool) -> dict[str, Optional[str]]:
-    """Extract state injection mappings from tool annotations.
-
-    This function analyzes a tool's input schema to identify arguments that should
-    be injected with graph state. It processes InjectedState annotations to build
-    a mapping of tool argument names to state field names.
-
-    Args:
-        tool: The tool to analyze for state injection requirements.
-
-    Returns:
-        A dictionary mapping tool argument names to state field names. If a field
-        name is None, the entire state should be injected for that argument.
-    """
     full_schema = tool.get_input_schema()
     tool_args_to_state_fields: dict = {}
 
@@ -1057,22 +844,6 @@ def _get_state_args(tool: BaseTool) -> dict[str, Optional[str]]:
 
 
 def _get_store_arg(tool: BaseTool) -> Optional[str]:
-    """Extract store injection argument from tool annotations.
-
-    This function analyzes a tool's input schema to identify the argument that
-    should be injected with the graph store. Only one store argument is supported
-    per tool.
-
-    Args:
-        tool: The tool to analyze for store injection requirements.
-
-    Returns:
-        The name of the argument that should receive the store injection, or None
-        if no store injection is required.
-
-    Raises:
-        ValueError: If a tool argument has multiple InjectedStore annotations.
-    """
     full_schema = tool.get_input_schema()
     for name, type_ in get_all_basemodel_annotations(full_schema).items():
         injections = [

libs/prebuilt/uv.lock

@@ -507,7 +507,7 @@ dev = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.73"
+version = "0.1.72"
 source = { editable = "../sdk-py" }
 dependencies = [
     { name = "httpx" },

libs/sdk-py/langgraph_sdk/auth/__init__.py

@@ -335,10 +335,8 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
     @typing.overload
     def __call__(
         self,
-        fn: (
-            _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
-            | _ActionHandler[dict[str, typing.Any]]
-        ),
+        fn: _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
+        | _ActionHandler[dict[str, typing.Any]],
     ) -> _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]: ...
 
     @typing.overload
@@ -354,11 +352,9 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
 
     def __call__(
         self,
-        fn: (
-            _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
-            | _ActionHandler[dict[str, typing.Any]]
-            | None
-        ) = None,
+        fn: _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
+        | _ActionHandler[dict[str, typing.Any]]
+        | None = None,
         *,
         resources: str | Sequence[str] | None = None,
         actions: str | Sequence[str] | None = None,
@@ -480,13 +476,9 @@ class _StoreOn:
     def __call__(
         self,
         *,
-        actions: (
-            typing.Literal["put", "get", "search", "list_namespaces", "delete"]
-            | Sequence[
-                typing.Literal["put", "get", "search", "list_namespaces", "delete"]
-            ]
-            | None
-        ) = None,
+        actions: typing.Literal["put", "get", "search", "list_namespaces", "delete"]
+        | Sequence[typing.Literal["put", "get", "search", "list_namespaces", "delete"]]
+        | None = None,
     ) -> Callable[[AHO], AHO]: ...
 
     @typing.overload
@@ -496,13 +488,9 @@ class _StoreOn:
         self,
         fn: AHO | None = None,
         *,
-        actions: (
-            typing.Literal["put", "get", "search", "list_namespaces", "delete"]
-            | Sequence[
-                typing.Literal["put", "get", "search", "list_namespaces", "delete"]
-            ]
-            | None
-        ) = None,
+        actions: typing.Literal["put", "get", "search", "list_namespaces", "delete"]
+        | Sequence[typing.Literal["put", "get", "search", "list_namespaces", "delete"]]
+        | None = None,
     ) -> AHO | Callable[[AHO], AHO]:
         """Register a handler for specific resources and actions.
 
