better tests

libs/langchain_v1/langchain/agents/factory.py

@@ -18,6 +18,7 @@ from typing_extensions import NotRequired, Required, TypedDict, TypeVar
 
 from langchain.agents.middleware.types import (
     AgentMiddleware,
+    AgentRuntime,
     AgentState,
     JumpTo,
     ModelRequest,
@@ -1018,6 +1019,9 @@ def create_agent(  # noqa: PLR0915
 
     def model_node(state: AgentState, runtime: Runtime[ContextT]) -> dict[str, Any]:
         """Sync model request handler with sequential middleware processing."""
+        # Create flat AgentRuntime with all runtime properties
+        agent_runtime = AgentRuntime.from_runtime(name or "agent", runtime)
+
         request = ModelRequest(
             model=model,
             tools=default_tools,
@@ -1026,7 +1030,7 @@ def create_agent(  # noqa: PLR0915
             messages=state["messages"],
             tool_choice=None,
             state=state,
-            runtime=runtime,
+            runtime=agent_runtime,
         )
 
         if wrap_model_call_handler is None:
@@ -1071,6 +1075,9 @@ def create_agent(  # noqa: PLR0915
 
     async def amodel_node(state: AgentState, runtime: Runtime[ContextT]) -> dict[str, Any]:
         """Async model request handler with sequential middleware processing."""
+        # Create flat AgentRuntime with all runtime properties
+        agent_runtime = AgentRuntime.from_runtime(name or "agent", runtime)
+
         request = ModelRequest(
             model=model,
             tools=default_tools,
@@ -1079,7 +1086,7 @@ def create_agent(  # noqa: PLR0915
             messages=state["messages"],
             tool_choice=None,
             state=state,
-            runtime=runtime,
+            runtime=agent_runtime,
         )
 
         if awrap_model_call_handler is None:

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

@@ -32,6 +32,7 @@ from .tool_retry import ToolRetryMiddleware
 from .tool_selection import LLMToolSelectorMiddleware
 from .types import (
     AgentMiddleware,
+    AgentRuntime,
     AgentState,
     ModelRequest,
     ModelResponse,
@@ -47,6 +48,7 @@ from .types import (
 
 __all__ = [
     "AgentMiddleware",
+    "AgentRuntime",
     "AgentState",
     "ClearToolUsesEdit",
     "CodexSandboxExecutionPolicy",

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

@@ -27,7 +27,8 @@ from typing import TypeAlias
 from langchain_core.messages import AIMessage, AnyMessage, BaseMessage, ToolMessage  # noqa: TC002
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.graph.message import add_messages
-from langgraph.types import Command  # noqa: TC002
+from langgraph.store.base import BaseStore  # noqa: TC002
+from langgraph.types import Command, StreamWriter  # noqa: TC002
 from langgraph.typing import ContextT
 from typing_extensions import NotRequired, Required, TypedDict, TypeVar, Unpack
 
@@ -60,6 +61,75 @@ JumpTo = Literal["tools", "model", "end"]
 ResponseT = TypeVar("ResponseT")
 
