Merge branch 'master' into imnishitha/openai/responses-api-strict-default

libs/core/langchain_core/callbacks/usage.py

@@ -114,6 +114,11 @@ def get_usage_metadata_callback(
     )
     register_configure_hook(usage_metadata_callback_var, inheritable=True)
     cb = UsageMetadataCallbackHandler()
-    usage_metadata_callback_var.set(cb)
-    yield cb
-    usage_metadata_callback_var.set(None)
+    token = usage_metadata_callback_var.set(cb)
+    try:
+        yield cb
+    finally:
+        # Always clear the context var, including when the with-block raises.
+        # Without finally, post-block model calls keep accumulating into cb
+        # (see #38989).
+        usage_metadata_callback_var.reset(token)

libs/core/langchain_core/language_models/chat_models.py

@@ -1888,9 +1888,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         # We should check the cache unless it's explicitly set to False
         # A None cache means we should use the default global cache
         # if it's configured.
-        check_cache = self.cache or self.cache is None
+        check_cache = self.cache is not False
         if check_cache:
-            if llm_cache:
+            if llm_cache is not None:
                 llm_string = self._get_llm_string(stop=stop, **kwargs)
                 normalized_messages = [
                     (
@@ -2032,7 +2032,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                 **result.llm_output,
                 **result.generations[0].message.response_metadata,
             }
-        if check_cache and llm_cache:
+        if check_cache and llm_cache is not None:
             llm_cache.update(prompt, llm_string, result.generations)
         return result
 
@@ -2047,9 +2047,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
         # We should check the cache unless it's explicitly set to False
         # A None cache means we should use the default global cache
         # if it's configured.
-        check_cache = self.cache or self.cache is None
+        check_cache = self.cache is not False
         if check_cache:
-            if llm_cache:
+            if llm_cache is not None:
                 llm_string = self._get_llm_string(stop=stop, **kwargs)
                 normalized_messages = [
                     (
@@ -2189,7 +2189,7 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                 **result.llm_output,
                 **result.generations[0].message.response_metadata,
             }
-        if check_cache and llm_cache:
+        if check_cache and llm_cache is not None:
             await llm_cache.aupdate(prompt, llm_string, result.generations)
         return result
 

libs/core/langchain_core/language_models/llms.py

@@ -182,13 +182,13 @@ def get_prompts(
 
     llm_cache = _resolve_cache(cache=cache)
     for i, prompt in enumerate(prompts):
-        if llm_cache:
+        if llm_cache is not None:
             cache_val = llm_cache.lookup(prompt, llm_string)
             if isinstance(cache_val, list):
                 existing_prompts[i] = cache_val
-            else:
-                missing_prompts.append(prompt)
-                missing_prompt_idxs.append(i)
+                continue
+        missing_prompts.append(prompt)
+        missing_prompt_idxs.append(i)
     return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts
 
 
@@ -217,13 +217,13 @@ async def aget_prompts(
     existing_prompts = {}
     llm_cache = _resolve_cache(cache=cache)
     for i, prompt in enumerate(prompts):
-        if llm_cache:
+        if llm_cache is not None:
             cache_val = await llm_cache.alookup(prompt, llm_string)
             if isinstance(cache_val, list):
                 existing_prompts[i] = cache_val
-            else:
-                missing_prompts.append(prompt)
-                missing_prompt_idxs.append(i)
+                continue
+        missing_prompts.append(prompt)
+        missing_prompt_idxs.append(i)
     return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts
 
 
@@ -288,7 +288,7 @@ async def aupdate_cache(
     for i, result in enumerate(new_results.generations):
         existing_prompts[missing_prompt_idxs[i]] = result
         prompt = prompts[missing_prompt_idxs[i]]
-        if llm_cache:
+        if llm_cache is not None:
             await llm_cache.aupdate(prompt, llm_string, result)
     return new_results.llm_output
 

libs/core/langchain_core/load/serializable.py

@@ -12,6 +12,7 @@ from typing import (
 
 from pydantic import BaseModel, ConfigDict
 from pydantic.fields import FieldInfo
+from pydantic_core import PydanticUndefined
 from typing_extensions import NotRequired, override
 
 logger = logging.getLogger(__name__)
@@ -70,14 +71,31 @@ def try_neq_default(value: Any, key: str, model: BaseModel) -> bool:
     return _try_neq_default(value, field)
 
 
+def _get_field_default(field: FieldInfo) -> Any:
+    # Pydantic 2.14+ returns ``PydanticUndefined`` (rather than ``None``) from
+    # ``get_default()`` for an un-called ``default_factory``. Restore the historical
+    # ``None`` so a factory-defaulted field at its default is still treated as
+    # unchanged. The factory is intentionally not called: ``get_default()`` keeps
+    # ``call_default_factory=False`` precisely because factories may have side effects.
+    default = field.get_default()
+    if default is PydanticUndefined and field.default_factory is not None:
+        return None
+    return default
+
+
 def _try_neq_default(value: Any, field: FieldInfo) -> bool:
     # Handle edge case: inequality of two objects does not evaluate to a bool (e.g. two
     # Pandas DataFrames).
     try:
-        return bool(field.get_default() != value)
+        default = _get_field_default(field)
+    except Exception as _:
+        # A raising default_factory means we cannot compare; treat as non-default.
+        return True
+    try:
+        return bool(default != value)
     except Exception as _:
         try:
-            return all(field.get_default() != value)
+            return all(default != value)
         except Exception as _:
             try:
                 return value is not field.default

libs/core/langchain_core/messages/block_translators/anthropic.py

@@ -26,6 +26,10 @@ def _populate_extras(
     return standard_block
 
 
+def _non_standard_block(block: dict[str, Any]) -> types.NonStandardContentBlock:
+    return {"type": "non_standard", "value": block}
+
+
 def _convert_to_v1_from_anthropic_input(
     content: list[types.ContentBlock],
 ) -> list[types.ContentBlock]:
@@ -54,87 +58,107 @@ def _convert_to_v1_from_anthropic_input(
         ]
         for block in blocks:
             block_type = block.get("type")
+            source = block.get("source")
 
             if (
                 block_type == "document"
-                and "source" in block
-                and "type" in block["source"]
+                and isinstance(source, dict)
+                and "type" in source
             ):
-                if block["source"]["type"] == "base64":
+                if source["type"] == "base64":
+                    if "data" not in source or "media_type" not in source:
+                        yield _non_standard_block(block)
+                        continue
                     file_block: types.FileContentBlock = {
                         "type": "file",
-                        "base64": block["source"]["data"],
-                        "mime_type": block["source"]["media_type"],
+                        "base64": source["data"],
+                        "mime_type": source["media_type"],
                     }
                     _populate_extras(file_block, block, {"type", "source"})
                     yield file_block
 
-                elif block["source"]["type"] == "url":
+                elif source["type"] == "url":
+                    if "url" not in source:
+                        yield _non_standard_block(block)
+                        continue
                     file_block = {
                         "type": "file",
-                        "url": block["source"]["url"],
+                        "url": source["url"],
                     }
                     _populate_extras(file_block, block, {"type", "source"})
                     yield file_block
 
-                elif block["source"]["type"] == "file":
+                elif source["type"] == "file":
+                    if "file_id" not in source:
+                        yield _non_standard_block(block)
+                        continue
                     file_block = {
                         "type": "file",
-                        "id": block["source"]["file_id"],
+                        "id": source["file_id"],
                     }
                     _populate_extras(file_block, block, {"type", "source"})
                     yield file_block
 
-                elif block["source"]["type"] == "text":
+                elif source["type"] == "text":
+                    if "data" not in source:
+                        yield _non_standard_block(block)
+                        continue
                     plain_text_block: types.PlainTextContentBlock = {
                         "type": "text-plain",
-                        "text": block["source"]["data"],
+                        "text": source["data"],
                         "mime_type": block.get("media_type", "text/plain"),
                     }
                     _populate_extras(plain_text_block, block, {"type", "source"})
                     yield plain_text_block
 
                 else:
-                    yield {"type": "non_standard", "value": block}
+                    yield _non_standard_block(block)
 
             elif (
-                block_type == "image"
-                and "source" in block
-                and "type" in block["source"]
+                block_type == "image" and isinstance(source, dict) and "type" in source
             ):
-                if block["source"]["type"] == "base64":
+                if source["type"] == "base64":
+                    if "data" not in source or "media_type" not in source:
+                        yield _non_standard_block(block)
+                        continue
                     image_block: types.ImageContentBlock = {
                         "type": "image",
-                        "base64": block["source"]["data"],
-                        "mime_type": block["source"]["media_type"],
+                        "base64": source["data"],
+                        "mime_type": source["media_type"],
                     }
                     _populate_extras(image_block, block, {"type", "source"})
                     yield image_block
 
-                elif block["source"]["type"] == "url":
+                elif source["type"] == "url":
+                    if "url" not in source:
+                        yield _non_standard_block(block)
+                        continue
                     image_block = {
                         "type": "image",
-                        "url": block["source"]["url"],
+                        "url": source["url"],
                     }
                     _populate_extras(image_block, block, {"type", "source"})
                     yield image_block
 
-                elif block["source"]["type"] == "file":
+                elif source["type"] == "file":
+                    if "file_id" not in source:
+                        yield _non_standard_block(block)
+                        continue
                     image_block = {
                         "type": "image",
-                        "id": block["source"]["file_id"],
+                        "id": source["file_id"],
                     }
                     _populate_extras(image_block, block, {"type", "source"})
                     yield image_block
 
                 else:
-                    yield {"type": "non_standard", "value": block}
+                    yield _non_standard_block(block)
 
             elif block_type in types.KNOWN_BLOCK_TYPES:
                 yield cast("types.ContentBlock", block)
 
             else:
-                yield {"type": "non_standard", "value": block}
+                yield _non_standard_block(block)
 
     return list(_iter_blocks())
 
@@ -143,6 +167,11 @@ def _convert_citation_to_v1(citation: dict[str, Any]) -> types.Annotation:
     citation_type = citation.get("type")
 
     if citation_type == "web_search_result_location":
+        if "cited_text" not in citation or "url" not in citation:
+            return {
+                "type": "non_standard_annotation",
+                "value": citation,
+            }
         url_citation: types.Citation = {
             "type": "citation",
             "cited_text": citation["cited_text"],
@@ -165,6 +194,11 @@ def _convert_citation_to_v1(citation: dict[str, Any]) -> types.Annotation:
         "page_location",
         "search_result_location",
     }:
+        if "cited_text" not in citation:
+            return {
+                "type": "non_standard_annotation",
+                "value": citation,
+            }
         document_citation: types.Citation = {
             "type": "citation",
             "cited_text": citation["cited_text"],

libs/core/langchain_core/prompts/dict.py

@@ -161,7 +161,7 @@ def _insert_input_variables(
                 warnings.warn(msg, stacklevel=2)
             formatted[k] = _insert_input_variables(v, inputs, template_format)
         elif isinstance(v, (list, tuple)):
-            formatted_v: list[str | dict[str, Any]] = []
+            formatted_v: list[Any] = []
             for x in v:
                 if isinstance(x, str):
                     formatted_v.append(formatter(x, **inputs))
@@ -169,6 +169,8 @@ def _insert_input_variables(
                     formatted_v.append(
                         _insert_input_variables(x, inputs, template_format)
                     )
+                else:
+                    formatted_v.append(x)
             formatted[k] = type(v)(formatted_v)
         else:
             formatted[k] = v

libs/core/langchain_core/prompts/structured.py

@@ -69,7 +69,8 @@ class StructuredPrompt(ChatPromptTemplate):
                 f"{schema_}"
             )
             raise ValueError(err_msg)
-        structured_output_kwargs = structured_output_kwargs or {}
+        # Avoid mutating a caller-provided dict when merging extra kwargs.
+        structured_output_kwargs = dict(structured_output_kwargs or {})
         for k in set(kwargs).difference(get_pydantic_field_names(self.__class__)):
             structured_output_kwargs[k] = kwargs.pop(k)
         super().__init__(

libs/core/langchain_core/runnables/utils.py

@@ -477,6 +477,9 @@ class AddableDict(dict[str, Any]):
 
         Returns:
             A dictionary that is the result of adding the two dictionaries.
+
+        Raises:
+            TypeError: If a shared key holds values of incompatible types.
         """
         chunk = AddableDict(self)
         for key in other:
@@ -485,8 +488,13 @@ class AddableDict(dict[str, Any]):
             elif other[key] is not None:
                 try:
                     added = chunk[key] + other[key]
-                except TypeError:
-                    added = other[key]
+                except TypeError as exc:
+                    msg = (
+                        f"Cannot add incompatible types for key {key!r}: "
+                        f"{type(chunk[key]).__name__!r} and "
+                        f"{type(other[key]).__name__!r}."
+                    )
+                    raise TypeError(msg) from exc
                 chunk[key] = added
         return chunk
 
@@ -498,6 +506,9 @@ class AddableDict(dict[str, Any]):
 
         Returns:
             A dictionary that is the result of adding the two dictionaries.
+
+        Raises:
+            TypeError: If a shared key holds values of incompatible types.
         """
         chunk = AddableDict(other)
         for key in self:
@@ -506,8 +517,13 @@ class AddableDict(dict[str, Any]):
             elif self[key] is not None:
                 try:
                     added = chunk[key] + self[key]
-                except TypeError:
-                    added = self[key]
+                except TypeError as exc:
+                    msg = (
+                        f"Cannot add incompatible types for key {key!r}: "
+                        f"{type(chunk[key]).__name__!r} and "
+                        f"{type(self[key]).__name__!r}."
+                    )
+                    raise TypeError(msg) from exc
                 chunk[key] = added
         return chunk
 

libs/core/langchain_core/tools/base.py

@@ -1238,7 +1238,7 @@ class ChildTool(BaseTool):
                         error_to_raise = ValueError(msg)
             else:
                 content = response
-        except ValidationError as e:
+        except (ValidationError, ValidationErrorV1) as e:
             if not self.handle_validation_error:
                 error_to_raise = e
             else:

libs/core/langchain_core/tools/convert.py

@@ -4,7 +4,7 @@ import inspect
 from collections.abc import Callable
 from typing import Any, Literal, cast, get_type_hints, overload
 
-from pydantic import BaseModel, Field, create_model
+from pydantic import BaseModel, Field, RootModel, create_model
 
 from langchain_core.callbacks import Callbacks
 from langchain_core.runnables import Runnable
@@ -329,16 +329,17 @@ def tool(
                 )
             # If someone doesn't want a schema applied, we must treat it as
             # a simple string->string function
-            if dec_func.__doc__ is None:
+            tool_description = tool_description or dec_func.__doc__
+            if tool_description is None:
                 msg = (
-                    "Function must have a docstring if "
-                    "description not provided and infer_schema is False."
+                    "Function must have either a docstring or description "
+                    "when infer_schema is False."
                 )
                 raise ValueError(msg)
             return Tool(
                 name=tool_name,
                 func=func,
-                description=f"{tool_name} tool",
+                description=tool_description,
                 return_direct=return_direct,
                 coroutine=coroutine,
                 response_format=response_format,
@@ -469,12 +470,19 @@ def convert_runnable_to_tool(
     def invoke_wrapper(callbacks: Callbacks | None = None, **kwargs: Any) -> Any:
         return runnable.invoke(kwargs, config={"callbacks": callbacks})
 