@@ -720,12 +708,4 @@ def _validate_handler(fn: Callable[..., typing.Any]) -> None:
         )
 
 
-def is_studio_user(user: types.MinimalUser | types.User | types.UserDict) -> bool:
-    return (
-        isinstance(user, types.StudioUser)
-        or isinstance(user, dict)
-        and user.get("kind") == "StudioUser"
-    )
-
-
 __all__ = ["Auth", "types", "exceptions"]

libs/sdk-py/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-sdk"
-version = "0.1.73"
+version = "0.1.72"
 description = "SDK for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"

libs/sdk-py/uv.lock

@@ -19,11 +19,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2025.7.14"
+version = "2025.7.9"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b3/76/52c535bcebe74590f296d6c77c86dabf761c41980e1347a2422e4aa2ae41/certifi-2025.7.14.tar.gz", hash = "sha256:8ea99dbdfaaf2ba2f9bac77b9249ef62ec5218e7c2b2e903378ed5fccf765995", size = 163981, upload-time = "2025-07-14T03:29:28.449Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/de/8a/c729b6b60c66a38f590c4e774decc4b2ec7b0576be8f1aa984a53ffa812a/certifi-2025.7.9.tar.gz", hash = "sha256:c1d2ec05395148ee10cf672ffc28cd37ea0ab0d99f9cc74c43e588cbd111b079", size = 160386, upload-time = "2025-07-09T02:13:58.874Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/4f/52/34c6cf5bb9285074dc3531c437b3919e825d976fde097a7a73f79e726d03/certifi-2025.7.14-py3-none-any.whl", hash = "sha256:6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2", size = 162722, upload-time = "2025-07-14T03:29:26.863Z" },
+    { url = "https://files.pythonhosted.org/packages/66/f3/80a3f974c8b535d394ff960a11ac20368e06b736da395b551a49ce950cce/certifi-2025.7.9-py3-none-any.whl", hash = "sha256:d842783a14f8fdd646895ac26f719a061408834473cfc10203f6a575beb15d39", size = 159230, upload-time = "2025-07-09T02:13:57.007Z" },
 ]
 
 [[package]]
@@ -119,7 +119,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.73"
+version = "0.1.72"
 source = { editable = "." }
 dependencies = [
     { name = "httpx" },
@@ -156,7 +156,7 @@ dev = [
 
