Tag middleware runs with ls_agent_type

.gitignore

@@ -166,4 +166,3 @@ virtualenv/
 scratch/
 
 .langgraph_api/
-.worktrees/

libs/core/langchain_core/runnables/base.py

@@ -82,6 +82,18 @@ from langchain_core.runnables.utils import (
     is_async_generator,
 )
 from langchain_core.tracers._streaming import _StreamingCallbackHandler
+from langchain_core.tracers.event_stream import (
+    _astream_events_implementation_v1,
+    _astream_events_implementation_v2,
+)
+from langchain_core.tracers.log_stream import (
+    LogStreamCallbackHandler,
+    _astream_log_implementation,
+)
+from langchain_core.tracers.root_listeners import (
+    AsyncRootListenersTracer,
+    RootListenersTracer,
+)
 from langchain_core.utils.aiter import aclosing, atee
 from langchain_core.utils.iter import safetee
 from langchain_core.utils.pydantic import create_model_v2
@@ -99,21 +111,8 @@ if TYPE_CHECKING:
     from langchain_core.runnables.retry import ExponentialJitterParams
     from langchain_core.runnables.schema import StreamEvent
     from langchain_core.tools import BaseTool
-    from langchain_core.tracers.event_stream import (
-        _astream_events_implementation_v1,  # noqa: F401
-        _astream_events_implementation_v2,  # noqa: F401
-    )
-    from langchain_core.tracers.log_stream import (
-        LogStreamCallbackHandler,  # noqa: F401
-        RunLog,
-        RunLogPatch,
-        _astream_log_implementation,  # noqa: F401
-    )
-    from langchain_core.tracers.root_listeners import (
-        AsyncListener,
-        AsyncRootListenersTracer,  # noqa: F401
-        RootListenersTracer,  # noqa: F401
-    )
+    from langchain_core.tracers.log_stream import RunLog, RunLogPatch
+    from langchain_core.tracers.root_listeners import AsyncListener
     from langchain_core.tracers.schemas import Run
 
 
@@ -1246,11 +1245,6 @@ class Runnable(ABC, Generic[Input, Output]):
             A `RunLogPatch` or `RunLog` object.
 
         """
-        from langchain_core.tracers.log_stream import (  # noqa: PLC0415
-            LogStreamCallbackHandler,
-            _astream_log_implementation,
-        )
-
         stream = LogStreamCallbackHandler(
             auto_close=False,
             include_names=include_names,
@@ -1486,11 +1480,6 @@ class Runnable(ABC, Generic[Input, Output]):
             NotImplementedError: If the version is not `'v1'` or `'v2'`.
 
         """  # noqa: E501
-        from langchain_core.tracers.event_stream import (  # noqa: PLC0415
-            _astream_events_implementation_v1,
-            _astream_events_implementation_v2,
-        )
-
         if version == "v2":
             event_stream = _astream_events_implementation_v2(
                 self,
@@ -1733,10 +1722,6 @@ class Runnable(ABC, Generic[Input, Output]):
             chain.invoke(2)
             ```
         """
-        from langchain_core.tracers.root_listeners import (  # noqa: PLC0415
-            RootListenersTracer,
-        )
-
         return RunnableBinding(
             bound=self,
             config_factories=[
@@ -1834,10 +1819,6 @@ class Runnable(ABC, Generic[Input, Output]):
             # on end callback ends at 2025-03-01T07:05:30.884831+00:00
             ```
         """
-        from langchain_core.tracers.root_listeners import (  # noqa: PLC0415
-            AsyncRootListenersTracer,
-        )
-
         return RunnableBinding(
             bound=self,
             config_factories=[
@@ -6068,9 +6049,6 @@ class RunnableBinding(RunnableBindingBase[Input, Output]):  # type: ignore[no-re
         Returns:
             A new `Runnable` with the listeners bound.
         """
-        from langchain_core.tracers.root_listeners import (  # noqa: PLC0415
-            RootListenersTracer,
-        )
 
         def listener_config_factory(config: RunnableConfig) -> RunnableConfig:
             return {

libs/core/langchain_core/tools/base.py

@@ -552,15 +552,8 @@ class ChildTool(BaseTool):
 
     model_config = ConfigDict(
         arbitrary_types_allowed=True,
-        ignored_types=(functools.cached_property,),
     )
 
-    def __setattr__(self, name: str, value: Any) -> None:
-        super().__setattr__(name, value)
-        if name in {"args_schema", "description", "name"}:
-            self.__dict__.pop("tool_call_schema", None)
-            self.__dict__.pop("args", None)
-
     @property
     def is_single_input(self) -> bool:
         """Check if the tool accepts only a single input argument.
@@ -571,7 +564,7 @@ class ChildTool(BaseTool):
         keys = {k for k in self.args if k != "kwargs"}
         return len(keys) == 1
 
-    @functools.cached_property
+    @property
     def args(self) -> dict:
         """Get the tool's input arguments schema.
 