+    input_schema_cls = runnable.input_schema
+    # Detect `RootModel` input schemas so we don't use them directly as the tool
+    # args schema.
+    is_root_model = isinstance(input_schema_cls, type) and issubclass(
+        input_schema_cls, RootModel
+    )
     if (
         arg_types is None
         and schema.get("type") == "object"
         and schema.get("properties")
+        and not is_root_model
     ):
-        args_schema = runnable.input_schema
+        args_schema = input_schema_cls
     else:
         args_schema = _get_schema_from_runnable_and_arg_types(
             runnable, name, arg_types=arg_types

libs/core/langchain_core/utils/_merge.py

@@ -68,6 +68,15 @@ def merge_dicts(left: dict[str, Any], *others: dict[str, Any]) -> dict[str, Any]
                 merged[right_k] = merge_lists(merged[right_k], right_v)
             elif merged[right_k] == right_v:
                 continue
+            elif isinstance(merged[right_k], bool):
+                # `bool` is a subclass of `int`, so without this check differing
+                # booleans would fall into the int branch below and get summed,
+                # silently turning e.g. `True + False` into the int `1`.
+                msg = (
+                    f"Additional kwargs key {right_k} already exists in left dict and "
+                    f"value has unsupported type {type(merged[right_k])}."
+                )
+                raise TypeError(msg)
             elif isinstance(merged[right_k], int):
                 # Preserve identification and temporal fields using last-wins strategy
                 # instead of summing:
@@ -118,7 +127,8 @@ def merge_lists(left: list[Any] | None, *others: list[Any] | None) -> list[Any]
                         i
                         for i, e_left in enumerate(merged)
                         if (
-                            "index" in e_left
+                            isinstance(e_left, dict)
+                            and "index" in e_left
                             and e_left["index"] == e["index"]  # index matches
                             and (  # IDs not inconsistent
                                 e_left.get("id") in {None, ""}

libs/core/langchain_core/utils/function_calling.py

@@ -713,6 +713,13 @@ def tool_example_to_messages(
     messages.append(
         AIMessage(content="", additional_kwargs={"tool_calls": openai_tool_calls})
     )
+    if tool_outputs is not None and len(tool_outputs) != len(openai_tool_calls):
+        msg = (
+            f"The number of tool_outputs ({len(tool_outputs)}) must match the number "
+            f"of tool_calls ({len(openai_tool_calls)}). Got {len(tool_outputs)} "
+            f"output(s) for {len(openai_tool_calls)} tool call(s)."
+        )
+        raise ValueError(msg)
     tool_outputs = tool_outputs or ["You have correctly called this tool."] * len(
         openai_tool_calls
     )

libs/core/langchain_core/version.py

@@ -1,3 +1,3 @@
 """Version information for `langchain-core`."""
 
-VERSION = "1.5.3"
+VERSION = "1.5.4"

libs/core/pyproject.toml

@@ -21,10 +21,11 @@ classifiers = [
     "Topic :: Software Development :: Libraries :: Python Modules",
 ]
 
-version = "1.5.3"
+version = "1.5.4"
 requires-python = ">=3.10.0,<4.0.0"
 dependencies = [
     "langsmith>=0.3.45,<1.0.0",
+    "httpx>=0.23.0,<1.0.0",
     "tenacity!=8.4.0,>=8.1.0,<10.0.0",
     "jsonpatch>=1.33.0,<2.0.0",
     "PyYAML>=5.3.0,<7.0.0",

libs/core/tests/unit_tests/callbacks/test_usage_callback.py

@@ -1,3 +1,4 @@
+import contextlib
 from typing import Any
 
 from langchain_core.callbacks import (
@@ -120,3 +121,15 @@ async def test_usage_callback_async() -> None:
     callback = UsageMetadataCallbackHandler()
     _ = await llm.abatch(["Message 1", "Message 2"], config={"callbacks": [callback]})
     assert callback.usage_metadata == {"test_model": total_1_2}
+
+
+def test_usage_callback_clears_on_exception() -> None:
+    """Callback must stop tracking after with-block exits via exception (#38989)."""
+    llm = FakeChatModelWithResponseMetadata(messages=iter(messages), model_name="fake")
+    with contextlib.suppress(RuntimeError), get_usage_metadata_callback() as cb:
+        _ = llm.invoke("in block")
+        raise RuntimeError
+
+    # Calls after the block must not accumulate into the previous callback.
+    _ = llm.invoke("outside block")
+    assert cb.usage_metadata == {"fake": usage1}

libs/core/tests/unit_tests/language_models/chat_models/test_cache.py

@@ -38,6 +38,10 @@ class InMemoryCache(BaseCache):
         """Clear cache."""
         self._cache = {}
 
+    def __len__(self) -> int:
+        """Return the number of cached entries."""
+        return len(self._cache)
+
 
 def test_local_cache_sync() -> None:
     """Test that the local cache is being populated but not the global one."""

libs/core/tests/unit_tests/language_models/llms/test_cache.py

@@ -27,6 +27,10 @@ class InMemoryCache(BaseCache):
         """Clear cache."""
         self._cache = {}
 
+    def __len__(self) -> int:
+        """Return the number of cached entries."""
+        return len(self._cache)
+
 
 async def test_local_cache_generate_async() -> None:
     global_cache = InMemoryCache()

libs/core/tests/unit_tests/load/test_serializable.py

@@ -5,6 +5,8 @@ from typing import Any
 
 import pytest
 from pydantic import BaseModel, ConfigDict, Field, SecretStr
+from pydantic.fields import FieldInfo
+from pydantic_core import PydanticUndefined
 
 from langchain_core._api import LangChainDeprecationWarning
 from langchain_core._api.deprecation import LangChainPendingDeprecationWarning
@@ -13,7 +15,7 @@ from langchain_core.load import InitValidator, Serializable, dumpd, dumps, load,
 from langchain_core.load.load import (
     _get_default_allowed_class_paths,
 )
-from langchain_core.load.serializable import _is_field_useful
+from langchain_core.load.serializable import _is_field_useful, _try_neq_default
 from langchain_core.messages import AIMessage
 from langchain_core.outputs import ChatGeneration, Generation
 from langchain_core.prompts import (
@@ -23,6 +25,7 @@ from langchain_core.prompts import (
 )
 from langchain_core.runnables.history import RunnableWithMessageHistory
 from langchain_core.tracers import log_stream
+from langchain_core.utils import from_env
 
 OPENAI_TEST_MODEL = "gpt-5.5"
 
@@ -144,6 +147,62 @@ def test__is_field_useful() -> None:
     assert not _is_field_useful(foo, "y", foo.y)
 
 
+def test_try_neq_default_none_factory(monkeypatch: pytest.MonkeyPatch) -> None:
+    """An env-backed `default_factory` at its `None` default is not flagged as changed.
+
+    Mirrors the real `output_version` field on chat models
+    (`Field(default_factory=from_env(..., default=None))`). Regression test for issue
+    #39157.
+    """
+    monkeypatch.delenv("LC_TEST_OUTPUT_VERSION", raising=False)
+
+    class Model(BaseModel):
+        none_factory: str | None = Field(
+            default_factory=from_env("LC_TEST_OUTPUT_VERSION", default=None)
+        )
+
+    field = Model.model_fields["none_factory"]
+    assert not _try_neq_default(None, field)
+    assert _try_neq_default("set", field)
+
+
+def test_try_neq_default_simulating_pydantic_2_14(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """A `None`-factory default is still recognized when `get_default()` is undefined.
+
+    Pydantic 2.14+ returns `PydanticUndefined` (instead of `None`) for an un-called
+    `default_factory`; this forces that behavior on the current pydantic. The factory
+    must not be invoked, so the sentinel is mapped back to `None` for comparison.
+    """
+    monkeypatch.delenv("LC_TEST_OUTPUT_VERSION", raising=False)
+
+    called = False
+
+    def _factory() -> None:
+        nonlocal called
+        called = True
+
+    class Model(BaseModel):
+        none_factory: str | None = Field(default_factory=_factory)
+
+    real_get_default = FieldInfo.get_default
+
+    def fake_get_default(self: FieldInfo, **kwargs: Any) -> Any:
+        if self.default_factory is not None and not kwargs.get("call_default_factory"):
+            return PydanticUndefined
+        return real_get_default(self, **kwargs)
+
+    monkeypatch.setattr(FieldInfo, "get_default", fake_get_default)
+
+    field = Model.model_fields["none_factory"]
+    assert field.get_default() is PydanticUndefined
+    assert not _try_neq_default(None, field)
+    assert _try_neq_default("set", field)
+    # The factory must never be re-executed during comparison (possible side effects).
+    assert called is False
+
+
 class Foo(Serializable):
     bar: int
     baz: str

libs/core/tests/unit_tests/messages/block_translators/test_anthropic.py

@@ -1,3 +1,5 @@
+from typing import Any
+
 from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage
 from langchain_core.messages import content as types
 
@@ -507,3 +509,66 @@ def test_convert_to_v1_from_anthropic_input() -> None:
     ]
 
     assert message.content_blocks == expected
+
+
+def test_convert_to_v1_from_anthropic_input_malformed_sources() -> None:
+    content: list[str | dict[Any, Any]] = [
+        {"type": "document", "source": {"type": "base64", "media_type": "app/pdf"}},
+        {"type": "document", "source": {"type": "url"}},
+        {"type": "document", "source": {"type": "file"}},
+        {"type": "document", "source": {"type": "text"}},
+        {"type": "image", "source": {"type": "base64", "media_type": "image/jpeg"}},
+        {"type": "image", "source": {"type": "url"}},
+        {"type": "image", "source": {"type": "file"}},
+    ]
+    message = HumanMessage(content)
+
+    assert message.content_blocks == [
+        *[{"type": "non_standard", "value": block} for block in content[:4]],
+        *content[4:],
+    ]
+
+
+def test_convert_to_v1_from_anthropic_malformed_citations() -> None:
+    message = AIMessage(
+        [
+            {
+                "type": "text",
+                "text": "Source-backed answer.",
+                "citations": [
+                    {
+                        "type": "web_search_result_location",
+                        "cited_text": "Source text",
+                    },
+                    {
+                        "type": "search_result_location",
+                        "title": "Document Title",
+                    },
+                ],
+            },
+        ],
+        response_metadata={"model_provider": "anthropic"},
+    )
+
+    assert message.content_blocks == [
+        {
+            "type": "text",
+            "text": "Source-backed answer.",
+            "annotations": [
+                {
+                    "type": "non_standard_annotation",
+                    "value": {
+                        "type": "web_search_result_location",
+                        "cited_text": "Source text",
+                    },
+                },
+                {
+                    "type": "non_standard_annotation",
+                    "value": {
+                        "type": "search_result_location",
+                        "title": "Document Title",
+                    },
+                },
+            ],
+        },
+    ]

libs/core/tests/unit_tests/prompts/test_dict.py

@@ -116,3 +116,46 @@ def test_prompt_template_blocks_attribute_access() -> None:
         ValueError, match="Variable names cannot contain attribute access"
     ):
         PromptTemplate.from_template("{name.__class__}", template_format="f-string")
+
+
+def test_dict_prompt_template_preserves_non_str_items_in_list() -> None:
+    """Non-str/non-dict items inside lists must be preserved, not silently dropped.
+
+    Regression test for #39152: int, float, bool, None and other scalars nested
+    inside a list value were silently discarded by _insert_input_variables.
+    """
+    template = {
+        "type": "tool_use",
+        "id": "call_1",
+        "name": "search",
+        "input": {
+            "query": "{q}",
+            "top_k_scores": [1, 2, 3],
+            "flags": [True, None],
+        },
+    }
+    prompt = DictPromptTemplate(template=template, template_format="f-string")
+    result = prompt.format(q="cats")
+
+    assert result["input"]["query"] == "cats"
+    assert result["input"]["top_k_scores"] == [1, 2, 3]
+    assert result["input"]["flags"] == [True, None]
+
+
+def test_dict_prompt_template_preserves_mixed_list() -> None:
+    """Mixed-type lists must survive formatting unchanged (except str interpolation)."""
+    template = {
+        "type": "x",
+        "mixed": ["a {v}", 1, 2.5, {"k": "val"}, None, True],
+    }
+    prompt = DictPromptTemplate(template=template, template_format="f-string")
+    result = prompt.format(v="b")
+
+    assert result["mixed"] == ["a b", 1, 2.5, {"k": "val"}, None, True]
+
+
+def test_dict_prompt_template_preserves_list_with_no_variables() -> None:
+    """Items must survive even when the template has no variables at all."""
+    template = {"nums": [1, 2]}
+    prompt = DictPromptTemplate(template=template, template_format="mustache")
+    assert prompt.format() == {"nums": [1, 2]}

libs/core/tests/unit_tests/prompts/test_structured.py

@@ -145,3 +145,36 @@ def test_structured_prompt_template_empty_vars() -> None:
             schema={"type": "object", "properties": {}, "title": "foo"},
             template_format="mustache",
         )
+
+
+def test_structured_prompt_does_not_mutate_caller_kwargs() -> None:
+    """StructuredPrompt must not mutate caller-provided kwargs.
+
+    Merging extra structured-output kwargs into a caller-owned dict would leak
+    options into later prompts that reuse the same dict.
+    """
+    schema = {
+        "type": "object",
+        "properties": {"answer": {"type": "string"}},
+    }
+    shared_options = {"method": "json_schema"}
+
+    first = StructuredPrompt(
+        [("human", "one")],
+        schema,
+        structured_output_kwargs=shared_options,
+        strict=True,
+    )
+    assert first.structured_output_kwargs == {
+        "method": "json_schema",
+        "strict": True,
+    }
+    assert shared_options == {"method": "json_schema"}
+
+    second = StructuredPrompt(
+        [("human", "two")],
+        schema,
+        structured_output_kwargs=shared_options,
+    )
+    assert second.structured_output_kwargs == {"method": "json_schema"}
+    assert shared_options == {"method": "json_schema"}

libs/core/tests/unit_tests/runnables/__snapshots__/test_fallbacks.ambr

@@ -84,7 +84,7 @@
                   "fake",
                   "FakeListLLM"
                 ],
-                "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo'], i=1)",
+                "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo'], i=1)",
                 "name": "FakeListLLM"
               }
             },
@@ -128,7 +128,7 @@
                     "fake",
                     "FakeListLLM"
                   ],
-                  "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['bar'])",
+                  "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['bar'])",
                   "name": "FakeListLLM"
                 }
               },
@@ -268,7 +268,7 @@
           "fake",
           "FakeListLLM"
         ],
-        "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo'], i=1)",
+        "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo'], i=1)",
         "name": "FakeListLLM"
       },
       "fallbacks": [
@@ -281,7 +281,7 @@
             "fake",
             "FakeListLLM"
           ],
-          "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['bar'])",
+          "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['bar'])",
           "name": "FakeListLLM"
         }
       ],
@@ -322,7 +322,7 @@
           "fake",
           "FakeListLLM"
         ],
-        "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo'], i=1)",
+        "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo'], i=1)",
         "name": "FakeListLLM"
       },
       "fallbacks": [
@@ -335,7 +335,7 @@
             "fake",
             "FakeListLLM"
           ],
-          "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['baz'], i=1)",
+          "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['baz'], i=1)",
           "name": "FakeListLLM"
         },
         {
@@ -347,7 +347,7 @@
             "fake",
             "FakeListLLM"
           ],
-          "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['bar'])",
+          "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['bar'])",
           "name": "FakeListLLM"
         }
       ],

libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr

@@ -97,7 +97,7 @@
             "fake_chat_models",
             "FakeListChatModel"
           ],
-          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo, bar'])",
+          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo, bar'])",
           "name": "FakeListChatModel"
         }
       ],
@@ -227,7 +227,7 @@
             "fake_chat_models",
             "FakeListChatModel"
           ],
-          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['baz, qux'])",
+          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['baz, qux'])",
           "name": "FakeListChatModel"
         }
       ],
@@ -346,7 +346,7 @@
             "fake_chat_models",
             "FakeListChatModel"
           ],
-          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo, bar'])",
+          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo, bar'])",
           "name": "FakeListChatModel"
         },
         {
@@ -457,7 +457,7 @@
             "fake_chat_models",
             "FakeListChatModel"
           ],
-          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['baz, qux'])",
+          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['baz, qux'])",
           "name": "FakeListChatModel"
         }
       ],
@@ -848,7 +848,7 @@
             "fake",
             "FakeStreamingListLLM"
           ],
-          "repr": "FakeStreamingListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['first item, second item, third item'])",
+          "repr": "FakeStreamingListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['first item, second item, third item'])",
           "name": "FakeStreamingListLLM"
         },
         {
@@ -884,7 +884,7 @@
               "fake",
               "FakeStreamingListLLM"
             ],
-            "repr": "FakeStreamingListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['this', 'is', 'a', 'test'])",
+            "repr": "FakeStreamingListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['this', 'is', 'a', 'test'])",
             "name": "FakeStreamingListLLM"
           }
         },
@@ -1009,7 +1009,7 @@
 # name: test_prompt_with_chat_model
   '''
   ChatPromptTemplate(input_variables=['question'], input_types={}, partial_variables={}, messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], input_types={}, partial_variables={}, template='You are a nice assistant.'), additional_kwargs={}), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question'], input_types={}, partial_variables={}, template='{question}'), additional_kwargs={})])
-  | FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo'])
+  | FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo'])
   '''
 # ---
 # name: test_prompt_with_chat_model.1
@@ -1109,7 +1109,7 @@
           "fake_chat_models",
           "FakeListChatModel"
         ],
-        "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo'])",
+        "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo'])",
         "name": "FakeListChatModel"
       }
     },
@@ -1220,7 +1220,7 @@
             "fake_chat_models",
             "FakeListChatModel"
           ],
-          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo, bar'])",
+          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo, bar'])",
           "name": "FakeListChatModel"
         }
       ],
@@ -1249,7 +1249,7 @@
 # name: test_prompt_with_chat_model_async
   '''
   ChatPromptTemplate(input_variables=['question'], input_types={}, partial_variables={}, messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], input_types={}, partial_variables={}, template='You are a nice assistant.'), additional_kwargs={}), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question'], input_types={}, partial_variables={}, template='{question}'), additional_kwargs={})])
-  | FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo'])
+  | FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo'])
   '''
 # ---
 # name: test_prompt_with_chat_model_async.1
@@ -1349,7 +1349,7 @@
           "fake_chat_models",
           "FakeListChatModel"
         ],
-        "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo'])",
+        "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo'])",
         "name": "FakeListChatModel"
       }
     },
@@ -1459,7 +1459,7 @@
           "fake",
           "FakeListLLM"
         ],
-        "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo', 'bar'])",
+        "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo', 'bar'])",
         "name": "FakeListLLM"
       }
     },
@@ -1576,7 +1576,7 @@
             "fake",
             "FakeListLLM"
           ],
-          "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo', 'bar'])",
+          "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo', 'bar'])",
           "name": "FakeListLLM"
         }
       ],
@@ -1699,7 +1699,7 @@
             "fake",
             "FakeStreamingListLLM"
           ],
-          "repr": "FakeStreamingListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['bear, dog, cat', 'tomato, lettuce, onion'])",
+          "repr": "FakeStreamingListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['bear, dog, cat', 'tomato, lettuce, onion'])",
           "name": "FakeStreamingListLLM"
         }
       ],
@@ -1867,7 +1867,7 @@
                     "fake",
                     "FakeListLLM"
                   ],
-                  "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['4'])",
+                  "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['4'])",
                   "name": "FakeListLLM"
                 }
               },
@@ -1940,7 +1940,7 @@
                     "fake",
                     "FakeListLLM"
                   ],
-                  "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['2'])",
+                  "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['2'])",
                   "name": "FakeListLLM"
                 }
               },
@@ -13407,7 +13407,7 @@
     just_to_test_lambda: RunnableLambda(...)
   }
   | ChatPromptTemplate(input_variables=['documents', 'question'], input_types={}, partial_variables={}, messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], input_types={}, partial_variables={}, template='You are a nice assistant.'), additional_kwargs={}), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['documents', 'question'], input_types={}, partial_variables={}, template='Context:\n{documents}\n\nQuestion:\n{question}'), additional_kwargs={})])
-  | FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo, bar'])
+  | FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo, bar'])
   | CommaSeparatedListOutputParser()
   '''
 # ---
@@ -13610,7 +13610,7 @@
             "fake_chat_models",
             "FakeListChatModel"
           ],
-          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=['foo, bar'])",
+          "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=['foo, bar'])",
           "name": "FakeListChatModel"
         }
       ],
@@ -13636,8 +13636,8 @@
   ChatPromptTemplate(input_variables=['question'], input_types={}, partial_variables={}, messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], input_types={}, partial_variables={}, template='You are a nice assistant.'), additional_kwargs={}), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question'], input_types={}, partial_variables={}, template='{question}'), additional_kwargs={})])
   | RunnableLambda(...)
   | {
-      chat: FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=["i'm a chatbot"]),
-      llm: FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=["i'm a textbot"])
+      chat: FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=["i'm a chatbot"]),
+      llm: FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=["i'm a textbot"])
     }
   '''
 # ---
@@ -13762,7 +13762,7 @@
                 "fake_chat_models",
                 "FakeListChatModel"
               ],
-              "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=[\"i'm a chatbot\"])",
+              "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=[\"i'm a chatbot\"])",
               "name": "FakeListChatModel"
             },
             "llm": {
@@ -13774,7 +13774,7 @@
                 "fake",
                 "FakeListLLM"
               ],
-              "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=[\"i'm a textbot\"])",
+              "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=[\"i'm a textbot\"])",
               "name": "FakeListLLM"
             }
           }
@@ -13917,7 +13917,7 @@
                     "fake_chat_models",
                     "FakeListChatModel"
                   ],
-                  "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=[\"i'm a chatbot\"])",
+                  "repr": "FakeListChatModel(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=[\"i'm a chatbot\"])",
                   "name": "FakeListChatModel"
                 },
                 "kwargs": {
@@ -13938,7 +13938,7 @@
                 "fake",
                 "FakeListLLM"
               ],
-              "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.3'}}, responses=[\"i'm a textbot\"])",
+              "repr": "FakeListLLM(metadata={'lc_versions': {'langchain-core': '1.5.4'}}, responses=[\"i'm a textbot\"])",
               "name": "FakeListLLM"
             },
             "passthrough": {

libs/core/tests/unit_tests/runnables/test_utils.py

@@ -5,6 +5,7 @@ import pytest
 
 from langchain_core.runnables.base import RunnableLambda
 from langchain_core.runnables.utils import (
+    AddableDict,
     get_function_nonlocals,
     get_lambda_source,
     indent_lines_after_first,
@@ -73,3 +74,29 @@ def test_nonlocals() -> None:
     assert RunnableLambda(my_func3).deps == [agent]
     assert RunnableLambda(my_func4).deps == [global_agent]
     assert RunnableLambda(func).deps == [nl]
+
+
+def test_addable_dict_add_incompatible_types_raises() -> None:
+    left = AddableDict({"count": 1})
+    right = AddableDict({"count": "some_string"})
+    with pytest.raises(
+        TypeError,
+        match=r"Cannot add incompatible types for key 'count': 'int' and 'str'\.",
+    ):
+        left + right
+
+
+def test_addable_dict_radd_incompatible_types_raises() -> None:
+    left = AddableDict({"count": 1})
+    right = AddableDict({"count": "some_string"})
+    with pytest.raises(
+        TypeError,
+        match=r"Cannot add incompatible types for key 'count': 'int' and 'str'\.",
+    ):
+        right.__radd__(left)
+
+
+def test_addable_dict_add_none_seeded_key_is_unaffected() -> None:
+    left = AddableDict({"data": None})
+    right = AddableDict({"data": {"a": 1}})
+    assert (left + right) == AddableDict({"data": {"a": 1}})

libs/core/tests/unit_tests/test_messages.py

@@ -225,6 +225,14 @@ def test_message_chunks() -> None:
     assert (default_id_chunk + provider_chunk).id == meaningful_id
 
 
+def test_message_chunks_bool_additional_kwargs_raises() -> None:
+    """Differing booleans (e.g. `refusal`) must not silently coerce to `int`."""
+    a = AIMessageChunk(content="", additional_kwargs={"refusal": True})
+    b = AIMessageChunk(content="", additional_kwargs={"refusal": False})
+    with pytest.raises(TypeError, match="unsupported type"):
+        a + b
+
+
 def test_chat_message_chunks() -> None:
     assert ChatMessageChunk(role="User", content="I am", id="ai4") + ChatMessageChunk(
         role="User", content=" indeed."

libs/core/tests/unit_tests/test_tools.py

@@ -23,7 +23,7 @@ from typing import (
 )
 
 import pytest
-from pydantic import BaseModel, ConfigDict, Field, ValidationError
+from pydantic import BaseModel, ConfigDict, Field, RootModel, ValidationError
 from pydantic.v1 import BaseModel as BaseModelV1
 from pydantic.v1 import ValidationError as ValidationErrorV1
 from typing_extensions import TypedDict, override
@@ -365,9 +365,34 @@ def test_structured_single_str_decorator_no_infer_schema() -> None:
 
     assert isinstance(unstructured_tool_input, BaseTool)
     assert unstructured_tool_input.args_schema is None
+    assert unstructured_tool_input.description == "Return the arguments directly."
     assert unstructured_tool_input.run("foo") == "foo"
 
 
+def test_simple_tool_decorator_no_infer_schema_uses_explicit_description() -> None:
+    """Test that a simple tool preserves an explicit description."""
+
+    @tool(infer_schema=False, description="Echo the supplied input.")
+    def echo(tool_input: str) -> str:
+        return tool_input
+
+    assert echo.description == "Echo the supplied input."
+
+
+def test_simple_tool_decorator_no_infer_schema_requires_description_or_docstring() -> (
+    None
+):
+    """Test that a simple tool requires an authored description."""
+    with pytest.raises(
+        ValueError,
+        match="Function must have either a docstring or description",
+    ):
+
+        @tool(infer_schema=False)
+        def echo(tool_input: str) -> str:
+            return tool_input
+
+
 def test_structured_tool_types_parsed() -> None:
     """Test the non-primitive types are correctly passed to structured tools."""
 
@@ -1123,6 +1148,30 @@ async def test_async_validation_error_handling_callable() -> None:
     assert expected == actual
 
 
+@pytest.mark.skipif(
+    sys.version_info >= (3, 14),
+    reason="pydantic.v1 namespace not supported with Python 3.14+",
+)
+async def test_async_validation_error_handling_pydantic_v1_schema() -> None:
+    """Test async validation error handling for Pydantic V1 schemas."""
+
+    class Args(BaseModelV1):
+        x: int
+
+    def foo(x: int) -> str:
+        """Return x as text."""
+        return str(x)
+
+    tool_ = StructuredTool.from_function(
+        foo,
+        args_schema=cast("ArgsSchema", Args),
+        handle_validation_error=True,
+    )
+
+    assert tool_.run({"x": "not-an-integer"}) == "Tool input validation error"
+    assert await tool_.arun({"x": "not-an-integer"}) == "Tool input validation error"
+
+
 @pytest.mark.parametrize(
     "handler",
     [
@@ -1759,6 +1808,57 @@ def test_convert_from_runnable_dict() -> None:
     assert result == "6"
 
 
+def test_convert_from_runnable_root_model_input_schema() -> None:
+    """`as_tool` should not advertise a `TypedDict` input nested under `root`.
+
+    Some `Runnable`s (e.g. a compiled `langgraph` `StateGraph`) expose a
+    `pydantic.RootModel` as `input_schema` even though `get_input_jsonschema`
+    reports a flat object schema. See:
+    """
+
+    class Args(TypedDict):
+        foo: str
+        bar: str
+
+    class _RootModelInputRunnable(RunnableLambda[Args, str]):
+        @override
+        def get_input_schema(
+            self, config: RunnableConfig | None = None
+        ) -> TypeBaseModel:
+            return RootModel[Args]
+
+        @override
+        def get_input_jsonschema(
+            self, config: RunnableConfig | None = None
+        ) -> dict[str, Any]:
+            return {
+                "type": "object",
+                "properties": {
+                    "foo": {"type": "string"},
+                    "bar": {"type": "string"},
+                },
+                "required": ["foo", "bar"],
+            }
+
+    def f(x: Args) -> str:
+        return f"{x['foo']} {x['bar']}"
+
+    runnable = _RootModelInputRunnable(f)
+    as_tool = runnable.as_tool(name="my_tool", description="Example tool.")
+
+    assert as_tool.args_schema is not None
+    assert isinstance(as_tool.args_schema, type)
+    assert not issubclass(as_tool.args_schema, RootModel)
+
+    oai_schema = convert_to_openai_tool(as_tool)
+    parameters = oai_schema["function"]["parameters"]
+    assert parameters["properties"].keys() == {"foo", "bar"}
+    assert "root" not in parameters["properties"]
+
+    result = as_tool.invoke({"foo": "hello", "bar": "world"})
+    assert result == "hello world"
+
+
 def test_convert_from_runnable_other() -> None:
     # String input
     def f(x: str) -> str:

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

@@ -795,6 +795,24 @@ def test_tool_outputs() -> None:
     assert not response.tool_calls
 
 
+def test_tool_outputs_fewer_than_tool_calls_raises() -> None:
+    with pytest.raises(ValueError, match="must match"):
+        tool_example_to_messages(
+            input="Extract both values",
+            tool_calls=[FakeCall(data="a"), FakeCall(data="b")],
+            tool_outputs=["only one output"],
+        )
+
+
+def test_tool_outputs_more_than_tool_calls_raises() -> None:
+    with pytest.raises(ValueError, match="must match"):
+        tool_example_to_messages(
+            input="Extract one value",
+            tool_calls=[FakeCall(data="a")],
+            tool_outputs=["output1", "extra output"],
+        )
+
+
 @pytest.mark.parametrize(
     "typed_dict",
     [ExtensionsTypedDict, TypingTypedDict],

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

@@ -129,6 +129,18 @@ def test_check_package_version(
         # Other integer fields should still be summed (e.g., token counts)
         ({"tokens": 10}, {"tokens": 5}, {"tokens": 15}),
         ({"count": 1}, {"count": 2}, {"count": 3}),
+        # Differing booleans must not silently coerce to `int` (e.g. `True + False`).
+        (
+            {"a": True},
+            {"a": False},
+            pytest.raises(
+                TypeError,
+                match=(
+                    "Additional kwargs key a already exists in left dict and value "
+                    r"has unsupported type .+bool.+."
+                ),
+            ),
+        ),
     ],
 )
 def test_merge_dicts(
@@ -437,6 +449,16 @@ def test_generation_chunk_addition_type_error() -> None:
             [{"no_index": "b"}],
             [{"no_index": "a"}, {"no_index": "b"}],
         ),
+        # A string element whose text happens to contain the literal substring
+        # "index" must not be treated as index-keyed (it isn't a dict).
+        (
+            ["the index is here", {"index": 0, "reference_ids": ["a"]}],
+            [{"index": 0, "reference_ids": ["b"]}],
+            [
+                "the index is here",
+                {"index": 0, "reference_ids": ["a", "b"]},
+            ],
+        ),
     ],
 )
 def test_merge_lists(

libs/core/uv.lock

@@ -1040,9 +1040,10 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.5.3"
+version = "1.5.4"
 source = { editable = "." }
 dependencies = [
+    { name = "httpx" },
     { name = "jsonpatch" },
     { name = "langchain-protocol" },
     { name = "langsmith" },
@@ -1090,6 +1091,7 @@ typing = [
 