 [[package]]
 name = "mypy"
-version = "1.17.0"
+version = "1.16.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mypy-extensions" },
@@ -164,39 +164,39 @@ dependencies = [
     { name = "tomli", marker = "python_full_version < '3.11'" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/1e/e3/034322d5a779685218ed69286c32faa505247f1f096251ef66c8fd203b08/mypy-1.17.0.tar.gz", hash = "sha256:e5d7ccc08ba089c06e2f5629c660388ef1fee708444f1dee0b9203fa031dee03", size = 3352114, upload-time = "2025-07-14T20:34:30.181Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/81/69/92c7fa98112e4d9eb075a239caa4ef4649ad7d441545ccffbd5e34607cbb/mypy-1.16.1.tar.gz", hash = "sha256:6bd00a0a2094841c5e47e7374bb42b83d64c527a502e3334e1173a0c24437bab", size = 3324747, upload-time = "2025-06-16T16:51:35.145Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6a/31/e762baa3b73905c856d45ab77b4af850e8159dffffd86a52879539a08c6b/mypy-1.17.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f8e08de6138043108b3b18f09d3f817a4783912e48828ab397ecf183135d84d6", size = 10998313, upload-time = "2025-07-14T20:33:24.519Z" },
-    { url = "https://files.pythonhosted.org/packages/1c/c1/25b2f0d46fb7e0b5e2bee61ec3a47fe13eff9e3c2f2234f144858bbe6485/mypy-1.17.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ce4a17920ec144647d448fc43725b5873548b1aae6c603225626747ededf582d", size = 10128922, upload-time = "2025-07-14T20:34:06.414Z" },
-    { url = "https://files.pythonhosted.org/packages/02/78/6d646603a57aa8a2886df1b8881fe777ea60f28098790c1089230cd9c61d/mypy-1.17.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6ff25d151cc057fdddb1cb1881ef36e9c41fa2a5e78d8dd71bee6e4dcd2bc05b", size = 11913524, upload-time = "2025-07-14T20:33:19.109Z" },
-    { url = "https://files.pythonhosted.org/packages/4f/19/dae6c55e87ee426fb76980f7e78484450cad1c01c55a1dc4e91c930bea01/mypy-1.17.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:93468cf29aa9a132bceb103bd8475f78cacde2b1b9a94fd978d50d4bdf616c9a", size = 12650527, upload-time = "2025-07-14T20:32:44.095Z" },
-    { url = "https://files.pythonhosted.org/packages/86/e1/f916845a235235a6c1e4d4d065a3930113767001d491b8b2e1b61ca56647/mypy-1.17.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:98189382b310f16343151f65dd7e6867386d3e35f7878c45cfa11383d175d91f", size = 12897284, upload-time = "2025-07-14T20:33:38.168Z" },
-    { url = "https://files.pythonhosted.org/packages/ae/dc/414760708a4ea1b096bd214d26a24e30ac5e917ef293bc33cdb6fe22d2da/mypy-1.17.0-cp310-cp310-win_amd64.whl", hash = "sha256:c004135a300ab06a045c1c0d8e3f10215e71d7b4f5bb9a42ab80236364429937", size = 9506493, upload-time = "2025-07-14T20:34:01.093Z" },
-    { url = "https://files.pythonhosted.org/packages/d4/24/82efb502b0b0f661c49aa21cfe3e1999ddf64bf5500fc03b5a1536a39d39/mypy-1.17.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:9d4fe5c72fd262d9c2c91c1117d16aac555e05f5beb2bae6a755274c6eec42be", size = 10914150, upload-time = "2025-07-14T20:31:51.985Z" },
-    { url = "https://files.pythonhosted.org/packages/03/96/8ef9a6ff8cedadff4400e2254689ca1dc4b420b92c55255b44573de10c54/mypy-1.17.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d96b196e5c16f41b4f7736840e8455958e832871990c7ba26bf58175e357ed61", size = 10039845, upload-time = "2025-07-14T20:32:30.527Z" },
-    { url = "https://files.pythonhosted.org/packages/df/32/7ce359a56be779d38021d07941cfbb099b41411d72d827230a36203dbb81/mypy-1.17.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:73a0ff2dd10337ceb521c080d4147755ee302dcde6e1a913babd59473904615f", size = 11837246, upload-time = "2025-07-14T20:32:01.28Z" },
-    { url = "https://files.pythonhosted.org/packages/82/16/b775047054de4d8dbd668df9137707e54b07fe18c7923839cd1e524bf756/mypy-1.17.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:24cfcc1179c4447854e9e406d3af0f77736d631ec87d31c6281ecd5025df625d", size = 12571106, upload-time = "2025-07-14T20:34:26.942Z" },