@@ -590,7 +583,7 @@ class ChildTool(BaseTool):
                 json_schema = input_schema.model_json_schema()
         return cast("dict", json_schema["properties"])
 
-    @functools.cached_property
+    @property
     def tool_call_schema(self) -> ArgsSchema:
         """Get the schema for tool calls, excluding injected arguments.
 

libs/core/langchain_core/utils/function_calling.py

@@ -340,18 +340,17 @@ def _format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
     is_simple_oai_tool = (
         isinstance(tool, langchain_core.tools.simple.Tool) and not tool.args_schema
     )
-    schema = tool.tool_call_schema
-    if schema and not is_simple_oai_tool:
-        if isinstance(schema, dict):
+    if tool.tool_call_schema and not is_simple_oai_tool:
+        if isinstance(tool.tool_call_schema, dict):
             return _convert_json_schema_to_openai_function(
-                schema, name=tool.name, description=tool.description
+                tool.tool_call_schema, name=tool.name, description=tool.description
             )
-        if issubclass(schema, (BaseModel, BaseModelV1)):
+        if issubclass(tool.tool_call_schema, (BaseModel, BaseModelV1)):
             return _convert_pydantic_to_openai_function(
-                schema, name=tool.name, description=tool.description
+                tool.tool_call_schema, name=tool.name, description=tool.description
             )
         error_msg = (
-            f"Unsupported tool call schema: {schema}. "
+            f"Unsupported tool call schema: {tool.tool_call_schema}. "
             "Tool call schema must be a JSON schema dict or a Pydantic model."
         )
         raise ValueError(error_msg)

libs/core/langchain_core/utils/pydantic.py

@@ -199,21 +199,18 @@ class _IgnoreUnserializable(GenerateJsonSchema):
         return {}
 
 
-@lru_cache(maxsize=256)
 def _create_subset_model_v1(
     name: str,
     model: type[BaseModelV1],
-    field_names: tuple[str, ...],
+    field_names: list,
     *,
-    descriptions: tuple[tuple[str, str], ...] | None = None,
+    descriptions: dict | None = None,
     fn_description: str | None = None,
 ) -> type[BaseModelV1]:
     """Create a Pydantic model with only a subset of model's fields."""
-    descriptions_ = dict(descriptions) if descriptions else {}
-    field_names_list = list(field_names)
     fields = {}
 
-    for field_name in field_names_list:
+    for field_name in field_names:
         # Using pydantic v1 so can access __fields__ as a dict.
         field = model.__fields__[field_name]
         t = (
@@ -222,8 +219,8 @@ def _create_subset_model_v1(
             if field.required and not field.allow_none
             else field.outer_type_ | None
         )
-        if descriptions_ and field_name in descriptions_:
-            field.field_info.description = descriptions_[field_name]
+        if descriptions and field_name in descriptions:
+            field.field_info.description = descriptions[field_name]
         fields[field_name] = (t, field.field_info)
 
     rtn = cast("type[BaseModelV1]", create_model_v1(name, **fields))  # type: ignore[call-overload]
@@ -231,20 +228,18 @@ def _create_subset_model_v1(
     return rtn
 
 
-@lru_cache(maxsize=256)
 def _create_subset_model_v2(
     name: str,
     model: type[BaseModel],
-    field_names: tuple[str, ...],
+    field_names: list[str],
     *,
-    descriptions: tuple[tuple[str, str], ...] | None = None,
+    descriptions: dict | None = None,
     fn_description: str | None = None,
 ) -> type[BaseModel]:
     """Create a Pydantic model with a subset of the model fields."""
-    descriptions_ = dict(descriptions) if descriptions else {}
-    field_names_list = list(field_names)
+    descriptions_ = descriptions or {}
     fields = {}
-    for field_name in field_names_list:
+    for field_name in field_names:
         field = model.model_fields[field_name]
         description = descriptions_.get(field_name, field.description)
         field_kwargs: dict[str, Any] = {"description": description}
@@ -296,20 +291,19 @@ def _create_subset_model(
     Returns:
         The created subset model.
     """
-    descriptions_tuple = tuple(descriptions.items()) if descriptions else None
     if issubclass(model, BaseModelV1):
         return _create_subset_model_v1(
             name,
             model,
-            tuple(field_names),
-            descriptions=descriptions_tuple,
+            field_names,
+            descriptions=descriptions,
             fn_description=fn_description,
         )
     return _create_subset_model_v2(
         name,
         model,
-        tuple(field_names),
-        descriptions=descriptions_tuple,
+        field_names,
+        descriptions=descriptions,
         fn_description=fn_description,
     )
 

libs/core/tests/unit_tests/test_tools.py

@@ -3653,42 +3653,3 @@ def test_tool_default_factory_not_required() -> None:
     schema = convert_to_openai_tool(some_func)
     params = schema["function"]["parameters"]
     assert "names" not in params.get("required", [])
-
-
-def test_tool_call_schema_cache_invalidated_on_field_mutation() -> None:
-    """Verify cached tool_call_schema is invalidated when schema-affecting fields change."""
-
-    @tool
-    def my_tool(x: int) -> str:
-        """Original description."""
-        return str(x)
-
-    schema_before = my_tool.tool_call_schema
-    my_tool.description = "Updated description"
-    schema_after = my_tool.tool_call_schema
-
-    assert schema_before is not schema_after
-    assert schema_after.__doc__ == "Updated description"
-
-
-def test_args_cache_invalidated_on_args_schema_change() -> None:
-    """Verify cached args is invalidated when args_schema changes."""
-
-    class SchemaA(BaseModel):
-        x: int
-
-    class SchemaB(BaseModel):
-        y: str
-
-    @tool(args_schema=SchemaA)
-    def my_tool(x: int) -> str:
-        """A tool."""
-        return str(x)
-
-    args_before = my_tool.args
-    assert "x" in args_before
-
-    my_tool.args_schema = SchemaB
-    args_after = my_tool.args
-    assert "y" in args_after
-    assert "x" not in args_after

libs/core/tests/unit_tests/utils/test_pydantic.py

@@ -115,7 +115,7 @@ def test_with_field_metadata() -> None:
             description="List of integers", min_length=10, max_length=15
         )
 