 [package.metadata]
 requires-dist = [
+    { name = "httpx", specifier = ">=0.23.0,<1.0.0" },
     { name = "jsonpatch", specifier = ">=1.33.0,<2.0.0" },
     { name = "langchain-protocol", specifier = ">=0.0.17" },
     { name = "langsmith", specifier = ">=0.3.45,<1.0.0" },
@@ -1107,7 +1109,7 @@ dev = [
     { name = "jupyter", specifier = ">=1.0.0,<2.0.0" },
     { name = "setuptools", specifier = ">=67.6.1,<84.0.0" },
 ]
-lint = [{ name = "ruff", specifier = ">=0.15.0,<0.16.0" }]
+lint = [{ name = "ruff", specifier = ">=0.15.0,<0.17.0" }]
 test = [
     { name = "blockbuster", specifier = ">=1.5.18,<1.6.0" },
     { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
@@ -1182,11 +1184,11 @@ requires-dist = [
 ]
 
 [package.metadata.requires-dev]
-lint = [{ name = "ruff", specifier = ">=0.15.0,<0.16.0" }]
+lint = [{ name = "ruff", specifier = ">=0.15.0,<0.17.0" }]
 test = []
 test-integration = []
 typing = [
-    { name = "mypy", specifier = ">=2.1.0,<2.2.0" },
+    { name = "mypy", specifier = ">=2.1.0,<2.4.0" },
     { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" },
 ]
 
@@ -1203,7 +1205,7 @@ requires-dist = [{ name = "langchain-core", editable = "." }]
 
 [package.metadata.requires-dev]
 dev = [{ name = "jupyter", specifier = ">=1.0.0,<2.0.0" }]
-lint = [{ name = "ruff", specifier = ">=0.15.0,<0.16.0" }]
+lint = [{ name = "ruff", specifier = ">=0.15.0,<0.17.0" }]
 test = [
     { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
     { name = "pytest", specifier = ">=9.0.3,<10.0.0" },

libs/langchain/uv.lock

@@ -1970,15 +1970,15 @@ wheels = [
 
 [[package]]
 name = "h2"
-version = "4.3.0"
+version = "4.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "hpack" },
     { name = "hyperframe" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/1d/17/afa56379f94ad0fe8defd37d6eb3f89a25404ffc71d4d848893d270325fc/h2-4.3.0.tar.gz", hash = "sha256:6c59efe4323fa18b47a632221a1888bd7fde6249819beda254aeca909f221bf1", size = 2152026, upload-time = "2025-08-23T18:12:19.778Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e7/85/7c366e69d84c17bb778fe41419e1fbcce3033d5b7ce29bbffff0a98b859f/h2-4.4.1.tar.gz", hash = "sha256:4e866ffb1a869ae14dd9b5e6beb5c24a13da0495ad72b65925ded182521c1516", size = 2157281, upload-time = "2026-08-03T11:45:09.509Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/69/b2/119f6e6dcbd96f9069ce9a2665e0146588dc9f88f29549711853645e736a/h2-4.3.0-py3-none-any.whl", hash = "sha256:c438f029a25f7945c69e0ccf0fb951dc3f73a5f6412981daee861431b70e2bdd", size = 61779, upload-time = "2025-08-23T18:12:17.779Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/22/e85faf23bd72a92d1921e37d674ca56eb298a3c8be31fdecef0ff2b3aaac/h2-4.4.1-py3-none-any.whl", hash = "sha256:0e25f1462b23c9cb82d9eb02e28bc706dac2a68cb457c6a0d74d63c8a2a5d0e6", size = 62636, upload-time = "2026-08-03T11:44:59.164Z" },
 ]
 
 [[package]]
@@ -1998,11 +1998,11 @@ wheels = [
 
 [[package]]
 name = "hpack"
-version = "4.1.0"
+version = "4.2.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/2c/48/71de9ed269fdae9c8057e5a4c0aa7402e8bb16f2c6e90b3aa53327b113f8/hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca", size = 51276, upload-time = "2025-01-22T21:44:58.347Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/5b/fcabf6028144a8723726318b07a32c2f3314acdff6265743cf08a344b18e/hpack-4.2.0.tar.gz", hash = "sha256:0895cfa3b5531fc65fe439c05eb65144f123bf7a394fcaa56aa423548d8e45c0", size = 51300, upload-time = "2026-06-23T18:34:46.667Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/07/c6/80c95b1b2b94682a72cbdbfb85b81ae2daffa4291fbfa1b1464502ede10d/hpack-4.1.0-py3-none-any.whl", hash = "sha256:157ac792668d995c657d93111f46b4535ed114f0c9c8d672271bbec7eae1b496", size = 34357, upload-time = "2025-01-22T21:44:56.92Z" },
+    { url = "https://files.pythonhosted.org/packages/71/b4/4a9fcfb2aef6ba44d9073ecd301443aa00b3dac95de5619f2a7de7ec8a91/hpack-4.2.0-py3-none-any.whl", hash = "sha256:858ac0b02280fa582b5080d68db0899c62a80375e0e5413a74970c5e518b6986", size = 34246, upload-time = "2026-06-23T18:34:45.472Z" },
 ]
 
 [[package]]
@@ -3114,7 +3114,7 @@ wheels = [
 
 [[package]]
 name = "langchain-openai"
-version = "1.4.1"
+version = "1.4.2"
 source = { editable = "../partners/openai" }
 dependencies = [
     { name = "langchain-core" },

libs/langchain_v1/langchain/__init__.py

@@ -1,3 +1,3 @@
 """Main entrypoint into LangChain."""
 
-__version__ = "1.3.14"
+__version__ = "1.3.15"

libs/langchain_v1/langchain/agents/factory.py

@@ -31,6 +31,10 @@ from langsmith import traceable
 from typing_extensions import NotRequired, Required, TypedDict, overload
 
 from langchain.agents._subagent_transformer import SubagentTransformer
+from langchain.agents.middleware._trace_policy import (
+    _node_trace_policy,
+    _resolved_transform,
+)
 from langchain.agents.middleware.types import (
     AgentMiddleware,
     AgentState,
@@ -151,6 +155,22 @@ def _scrub_inputs(inputs: dict[str, Any]) -> dict[str, Any]:
     return filtered
 
 
+def _wrap_trace_kwargs(middleware: AgentMiddleware[Any, Any]) -> dict[str, Any]:
+    """`traceable` kwargs for a middleware's `wrap_*` hook spans.
+
+    The `_scrub_inputs` baseline (strip the unserializable `handler`/`runtime`) always
+    runs first; the effective `TracePolicy` (the middleware's own, else the process-wide
+    default) composes on top. The effective policy is resolved at call time, so a
+    `configure_trace_policy` call after `create_agent` still applies.
+    """
+    process_inputs = _resolved_transform(middleware.trace_policy, "process_inputs")
+    process_outputs = _resolved_transform(middleware.trace_policy, "process_outputs")
+    return {
+        "process_inputs": lambda inputs: process_inputs(_scrub_inputs(inputs)),
+        "process_outputs": process_outputs,
+    }
+
+
 FALLBACK_MODELS_WITH_STRUCTURED_OUTPUT = [
     # If model profile data are not available, model names matching these patterns
     # are assumed to support provider-native structured output. These are regexes
@@ -436,7 +456,11 @@ def _resolve_schemas(schemas: list[type]) -> tuple[type, type, type]:
     same field is declared by multiple schemas.  Duplicates are harmless — a type
     that appears more than once is processed at its last position.
     """
-    schema_hints = {schema: _get_schema_type_hints(schema) for schema in schemas}
+    schema_hints: dict[type, dict[str, Any]] = {}
+    for schema in schemas:
+        # Reinsert duplicates so dict iteration reflects their final position.
+        schema_hints.pop(schema, None)
+        schema_hints[schema] = _get_schema_type_hints(schema)
     return (
         _resolve_schema(schema_hints, "StateSchema", None),
         _resolve_schema(schema_hints, "InputSchema", "input"),
@@ -1026,9 +1050,7 @@ 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", **_wrap_trace_kwargs(m))(m.wrap_tool_call)
             for m in middleware_w_wrap_tool_call
         ]
         wrap_tool_call_wrapper = _chain_tool_call_wrappers(wrappers)
@@ -1047,9 +1069,7 @@ 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", **_wrap_trace_kwargs(m))(m.awrap_tool_call)
             for m in middleware_w_awrap_tool_call
         ]
         awrap_tool_call_wrapper = _chain_async_tool_call_wrappers(async_wrappers)
@@ -1135,9 +1155,7 @@ 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", **_wrap_trace_kwargs(m))(m.wrap_model_call)
             for m in middleware_w_wrap_model_call
         ]
         wrap_model_call_handler = _chain_model_call_handlers(sync_handlers)
@@ -1146,7 +1164,7 @@ 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)(
+            traceable(name=f"{m.name}.awrap_model_call", **_wrap_trace_kwargs(m))(
                 m.awrap_model_call
             )
             for m in middleware_w_awrap_model_call
@@ -1548,7 +1566,10 @@ def create_agent(
             )
             before_agent_node = RunnableCallable(sync_before_agent, async_before_agent, trace=False)
             graph.add_node(
-                f"{m.name}.before_agent", before_agent_node, input_schema=resolved_state_schema
+                f"{m.name}.before_agent",
+                before_agent_node,
+                input_schema=resolved_state_schema,
+                trace_policy=_node_trace_policy(m.trace_policy),
             )
 
         if (
@@ -1569,7 +1590,10 @@ def create_agent(
             )
             before_node = RunnableCallable(sync_before, async_before, trace=False)
             graph.add_node(
-                f"{m.name}.before_model", before_node, input_schema=resolved_state_schema
+                f"{m.name}.before_model",
+                before_node,
+                input_schema=resolved_state_schema,
+                trace_policy=_node_trace_policy(m.trace_policy),
             )
 
         if (
@@ -1589,7 +1613,12 @@ def create_agent(
                 else None
             )
             after_node = RunnableCallable(sync_after, async_after, trace=False)
-            graph.add_node(f"{m.name}.after_model", after_node, input_schema=resolved_state_schema)
+            graph.add_node(
+                f"{m.name}.after_model",
+                after_node,
+                input_schema=resolved_state_schema,
+                trace_policy=_node_trace_policy(m.trace_policy),
+            )
 
         if (
             m.__class__.after_agent is not AgentMiddleware.after_agent
@@ -1609,7 +1638,10 @@ def create_agent(
             )
             after_agent_node = RunnableCallable(sync_after_agent, async_after_agent, trace=False)
             graph.add_node(
-                f"{m.name}.after_agent", after_agent_node, input_schema=resolved_state_schema
+                f"{m.name}.after_agent",
+                after_agent_node,
+                input_schema=resolved_state_schema,
+                trace_policy=_node_trace_policy(m.trace_policy),
             )
 
     # Determine the entry node (runs once at start): before_agent -> before_model -> model

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

@@ -2,6 +2,7 @@
 
 from langgraph.runtime import Runtime
 
+from langchain.agents.middleware._trace_policy import configure_trace_policy
 from langchain.agents.middleware.context_editing import ClearToolUsesEdit, ContextEditingMiddleware
 from langchain.agents.middleware.file_search import FilesystemFileSearchMiddleware
 from langchain.agents.middleware.human_in_the_loop import (
@@ -37,12 +38,14 @@ from langchain.agents.middleware.types import (
     ModelResponse,
     OutputAgentState,
     ToolCallRequest,
+    TracePolicy,
     after_agent,
     after_model,
     before_agent,
     before_model,
     dynamic_prompt,
     hook_config,
+    omit_payload,
     wrap_model_call,
     wrap_tool_call,
 )
@@ -82,13 +85,16 @@ __all__ = [
     "ToolCallRequest",
     "ToolErrorMiddleware",
     "ToolRetryMiddleware",
+    "TracePolicy",
     "TriggerClause",
     "after_agent",
     "after_model",
     "before_agent",
     "before_model",
+    "configure_trace_policy",
     "dynamic_prompt",
     "hook_config",
+    "omit_payload",
     "wrap_model_call",
     "wrap_tool_call",
 ]

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

@@ -0,0 +1,61 @@
+"""Process-global default `TracePolicy` for agent middleware.
+
+`TracePolicy` (from langgraph) shapes what a middleware hook span records. This module
+adds a process-wide default that individual middleware override via their own
+`trace_policy` attribute. Set it once at startup with `configure_trace_policy`; the
+default is read at span-creation time, so it applies to agents built before or after
+the call.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Literal
+
+from langgraph.types import TracePolicy
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+_DEFAULT_TRACE_POLICY: TracePolicy | None = None
+
+
+def configure_trace_policy(policy: TracePolicy | None) -> None:
+    """Set the process-wide default `TracePolicy` for agent middleware hook spans.
+
+    Call once at startup. A middleware's own `trace_policy` overrides this wholesale
+    (no field-level merge). Pass `None` to clear the default.
+    """
+    global _DEFAULT_TRACE_POLICY  # noqa: PLW0603 - process-wide default, set once at startup
+    _DEFAULT_TRACE_POLICY = policy
+
+
+def _resolve(mw_policy: TracePolicy | None) -> TracePolicy | None:
+    """The effective policy: the middleware's own if set, else the global default."""
+    return mw_policy if mw_policy is not None else _DEFAULT_TRACE_POLICY
+
+
+def _resolved_transform(
+    mw_policy: TracePolicy | None,
+    field: Literal["process_inputs", "process_outputs"],
+) -> Callable[[Any], Any]:
+    """A trace processor that resolves the effective policy at call time.
+
+    Reading the global at call time (not build time) means `configure_trace_policy` can
+    run after `create_agent` and still apply. Passes the value through unchanged when no
+    effective policy defines `field`.
+    """
+
+    def process(value: Any) -> Any:
+        effective = _resolve(mw_policy)
+        fn = getattr(effective, field) if effective is not None else None
+        return fn(value) if fn is not None else value
+
+    return process
+
+
+def _node_trace_policy(mw_policy: TracePolicy | None) -> TracePolicy:
+    """Build the resolver-backed `TracePolicy` for a middleware's node hook."""
+    return TracePolicy(
+        process_inputs=_resolved_transform(mw_policy, "process_inputs"),
+        process_outputs=_resolved_transform(mw_policy, "process_outputs"),
+    )

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

@@ -1,13 +1,8 @@
 """Tag and filter middleware-internal model calls.
 