-    { url = "https://files.pythonhosted.org/packages/a1/cf/fa33eaf29a606102c8d9ffa45a386a04c2203d9ad18bf4eef3e20c43ebc8/mypy-1.17.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:3c56f180ff6430e6373db7a1d569317675b0a451caf5fef6ce4ab365f5f2f6c3", size = 12759960, upload-time = "2025-07-14T20:33:42.882Z" },
-    { url = "https://files.pythonhosted.org/packages/94/75/3f5a29209f27e739ca57e6350bc6b783a38c7621bdf9cac3ab8a08665801/mypy-1.17.0-cp311-cp311-win_amd64.whl", hash = "sha256:eafaf8b9252734400f9b77df98b4eee3d2eecab16104680d51341c75702cad70", size = 9503888, upload-time = "2025-07-14T20:32:34.392Z" },
-    { url = "https://files.pythonhosted.org/packages/12/e9/e6824ed620bbf51d3bf4d6cbbe4953e83eaf31a448d1b3cfb3620ccb641c/mypy-1.17.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f986f1cab8dbec39ba6e0eaa42d4d3ac6686516a5d3dccd64be095db05ebc6bb", size = 11086395, upload-time = "2025-07-14T20:34:11.452Z" },
-    { url = "https://files.pythonhosted.org/packages/ba/51/a4afd1ae279707953be175d303f04a5a7bd7e28dc62463ad29c1c857927e/mypy-1.17.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:51e455a54d199dd6e931cd7ea987d061c2afbaf0960f7f66deef47c90d1b304d", size = 10120052, upload-time = "2025-07-14T20:33:09.897Z" },
-    { url = "https://files.pythonhosted.org/packages/8a/71/19adfeac926ba8205f1d1466d0d360d07b46486bf64360c54cb5a2bd86a8/mypy-1.17.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3204d773bab5ff4ebbd1f8efa11b498027cd57017c003ae970f310e5b96be8d8", size = 11861806, upload-time = "2025-07-14T20:32:16.028Z" },
-    { url = "https://files.pythonhosted.org/packages/0b/64/d6120eca3835baf7179e6797a0b61d6c47e0bc2324b1f6819d8428d5b9ba/mypy-1.17.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1051df7ec0886fa246a530ae917c473491e9a0ba6938cfd0ec2abc1076495c3e", size = 12744371, upload-time = "2025-07-14T20:33:33.503Z" },
-    { url = "https://files.pythonhosted.org/packages/1f/dc/56f53b5255a166f5bd0f137eed960e5065f2744509dfe69474ff0ba772a5/mypy-1.17.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f773c6d14dcc108a5b141b4456b0871df638eb411a89cd1c0c001fc4a9d08fc8", size = 12914558, upload-time = "2025-07-14T20:33:56.961Z" },
-    { url = "https://files.pythonhosted.org/packages/69/ac/070bad311171badc9add2910e7f89271695a25c136de24bbafc7eded56d5/mypy-1.17.0-cp312-cp312-win_amd64.whl", hash = "sha256:1619a485fd0e9c959b943c7b519ed26b712de3002d7de43154a489a2d0fd817d", size = 9585447, upload-time = "2025-07-14T20:32:20.594Z" },
-    { url = "https://files.pythonhosted.org/packages/be/7b/5f8ab461369b9e62157072156935cec9d272196556bdc7c2ff5f4c7c0f9b/mypy-1.17.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:2c41aa59211e49d717d92b3bb1238c06d387c9325d3122085113c79118bebb06", size = 11070019, upload-time = "2025-07-14T20:32:07.99Z" },
-    { url = "https://files.pythonhosted.org/packages/9c/f8/c49c9e5a2ac0badcc54beb24e774d2499748302c9568f7f09e8730e953fa/mypy-1.17.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0e69db1fb65b3114f98c753e3930a00514f5b68794ba80590eb02090d54a5d4a", size = 10114457, upload-time = "2025-07-14T20:33:47.285Z" },
-    { url = "https://files.pythonhosted.org/packages/89/0c/fb3f9c939ad9beed3e328008b3fb90b20fda2cddc0f7e4c20dbefefc3b33/mypy-1.17.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:03ba330b76710f83d6ac500053f7727270b6b8553b0423348ffb3af6f2f7b889", size = 11857838, upload-time = "2025-07-14T20:33:14.462Z" },
-    { url = "https://files.pythonhosted.org/packages/4c/66/85607ab5137d65e4f54d9797b77d5a038ef34f714929cf8ad30b03f628df/mypy-1.17.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:037bc0f0b124ce46bfde955c647f3e395c6174476a968c0f22c95a8d2f589bba", size = 12731358, upload-time = "2025-07-14T20:32:25.579Z" },
-    { url = "https://files.pythonhosted.org/packages/73/d0/341dbbfb35ce53d01f8f2969facbb66486cee9804048bf6c01b048127501/mypy-1.17.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:c38876106cb6132259683632b287238858bd58de267d80defb6f418e9ee50658", size = 12917480, upload-time = "2025-07-14T20:34:21.868Z" },