-    subset_model = _create_subset_model_v2("Foo", Foo, ("x",))
+    subset_model = _create_subset_model_v2("Foo", Foo, ["x"])
     assert subset_model.model_json_schema() == {
         "properties": {
             "x": {
@@ -199,7 +199,7 @@ def test_create_subset_model_v2_preserves_default_factory() -> None:
     subset = _create_subset_model_v2(
         "Subset",
         Original,
-        ("required_field", "names", "mapping"),
+        ["required_field", "names", "mapping"],
     )
     schema = subset.model_json_schema()
     assert schema.get("required") == ["required_field"]

libs/langchain_v1/langchain/agents/factory.py

@@ -896,9 +896,11 @@ def create_agent(
     wrap_tool_call_wrapper = None
     if middleware_w_wrap_tool_call:
         wrappers = [
-            traceable(name=f"{m.name}.wrap_tool_call", process_inputs=_scrub_inputs)(
-                m.wrap_tool_call
-            )
+            traceable(
+                name=f"{m.name}.wrap_tool_call",
+                process_inputs=_scrub_inputs,
+                metadata={"ls_agent_type": "middleware"},
+            )(m.wrap_tool_call)
             for m in middleware_w_wrap_tool_call
         ]
         wrap_tool_call_wrapper = _chain_tool_call_wrappers(wrappers)
@@ -917,9 +919,11 @@ def create_agent(
     awrap_tool_call_wrapper = None
     if middleware_w_awrap_tool_call:
         async_wrappers = [
-            traceable(name=f"{m.name}.awrap_tool_call", process_inputs=_scrub_inputs)(
-                m.awrap_tool_call
-            )
+            traceable(
+                name=f"{m.name}.awrap_tool_call",
+                process_inputs=_scrub_inputs,
+                metadata={"ls_agent_type": "middleware"},
+            )(m.awrap_tool_call)
             for m in middleware_w_awrap_tool_call
         ]
         awrap_tool_call_wrapper = _chain_async_tool_call_wrappers(async_wrappers)
@@ -1005,9 +1009,11 @@ def create_agent(
     wrap_model_call_handler = None
     if middleware_w_wrap_model_call:
         sync_handlers = [
-            traceable(name=f"{m.name}.wrap_model_call", process_inputs=_scrub_inputs)(
-                m.wrap_model_call
-            )
+            traceable(
+                name=f"{m.name}.wrap_model_call",
+                process_inputs=_scrub_inputs,
+                metadata={"ls_agent_type": "middleware"},
+            )(m.wrap_model_call)
             for m in middleware_w_wrap_model_call
         ]
         wrap_model_call_handler = _chain_model_call_handlers(sync_handlers)
@@ -1016,9 +1022,11 @@ def create_agent(
     awrap_model_call_handler = None
     if middleware_w_awrap_model_call:
         async_handlers = [
-            traceable(name=f"{m.name}.awrap_model_call", process_inputs=_scrub_inputs)(
-                m.awrap_model_call
-            )
+            traceable(
+                name=f"{m.name}.awrap_model_call",
+                process_inputs=_scrub_inputs,
+                metadata={"ls_agent_type": "middleware"},
+            )(m.awrap_model_call)
             for m in middleware_w_awrap_model_call
         ]
         awrap_model_call_handler = _chain_async_model_call_handlers(async_handlers)

libs/langchain_v1/pyproject.toml

@@ -69,7 +69,6 @@ test = [
     "pytest-xdist<4.0.0,>=3.6.1",
     "pytest-mock",
     "pytest-benchmark>=5.1.0,<6.0.0",
-    "pytest-codspeed>=3.0.0,<4.0.0",
     "syrupy>=5.0.0,<6.0.0",
     "toml>=0.10.2,<1.0.0",
     "blockbuster>=1.5.26,<1.6.0",

libs/langchain_v1/tests/benchmarks/test_create_agent.py

@@ -1,14 +1,8 @@
-from __future__ import annotations
-
-import tracemalloc
-from itertools import cycle
 from typing import TYPE_CHECKING, Any
 
 import pytest
 from langchain_core.language_models.fake_chat_models import GenericFakeChatModel
 from langchain_core.messages import AIMessage
-from langchain_core.tools import tool
-from pydantic import BaseModel, Field
 from pytest_benchmark.fixture import BenchmarkFixture
 
 from langchain.agents import create_agent
@@ -24,314 +18,27 @@ if TYPE_CHECKING:
     from langchain.agents.middleware import AgentMiddleware
 