-Middleware may make bookkeeping model calls (e.g. summarization or tool
-selection) in the same graph namespace as the main agent call, causing their
-tokens to appear in `run.messages`.
-
-Tag these calls with `internal_call_metadata()` and declare
-`transformers = (InternalCallTransformer,)` on the middleware class so it's
-only registered on agents that actually use it — see `AgentMiddleware.transformers`.
-Both are public so third-party middleware can adopt the same pattern.
+Tag internal calls with `internal_call_metadata()` and declare
+`transformers = (InternalCallTransformer,)` on the middleware class to keep
+them out of `run.messages`. Both APIs are public for third-party middleware.
 """
 
 from __future__ import annotations
@@ -22,25 +17,17 @@ if TYPE_CHECKING:
     from langgraph.stream._types import ProtocolEvent
 
 INTERNAL_CALL_METADATA_KEY = "lc_internal_call"
-"""`RunnableConfig` metadata key marking a model call as internal to middleware.
+"""Metadata key marking a model call as internal to middleware.
 
-Kept separate from `lc_source` (used by `SummarizationMiddleware` to advertise
-that a summarization call is in flight) so tagging a call for filtering here
-never changes what other consumers observe via that key.
+Kept separate from `lc_source` so filtering doesn't affect its existing
+semantics.
 """
 
 _INTERNAL_CALL_TOKEN = secrets.token_hex(16)
-"""Unguessable marker value, regenerated on import.
+"""Process-local marker used to prevent callers from spoofing internal calls.
 
-`config["metadata"]` ultimately comes from a `RunnableConfig`, which callers
-of `invoke`/`stream_events` can populate with arbitrary values — including
-the main agent turn's own call, since it goes through the same ambient
-config. If the marker were a fixed value like `True`, a caller who can
-influence invocation metadata (e.g. an API layer that forwards user-supplied
-metadata) could set `lc_internal_call` themselves and hide the agent's real
-answer from `run.messages`. Comparing against this process-local secret
-instead of truthiness means a caller can't forge it without already being
-able to run code in this process.
+A random token prevents user-supplied metadata from hiding real model calls
+from `run.messages`.
 """
 
 
@@ -56,33 +43,14 @@ def internal_call_metadata() -> dict[str, Any]:
 class InternalCallTransformer(StreamTransformer):
     """Keep internal model calls out of `run.messages` and the raw event log.
 
-    Declared on `transformers` by middleware that makes internal calls (e.g.
-    `SummarizationMiddleware`), so it's only registered on agents using one of
-    those, and runs before built-in transformers.
+    Used by middleware that makes internal model calls and runs before built-in
+    transformers.
 
-    `messages`-mode events come in two shapes, and influencing
-    `MessagesTransformer`'s built-in exclusion rules for either one requires
-    mutating the event in place (there's no metadata hook it consults). That
-    mutation would misrepresent the call if it also reached raw event
-    consumers — a real AI response reported as `role: "tool"`, or a payload
-    replaced with `None`, neither a real messages-mode shape — so tagged
-    events are dropped from the raw log entirely rather than published in a
-    mutated form:
+    For tagged events, streamed `message-start` events are marked as tool-role and
+    whole-`AIMessage` payloads are cleared so `MessagesTransformer` ignores them.
+    The mutated events are then dropped from the raw log.
 
-    - Streamed protocol events (`message-start` / `content-block-*` /
-      `message-finish`): `message-start`'s `role` is rewritten to `"tool"`,
-      reusing `MessagesTransformer`'s existing tool-result exclusion, then
-      the event is dropped.
-    - Whole-`AIMessage` events — the fallback `MessagesTransformer` uses when
-      a chat model doesn't stream (notably, streaming context isn't
-      propagated on Python 3.10) or when a node returns a finalized message
-      as state: the payload is cleared so `MessagesTransformer` has nothing
-      left to route, then the event is dropped.
-
-    Only events in this transformer's own scope are touched — nested
-    subgraphs get their own scoped instance (if the offending middleware runs
-    there too), and `MessagesTransformer` itself ignores events outside its
-    scope, so mutating them here would be both unnecessary and unsafe.
+    Only events within this transformer's scope are modified.
     """
 
     before_builtins: ClassVar[bool] = True
@@ -129,10 +97,9 @@ class InternalCallTransformer(StreamTransformer):
         if not is_internal:
             return True
 
-        # Only `message-start` needs mutating: once MessagesTransformer sees its
-        # `role` spoofed as "tool", its own tool-result bookkeeping ignores every
-        # later event for this run_id, so content-block-*/message-finish need no
-        # action here, the whole run gets dropped below regardless.
+        # Only `message-start` needs mutation: marking it as `"tool"` makes
+        # `MessagesTransformer` ignore the rest of that run. All events are still
+        # dropped from the raw log below.
         if isinstance(payload, dict) and payload.get("event") == "message-start":
             payload["role"] = "tool"
         elif isinstance(payload, BaseMessage):

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

@@ -1,5 +1,6 @@
 """Summarization middleware."""
 
+import logging
 import uuid
 import warnings
 from collections.abc import Callable, Iterable, Mapping
@@ -32,6 +33,8 @@ from langchain.agents.middleware.internal_call_transformer import (
 from langchain.agents.middleware.types import AgentMiddleware, AgentState, ContextT, ResponseT
 from langchain.chat_models import BaseChatModel, init_chat_model
 
+logger = logging.getLogger(__name__)
+
 TokenCounter = Callable[[Iterable[MessageLikeRepresentation]], int]
 
 DEFAULT_SUMMARY_PROMPT = """<role>
@@ -232,6 +235,11 @@ class SummarizationMiddleware(AgentMiddleware[AgentState[ResponseT], ContextT, R
     This middleware monitors message token counts and automatically summarizes older
     messages when a threshold is reached, preserving recent messages and maintaining
     context continuity by ensuring AI/Tool message pairs remain together.
+
+    Transient summary-generation errors (rate limits, timeouts) are retried
+    in-process, up to 3 attempts total, via `Runnable.with_retry`. If a summary call
+    still fails after those attempts, the underlying error propagates rather than
+    fabricating a summary.
     """
 
     transformers = (InternalCallTransformer,)
@@ -346,6 +354,9 @@ class SummarizationMiddleware(AgentMiddleware[AgentState[ResponseT], ContextT, R
             model = init_chat_model(model)
 
         self.model = model
+        # Retry transient errors (rate limits, timeouts) in-process, up to 3 attempts
+        # total, before giving up. See `Runnable.with_retry`.
+        self._summary_model = self.model.with_retry()
 
         self.trigger: ContextSize | TriggerClause | list[ContextSize | TriggerClause] | None = (
             self._copy_trigger(trigger)
@@ -395,6 +406,9 @@ class SummarizationMiddleware(AgentMiddleware[AgentState[ResponseT], ContextT, R
 
         Returns:
             An updated state with summarized messages if summarization was performed.
+
+        Raises:
+            Exception: If summary generation still fails once retries are exhausted.
         """
         messages = state["messages"]
         self._ensure_message_ids(messages)
@@ -433,6 +447,9 @@ class SummarizationMiddleware(AgentMiddleware[AgentState[ResponseT], ContextT, R
 
         Returns:
             An updated state with summarized messages if summarization was performed.
+
+        Raises:
+            Exception: If summary generation still fails once retries are exhausted.
         """
         messages = state["messages"]
         self._ensure_message_ids(messages)
@@ -817,6 +834,13 @@ class SummarizationMiddleware(AgentMiddleware[AgentState[ResponseT], ContextT, R
 
         Args:
             messages_to_summarize: Messages to summarize.
+
+        Returns:
+            The generated summary.
+
+        Raises:
+            Exception: If the summary model call still fails once retries (see
+                `Runnable.with_retry`) are exhausted.
         """
         if not messages_to_summarize:
             return "No previous conversation history."
@@ -829,20 +853,24 @@ class SummarizationMiddleware(AgentMiddleware[AgentState[ResponseT], ContextT, R
         # prompt while excluding raw message metadata from the token budget.
         formatted_messages = get_buffer_string(trimmed_messages, format="xml")
 
-        try:
-            response = self.model.invoke(
-                self.summary_prompt.format(messages=formatted_messages).rstrip(),
-                config={"metadata": {"lc_source": "summarization", **internal_call_metadata()}},
-            )
-            return response.text.strip()
-        except Exception as e:
-            return f"Error generating summary: {e!s}"
+        response = self._summary_model.invoke(
+            self.summary_prompt.format(messages=formatted_messages).rstrip(),
+            config={"metadata": {"lc_source": "summarization", **internal_call_metadata()}},
+        )
+        return response.text.strip()
 
     async def _acreate_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
         """Generate summary for the given messages.
 
         Args:
             messages_to_summarize: Messages to summarize.
+
+        Returns:
+            The generated summary.
+
+        Raises:
+            Exception: If the summary model call still fails once retries (see
+                `Runnable.with_retry`) are exhausted.
         """
         if not messages_to_summarize:
             return "No previous conversation history."
@@ -855,14 +883,11 @@ class SummarizationMiddleware(AgentMiddleware[AgentState[ResponseT], ContextT, R
         # prompt while excluding raw message metadata from the token budget.
         formatted_messages = get_buffer_string(trimmed_messages, format="xml")
 
-        try:
-            response = await self.model.ainvoke(
-                self.summary_prompt.format(messages=formatted_messages).rstrip(),
-                config={"metadata": {"lc_source": "summarization", **internal_call_metadata()}},
-            )
-            return response.text.strip()
-        except Exception as e:
-            return f"Error generating summary: {e!s}"
+        response = await self._summary_model.ainvoke(
+            self.summary_prompt.format(messages=formatted_messages).rstrip(),
+            config={"metadata": {"lc_source": "summarization", **internal_call_metadata()}},
+        )
+        return response.text.strip()
 
     def _trim_messages_for_summary(self, messages: list[AnyMessage]) -> list[AnyMessage]:
         """Trim messages to fit within summary generation limits."""

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

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import warnings
 from typing import TYPE_CHECKING, Any, Generic
 
 from langchain_core.language_models.chat_models import BaseChatModel
@@ -22,6 +23,8 @@ if TYPE_CHECKING:
     from langchain.agents.middleware.types import ToolCallRequest
     from langchain.tools import BaseTool
 
+_DEFAULT_EMULATOR_MODEL = "anthropic:claude-sonnet-4-5-20250929"
+
 
 class LLMToolEmulator(AgentMiddleware[AgentState[Any], ContextT], Generic[ContextT]):
     """Emulates specified tools using an LLM instead of executing them.
@@ -90,7 +93,14 @@ class LLMToolEmulator(AgentMiddleware[AgentState[Any], ContextT], Generic[Contex
                 If empty list, no tools will be emulated.
             model: Model to use for emulation.
 
-                Defaults to `'anthropic:claude-sonnet-4-5-20250929'`.
+                Defaults to `'anthropic:claude-sonnet-4-5-20250929'`, which requires
+                `langchain-anthropic` to be installed.
+
+                !!! warning "Deprecated"
+                    Relying on the implicit default is deprecated and will be
+                    removed in a future release, since it makes this middleware
+                    depend on `langchain-anthropic` even when unspecified. Pass
+                    `model` explicitly instead.
 
                 Can be a model identifier string or `BaseChatModel` instance.
         """
@@ -101,7 +111,7 @@ class LLMToolEmulator(AgentMiddleware[AgentState[Any], ContextT], Generic[Contex
         self.emulate_all = tools is None
         self.tools_to_emulate: set[str] = set()
 
-        if not self.emulate_all and tools is not None:
+        if tools is not None:
             for tool in tools:
                 if isinstance(tool, str):
                     self.tools_to_emulate.add(tool)
@@ -111,7 +121,22 @@ class LLMToolEmulator(AgentMiddleware[AgentState[Any], ContextT], Generic[Contex
 
         # Initialize emulator model
         if model is None:
-            self.model = init_chat_model("anthropic:claude-sonnet-4-5-20250929", temperature=1)
+            warnings.warn(
+                "LLMToolEmulator's default model "
+                f"({_DEFAULT_EMULATOR_MODEL!r}) is deprecated and will be removed "
+                "in a future release. Pass `model` explicitly instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            try:
+                self.model = init_chat_model(_DEFAULT_EMULATOR_MODEL, temperature=1)
+            except ImportError as e:
+                msg = (
+                    "LLMToolEmulator's default model requires `langchain-anthropic` "
+                    "to be installed. Install it with `pip install langchain-anthropic`, "
+                    "or pass `model=...` explicitly to use a different provider."
+                )
+                raise ImportError(msg) from e
         elif isinstance(model, BaseChatModel):
             self.model = model
         else:

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

@@ -28,7 +28,7 @@ from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt.tool_node import ToolCallRequest, ToolCallWrapper
 from langgraph.runtime import Runtime
-from langgraph.types import Command
+from langgraph.types import Command, TracePolicy, omit_payload
 from langgraph.typing import ContextT
 from typing_extensions import NotRequired, Required, TypedDict, TypeVar, Unpack
 
@@ -54,12 +54,14 @@ __all__ = [
     "StateT_co",
     "ToolCallRequest",
     "ToolCallWrapper",
+    "TracePolicy",
     "after_agent",
     "after_model",
     "before_agent",
     "before_model",
     "dynamic_prompt",
     "hook_config",
+    "omit_payload",
     "wrap_tool_call",
 ]
 
@@ -398,6 +400,16 @@ class AgentMiddleware(Generic[StateT, ContextT, ResponseT]):
     tools: Sequence[BaseTool]
     """Additional tools registered by the middleware."""
 
+    trace_policy: TracePolicy | None = None
+    """Optional trace policy for this middleware's hook spans (`wrap_model_call`/
+    `wrap_tool_call` and the `before_*`/`after_*` node hooks).
+
+    By default (`None`), hook spans are traced normally. Set a `TracePolicy` to shape
+    what they record -- e.g. `TracePolicy(process_inputs=omit_payload)` to drop the
+    conversation `messages`/`state` payload while keeping the span and its timing.
+    Messages are still captured on the inner model-call span.
+    """
+
     transformers: Sequence[TransformerFactory] = ()
     """Stream transformer factories registered by the middleware.
 
@@ -999,6 +1011,9 @@ def before_model(
 
         !!! example "With custom state schema"
 
+        Use a custom state schema when your middleware needs to read or write additional
+        state fields that aren't part of the default agent state.
+
             ```python
             @before_model(state_schema=MyCustomState)
             def custom_before_model(state: MyCustomState, runtime: Runtime) -> dict[str, Any]:
@@ -1173,6 +1188,9 @@ def after_model(
 
         !!! example "With custom state schema"
 
+        Use a custom state schema when your middleware needs to read or write additional
+        state fields that aren't part of the default agent state.
+
             ```python
             @after_model(state_schema=MyCustomState, name="MyAfterModelMiddleware")
             def custom_after_model(state: MyCustomState, runtime: Runtime) -> dict[str, Any]:
@@ -1359,6 +1377,9 @@ def before_agent(
 
         !!! example "With custom state schema"
 
+        Use a custom state schema when your middleware needs to read or write additional
+        state fields that aren't part of the default agent state.
+
             ```python
             @before_agent(state_schema=MyCustomState)
             def custom_before_agent(state: MyCustomState, runtime: Runtime) -> dict[str, Any]:
@@ -1558,6 +1579,9 @@ def after_agent(
 
         !!! example "With custom state schema"
 
+        Use a custom state schema when your middleware needs to read or write additional
+        state fields that aren't part of the default agent state.
+
             ```python
             @after_agent(state_schema=MyCustomState, name="MyAfterAgentMiddleware")
             def custom_after_agent(state: MyCustomState, runtime: Runtime) -> dict[str, Any]:
@@ -1994,32 +2018,34 @@ def wrap_model_call(
 @overload
 def wrap_tool_call(
     func: _CallableReturningToolResponse,
-) -> AgentMiddleware: ...
+) -> AgentMiddleware[StateT, ContextT]: ...
 