-    { url = "https://files.pythonhosted.org/packages/64/63/70c8b7dbfc520089ac48d01367a97e8acd734f65bd07813081f508a8c94c/mypy-1.17.0-cp313-cp313-win_amd64.whl", hash = "sha256:d30ba01c0f151998f367506fab31c2ac4527e6a7b2690107c7a7f9e3cb419a9c", size = 9589666, upload-time = "2025-07-14T20:34:16.841Z" },
-    { url = "https://files.pythonhosted.org/packages/9f/a0/6263dd11941231f688f0a8f2faf90ceac1dc243d148d314a089d2fe25108/mypy-1.17.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:63e751f1b5ab51d6f3d219fe3a2fe4523eaa387d854ad06906c63883fde5b1ab", size = 10988185, upload-time = "2025-07-14T20:33:04.797Z" },
-    { url = "https://files.pythonhosted.org/packages/02/13/b8f16d6b0dc80277129559c8e7dbc9011241a0da8f60d031edb0e6e9ac8f/mypy-1.17.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f7fb09d05e0f1c329a36dcd30e27564a3555717cde87301fae4fb542402ddfad", size = 10120169, upload-time = "2025-07-14T20:32:38.84Z" },
-    { url = "https://files.pythonhosted.org/packages/14/ef/978ba79df0d65af680e20d43121363cf643eb79b04bf3880d01fc8afeb6f/mypy-1.17.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b72c34ce05ac3a1361ae2ebb50757fb6e3624032d91488d93544e9f82db0ed6c", size = 11918121, upload-time = "2025-07-14T20:33:52.328Z" },
-    { url = "https://files.pythonhosted.org/packages/f4/10/55ef70b104151a0d8280474f05268ff0a2a79be8d788d5e647257d121309/mypy-1.17.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:434ad499ad8dde8b2f6391ddfa982f41cb07ccda8e3c67781b1bfd4e5f9450a8", size = 12648821, upload-time = "2025-07-14T20:32:59.631Z" },
-    { url = "https://files.pythonhosted.org/packages/26/8c/7781fcd2e1eef48fbedd3a422c21fe300a8e03ed5be2eb4bd10246a77f4e/mypy-1.17.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:f105f61a5eff52e137fd73bee32958b2add9d9f0a856f17314018646af838e97", size = 12896955, upload-time = "2025-07-14T20:32:49.543Z" },
-    { url = "https://files.pythonhosted.org/packages/78/13/03ac759dabe86e98ca7b6681f114f90ee03f3ff8365a57049d311bd4a4e3/mypy-1.17.0-cp39-cp39-win_amd64.whl", hash = "sha256:ba06254a5a22729853209550d80f94e28690d5530c661f9416a68ac097b13fc4", size = 9512957, upload-time = "2025-07-14T20:33:28.619Z" },
-    { url = "https://files.pythonhosted.org/packages/e3/fc/ee058cc4316f219078464555873e99d170bde1d9569abd833300dbeb484a/mypy-1.17.0-py3-none-any.whl", hash = "sha256:15d9d0018237ab058e5de3d8fce61b6fa72cc59cc78fd91f1b474bce12abf496", size = 2283195, upload-time = "2025-07-14T20:31:54.753Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/12/2bf23a80fcef5edb75de9a1e295d778e0f46ea89eb8b115818b663eff42b/mypy-1.16.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b4f0fed1022a63c6fec38f28b7fc77fca47fd490445c69d0a66266c59dd0b88a", size = 10958644, upload-time = "2025-06-16T16:51:11.649Z" },
+    { url = "https://files.pythonhosted.org/packages/08/50/bfe47b3b278eacf348291742fd5e6613bbc4b3434b72ce9361896417cfe5/mypy-1.16.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:86042bbf9f5a05ea000d3203cf87aa9d0ccf9a01f73f71c58979eb9249f46d72", size = 10087033, upload-time = "2025-06-16T16:35:30.089Z" },
+    { url = "https://files.pythonhosted.org/packages/21/de/40307c12fe25675a0776aaa2cdd2879cf30d99eec91b898de00228dc3ab5/mypy-1.16.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ea7469ee5902c95542bea7ee545f7006508c65c8c54b06dc2c92676ce526f3ea", size = 11875645, upload-time = "2025-06-16T16:35:48.49Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/d8/85bdb59e4a98b7a31495bd8f1a4445d8ffc86cde4ab1f8c11d247c11aedc/mypy-1.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:352025753ef6a83cb9e7f2427319bb7875d1fdda8439d1e23de12ab164179574", size = 12616986, upload-time = "2025-06-16T16:48:39.526Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/d0/bb25731158fa8f8ee9e068d3e94fcceb4971fedf1424248496292512afe9/mypy-1.16.