 
-# ---------------------------------------------------------------------------
-# Tool fixtures — a realistic mix of simple, structured, and nested schemas
-# ---------------------------------------------------------------------------
-
-
-@tool
-def simple_tool_1(x: int) -> str:
-    """Add one to a number."""
-    return str(x + 1)
-
-
-@tool
-def simple_tool_2(text: str) -> str:
-    """Reverse a string."""
-    return text[::-1]
-
-
-@tool
-def simple_tool_3(a: float, b: float) -> float:
-    """Multiply two numbers."""
-    return a * b
-
-
-@tool
-def simple_tool_4(items: list[str]) -> int:
-    """Count items in a list."""
-    return len(items)
-
-
-@tool
-def simple_tool_5(flag: bool, value: str) -> str:
-    """Return value if flag is true."""
-    return value if flag else ""
-
-
-class AddressSchema(BaseModel):
-    street: str = Field(description="Street address")
-    city: str = Field(description="City name")
-    zip_code: str = Field(description="ZIP code")
-
-
-class PersonSchema(BaseModel):
-    name: str = Field(description="Full name")
-    age: int = Field(description="Age in years")
-    address: AddressSchema = Field(description="Home address")
-    tags: list[str] = Field(default_factory=list, description="Tags")
-
-
-@tool(args_schema=PersonSchema)
-def structured_tool_1(name: str, age: int, address: AddressSchema, tags: list[str]) -> str:
-    """Look up a person by their details."""
-    return f"{name}, {age}"
-
-
-class SearchSchema(BaseModel):
-    query: str = Field(description="Search query string")
-    max_results: int = Field(default=10, description="Maximum results to return")
-    filters: dict[str, str] = Field(default_factory=dict, description="Filter map")
-
-
-@tool(args_schema=SearchSchema)
-def structured_tool_2(query: str, max_results: int, filters: dict[str, str]) -> list[str]:
-    """Search a database with filters."""
-    return [query] * min(max_results, 3)
-
-
-class FileSchema(BaseModel):
-    path: str = Field(description="File path")
-    encoding: str = Field(default="utf-8", description="File encoding")
-    lines: list[int] = Field(default_factory=list, description="Line numbers to read")
-
-
-@tool(args_schema=FileSchema)
-def structured_tool_3(path: str, encoding: str, lines: list[int]) -> str:
-    """Read a file at a given path."""
-    return f"content of {path}"
-
-
-class MatrixSchema(BaseModel):
-    rows: int = Field(description="Number of rows")
-    cols: int = Field(description="Number of columns")
-    fill: float = Field(default=0.0, description="Fill value")
-
-
-@tool(args_schema=MatrixSchema)
-def structured_tool_4(rows: int, cols: int, fill: float) -> list[list[float]]:
-    """Create a matrix filled with a value."""
-    return [[fill] * cols for _ in range(rows)]
-
-
-class CoordinateSchema(BaseModel):
-    lat: float = Field(description="Latitude")
-    lon: float = Field(description="Longitude")
-
-
-class LocationSchema(BaseModel):
-    name: str = Field(description="Location name")
-    coordinate: CoordinateSchema = Field(description="GPS coordinate")
-    altitude_m: float = Field(default=0.0, description="Altitude in meters")
-
-
-class RouteSchema(BaseModel):
-    origin: LocationSchema = Field(description="Starting location")
-    destination: LocationSchema = Field(description="Ending location")
-    waypoints: list[LocationSchema] = Field(default_factory=list, description="Intermediate stops")
-    max_distance_km: float = Field(default=1000.0, description="Maximum route distance")
-
-
-@tool(args_schema=RouteSchema)
-def deep_nested_tool(
-    origin: LocationSchema,
-    destination: LocationSchema,
-    waypoints: list[LocationSchema],
-    max_distance_km: float,
-) -> dict[str, Any]:
-    """Plan a route between locations with deep nested schema."""
-    return {"origin": origin.name, "destination": destination.name}
-
-
-@tool
-def complex_tool_1(
-    name: str,
-    metadata: dict[str, Any],
-    tags: list[str],
-    priority: int = 0,
-) -> dict[str, Any]:
-    """Create an item with metadata."""
-    return {"name": name, "metadata": metadata, "tags": tags, "priority": priority}
-
-
-@tool
-def complex_tool_2(
-    source: str,
-    destination: str,
-    options: dict[str, str] | None = None,
-) -> bool:
-    """Copy data from source to destination."""
-    return True
-
-
-@tool
-def complex_tool_3(
-    ids: list[int],
-    batch_size: int = 10,
-    retry: bool = False,
-) -> dict[str, list[int]]:
-    """Process a batch of IDs."""
-    return {"processed": ids[:batch_size]}
-
-
-@tool
-def complex_tool_4(
-    expression: str,
-    variables: dict[str, float],
-    precision: int = 6,
-) -> float:
-    """Evaluate a mathematical expression."""
-    return 0.0
-
-
-@tool
-def complex_tool_5(
-    url: str,
-    method: str = "GET",
-    headers: dict[str, str] | None = None,
-    body: str | None = None,
-    timeout: int = 30,
-) -> dict[str, Any]:
-    """Make an HTTP request."""
-    return {"status": 200, "body": ""}
-
-
-SMALL_TOOLS = [simple_tool_1, simple_tool_2, simple_tool_3]
-MEDIUM_TOOLS = [
-    simple_tool_1,
-    simple_tool_2,
-    simple_tool_3,
-    simple_tool_4,
-    simple_tool_5,
-    structured_tool_1,
-    structured_tool_2,
-]
-LARGE_TOOLS = [
-    simple_tool_1,
-    simple_tool_2,
-    simple_tool_3,
-    simple_tool_4,
-    simple_tool_5,
-    structured_tool_1,
-    structured_tool_2,
-    structured_tool_3,
-    structured_tool_4,
-    deep_nested_tool,
-    complex_tool_1,
-    complex_tool_2,
-    complex_tool_3,
-    complex_tool_4,
-    complex_tool_5,
-]
-
-
-def _make_model() -> GenericFakeChatModel:
-    return GenericFakeChatModel(messages=cycle([AIMessage(content="ok")]))
-
-
-# ---------------------------------------------------------------------------
-# Benchmarks
-# ---------------------------------------------------------------------------
-
-
 @pytest.mark.benchmark
 def test_create_agent_instantiation(benchmark: BenchmarkFixture) -> None:
-    """Baseline: no tools."""
-    benchmark(lambda: create_agent(model=_make_model()))
+    def instantiate_agent() -> None:
+        create_agent(model=GenericFakeChatModel(messages=iter([AIMessage(content="ok")])))
+
+    benchmark(instantiate_agent)
 