 
 @overload
 def wrap_tool_call(
     func: None = None,
     *,
+    state_schema: type[StateT] | None = None,
     tools: list[BaseTool] | None = None,
     name: str | None = None,
 ) -> Callable[
     [_CallableReturningToolResponse],
-    AgentMiddleware,
+    AgentMiddleware[StateT, ContextT],
 ]: ...
 
 
 def wrap_tool_call(
     func: _CallableReturningToolResponse | None = None,
     *,
+    state_schema: type[StateT] | None = None,
     tools: list[BaseTool] | None = None,
     name: str | None = None,
 ) -> (
     Callable[
         [_CallableReturningToolResponse],
-        AgentMiddleware,
+        AgentMiddleware[StateT, ContextT],
     ]
-    | AgentMiddleware
+    | AgentMiddleware[StateT, ContextT]
 ):
     """Create middleware with `wrap_tool_call` hook from a function.
 
@@ -2034,6 +2060,9 @@ def wrap_tool_call(
             `Command`.
 
             Can be sync or async.
+        state_schema: Optional custom state schema type.
+
+            If not provided, uses the default `AgentState` schema.
         tools: Additional tools to register with this middleware.
         name: Middleware class name.
 
@@ -2097,17 +2126,28 @@ def wrap_tool_call(
                 save_cache(request, result)
                 return result
             ```
+
+        !!! example "With custom state schema"
+
+        Use a custom state schema when your middleware needs to read or write additional
+        state fields that aren't part of the default agent state.
+
+            ```python
+            @wrap_tool_call(state_schema=MyCustomState)
+            def custom_wrap_tool_call(request, handler):
+                return handler(request)
+            ```
     """
 
     def decorator(
         func: _CallableReturningToolResponse,
-    ) -> AgentMiddleware:
+    ) -> AgentMiddleware[StateT, ContextT]:
         is_async = iscoroutinefunction(func)
 
         if is_async:
 
             async def async_wrapped(
-                _self: AgentMiddleware,
+                _self: AgentMiddleware[StateT, ContextT],
                 request: ToolCallRequest,
                 handler: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command[Any]]],
             ) -> ToolMessage | Command[Any]:
@@ -2120,12 +2160,12 @@ def wrap_tool_call(
             # `type(...)` builds the correct middleware subclass at runtime, but
             # type checkers cannot infer its generic `AgentMiddleware` parameters.
             return cast(
-                "AgentMiddleware",
+                "AgentMiddleware[StateT, ContextT]",
                 type(
                     middleware_name,
                     (AgentMiddleware,),
                     {
-                        "state_schema": AgentState,
+                        "state_schema": state_schema or AgentState,
                         "tools": tools or [],
                         "awrap_tool_call": async_wrapped,
                     },
@@ -2133,7 +2173,7 @@ def wrap_tool_call(
             )
 
         def wrapped(
-            _self: AgentMiddleware,
+            _self: AgentMiddleware[StateT, ContextT],
             request: ToolCallRequest,
             handler: Callable[[ToolCallRequest], ToolMessage | Command[Any]],
         ) -> ToolMessage | Command[Any]:
@@ -2144,12 +2184,12 @@ def wrap_tool_call(
         # `type(...)` builds the correct middleware subclass at runtime, but
         # type checkers cannot infer its generic `AgentMiddleware` parameters.
         return cast(
-            "AgentMiddleware",
+            "AgentMiddleware[StateT, ContextT]",
             type(
                 middleware_name,
                 (AgentMiddleware,),
                 {
-                    "state_schema": AgentState,
+                    "state_schema": state_schema or AgentState,
                     "tools": tools or [],
                     "wrap_tool_call": wrapped,
                 },

libs/langchain_v1/pyproject.toml

@@ -21,11 +21,11 @@ classifiers = [
     "Topic :: Software Development :: Libraries :: Python Modules",
 ]
 
-version = "1.3.14"
+version = "1.3.15"
 requires-python = ">=3.10.0,<4.0.0"
 dependencies = [
-    "langchain-core>=1.5.3,<2.0.0",
-    "langgraph>=1.2.5,<1.3.0",
+    "langchain-core>=1.5.4,<2.0.0",
+    "langgraph>=1.2.11,<1.3.0",
     "pydantic>=2.7.4,<3.0.0",
 ]
 

libs/langchain_v1/tests/unit_tests/agents/middleware/core/test_internal_call_transformer.py

@@ -1,5 +1,11 @@
 """Tests for `InternalCallTransformer` filtering middleware-internal model calls."""
 
+# `run.tool_calls`/`run.subagents` are stream projections registered dynamically by
+# `create_agent` (via langgraph-prebuilt's `ToolCallTransformer` and langchain's
+# `SubagentTransformer`), not declared on langgraph's typed `GraphRunStream`
+# (langgraph#8389). The `# type: ignore[attr-defined]` below self-remove once
+# langgraph adds a `__getattr__` fallback (strict mode's `warn_unused_ignores`).
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any
@@ -97,7 +103,7 @@ def test_internal_call_transformer_not_registered_without_offending_middleware()
     assert not any(isinstance(t, InternalCallTransformer) for t in transformers)
 
     # Drain to close cleanly.
-    list(run.tool_calls)
+    list(run.tool_calls)  # type: ignore[attr-defined]
 
 
 def test_internal_call_transformer_registered_before_messages_transformer() -> None:
@@ -125,7 +131,7 @@ def test_internal_call_transformer_registered_before_messages_transformer() -> N
     )
 
     # Drain to close cleanly.
-    list(run.tool_calls)
+    list(run.tool_calls)  # type: ignore[attr-defined]
 
 
 def test_internal_call_transformer_deduped_across_middleware() -> None:
@@ -147,7 +153,7 @@ def test_internal_call_transformer_deduped_across_middleware() -> None:
     assert sum(isinstance(t, InternalCallTransformer) for t in transformers) == 1
 
     # Drain to close cleanly.
-    list(run.tool_calls)
+    list(run.tool_calls)  # type: ignore[attr-defined]
 
 
 def test_internal_call_transformer_deduped_alongside_builtins() -> None:
@@ -170,7 +176,7 @@ def test_internal_call_transformer_deduped_alongside_builtins() -> None:
     assert sum(isinstance(t, InternalCallTransformer) for t in transformers) == 1
 
     # Drain to close cleanly.
-    list(run.tool_calls)
+    list(run.tool_calls)  # type: ignore[attr-defined]
 
 
 def test_internal_call_transformer_dedup_accepts_unhashable_factories() -> None:
@@ -193,7 +199,7 @@ def test_internal_call_transformer_dedup_accepts_unhashable_factories() -> None:
     )
 
     run = agent.stream_events({"messages": [HumanMessage("hi")]}, version="v3")
-    list(run.tool_calls)
+    list(run.tool_calls)  # type: ignore[attr-defined]
 
 
 def test_internal_model_calls_excluded_from_messages_projection_sync() -> None:

libs/langchain_v1/tests/unit_tests/agents/middleware/core/test_tracing.py

@@ -0,0 +1,159 @@
+"""End-to-end tests for `AgentMiddleware.trace_policy` and the global default."""
+
+from collections.abc import Iterator
+from typing import Any
+
+import pytest
+from langchain_core.messages import HumanMessage
+from langchain_core.tracers import BaseTracer, Run
+from langgraph.runtime import Runtime
+from typing_extensions import override
+
+from langchain.agents.factory import _wrap_trace_kwargs, create_agent
+from langchain.agents.middleware import (
+    AgentMiddleware,
+    TracePolicy,
+    configure_trace_policy,
+    omit_payload,
+)
+from langchain.agents.middleware.types import AgentState
+from tests.unit_tests.agents.model import FakeToolCallingModel
+
+
+@pytest.fixture(autouse=True)
+def _reset_global_trace_policy() -> Iterator[None]:
+    """Keep the process-global default from leaking across tests."""
+    configure_trace_policy(None)
+    yield
+    configure_trace_policy(None)
+
+
+class _CaptureInputs(BaseTracer):
+    """Tracer that records the inputs each run reports, keyed by run name.
+
+    A real tracer (not a plain callback) so `TracePolicy` processors, which run only
+    when a tracer is attached, actually fire.
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.inputs_by_name: dict[str, Any] = {}
+
+    def _persist_run(self, run: Run) -> None:
+        pass
+
+    def _on_chain_start(self, run: Run) -> None:
+        self.inputs_by_name.setdefault(run.name, run.inputs)
+
+
+def _recorded_before_model_inputs(middleware: AgentMiddleware) -> Any:
+    """Run the agent and return what the middleware's before_model node recorded."""
+    agent = create_agent(model=FakeToolCallingModel(), middleware=[middleware])
+    capture = _CaptureInputs()
+    agent.invoke({"messages": [HumanMessage("hi")]}, {"callbacks": [capture]})
+    return capture.inputs_by_name[f"{middleware.name}.before_model"]
+
+
+class _NoopBeforeModel(AgentMiddleware):
+    """Middleware with a before_model hook and no `trace_policy`."""
+
+    @override
+    def before_model(self, state: AgentState[Any], runtime: Runtime) -> None:
+        return None
+
+
+def test_tracing_records_hook_inputs_by_default() -> None:
+    # no `trace_policy`, no global -> hooks trace normally; real state is recorded
+    recorded = _recorded_before_model_inputs(_NoopBeforeModel())
+    assert "messages" in recorded
+    assert [m.content for m in recorded["messages"]] == ["hi"]
+
+
+def test_tracing_omits_hook_inputs_with_omit_payload() -> None:
+    class Scrubbed(AgentMiddleware):
+        trace_policy = TracePolicy(process_inputs=omit_payload)
+
+        @override
+        def before_model(self, state: AgentState[Any], runtime: Runtime) -> None:
+            return None
+
+    # opt in via omit_payload -> the before_model node records an empty payload
+    assert _recorded_before_model_inputs(Scrubbed()) == {}
+
+
+def test_global_default_applies_when_middleware_unset() -> None:
+    configure_trace_policy(TracePolicy(process_inputs=omit_payload))
+
+    # middleware leaves trace_policy=None -> inherits the global default
+    assert _recorded_before_model_inputs(_NoopBeforeModel()) == {}
+
+
+def test_middleware_overrides_global_no_merge() -> None:
+    configure_trace_policy(TracePolicy(process_inputs=omit_payload))
+
+    class Override(AgentMiddleware):
+        # sets only process_outputs; per override-not-merge this replaces the global
+        # wholesale, so the global's input scrub does NOT apply -> inputs recorded raw
+        trace_policy = TracePolicy(process_outputs=omit_payload)
+
+        @override
+        def before_model(self, state: AgentState[Any], runtime: Runtime) -> None:
+            return None
+
+    recorded = _recorded_before_model_inputs(Override())
+    assert "messages" in recorded
+
+
+def test_configure_after_create_agent_applies() -> None:
+    mw = _NoopBeforeModel()
+    agent = create_agent(model=FakeToolCallingModel(), middleware=[mw])
+    # configure AFTER the graph is built; the resolver reads the global at trace time
+    configure_trace_policy(TracePolicy(process_inputs=omit_payload))
+    capture = _CaptureInputs()
+    agent.invoke({"messages": [HumanMessage("hi")]}, {"callbacks": [capture]})
+    assert capture.inputs_by_name[f"{mw.name}.before_model"] == {}
+
+
+def test_configure_none_clears_global() -> None:
+    configure_trace_policy(TracePolicy(process_inputs=omit_payload))
+    configure_trace_policy(None)
+
+    recorded = _recorded_before_model_inputs(_NoopBeforeModel())
+    assert "messages" in recorded
+
+
+def test_wrap_trace_kwargs_composes_scrub_baseline() -> None:
+    seen: dict[str, Any] = {}
+
+    def process_inputs(inp: Any) -> Any:
+        seen["inputs"] = inp
+        return inp
+
+    class MW(AgentMiddleware):
+        trace_policy = TracePolicy(process_inputs=process_inputs)
+
+    process = _wrap_trace_kwargs(MW())["process_inputs"]
+    result = process({"request": {"x": 1}, "handler": lambda: None})
+    # the baseline (`_scrub_inputs`) strips `handler` before the policy callable runs
+    assert "handler" not in seen["inputs"]
+    assert seen["inputs"] == {"request": {"x": 1}}
+    assert result == {"request": {"x": 1}}
+
+
+def test_wrap_trace_kwargs_omit_drops_everything() -> None:
+    class MW(AgentMiddleware):
+        trace_policy = TracePolicy(process_inputs=omit_payload)
+
+    process = _wrap_trace_kwargs(MW())["process_inputs"]
+    assert process({"request": {"x": 1}, "handler": lambda: None}) == {}
+
+
+def test_wrap_trace_kwargs_inherits_global() -> None:
+    configure_trace_policy(TracePolicy(process_inputs=omit_payload))
+
+    class MW(AgentMiddleware):
+        pass
+
+    # no middleware policy -> wrap hook inherits the global, still after the baseline
+    process = _wrap_trace_kwargs(MW())["process_inputs"]
+    assert process({"request": {"x": 1}, "handler": lambda: None}) == {}

libs/langchain_v1/tests/unit_tests/agents/middleware/core/test_transformers.py

@@ -1,5 +1,11 @@
 """Tests for middleware-registered stream transformers."""
 
+# `run.tool_calls`/`run.subagents` are stream projections registered dynamically by
+# `create_agent` (via langgraph-prebuilt's `ToolCallTransformer` and langchain's
+# `SubagentTransformer`), not declared on langgraph's typed `GraphRunStream`
+# (langgraph#8389). The `# type: ignore[attr-defined]` below self-remove once
+# langgraph adds a `__getattr__` fallback (strict mode's `warn_unused_ignores`).
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any
@@ -62,7 +68,7 @@ def test_middleware_transformer_registered_on_compiled_graph() -> None:
 
     assert "middleware_marker" in run._mux.extensions
     # Drain to close the run cleanly.
-    list(run.tool_calls)
+    list(run.tool_calls)  # type: ignore[attr-defined]
 
 
 def test_middleware_and_user_transformers_compose_in_order() -> None:
@@ -91,7 +97,7 @@ def test_middleware_and_user_transformers_compose_in_order() -> None:
         "transformers must register as: built-in, then middleware, then user-supplied"
     )
 
-    list(run.tool_calls)
+    list(run.tool_calls)  # type: ignore[attr-defined]
 
 
 def test_transformers_from_multiple_middleware_preserve_middleware_order() -> None:
@@ -124,7 +130,7 @@ def test_transformers_from_multiple_middleware_preserve_middleware_order() -> No
     idx_b = next(i for i, t in enumerate(transformers) if isinstance(t, _MarkerB))
     assert idx_a < idx_b
 
-    list(run.tool_calls)
+    list(run.tool_calls)  # type: ignore[attr-defined]
 
 
 def test_middleware_without_transformers_does_not_affect_registry() -> None:
@@ -140,4 +146,4 @@ def test_middleware_without_transformers_does_not_affect_registry() -> None:
     assert any(isinstance(t, ToolCallTransformer) for t in transformers)
     assert not any(isinstance(t, _MiddlewareMarker) for t in transformers)
 
-    list(run.tool_calls)
+    list(run.tool_calls)  # type: ignore[attr-defined]

libs/langchain_v1/tests/unit_tests/agents/middleware/core/test_wrap_tool_call.py

@@ -6,7 +6,7 @@ focusing on the handler pattern (not generators).
 
 import time
 from collections.abc import Callable
-from typing import Any
+from typing import Any, TypedDict
 
 from langchain_core.messages import HumanMessage, ToolCall, ToolMessage
 from langchain_core.tools import BaseTool, tool
@@ -14,7 +14,7 @@ from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.types import Command
 
 from langchain.agents.factory import create_agent
-from langchain.agents.middleware.types import ToolCallRequest, wrap_tool_call
+from langchain.agents.middleware.types import AgentMiddleware, ToolCallRequest, wrap_tool_call
 from tests.unit_tests.agents.model import FakeToolCallingModel
 
 
@@ -74,6 +74,27 @@ def test_wrap_tool_call_basic_passthrough() -> None:
     assert "Results for: test" in tool_messages[0].content
 
 
+def test_wrap_tool_call_with_custom_state_schema() -> None:
+    """Test `state_schema` is accepted for consistency with other middleware decorators.
+
+    `before_model`, `after_model`, `wrap_model_call`, `before_agent`, and
+    `after_agent` all support a `state_schema` parameter.
+    """
+
+    class CustomState(TypedDict):
+        messages: list[Any]
+        custom_field: str
+
+    @wrap_tool_call(state_schema=CustomState)  # type: ignore[type-var]
+    def middleware_with_schema(
+        request: ToolCallRequest, handler: Callable[[ToolCallRequest], ToolMessage | Command[Any]]
+    ) -> ToolMessage | Command[Any]:
+        return handler(request)
+
+    assert isinstance(middleware_with_schema, AgentMiddleware)
+    assert middleware_with_schema.state_schema == CustomState
+
+
 def test_wrap_tool_call_logging() -> None:
     """Test logging tool call execution with wrap_tool_call decorator."""
     call_log = []

libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_pii.py

@@ -1,5 +1,11 @@
 """Tests for PII detection middleware."""
 