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:ff9fa5b16e4c1364eb89a4d16bcda9987f05d39604e1e6c35378a2987c1aac2d", size = 12878632, upload-time = "2025-06-16T16:36:08.195Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/11/822a9beb7a2b825c0cb06132ca0a5183f8327a5e23ef89717c9474ba0bc6/mypy-1.16.1-cp310-cp310-win_amd64.whl", hash = "sha256:1256688e284632382f8f3b9e2123df7d279f603c561f099758e66dd6ed4e8bd6", size = 9484391, upload-time = "2025-06-16T16:37:56.151Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/61/ec1245aa1c325cb7a6c0f8570a2eee3bfc40fa90d19b1267f8e50b5c8645/mypy-1.16.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:472e4e4c100062488ec643f6162dd0d5208e33e2f34544e1fc931372e806c0cc", size = 10890557, upload-time = "2025-06-16T16:37:21.421Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/bb/6eccc0ba0aa0c7a87df24e73f0ad34170514abd8162eb0c75fd7128171fb/mypy-1.16.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ea16e2a7d2714277e349e24d19a782a663a34ed60864006e8585db08f8ad1782", size = 10012921, upload-time = "2025-06-16T16:51:28.659Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/80/b337a12e2006715f99f529e732c5f6a8c143bb58c92bb142d5ab380963a5/mypy-1.16.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:08e850ea22adc4d8a4014651575567b0318ede51e8e9fe7a68f25391af699507", size = 11802887, upload-time = "2025-06-16T16:50:53.627Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/59/f7af072d09793d581a745a25737c7c0a945760036b16aeb620f658a017af/mypy-1.16.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22d76a63a42619bfb90122889b903519149879ddbf2ba4251834727944c8baca", size = 12531658, upload-time = "2025-06-16T16:33:55.002Z" },
+    { url = "https://files.pythonhosted.org/packages/82/c4/607672f2d6c0254b94a646cfc45ad589dd71b04aa1f3d642b840f7cce06c/mypy-1.16.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:2c7ce0662b6b9dc8f4ed86eb7a5d505ee3298c04b40ec13b30e572c0e5ae17c4", size = 12732486, upload-time = "2025-06-16T16:37:03.301Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/5e/136555ec1d80df877a707cebf9081bd3a9f397dedc1ab9750518d87489ec/mypy-1.16.1-cp311-cp311-win_amd64.whl", hash = "sha256:211287e98e05352a2e1d4e8759c5490925a7c784ddc84207f4714822f8cf99b6", size = 9479482, upload-time = "2025-06-16T16:47:37.48Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/d6/39482e5fcc724c15bf6280ff5806548c7185e0c090712a3736ed4d07e8b7/mypy-1.16.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:af4792433f09575d9eeca5c63d7d90ca4aeceda9d8355e136f80f8967639183d", size = 11066493, upload-time = "2025-06-16T16:47:01.683Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/e5/26c347890efc6b757f4d5bb83f4a0cf5958b8cf49c938ac99b8b72b420a6/mypy-1.16.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:66df38405fd8466ce3517eda1f6640611a0b8e70895e2a9462d1d4323c5eb4b9", size = 10081687, upload-time = "2025-06-16T16:48:19.367Z" },
+    { url = "https://files.pythonhosted.org/packages/44/c7/b5cb264c97b86914487d6a24bd8688c0172e37ec0f43e93b9691cae9468b/mypy-1.16.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:44e7acddb3c48bd2713994d098729494117803616e116032af192871aed80b79", size = 11839723, upload-time = "2025-06-16T16:49:20.912Z" },
+    { url = "https://files.pythonhosted.org/packages/15/f8/491997a9b8a554204f834ed4816bda813aefda31cf873bb099deee3c9a99/mypy-1.16.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0ab5eca37b50188163fa7c1b73c685ac66c4e9bdee4a85c9adac0e91d8895e15", size = 12722980, upload-time = "2025-06-16T16:37:40.929Z" },
+    { url = "https://files.pythonhosted.org/packages/df/f0/2bd41e174b5fd93bc9de9a28e4fb673113633b8a7f3a607fa4a73595e468/mypy-1.16.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:dedb6229b2c9086247e21a83c309754b9058b438704ad2f6807f0d8227f6ebdd", size = 12903328, upload-time = "2025-06-16T16:34:35.099Z" },
+    { url = "https://files.pythonhosted.org/packages/61/81/5572108a7bec2c46b8aff7e9b524f371fe6ab5efb534d38d6b37b5490da8/mypy-1.16.1-cp312-cp312-win_amd64.whl", hash = "sha256:1f0435cf920e287ff68af3d10a118a73f212deb2ce087619eb4e648116d1fe9b", size = 9562321, upload-time = "2025-06-16T16:48:58.823Z" },