 
 @pytest.mark.benchmark
-def test_create_agent_small_tools(benchmark: BenchmarkFixture) -> None:
-    """3 simple tools."""
-    benchmark(lambda: create_agent(model=_make_model(), tools=SMALL_TOOLS))
-
-
-@pytest.mark.benchmark
-def test_create_agent_medium_tools(benchmark: BenchmarkFixture) -> None:
-    """7 mixed tools."""
-    benchmark(lambda: create_agent(model=_make_model(), tools=MEDIUM_TOOLS))
-
-
-@pytest.mark.benchmark
-def test_create_agent_large_tools(benchmark: BenchmarkFixture) -> None:
-    """15 tools including complex nested schemas."""
-    benchmark(lambda: create_agent(model=_make_model(), tools=LARGE_TOOLS))
-
-
-@pytest.mark.benchmark
-def test_create_agent_large_tools_with_middleware(benchmark: BenchmarkFixture) -> None:
-    """15 tools + full middleware stack."""
-    def run() -> None:
+def test_create_agent_instantiation_with_middleware(
+    benchmark: BenchmarkFixture,
+) -> None:
+    def instantiate_agent() -> None:
         middleware: Sequence[AgentMiddleware[Any, Any]] = (
             TodoListMiddleware(),
             ToolRetryMiddleware(),
             ModelRetryMiddleware(),
         )
         create_agent(
-            model=_make_model(),
-            tools=LARGE_TOOLS,
+            model=GenericFakeChatModel(messages=iter([AIMessage(content="ok")])),
             middleware=middleware,
         )
 
-    benchmark(run)
-
-
-@pytest.mark.benchmark
-def test_tool_call_schema_repeated_access(benchmark: BenchmarkFixture) -> None:
-    """Measure cost of repeated .tool_call_schema access on a complex tool (10 accesses per iteration)."""
-    t = structured_tool_1
-
-    def access_schema_10x() -> None:
-        for _ in range(10):
-            t.tool_call_schema
-
-    benchmark(access_schema_10x)
-
-
-@pytest.mark.benchmark
-def test_tool_args_repeated_access(benchmark: BenchmarkFixture) -> None:
-    """Measure cost of repeated .args access on a complex tool (10 accesses per iteration)."""
-    t = structured_tool_1
-
-    def access_args_10x() -> None:
-        for _ in range(10):
-            t.args
-
-    benchmark(access_args_10x)
-
-
-@pytest.mark.benchmark
-def test_create_agent_instantiation_with_middleware(benchmark: BenchmarkFixture) -> None:
-    """Baseline with middleware, no tools."""
-    def run() -> None:
-        middleware: Sequence[AgentMiddleware[Any, Any]] = (
-            TodoListMiddleware(),
-            ToolRetryMiddleware(),
-            ModelRetryMiddleware(),
-        )
-        create_agent(model=_make_model(), middleware=middleware)
-
-    benchmark(run)
-
-
-# ---------------------------------------------------------------------------
-# Memory snapshot (not a codspeed benchmark — uses tracemalloc directly)
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.benchmark
-def test_create_agent_large_tools_memory() -> None:
-    """Observe peak memory for large-tools agent creation.
-
-    This is not a hard assertion — it records the tracemalloc peak for the
-    memory allocated *during* create_agent. Run before and after optimization
-    passes to track improvement. Update the printed baseline comment below
-    when the number changes significantly.
-    """
-    tracemalloc.start()
-    create_agent(model=_make_model(), tools=LARGE_TOOLS)
-    _, peak = tracemalloc.get_traced_memory()
-    tracemalloc.stop()
-    peak_kb = peak / 1024
-    # Baseline (pre-optimization): ~recorded after first run
-    print(f"\nPeak memory during create_agent (15 tools): {peak_kb:.1f} KB")
+    benchmark(instantiate_agent)

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

@@ -29,11 +29,19 @@ from langsmith import Client
 from langsmith.run_helpers import tracing_context
 
 from langchain.agents import create_agent
-from langchain.agents.middleware.types import AgentMiddleware, AgentState
+from langchain.agents.middleware.types import (
+    AgentMiddleware,
+    AgentState,
+    ModelCallResult,
+    ModelRequest,
+    ModelResponse,
+)
 from langchain.tools import InjectedState, ToolRuntime
 from tests.unit_tests.agents.model import FakeToolCallingModel
 
 if TYPE_CHECKING:
+    from collections.abc import Callable
+
     from langgraph.runtime import Runtime
 
 
@@ -849,107 +857,170 @@ async def test_combined_injected_state_runtime_store_async() -> None:
     assert injected_data["store_write_success"] is True
 
 
-def test_ls_agent_type_is_trace_only_metadata() -> None:
-    """Test that ls_agent_type is added to metadata on tracing only, not in streamed chunks."""
-    # Capture metadata from regular callback handler (simulates streamed metadata)
-    captured_callback_metadata: list[dict[str, Any]] = []
+# ---------------------------------------------------------------------------
+# ls_agent_type tracing metadata
+# ---------------------------------------------------------------------------
 