 
+@dataclass
+class AgentRuntime(Generic[ContextT]):
+    """Runtime context for agent execution, extending LangGraph's Runtime.
+
+    This class provides agent-specific execution context to middleware, including
+    the name of the currently executing graph and all Runtime properties flattened
+    for convenient access.
+
+    The AgentRuntime follows the same pattern as ToolRuntime, providing a flat
+    structure with all runtime properties directly accessible.
+
+    Attributes:
+        agent_name: The name of the currently executing graph/agent. This is the
+            name passed to `create_agent(name=...)` or defaults to "LangGraph".
+        context: Static context for the graph run (e.g., `user_id`, `db_conn`).
+        store: Store for persistence and memory, if configured.
+        stream_writer: Function for writing to the custom stream.
+        previous: The previous return value for the given thread (functional API only).
+
+    Example:
+        ```python
+        from langchain.agents.middleware import wrap_model_call, AgentRuntime
+        from langchain.agents.middleware.types import ModelRequest, ModelResponse
+
+
+        @wrap_model_call
+        def log_agent_name(
+            request: ModelRequest,
+            handler: Callable[[ModelRequest], ModelResponse],
+        ) -> ModelResponse:
+            '''Log which agent is making the model call.'''
+            agent_name = request.runtime.agent_name
+            print(f"Agent '{agent_name}' is calling the model")
+
+            # Access runtime context directly (flattened)
+            user_id = request.runtime.context.get("user_id")
+            print(f"User: {user_id}")
+
+            return handler(request)
+        ```
+    """
+
+    agent_name: str
+    """The name of the currently executing graph/agent."""
+
+    context: ContextT = field(default=None)  # type: ignore[assignment]
+    """Static context for the graph run, like `user_id`, `db_conn`, etc."""
+
+    store: BaseStore | None = field(default=None)
+    """Store for the graph run, enabling persistence and memory."""
+
+    stream_writer: StreamWriter = field(default=None)  # type: ignore[assignment]
+    """Function that writes to the custom stream."""
+
+    previous: Any = field(default=None)
+    """The previous return value for the given thread."""
+
+    @classmethod
+    def from_runtime(cls, name: str, runtime: Runtime[ContextT]) -> AgentRuntime[ContextT]:
+        """Create an AgentRuntime from a Runtime."""
+        return AgentRuntime[ContextT](
+            agent_name=name,
+            context=runtime.context,
+            store=runtime.store,
+            stream_writer=runtime.stream_writer,
+            previous=runtime.previous,
+        )
+
+
 class _ModelRequestOverrides(TypedDict, total=False):
     """Possible overrides for ModelRequest.override() method."""
 
@@ -74,7 +144,23 @@ class _ModelRequestOverrides(TypedDict, total=False):
 
 @dataclass
 class ModelRequest:
-    """Model request information for the agent."""
+    """Model request information for the agent.
+
+    This dataclass contains all the information needed for a model invocation,
+    including the model, messages, tools, and runtime context.
+
+    Attributes:
+        model: The chat model to invoke.
+        system_prompt: Optional system prompt to prepend to messages.
+        messages: List of conversation messages (excluding system prompt).
+        tool_choice: Tool selection configuration for the model.
+        tools: Available tools for the model to use.
+        response_format: Structured output format specification.
+        state: Complete agent state at the time of model invocation.
+        runtime: Agent runtime context including agent name and underlying
+            LangGraph Runtime with context, store, and stream_writer.
+        model_settings: Additional model-specific settings.
+    """
 
     model: BaseChatModel
     system_prompt: str | None
@@ -83,7 +169,7 @@ class ModelRequest:
     tools: list[BaseTool | dict]
     response_format: ResponseFormat | None
     state: AgentState
-    runtime: Runtime[ContextT]  # type: ignore[valid-type]
+    runtime: AgentRuntime[ContextT]  # type: ignore[valid-type]
     model_settings: dict[str, Any] = field(default_factory=dict)
 
     def override(self, **overrides: Unpack[_ModelRequestOverrides]) -> ModelRequest:
@@ -932,7 +1018,7 @@ def before_agent(
         ```python
         @before_agent
         def log_before_agent(state: AgentState, runtime: Runtime) -> None:
-            print(f"Starting agent with {len(state['messages'])} messages")
+            print(f"Starting agent '{runtime.agent_name}' with {len(state['messages'])} messages")
         ```
 
         With conditional jumping:

libs/langchain_v1/tests/unit_tests/agents/test_agent_runtime.py

@@ -0,0 +1,108 @@
+"""Tests for AgentRuntime access via wrap_model_call middleware."""
+
+import pytest
+from langchain_core.language_models.fake_chat_models import GenericFakeChatModel
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.tools import tool
+
+from langchain.agents import create_agent
+from langchain.agents.middleware import wrap_model_call
+from langchain.agents.middleware.types import ModelRequest
+from langchain.tools import ToolRuntime
+
+from .model import FakeToolCallingModel
+
+
+@pytest.fixture
+def fake_chat_model():
+    """Fixture providing a fake chat model for testing."""
+    return GenericFakeChatModel(messages=iter([AIMessage(content="test response")]))
+
+
+def test_agent_name_accessible_in_middleware(fake_chat_model):
+    """Test that agent name can be accessed via middleware."""
+    captured_agent_name = None
+
+    @wrap_model_call
+    def capture_agent_name(request: ModelRequest, handler):
+        nonlocal captured_agent_name
+        captured_agent_name = request.runtime.agent_name
+        return handler(request)
+
+    agent = create_agent(
+        fake_chat_model,
+        tools=[],
+        middleware=[capture_agent_name],
+        name="TestAgent",
+    )
+
+    agent.invoke({"messages": [HumanMessage("Hello")]})
+    assert captured_agent_name == "TestAgent"
+
+
+def test_nested_agent_name_accessible_in_tool():
+    """Test that nested agent's name is accessible when agent is used in a tool."""
+    # Track which agent names were captured
+    captured_agent_names = []
+
+    @wrap_model_call
+    def capture_agent_name(request: ModelRequest, handler):
+        captured_agent_names.append(request.runtime.agent_name)
+        return handler(request)
+
+    # Create a nested agent that will be called from within a tool
+    nested_agent = create_agent(
+        FakeToolCallingModel(),
+        tools=[],
+        middleware=[capture_agent_name],
+        name="NestedAgent",
+    )
+
+    # Create a tool that invokes the nested agent
+    @tool
+    def call_nested_agent(query: str, runtime: ToolRuntime) -> str:
+        """Tool that calls a nested agent."""
+        result = nested_agent.invoke({"messages": [HumanMessage(query)]})
+        return result["messages"][-1].content
+
+    # Create outer agent that uses the tool
+    outer_agent = create_agent(
+        FakeToolCallingModel(
+            tool_calls=[
+                [{"name": "call_nested_agent", "args": {"query": "test"}, "id": "1"}],
+                [],
+            ]
+        ),
+        tools=[call_nested_agent],
+        middleware=[capture_agent_name],
+        name="OuterAgent",
+    )
+
+    # Invoke the outer agent, which should call the tool, which calls the nested agent
+    outer_agent.invoke({"messages": [HumanMessage("Hello")]})
+
+    # Both agents should have captured their names
+    assert "OuterAgent" in captured_agent_names
+    assert "NestedAgent" in captured_agent_names
+
+
+async def test_agent_name_accessible_in_async_middleware():
+    """Test that agent name can be accessed in async middleware."""
+    captured_agent_name = None
+
+    @wrap_model_call
+    async def capture_agent_name_async(request: ModelRequest, handler):
+        nonlocal captured_agent_name
+        captured_agent_name = request.runtime.agent_name
+        return await handler(request)
+
+    fake_model = GenericFakeChatModel(messages=iter([AIMessage(content="async response")]))
+    agent = create_agent(
+        fake_model,
+        tools=[],
+        middleware=[capture_agent_name_async],
+        name="AsyncAgent",
+    )
+
+    await agent.ainvoke({"messages": [HumanMessage("Hello")]})
+    assert captured_agent_name == "AsyncAgent"

libs/langchain_v1/tests/unit_tests/agents/test_middleware_agent.py

@@ -1351,7 +1351,7 @@ def test_public_private_state_for_custom_middleware() -> None:
     class CustomMiddleware(AgentMiddleware[CustomState]):
         state_schema: type[CustomState] = CustomState
 
-        def before_model(self, state: CustomState) -> dict[str, Any]:
+        def before_model(self, state: CustomState, runtime) -> dict[str, Any]:
             assert "omit_input" not in state
             assert "omit_output" in state
             assert "private_state" not in state
@@ -1456,11 +1456,11 @@ def test_injected_state_in_middleware_agent() -> None:
 
 def test_jump_to_is_ephemeral() -> None:
     class MyMiddleware(AgentMiddleware):
-        def before_model(self, state: AgentState) -> dict[str, Any]:
+        def before_model(self, state: AgentState, runtime) -> dict[str, Any]:
             assert "jump_to" not in state
             return {"jump_to": "model"}
 
-        def after_model(self, state: AgentState) -> dict[str, Any]:
+        def after_model(self, state: AgentState, runtime) -> dict[str, Any]:
             assert "jump_to" not in state
             return {"jump_to": "model"}