+# `run.tool_calls`/`run.subagents` are stream projections registered dynamically by
+# `create_agent` (via langgraph-prebuilt's `ToolCallTransformer` and langchain's
+# `SubagentTransformer`), not declared on langgraph's typed `GraphRunStream`
+# (langgraph#8389). The `# type: ignore[attr-defined]` below self-remove once
+# langgraph adds a `__getattr__` fallback (strict mode's `warn_unused_ignores`).
+
 import re
 from typing import Any
 
@@ -1973,7 +1979,7 @@ class TestPIIStreamTransformer:
         )
 
         # Drain to close cleanly.
-        list(run.tool_calls)
+        list(run.tool_calls)  # type: ignore[attr-defined]
 
 
 class TestPIIStreamingEndToEnd:
@@ -2113,8 +2119,6 @@ class TestPIIStreamingEndToEnd:
         surfaces: list[str] = []
         run = await agent.astream_events({"messages": [HumanMessage("hi")]}, version="v3")
         async for event in run:
-            if not isinstance(event, dict):
-                continue
             data = event.get("params", {}).get("data")
             if isinstance(data, tuple) and len(data) == 2:
                 p = data[0]
@@ -2187,8 +2191,6 @@ class TestPIIStreamingEndToEnd:
         surfaces: list[str] = []
         run = await agent.astream_events({"messages": [HumanMessage("hi")]}, version="v3")
         async for event in run:
-            if not isinstance(event, dict):
-                continue
             data = event.get("params", {}).get("data")
             if isinstance(data, tuple) and len(data) == 2:
                 p = data[0]

libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_summarization.py

@@ -24,6 +24,7 @@ from langchain_core.messages import (
 from langchain_core.messages.utils import count_tokens_approximately, get_buffer_string
 from langchain_core.outputs import ChatGeneration, ChatResult
 from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph.message import REMOVE_ALL_MESSAGES
 from langgraph.runtime import Runtime
 from pydantic import Field
@@ -246,8 +247,11 @@ def test_summarization_middleware_summary_creation() -> None:
             return "mock"
 
     middleware_error = SummarizationMiddleware(model=ErrorModel(), trigger=("tokens", 1000))
-    summary = middleware_error._create_summary(messages)
-    assert "Error generating summary: Model error" in summary
+    # Bypass the retry wrapper so this test isn't slowed by real backoff delay; retry
+    # behavior itself is covered by test_summarization_middleware_retries_transient_failure_in_call.
+    middleware_error._summary_model = middleware_error.model
+    with pytest.raises(ValueError, match="Model error"):
+        middleware_error._create_summary(messages)
 
     # Test we raise warning if max_tokens_before_summary or messages_to_keep is specified
     with pytest.warns(DeprecationWarning, match="max_tokens_before_summary is deprecated"):
@@ -256,6 +260,226 @@ def test_summarization_middleware_summary_creation() -> None:
         SummarizationMiddleware(model=MockChatModel(), messages_to_keep=5)
 
 
+def test_summarization_middleware_retries_transient_failure_in_call() -> None:
+    """A transient failure must be retried in-process via `Runnable.with_retry`.
+
+    A single `_create_summary` call must succeed if the underlying model recovers
+    within 3 attempts, without the caller ever observing a failure.
+    """
+
+    class FlakyModel(BaseChatModel):
+        """Model that fails a fixed number of times, then succeeds."""
+
+        fail_count: int = Field(default=0)
+        attempts: int = Field(default=0)
+
+        @override
+        def _generate(
+            self,
+            messages: list[BaseMessage],
+            stop: list[str] | None = None,
+            run_manager: CallbackManagerForLLMRun | None = None,
+            **kwargs: Any,
+        ) -> ChatResult:
+            self.attempts += 1
+            if self.attempts <= self.fail_count:
+                msg = "429 Too Many Requests (simulated transient failure)"
+                raise RuntimeError(msg)
+            return ChatResult(generations=[ChatGeneration(message=AIMessage(content="Summary."))])
+
+        @property
+        def _llm_type(self) -> str:
+            return "mock"
+
+    model = FlakyModel(fail_count=2)
+    middleware = SummarizationMiddleware(model=model, trigger=("messages", 2))
+    # No delay between attempts so the test runs fast and deterministically; the
+    # retry count itself (3, `Runnable.with_retry`'s own default) is untouched.
+    middleware._summary_model.wait_exponential_jitter = False  # type: ignore[attr-defined]
+
+    summary = middleware._create_summary([HumanMessage(content="hi")])
+
+    assert summary == "Summary."
+    assert model.attempts == 3
+
+
+class _AlwaysFailingModel(BaseChatModel):
+    """Chat model whose sync and async summary calls always raise."""
+
+    @override
+    def _generate(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        msg = "429 Too Many Requests"
+        raise RuntimeError(msg)
+
+    @override
+    async def _agenerate(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: AsyncCallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        msg = "429 Too Many Requests"
+        raise RuntimeError(msg)
+
+    @property
+    def _llm_type(self) -> str:
+        return "mock"
+
+
+def _skip_retry(middleware: SummarizationMiddleware) -> None:
+    """Bypass the retry wrapper so failure-path tests aren't slowed by real backoff.
+
+    Retry behavior itself is covered by
+    `test_summarization_middleware_retries_transient_failure_in_call`.
+    """
+    middleware._summary_model = middleware.model
+
+
+def test_create_summary_raises_on_failure() -> None:
+    """`_create_summary` must raise, never fabricate a fake summary string."""
+    middleware = SummarizationMiddleware(model=_AlwaysFailingModel())
+    _skip_retry(middleware)
+
+    with pytest.raises(RuntimeError, match="429 Too Many Requests"):
+        middleware._create_summary([HumanMessage(content="hi")])
+
+
+async def test_acreate_summary_raises_on_failure() -> None:
+    """Async: `_acreate_summary` must raise on failure."""
+    middleware = SummarizationMiddleware(model=_AlwaysFailingModel())
+    _skip_retry(middleware)
+
+    with pytest.raises(RuntimeError, match="429 Too Many Requests"):
+        await middleware._acreate_summary([HumanMessage(content="hi")])
+
+
+def test_summarization_middleware_before_model_raises_on_summary_failure() -> None:
+    """A failed summary generation must raise, never fabricate a summary or delete history."""
+    middleware = SummarizationMiddleware(
+        model=_AlwaysFailingModel(), trigger=("messages", 2), keep=("messages", 1)
+    )
+    _skip_retry(middleware)
+    messages: list[AnyMessage] = [
+        HumanMessage(content="hi", id="h0"),
+        AIMessage(content="hello", id="a0"),
+        HumanMessage(content="how are you", id="h1"),
+    ]
+    state = AgentState[Any](messages=messages)
+
+    with pytest.raises(RuntimeError, match="429 Too Many Requests"):
+        middleware.before_model(state, Runtime())
+
+    # The original message list is untouched - no fabricated summary, no deletion.
+    assert state["messages"] == messages
+
+
+async def test_summarization_middleware_abefore_model_raises_on_summary_failure() -> None:
+    """Async: same raise-on-failure behavior as `before_model`."""
+    middleware = SummarizationMiddleware(
+        model=_AlwaysFailingModel(), trigger=("messages", 2), keep=("messages", 1)
+    )
+    _skip_retry(middleware)
+    messages: list[AnyMessage] = [
+        HumanMessage(content="hi", id="h0"),
+        AIMessage(content="hello", id="a0"),
+        HumanMessage(content="how are you", id="h1"),
+    ]
+    state = AgentState[Any](messages=messages)
+
+    with pytest.raises(RuntimeError, match="429 Too Many Requests"):
+        await middleware.abefore_model(state, Runtime())
+
+    assert state["messages"] == messages
+
+
+def test_summarization_middleware_e2e_failure_raises_without_corrupting_history() -> None:
+    """End-to-end regression test.
+
+    Verifies that exhausted summary retries raise an error without corrupting
+    checkpointed history, and that summarization succeeds on a later invocation
+    once the model recovers.
+    """
+
+    class TemporarilyFailingSummaryModel(BaseChatModel):
+        """Summary-only model that fails until `working` is set to `True`."""
+
+        working: bool = Field(default=False)
+
+        @override
+        def _generate(
+            self,
+            messages: list[BaseMessage],
+            stop: list[str] | None = None,
+            run_manager: CallbackManagerForLLMRun | None = None,
+            **kwargs: Any,
+        ) -> ChatResult:
+            if not self.working:
+                msg = "429 Too Many Requests (simulated persistent failure)"
+                raise RuntimeError(msg)
+            summary = AIMessage(content="Summary of the conversation so far.")
+            return ChatResult(generations=[ChatGeneration(message=summary)])
+
+        @property
+        def _llm_type(self) -> str:
+            return "mock"
+
+    main_model = FakeToolCallingModel()
+    summary_model = TemporarilyFailingSummaryModel()
+    middleware = SummarizationMiddleware(
+        model=summary_model, trigger=("messages", 6), keep=("messages", 2)
+    )
+    _skip_retry(middleware)
+
+    agent = create_agent(
+        model=main_model,
+        tools=[],
+        middleware=[middleware],
+        checkpointer=InMemorySaver(),
+    )
+    config: RunnableConfig = {"configurable": {"thread_id": "e2e-summary-failure"}}
+
+    # Build up history below the ("messages", 6) trigger threshold.
+    for i in range(3):
+        agent.invoke({"messages": [HumanMessage(f"turn {i}")]}, config)
+
+    messages_below_threshold = agent.get_state(config).values["messages"]
+    assert len(messages_below_threshold) == 6
+
+    # This turn triggers summarization, which fails and raises. The new human
+    # message is checkpointed, but no AI response, summary, or message removal occurs.
+    with pytest.raises(RuntimeError, match="429 Too Many Requests"):
+        agent.invoke({"messages": [HumanMessage("turn 3")]}, config)
+
+    messages_after_failed_summary = agent.get_state(config).values["messages"]
+    assert messages_after_failed_summary[:-1] == messages_below_threshold
+    assert messages_after_failed_summary[-1].content == "turn 3"
+    assert not any(
+        "Error generating summary" in (m.content or "") for m in messages_after_failed_summary
+    )
+
+    # Once the summarizer is fixed, retrying the same turn must succeed and
+    # actually summarize, proving the earlier failure didn't corrupt anything.
+    summary_model.working = True
+    agent.invoke({"messages": [HumanMessage("turn 3")]}, config)
+
+    messages_after_recovered_summary = agent.get_state(config).values["messages"]
+    assert len(messages_after_recovered_summary) < len(messages_after_failed_summary)
+    assert any(
+        "summary of the conversation" in (m.content or "").lower()
+        for m in messages_after_recovered_summary
+    )
+    assert not any(m.content == "turn 0" for m in messages_after_recovered_summary), (
+        "history should actually be trimmed once summarization succeeds"
+    )
+
+
 def test_summarization_middleware_trim_limit_none_keeps_all_messages() -> None:
     """Verify disabling trim limit preserves full message sequence."""
     messages: list[AnyMessage] = [HumanMessage(content=str(i)) for i in range(10)]
@@ -941,9 +1165,10 @@ async def test_summarization_middleware_async_error_handling() -> None:
             return "mock"
 
     middleware = SummarizationMiddleware(model=ErrorAsyncModel(), trigger=("messages", 5))
+    _skip_retry(middleware)
     messages: list[AnyMessage] = [HumanMessage(content="test")]
-    summary = await middleware._acreate_summary(messages)
-    assert "Error generating summary: Async model error" in summary
+    with pytest.raises(ValueError, match="Async model error"):
+        await middleware._acreate_summary(messages)
 
 
 def test_summarization_middleware_cutoff_at_boundary() -> None:

libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_tool_emulator.py

@@ -4,6 +4,7 @@ from collections.abc import Callable, Sequence
 from itertools import cycle
 from typing import Any, Literal
 
+import pytest
 from langchain_core.language_models import LanguageModelInput
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.language_models.fake_chat_models import GenericFakeChatModel
@@ -12,6 +13,7 @@ from langchain_core.outputs import ChatGeneration, ChatResult
 from langchain_core.runnables import Runnable, RunnableConfig
 from langchain_core.tools import BaseTool, tool
 from pydantic import BaseModel, Field
+from pytest_mock import MockerFixture
 from typing_extensions import override
 
 from langchain.agents import create_agent
@@ -460,17 +462,38 @@ class TestLLMToolEmulatorModelConfiguration:
         # Should use the custom model for emulation
         assert isinstance(result["messages"][-1], AIMessage)
 
-    def test_default_model_used_when_none(self) -> None:
-        """Test that default model is used when model=None."""
-        # Just test that initialization doesn't fail - don't require anthropic package
-        # The actual default model requires langchain_anthropic which may not be installed
-        try:
-            emulator = LLMToolEmulator(tools=["get_weather"], model=None)
-            assert emulator.model is not None
-        except ImportError:
-            # If anthropic isn't installed, that's fine for this unit test
-            # The integration tests will verify the full functionality
-            pass
+    def test_default_model_deprecated_and_missing_langchain_anthropic_raises_clear_error(
+        self,
+    ) -> None:
+        """Test the `model=None` default path without `langchain-anthropic` installed.
+
+        Regression test: omitting `model` used to either silently depend on
+        `langchain-anthropic` or (in an earlier draft of this fix) raise a
+        `TypeError` for a previously-supported call shape. It should instead
+        keep working when a model provider is available, and raise an
+        actionable `ImportError` (plus a `DeprecationWarning`) when it isn't.
+        """
+        with (
+            pytest.warns(DeprecationWarning, match="deprecated"),
+            pytest.raises(ImportError, match="langchain-anthropic"),
+        ):
+            LLMToolEmulator(tools=["get_weather"])
+
+    def test_default_model_used_when_none(self, mocker: MockerFixture) -> None:
+        """Test that the default model is used and a deprecation warning is raised."""
+        fake_model = FakeEmulatorModel(responses=["response"])
+        init_chat_model_mock = mocker.patch(
+            "langchain.agents.middleware.tool_emulator.init_chat_model",
+            return_value=fake_model,
+        )
+
+        with pytest.warns(DeprecationWarning, match="deprecated"):
+            emulator = LLMToolEmulator(tools=["get_weather"])
+
+        assert emulator.model is fake_model
+        init_chat_model_mock.assert_called_once_with(
+            "anthropic:claude-sonnet-4-5-20250929", temperature=1
+        )
 
 
 class TestLLMToolEmulatorAsync:

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

@@ -1,5 +1,11 @@
 """Unit tests for create_agent graphs streaming via `stream_events(version="v3")`."""
 