-    class CaptureHandler(BaseCallbackHandler):
-        def on_chain_start(
+
+class _CaptureCallbackHandler(BaseCallbackHandler):
+    """Records metadata observed on every ``on_chain_start`` callback."""
+
+    def __init__(self) -> None:
+        self.captured: list[dict[str, Any]] = []
+
+    def on_chain_start(
+        self,
+        serialized: dict[str, Any],
+        inputs: dict[str, Any],
+        *,
+        run_id: str,
+        parent_run_id: str | None = None,
+        tags: list[str] | None = None,
+        metadata: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        self.captured.append(
+            {
+                "name": kwargs.get("name") or (serialized or {}).get("name"),
+                "tags": tags,
+                "metadata": metadata or {},
+            }
+        )
+
+
+def _build_mock_langsmith_client() -> tuple[MagicMock, Client]:
+    """Return a (session, client) pair where the session records tracing POSTs."""
+    mock_session = MagicMock()
+    mock_client = Client(session=mock_session, api_key="test", auto_batch_tracing=False)
+    return mock_session, mock_client
+
+
+def _posted_runs(mock_session: MagicMock) -> list[dict[str, Any]]:
+    """Extract the run dicts POSTed to the LangSmith API by the mock session."""
+    posts: list[dict[str, Any]] = []
+    for call in mock_session.request.mock_calls:
+        if call.args and call.args[0] == "POST":
+            body = json.loads(call.kwargs["data"])
+            if "post" in body:
+                posts.extend(body["post"])
+            else:
+                posts.append(body)
+    return posts
+
+
+def _run_metadata(post: dict[str, Any]) -> dict[str, Any]:
+    return post.get("extra", {}).get("metadata", {}) or {}
+
+
+def test_ls_agent_type_root_is_trace_only_metadata() -> None:
+    """``ls_agent_type='root'`` reaches the LangSmith tracer but not callback metadata."""
+    handler = _CaptureCallbackHandler()
+    mock_session, mock_client = _build_mock_langsmith_client()
+
+    agent = create_agent(
+        model=FakeToolCallingModel(tool_calls=[[], []]),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+    )
+
+    with tracing_context(client=mock_client, enabled=True):
+        agent.invoke(
+            {"messages": [HumanMessage("hi?")]},
+            config={"callbacks": [handler]},
+        )
+
+    # ls_agent_type must not leak into callback metadata.
+    assert handler.captured, "expected on_chain_start to fire at least once"
+    for entry in handler.captured:
+        assert entry["metadata"].get("ls_agent_type") is None, (
+            f"ls_agent_type leaked into callback metadata: {entry['metadata']}"
+        )
+
+    # ls_agent_type='root' must reach the tracer on the root run.
+    posts = _posted_runs(mock_session)
+    assert posts, "expected at least one LangSmith POST"
+    assert _run_metadata(posts[0]).get("ls_agent_type") == "root"
+
+
+def test_ls_agent_type_is_overridable_via_configurable() -> None:
+    """A caller can override ``ls_agent_type`` (and add keys) via ``configurable``."""
+    mock_session, mock_client = _build_mock_langsmith_client()
+
+    agent = create_agent(
+        model=FakeToolCallingModel(tool_calls=[[], []]),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+    )
+
+    with tracing_context(client=mock_client, enabled=True):
+        agent.invoke(
+            {"messages": [HumanMessage("hi?")]},
+            config={
+                "configurable": {
+                    "ls_agent_type": "subagent",
+                    "custom_key": "custom_value",
+                }
+            },
+        )
+
+    posts = _posted_runs(mock_session)
+    assert posts, "expected at least one LangSmith POST"
+    root_metadata = _run_metadata(posts[0])
+    assert root_metadata.get("ls_agent_type") == "subagent"
+    # Extra configurable keys also flow into tracer metadata.
+    assert root_metadata.get("custom_key") == "custom_value"
+
+
+def test_ls_agent_type_middleware_is_trace_only_metadata() -> None:
+    """Middleware traceable runs are tagged with ``ls_agent_type='middleware'``.
+
+    The tag is attached via the ``metadata=`` argument of langsmith's
+    ``traceable`` decorator, which routes it to ``run.extra.metadata`` for
+    LangSmith only -- it must not leak into on_chain_start callback metadata.
+    """
+
+    class PassthroughMiddleware(AgentMiddleware):
+        name = "test-passthrough"
+
+        def wrap_model_call(
             self,
-            serialized: dict[str, Any],
-            inputs: dict[str, Any],
-            *,
-            run_id: str,
-            parent_run_id: str | None = None,
-            tags: list[str] | None = None,
-            metadata: dict[str, Any] | None = None,
-            **kwargs: Any,
-        ) -> None:
-            captured_callback_metadata.append({"tags": tags, "metadata": metadata})
+            request: ModelRequest,
+            handler: Callable[[ModelRequest], ModelResponse],
+        ) -> ModelCallResult:
+            return handler(request)
 
-    # Create a mock client to capture what gets sent to LangSmith
-    mock_session = MagicMock()
-    mock_client = Client(session=mock_session, api_key="test", auto_batch_tracing=False)
+    handler = _CaptureCallbackHandler()
+    mock_session, mock_client = _build_mock_langsmith_client()
 
     agent = create_agent(
         model=FakeToolCallingModel(tool_calls=[[], []]),
         tools=[],
         system_prompt="You are a helpful assistant.",
+        middleware=[PassthroughMiddleware()],
     )
 
-    # Use tracing_context to enable tracing with the mock client
     with tracing_context(client=mock_client, enabled=True):
         agent.invoke(
             {"messages": [HumanMessage("hi?")]},
-            config={"callbacks": [CaptureHandler()]},
+            config={"callbacks": [handler]},
         )
 
-    # Verify that ls_agent_type is NOT in the regular callback metadata
-    # (it should only go to the tracer via langsmith_inheritable_metadata)
-    assert len(captured_callback_metadata) > 0
-    for captured in captured_callback_metadata:
-        metadata = captured.get("metadata") or {}
-        assert metadata.get("ls_agent_type") is None, (
-            f"ls_agent_type should not be in callback metadata, but got: {metadata}"
+    # (1) ls_agent_type='middleware' must not leak into callback metadata.
+    for entry in handler.captured:
+        assert entry["metadata"].get("ls_agent_type") != "middleware", (
+            f"ls_agent_type='middleware' leaked into callback metadata for "
+            f"run {entry['name']!r}: {entry['metadata']}"
         )
 
