some linting

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

@@ -1,4 +1,3 @@
-from langchain.agents.types import AgentJump, AgentMiddleware, AgentState, AgentUpdate
 from langgraph.prebuilt.interrupt import (
     ActionRequest,
     HumanInterrupt,
@@ -7,6 +6,8 @@ from langgraph.prebuilt.interrupt import (
 )
 from langgraph.types import interrupt
 
+from langchain.agents.types import AgentJump, AgentMiddleware, AgentState, AgentUpdate
+
 ToolInterruptConfig = dict[str, HumanInterruptConfig]
 
 
@@ -23,12 +24,12 @@ class HumanInTheLoopMiddleware(AgentMiddleware):
     def after_model(self, state: AgentState) -> AgentUpdate | AgentJump | None:
         messages = state["messages"]
         if not messages:
-            return
+            return None
 
         last_message = messages[-1]
 
         if not hasattr(last_message, "tool_calls") or not last_message.tool_calls:
-            return
+            return None
 
         # Separate tool calls that need interrupts from those that don't
         interrupt_tool_calls = []
@@ -43,7 +44,7 @@ class HumanInTheLoopMiddleware(AgentMiddleware):
 
         # If no interrupts needed, return early
         if not interrupt_tool_calls:
-            return
+            return None
 
         approved_tool_calls = auto_approved_tool_calls.copy()
 
@@ -53,9 +54,7 @@ class HumanInTheLoopMiddleware(AgentMiddleware):
         for tool_call in interrupt_tool_calls:
             tool_name = tool_call["name"]
             tool_args = tool_call["args"]
-            description = (
-                f"{self.message_prefix}\n\nTool: {tool_name}\nArgs: {tool_args}"
-            )
+            description = f"{self.message_prefix}\n\nTool: {tool_name}\nArgs: {tool_args}"
             tool_config = self.tool_configs[tool_name]
 
             request: HumanInterrupt = {

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

@@ -1,18 +1,13 @@
-from langchain.agents.new_agent import create_agent
-from langchain_openai import ChatOpenAI
-from langchain_core.messages import HumanMessage
-from langchain_core.tools import tool
 import operator
-from dataclasses import dataclass
 from typing import Annotated
-from pydantic import BaseModel
-from langchain.agents.structured_output import ToolStrategy
 
 from langchain.agents.types import AgentJump, AgentMiddleware, AgentState, AgentUpdate
 
+
 class State(AgentState):
     model_request_count: Annotated[int, operator.add]
 
+
 class ModelRequestLimitMiddleware(AgentMiddleware):
     """Terminates after N model requests"""
 

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

@@ -1,6 +1,5 @@
 import uuid
-from collections.abc import Sequence
-from typing import Callable, Iterable
+from collections.abc import Callable, Iterable, Sequence
 
 from langchain_core.language_models import LanguageModelLike
 from langchain_core.messages import (
@@ -85,10 +84,7 @@ class SummarizationMiddleware(AgentMiddleware):
             ),
             ToolMessage(tool_call_id=fake_tool_call_id, content=summary),
         ]
-        return {
-            "messages": [RemoveMessage(id=m.id) for m in messages_to_summarize]
-            + fake_messages
-        }
+        return {"messages": [RemoveMessage(id=m.id) for m in messages_to_summarize] + fake_messages}
 
     def _summarize_messages(self, messages_to_summarize: Sequence[AnyMessage]) -> str:
         system_message = self.summary_system_prompt

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

@@ -1,11 +1,10 @@
-from dataclasses import dataclass, field
-from typing import Annotated, Any, Dict, List, cast
+from dataclasses import field
+from typing import cast
 
 from langchain_core.messages import AIMessage
-from typing_extensions import Annotated
-
 from langgraph.agents.types import AgentJump, AgentMiddleware, AgentState, AgentUpdate
 
+
 class State(AgentState):
     tool_call_count: dict[str, int] = field(default_factory=dict)
 
@@ -19,14 +18,14 @@ class ToolCallLimitMiddleware(AgentMiddleware):
         self.tool_limits = tool_limits
 
     def after_model(self, state: State) -> AgentUpdate | AgentJump | None:
-        ai_msg: AIMessage = cast(AIMessage, state["messages"][-1])
+        ai_msg: AIMessage = cast("AIMessage", state["messages"][-1])
 
         tool_calls = {}
         for call in ai_msg.tool_calls or []:
             tool_calls[call["name"]] = tool_calls.get(call["name"], 0) + 1
 
         aggregate_calls = state["tool_call_count"].copy()
-        for tool_name in tool_calls.keys():
+        for tool_name in tool_calls:
             aggregate_calls[tool_name] = aggregate_calls.get(tool_name, 0) + 1
 
         for tool_name, max_calls in self.tool_limits.items():

libs/langchain_v1/langchain/agents/new_agent.py

@@ -422,7 +422,9 @@ def create_agent(
         )
 
     if middleware_w_modify_model_request:
-        for m1, m2 in zip(middleware_w_modify_model_request, middleware_w_modify_model_request[1:], strict=False):
+        for m1, m2 in zip(
+            middleware_w_modify_model_request, middleware_w_modify_model_request[1:], strict=False
+        ):
             _add_middleware_edge(
                 graph,
                 m1.modify_model_request,

testing_middleware.py

@@ -8,25 +8,7 @@ from typing import Annotated
 from pydantic import BaseModel
 from langchain.agents.structured_output import ToolStrategy
 
-from langchain.agents.types import AgentJump, AgentMiddleware, AgentState, AgentUpdate
-
-class State(AgentState):
-    model_request_count: Annotated[int, operator.add]
-
-class ModelRequestLimitMiddleware(AgentMiddleware):
-    """Terminates after N model requests"""
-
-    state_schema = State
-
-    def __init__(self, max_requests: int = 10):
-        self.max_requests = max_requests
-
-    def before_model(self, state: State) -> AgentUpdate | AgentJump | None:
-        # TODO: want to be able to configure end behavior here
-        if state.get("model_request_count", 0) == self.max_requests:
-            return {"jump_to": "__end__"}
-
-        return {"model_request_count": 1}
+from langchain.agents.middleware.model_call_limits import ModelRequestLimitMiddleware