+    { url = "https://files.pythonhosted.org/packages/28/e3/96964af4a75a949e67df4b95318fe2b7427ac8189bbc3ef28f92a1c5bc56/mypy-1.16.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ddc91eb318c8751c69ddb200a5937f1232ee8efb4e64e9f4bc475a33719de438", size = 11063480, upload-time = "2025-06-16T16:47:56.205Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/4d/cd1a42b8e5be278fab7010fb289d9307a63e07153f0ae1510a3d7b703193/mypy-1.16.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:87ff2c13d58bdc4bbe7dc0dedfe622c0f04e2cb2a492269f3b418df2de05c536", size = 10090538, upload-time = "2025-06-16T16:46:43.92Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/4f/c3c6b4b66374b5f68bab07c8cabd63a049ff69796b844bc759a0ca99bb2a/mypy-1.16.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0a7cfb0fe29fe5a9841b7c8ee6dffb52382c45acdf68f032145b75620acfbd6f", size = 11836839, upload-time = "2025-06-16T16:36:28.039Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/7e/81ca3b074021ad9775e5cb97ebe0089c0f13684b066a750b7dc208438403/mypy-1.16.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:051e1677689c9d9578b9c7f4d206d763f9bbd95723cd1416fad50db49d52f359", size = 12715634, upload-time = "2025-06-16T16:50:34.441Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/95/bdd40c8be346fa4c70edb4081d727a54d0a05382d84966869738cfa8a497/mypy-1.16.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d5d2309511cc56c021b4b4e462907c2b12f669b2dbeb68300110ec27723971be", size = 12895584, upload-time = "2025-06-16T16:34:54.857Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/fd/d486a0827a1c597b3b48b1bdef47228a6e9ee8102ab8c28f944cb83b65dc/mypy-1.16.1-cp313-cp313-win_amd64.whl", hash = "sha256:4f58ac32771341e38a853c5d0ec0dfe27e18e27da9cdb8bbc882d2249c71a3ee", size = 9573886, upload-time = "2025-06-16T16:36:43.589Z" },
+    { url = "https://files.pythonhosted.org/packages/49/5e/ed1e6a7344005df11dfd58b0fdd59ce939a0ba9f7ed37754bf20670b74db/mypy-1.16.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7fc688329af6a287567f45cc1cefb9db662defeb14625213a5b7da6e692e2069", size = 10959511, upload-time = "2025-06-16T16:47:21.945Z" },
+    { url = "https://files.pythonhosted.org/packages/30/88/a7cbc2541e91fe04f43d9e4577264b260fecedb9bccb64ffb1a34b7e6c22/mypy-1.16.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:5e198ab3f55924c03ead626ff424cad1732d0d391478dfbf7bb97b34602395da", size = 10075555, upload-time = "2025-06-16T16:50:14.084Z" },
+    { url = "https://files.pythonhosted.org/packages/93/f7/c62b1e31a32fbd1546cca5e0a2e5f181be5761265ad1f2e94f2a306fa906/mypy-1.16.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:09aa4f91ada245f0a45dbc47e548fd94e0dd5a8433e0114917dc3b526912a30c", size = 11874169, upload-time = "2025-06-16T16:49:42.276Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/15/db580a28034657fb6cb87af2f8996435a5b19d429ea4dcd6e1c73d418e60/mypy-1.16.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:13c7cd5b1cb2909aa318a90fd1b7e31f17c50b242953e7dd58345b2a814f6383", size = 12610060, upload-time = "2025-06-16T16:34:15.215Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/78/c17f48f6843048fa92d1489d3095e99324f2a8c420f831a04ccc454e2e51/mypy-1.16.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:58e07fb958bc5d752a280da0e890c538f1515b79a65757bbdc54252ba82e0b40", size = 12875199, upload-time = "2025-06-16T16:35:14.448Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/d6/ed42167d0a42680381653fd251d877382351e1bd2c6dd8a818764be3beb1/mypy-1.16.1-cp39-cp39-win_amd64.whl", hash = "sha256:f895078594d918f93337a505f8add9bd654d1a24962b4c6ed9390e12531eb31b", size = 9487033, upload-time = "2025-06-16T16:49:57.907Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/d3/53e684e78e07c1a2bf7105715e5edd09ce951fc3f47cf9ed095ec1b7a037/mypy-1.16.1-py3-none-any.whl", hash = "sha256:5fc2ac4027d0ef28d6ba69a0343737a23c4d1b83672bf38d1fe237bdc0643b37", size = 2265923, upload-time = "2025-06-16T16:48:02.366Z" },
 ]
 
 [[package]]