fix(langchain): add types to human_in_the_loop middleware (#34137)

The `HumanInTheLoopMiddleware` is missing a type annotation for the
context schema. Without the fix in this PR, the following code does not
type check:

```
graph = create_agent(
    "gpt-5",
    tools=[send_email_tool, read_email_tool],
    middleware=[
        HumanInTheLoopMiddleware(
            interrupt_on={
                # Require approval or rejection for sending emails
                "send_email_tool": {
                    "allowed_decisions": ["approve", "reject"],
                },
                # Auto-approve reading emails
                "read_email_tool": False,
            }
        ),
    ],
    context_schema=ContextSchema,
)
```

```
Argument of type "list[HumanInTheLoopMiddleware]" cannot be assigned to parameter "middleware" of type "Sequence[AgentMiddleware[StateT_co@create_agent, ContextT@create_agent]]" in function "create_agent"
  "HumanInTheLoopMiddleware" is not assignable to "AgentMiddleware[AgentState[Unknown], ContextSchema | None]"
    Type parameter "ContextT@AgentMiddleware" is invariant, but "None" is not the same as "ContextSchema | None"
```

libs/langchain_v1/langchain/agents/middleware/human_in_the_loop.py

@@ -7,7 +7,7 @@ from langgraph.runtime import Runtime
 from langgraph.types import interrupt
 from typing_extensions import NotRequired, TypedDict
 
-from langchain.agents.middleware.types import AgentMiddleware, AgentState
+from langchain.agents.middleware.types import AgentMiddleware, AgentState, ContextT, StateT
 
 
 class Action(TypedDict):
@@ -102,7 +102,7 @@ class HITLResponse(TypedDict):
 class _DescriptionFactory(Protocol):
     """Callable that generates a description for a tool call."""
 
-    def __call__(self, tool_call: ToolCall, state: AgentState, runtime: Runtime) -> str:
+    def __call__(self, tool_call: ToolCall, state: AgentState, runtime: Runtime[ContextT]) -> str:
         """Generate a description for a tool call."""
         ...
 
@@ -138,7 +138,7 @@ class InterruptOnConfig(TypedDict):
         def format_tool_description(
             tool_call: ToolCall,
             state: AgentState,
-            runtime: Runtime
+            runtime: Runtime[ContextT]
         ) -> str:
             import json
             return (
@@ -156,7 +156,7 @@ class InterruptOnConfig(TypedDict):
     """JSON schema for the args associated with the action, if edits are allowed."""
 
 
-class HumanInTheLoopMiddleware(AgentMiddleware):
+class HumanInTheLoopMiddleware(AgentMiddleware[StateT, ContextT]):
     """Human in the loop middleware."""
 
     def __init__(
@@ -204,7 +204,7 @@ class HumanInTheLoopMiddleware(AgentMiddleware):
         tool_call: ToolCall,
         config: InterruptOnConfig,
         state: AgentState,
-        runtime: Runtime,
+        runtime: Runtime[ContextT],
     ) -> tuple[ActionRequest, ReviewConfig]:
         """Create an ActionRequest and ReviewConfig for a tool call."""
         tool_name = tool_call["name"]
@@ -277,7 +277,7 @@ class HumanInTheLoopMiddleware(AgentMiddleware):
         )
         raise ValueError(msg)
 
-    def after_model(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
+    def after_model(self, state: AgentState, runtime: Runtime[ContextT]) -> dict[str, Any] | None:
         """Trigger interrupt flows for relevant tool calls after an `AIMessage`."""
         messages = state["messages"]
         if not messages:
@@ -350,6 +350,8 @@ class HumanInTheLoopMiddleware(AgentMiddleware):
 
         return {"messages": [last_ai_msg, *artificial_tool_messages]}
 
-    async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
+    async def aafter_model(
+        self, state: AgentState, runtime: Runtime[ContextT]
+    ) -> dict[str, Any] | None:
         """Async trigger interrupt flows for relevant tool calls after an `AIMessage`."""
         return self.after_model(state, runtime)