-    # Verify that ls_agent_type IS in the tracer metadata (sent to LangSmith)
-    # Get the POST requests to the LangSmith API
-    posts = []
-    for call in mock_session.request.mock_calls:
-        if call.args and call.args[0] == "POST":
-            body = json.loads(call.kwargs["data"])
-            if "post" in body:
-                posts.extend(body["post"])
-            else:
-                posts.append(body)
-
-    assert len(posts) >= 1
-    # Find the root run (the agent execution)
-    root_post = posts[0]
-    metadata = root_post.get("extra", {}).get("metadata", {})
-    assert metadata.get("ls_agent_type") == "root", (
-        f"ls_agent_type should be 'root' in tracer metadata, but got: {metadata}"
+    # (2) ls_agent_type='middleware' must reach the LangSmith tracer, on a run
+    # named after the middleware's traceable (e.g. 'test-passthrough.wrap_model_call').
+    posts = _posted_runs(mock_session)
+    middleware_posts = [p for p in posts if _run_metadata(p).get("ls_agent_type") == "middleware"]
+    assert middleware_posts, (
+        f"expected a LangSmith post with ls_agent_type='middleware'; "
+        f"saw metadatas: {[_run_metadata(p) for p in posts]}"
     )
-
-
-def test_ls_agent_type_is_overridable() -> None:
-    """Test that ls_agent_type can be overridden via configurable in invoke config."""
-    # Create a mock client to capture what gets sent to LangSmith
-    mock_session = MagicMock()
-    mock_client = Client(session=mock_session, api_key="test", auto_batch_tracing=False)
-
-    agent = create_agent(
-        model=FakeToolCallingModel(tool_calls=[[], []]),
-        tools=[],
-        system_prompt="You are a helpful assistant.",
-    )
-
-    # Use tracing_context to enable tracing with the mock client
-    with tracing_context(client=mock_client, enabled=True):
-        agent.invoke(
-            {"messages": [HumanMessage("hi?")]},
-            config={"configurable": {"ls_agent_type": "subagent", "custom_key": "custom_value"}},
-        )
-
-    # Verify that ls_agent_type is overridden and configurable is merged in the tracer metadata
-    posts = []
-    for call in mock_session.request.mock_calls:
-        if call.args and call.args[0] == "POST":
-            body = json.loads(call.kwargs["data"])
-            if "post" in body:
-                posts.extend(body["post"])
-            else:
-                posts.append(body)
-
-    assert len(posts) >= 1
-    root_post = posts[0]
-    metadata = root_post.get("extra", {}).get("metadata", {})
-    assert metadata.get("ls_agent_type") == "subagent", (
-        f"ls_agent_type should be 'subagent' in tracer metadata, but got: {metadata}"
-    )
-    # Verify that the additional configurable key is merged into metadata
-    assert metadata.get("custom_key") == "custom_value", (
-        f"custom_key should be 'custom_value' in tracer metadata, but got: {metadata}"
+    assert any("test-passthrough" in (p.get("name") or "") for p in middleware_posts), (
+        f"expected a middleware run named like 'test-passthrough.wrap_model_call', "
+        f"got: {[p.get('name') for p in middleware_posts]}"
     )

libs/langchain_v1/uv.lock

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     { name = "pytest-asyncio" },
     { name = "pytest-benchmark" },
-    { name = "pytest-codspeed" },
     { name = "pytest-cov" },
     { name = "pytest-mock" },
     { name = "pytest-socket" },
@@ -2045,7 +2039,6 @@ test = [
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     { name = "pytest-asyncio", specifier = ">=1.3.0,<2.0.0" },
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-    { name = "pytest-codspeed", specifier = ">=3.0.0,<4.0.0" },
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     { name = "pytest-mock" },
     { name = "pytest-socket", specifier = ">=0.6.0,<1.0.0" },
@@ -4172,24 +4165,28 @@ wheels = [
 
 [[package]]
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-version = "3.2.0"
+version = "4.2.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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 ]
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 ]
 
 [[package]]

libs/partners/openai/langchain_openai/chat_models/azure.py

@@ -705,10 +705,6 @@ class AzureChatOpenAI(BaseChatOpenAI):
         return self
 
     def _resolve_model_profile(self) -> ModelProfile | None:
-        if (self.model_name is not None) and (
-            profile := _get_default_model_profile(self.model_name) or None
-        ):
-            return profile
         if self.deployment_name is not None:
             return _get_default_model_profile(self.deployment_name) or None
         return None

libs/partners/openai/langchain_openai/data/_profiles.py

@@ -41,6 +41,31 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "image_tool_message": True,
         "tool_choice": True,
     },
+    "codex-mini-latest": {
+        "name": "Codex Mini",
+        "release_date": "2025-05-16",
+        "last_updated": "2025-05-16",
+        "open_weights": False,
+        "max_input_tokens": 200000,
+        "max_output_tokens": 100000,
+        "text_inputs": True,
+        "image_inputs": False,
+        "audio_inputs": False,
+        "video_inputs": False,
+        "text_outputs": True,
+        "image_outputs": False,
+        "audio_outputs": False,
+        "video_outputs": False,
+        "reasoning_output": True,
+        "tool_calling": True,
+        "attachment": True,
+        "temperature": False,
+        "image_url_inputs": True,
+        "pdf_inputs": True,
+        "pdf_tool_message": True,
+        "image_tool_message": True,
+        "tool_choice": True,
+    },
     "gpt-3.5-turbo": {
         "name": "GPT-3.5-turbo",
         "release_date": "2023-03-01",

libs/partners/openai/tests/unit_tests/chat_models/test_azure.py

@@ -16,7 +16,6 @@ def test_initialize_azure_openai() -> None:
         azure_deployment="35-turbo-dev",
         openai_api_version="2023-05-15",
         azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
     )
     assert llm.deployment_name == "35-turbo-dev"
     assert llm.openai_api_version == "2023-05-15"
@@ -46,92 +45,6 @@ def test_initialize_more() -> None:
     assert ls_params.get("ls_model_name") == "gpt-35-turbo-0125"
 
 
-def test_profile_resolves_from_model_name() -> None:
-    llm = AzureChatOpenAI(
-        model="gpt-4o",
-        azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
-        api_version="2023-05-15",
-    )
-
-    assert llm.profile
-    assert llm.profile["name"] == "GPT-4o"
-    assert llm.profile["max_input_tokens"] == 128_000
-
-
-def test_profile_resolves_from_model_name_with_custom_deployment_alias() -> None:
-    llm = AzureChatOpenAI(
-        model="gpt-4o",
-        azure_deployment="35-turbo-dev",
-        azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
-        api_version="2023-05-15",
-    )
-
-    assert llm.profile
-    assert llm.profile["name"] == "GPT-4o"
-
-
-def test_profile_prefers_model_name_over_known_deployment_name() -> None:
-    llm = AzureChatOpenAI(
-        model="gpt-4o",
-        azure_deployment="gpt-4",
-        azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
-        api_version="2023-05-15",
-    )
-
-    assert llm.profile
-    assert llm.profile["name"] == "GPT-4o"
-
-
-def test_profile_falls_back_to_deployment_name_with_unknown_model() -> None:
-    llm = AzureChatOpenAI(
-        model="unknown-model",
-        azure_deployment="gpt-4o",
-        azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
-        api_version="2023-05-15",
-    )
-
-    assert llm.profile
-
-
-def test_profile_resolves_from_deployment_name_without_model() -> None:
-    llm = AzureChatOpenAI(
-        azure_deployment="gpt-4o",
-        azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
-        api_version="2023-05-15",
-    )
-
-    assert llm.profile
-    assert llm.profile["name"] == "GPT-4o"
-
-
-def test_profile_respects_explicit_profile() -> None:
-    llm = AzureChatOpenAI(
-        model="gpt-4o",
-        azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
-        api_version="2023-05-15",
-        profile={"tool_calling": False},
-    )
-
-    assert llm.profile == {"tool_calling": False}
-
-
-def test_profile_is_none_for_unknown_deployment_without_model() -> None:
-    llm = AzureChatOpenAI(
-        azure_deployment="unknown-deployment",
-        azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
-        api_version="2023-05-15",
-    )
-
-    assert llm.profile is None
-
-
 def test_initialize_azure_openai_with_openai_api_base_set() -> None:
     with mock.patch.dict(os.environ, {"OPENAI_API_BASE": "https://api.openai.com"}):
         llm = AzureChatOpenAI(  # type: ignore[call-arg, call-arg]
@@ -166,7 +79,6 @@ def test_structured_output_old_model() -> None:
             azure_deployment="35-turbo-dev",
             openai_api_version="2023-05-15",
             azure_endpoint="my-base-url",
-            api_key=SecretStr("test"),
         ).with_structured_output(Output)
 
     # assert tool calling was used instead of json_schema
@@ -179,7 +91,6 @@ def test_max_completion_tokens_in_payload() -> None:
         azure_deployment="o1-mini",
         api_version="2024-12-01-preview",
         azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
         model_kwargs={"max_completion_tokens": 300},
     )
     messages = [HumanMessage("Hello")]
@@ -237,7 +148,6 @@ def test_max_completion_tokens_parameter() -> None:
         azure_deployment="gpt-5",
         api_version="2024-12-01-preview",
         azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
         max_completion_tokens=1500,
     )
     messages = [HumanMessage("Hello")]
@@ -255,7 +165,6 @@ def test_max_tokens_converted_to_max_completion_tokens() -> None:
         azure_deployment="gpt-5",
         api_version="2024-12-01-preview",
         azure_endpoint="my-base-url",
-        api_key=SecretStr("test"),
         max_tokens=1000,  # type: ignore[call-arg]
     )
     messages = [HumanMessage("Hello")]

libs/partners/openrouter/langchain_openrouter/data/_profiles.py

@@ -167,28 +167,6 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "attachment": True,
         "temperature": True,
     },
-    "anthropic/claude-opus-4.7": {
-        "name": "Claude Opus 4.7",
-        "release_date": "2026-04-16",
-        "last_updated": "2026-04-16",
-        "open_weights": False,
-        "max_input_tokens": 1000000,
-        "max_output_tokens": 128000,
-        "text_inputs": True,
-        "image_inputs": True,
-        "audio_inputs": False,
-        "pdf_inputs": True,
-        "video_inputs": False,
-        "text_outputs": True,
-        "image_outputs": False,
-        "audio_outputs": False,
-        "video_outputs": False,
-        "reasoning_output": True,
-        "tool_calling": True,
-        "structured_output": True,
-        "attachment": True,
-        "temperature": False,
-    },
     "anthropic/claude-sonnet-4": {
         "name": "Claude Sonnet 4",
         "release_date": "2025-05-22",