+# `run.tool_calls`/`run.subagents` are stream projections registered dynamically by
+# `create_agent` (via langgraph-prebuilt's `ToolCallTransformer` and langchain's
+# `SubagentTransformer`), not declared on langgraph's typed `GraphRunStream`
+# (langgraph#8389). The `# type: ignore[attr-defined]` below self-remove once
+# langgraph adds a `__getattr__` fallback (strict mode's `warn_unused_ignores`).
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any
@@ -65,7 +71,7 @@ class TestAgentStreamV3Sync:
         run = agent.stream_events({"messages": [HumanMessage("hi")]}, version="v3")
 
         # Drain so the run closes cleanly.
-        list(run.tool_calls)
+        list(run.tool_calls)  # type: ignore[attr-defined]
 
     def test_tool_calls_populated_without_opt_in(self) -> None:
         """`ToolCallTransformer` is registered by default on the agent streamer."""
@@ -74,7 +80,7 @@ class TestAgentStreamV3Sync:
 
         run = agent.stream_events({"messages": [HumanMessage("hi")]}, version="v3")
 
-        collected: list[ToolCallStream] = list(run.tool_calls)
+        collected: list[ToolCallStream] = list(run.tool_calls)  # type: ignore[attr-defined]
         assert len(collected) == 1
         tc = collected[0]
         assert tc.tool_name == "echo"
@@ -89,7 +95,7 @@ class TestAgentStreamV3Sync:
         run = agent.stream_events({"messages": [HumanMessage("hi")]}, version="v3")
 
         tool_calls: list[ToolCallStream] = []
-        for tc in run.tool_calls:
+        for tc in run.tool_calls:  # type: ignore[attr-defined]
             tool_calls.append(tc)
             assert list(tc.output_deltas) == ["one", "two"]
         assert len(tool_calls) == 1
@@ -100,7 +106,7 @@ class TestAgentStreamV3Sync:
         agent = create_agent(model, [])
 
         run = agent.stream_events({"messages": [HumanMessage("hi")]}, version="v3")
-        assert list(run.tool_calls) == []
+        assert list(run.tool_calls) == []  # type: ignore[attr-defined]
         assert run.output is not None
 
     def test_messages_projection_present(self) -> None:
@@ -116,7 +122,7 @@ class TestAgentStreamV3Sync:
         assert "messages" in run._mux.extensions
         assert hasattr(run, "messages")
         # Drain so the run closes cleanly.
-        for tc in run.tool_calls:
+        for tc in run.tool_calls:  # type: ignore[attr-defined]
             list(tc.output_deltas)
 
     def test_caller_transformers_appended_not_replaced(self) -> None:
@@ -161,7 +167,7 @@ class TestAgentStreamV3Sync:
             "ToolCallTransformer must be registered before user-supplied transformers"
         )
 
-        list(run.tool_calls)
+        list(run.tool_calls)  # type: ignore[attr-defined]
 
     def test_tool_error_sets_error_field(self) -> None:
         """Tool errors are surfaced on the `ToolCallStream.error` field.
@@ -178,7 +184,7 @@ class TestAgentStreamV3Sync:
         collected: list[ToolCallStream] = []
 
         def _drive() -> None:
-            for tc in run.tool_calls:
+            for tc in run.tool_calls:  # type: ignore[attr-defined]
                 collected.append(tc)
                 list(tc.output_deltas)
 
@@ -265,7 +271,7 @@ class TestAgentStreamV3Async:
         agent = create_agent(model, [echo])
 
         run = await agent.astream_events({"messages": [HumanMessage("hi")]}, version="v3")
-        async for tc in run.tool_calls:
+        async for tc in run.tool_calls:  # type: ignore[attr-defined]
             async for _ in tc.output_deltas:
                 pass
 
@@ -277,7 +283,7 @@ class TestAgentStreamV3Async:
         run = await agent.astream_events({"messages": [HumanMessage("hi")]}, version="v3")
 
         collected: list[ToolCallStream] = []
-        async for tc in run.tool_calls:
+        async for tc in run.tool_calls:  # type: ignore[attr-defined]
             collected.append(tc)
             deltas = [d async for d in tc.output_deltas]
             assert deltas == ["hi", "hi!"]

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

@@ -302,6 +302,10 @@ def test_last_schema_wins_for_conflicting_field() -> None:
     hints = get_type_hints(resolved, include_extras=True)
     assert get_args(hints["shared_field"])[1] == "last"
 
+    resolved, _, _ = factory._resolve_schemas([LastState, MiddleState, LastState])
+    hints = get_type_hints(resolved, include_extras=True)
+    assert get_args(hints["shared_field"])[1] == "last"
+
 
 def test_get_schema_type_hints_cache_hits_for_reused_schema() -> None:
     """Test repeated schema resolution reuses cached type hints for the same schema."""

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

@@ -6,6 +6,12 @@ The transformer surfaces, on `run.subagents`, any nested run that carries an
 dispatches a nested `create_agent` from a tool, giving true end-to-end coverage.
 """
 
+# `run.tool_calls`/`run.subagents` are stream projections registered dynamically by
+# `create_agent` (via langgraph-prebuilt's `ToolCallTransformer` and langchain's
+# `SubagentTransformer`), not declared on langgraph's typed `GraphRunStream`
+# (langgraph#8389). The `# type: ignore[attr-defined]` below self-remove once
+# langgraph adds a `__getattr__` fallback (strict mode's `warn_unused_ignores`).
+
 from __future__ import annotations
 
 import sys
@@ -52,7 +58,7 @@ def test_subagents_surfaces_named_subagent() -> None:
     run = supervisor.stream_events({"messages": [HumanMessage("weather?")]}, version="v3")
 
     handles = []
-    for handle in run.subagents:
+    for handle in run.subagents:  # type: ignore[attr-defined]
         handles.append(handle)
         # Drain the nested run so it completes.
         for _ in handle:
@@ -101,7 +107,7 @@ async def test_subagents_surfaces_named_subagent_async() -> None:
     run = await supervisor.astream_events({"messages": [HumanMessage("weather?")]}, version="v3")
 
     handles = []
-    async for handle in run.subagents:
+    async for handle in run.subagents:  # type: ignore[attr-defined]
         handles.append(handle)
         # Drain the nested run so it completes.
         async for _ in handle:
@@ -130,7 +136,7 @@ def test_plain_tool_not_surfaced() -> None:
 
     run = supervisor.stream_events({"messages": [HumanMessage("weather?")]}, version="v3")
 
-    handles = list(run.subagents)
+    handles = list(run.subagents)  # type: ignore[attr-defined]
     # Drain the main run to completion.
     for _ in run:
         pass
@@ -166,7 +172,7 @@ def test_unnamed_inner_agent_surfaces_with_inherited_name() -> None:
     run = supervisor.stream_events({"messages": [HumanMessage("weather?")]}, version="v3")
 
     handles = []
-    for handle in run.subagents:
+    for handle in run.subagents:  # type: ignore[attr-defined]
         handles.append(handle)
         for _ in handle:
             pass
@@ -204,7 +210,7 @@ def test_same_name_nested_agent_surfaced() -> None:
     run = supervisor.stream_events({"messages": [HumanMessage("weather?")]}, version="v3")
 
     handles = []
-    for handle in run.subagents:
+    for handle in run.subagents:  # type: ignore[attr-defined]
         handles.append(handle)
         for _ in handle:
             pass

libs/langchain_v1/uv.lock

@@ -1044,7 +1044,7 @@ name = "exceptiongroup"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" }
 wheels = [
@@ -1650,15 +1650,15 @@ wheels = [
 
 [[package]]
 name = "h2"
-version = "4.3.0"
+version = "4.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "hpack" },
     { name = "hyperframe" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/1d/17/afa56379f94ad0fe8defd37d6eb3f89a25404ffc71d4d848893d270325fc/h2-4.3.0.tar.gz", hash = "sha256:6c59efe4323fa18b47a632221a1888bd7fde6249819beda254aeca909f221bf1", size = 2152026, upload-time = "2025-08-23T18:12:19.778Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e7/85/7c366e69d84c17bb778fe41419e1fbcce3033d5b7ce29bbffff0a98b859f/h2-4.4.1.tar.gz", hash = "sha256:4e866ffb1a869ae14dd9b5e6beb5c24a13da0495ad72b65925ded182521c1516", size = 2157281, upload-time = "2026-08-03T11:45:09.509Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/69/b2/119f6e6dcbd96f9069ce9a2665e0146588dc9f88f29549711853645e736a/h2-4.3.0-py3-none-any.whl", hash = "sha256:c438f029a25f7945c69e0ccf0fb951dc3f73a5f6412981daee861431b70e2bdd", size = 61779, upload-time = "2025-08-23T18:12:17.779Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/22/e85faf23bd72a92d1921e37d674ca56eb298a3c8be31fdecef0ff2b3aaac/h2-4.4.1-py3-none-any.whl", hash = "sha256:0e25f1462b23c9cb82d9eb02e28bc706dac2a68cb457c6a0d74d63c8a2a5d0e6", size = 62636, upload-time = "2026-08-03T11:44:59.164Z" },
 ]
 
 [[package]]
@@ -1692,11 +1692,11 @@ wheels = [
 
 [[package]]
 name = "hpack"
-version = "4.1.0"
+version = "4.2.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/2c/48/71de9ed269fdae9c8057e5a4c0aa7402e8bb16f2c6e90b3aa53327b113f8/hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca", size = 51276, upload-time = "2025-01-22T21:44:58.347Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/5b/fcabf6028144a8723726318b07a32c2f3314acdff6265743cf08a344b18e/hpack-4.2.0.tar.gz", hash = "sha256:0895cfa3b5531fc65fe439c05eb65144f123bf7a394fcaa56aa423548d8e45c0", size = 51300, upload-time = "2026-06-23T18:34:46.667Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/07/c6/80c95b1b2b94682a72cbdbfb85b81ae2daffa4291fbfa1b1464502ede10d/hpack-4.1.0-py3-none-any.whl", hash = "sha256:157ac792668d995c657d93111f46b4535ed114f0c9c8d672271bbec7eae1b496", size = 34357, upload-time = "2025-01-22T21:44:56.92Z" },
+    { url = "https://files.pythonhosted.org/packages/71/b4/4a9fcfb2aef6ba44d9073ecd301443aa00b3dac95de5619f2a7de7ec8a91/hpack-4.2.0-py3-none-any.whl", hash = "sha256:858ac0b02280fa582b5080d68db0899c62a80375e0e5413a74970c5e518b6986", size = 34246, upload-time = "2026-06-23T18:34:45.472Z" },
 ]
 
 [[package]]
@@ -1968,7 +1968,7 @@ wheels = [
 
 [[package]]
 name = "langchain"
-version = "1.3.14"
+version = "1.3.15"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -2084,7 +2084,7 @@ requires-dist = [
     { name = "langchain-perplexity", marker = "extra == 'perplexity'" },
     { name = "langchain-together", marker = "extra == 'together'" },
     { name = "langchain-xai", marker = "extra == 'xai'" },
-    { name = "langgraph", specifier = ">=1.2.5,<1.3.0" },
+    { name = "langgraph", specifier = ">=1.2.11,<1.3.0" },
     { name = "pydantic", specifier = ">=2.7.4,<3.0.0" },
 ]
 provides-extras = ["community", "anthropic", "openai", "azure-ai", "google-vertexai", "google-genai", "fireworks", "ollama", "together", "mistralai", "huggingface", "groq", "aws", "baseten", "deepseek", "xai", "perplexity", "meta"]
@@ -2120,7 +2120,7 @@ typing = [
 
 [[package]]
 name = "langchain-anthropic"
-version = "1.5.3"
+version = "1.5.5"
 source = { editable = "../partners/anthropic" }
 dependencies = [
     { name = "anthropic" },
@@ -2260,7 +2260,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.5.3"
+version = "1.5.4"
 source = { editable = "../core" }
 dependencies = [
     { name = "jsonpatch" },
@@ -2462,7 +2462,7 @@ wheels = [
 
 [[package]]
 name = "langchain-openai"
-version = "1.4.1"
+version = "1.4.3"
 source = { editable = "../partners/openai" }
 dependencies = [
     { name = "langchain-core" },
@@ -2665,7 +2665,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "1.2.5"
+version = "1.2.11"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "langchain-core" },
@@ -2675,22 +2675,22 @@ dependencies = [
     { name = "pydantic" },
     { name = "xxhash" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/77/9d/7c9ebd17b95569122e2d2e641f535cf086c870d66bb8e59be33cdba856b3/langgraph-1.2.5.tar.gz", hash = "sha256:09a3bdec6fdb3228623fc78b6f69a1400d383f66348d0b04d0efb692022cc6ef", size = 712532, upload-time = "2026-06-12T20:30:58.498Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/56/0d/c8e7ee98896659e1b6555db0ab115a9ca899844744645d5d894032bab1d7/langgraph-1.2.11.tar.gz", hash = "sha256:9ecfe11e50d338b34b15cf4d8a442642de103e8ae6971320efba84e4542eb363", size = 725753, upload-time = "2026-08-11T14:00:36.945Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a2/03/187281cf61845c5a9c397ae6cd9cd73bb54b39435e5575a7b83c853e5b76/langgraph-1.2.5-py3-none-any.whl", hash = "sha256:9286bb5def82fc865959c14378fe473518dc097d586225f622f029637a2a4bb9", size = 246150, upload-time = "2026-06-12T20:30:57.018Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/7f/c5c30e4be99ff821029c7ac872a480676bb179c9f3df85ea3f38d13f86d4/langgraph-1.2.11-py3-none-any.whl", hash = "sha256:8bab70de7b2d00b5300fb289bcf38d8b241400f3184c1e95e8ce706fb0e8686b", size = 248854, upload-time = "2026-08-11T14:00:35.494Z" },
 ]
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "4.1.1"
+version = "4.2.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "langchain-core" },
     { name = "ormsgpack" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/83/47/886af6f886f0bff2273164a45f008694e48a96ff3cd25ff0228f2aa9480e/langgraph_checkpoint-4.1.1.tar.gz", hash = "sha256:6c2bdb530c91f91d7d9c1bd100925d0fc4f498d418c17f3587d1526279482a25", size = 184020, upload-time = "2026-05-22T16:57:38.503Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/dc/e1/089c4c9e0a2fec7f883f82ae8e6a727138d50074cfeb6644bc2d13b1019b/langgraph_checkpoint-4.2.0.tar.gz", hash = "sha256:51a593b6bee684b0818e5d6e58e28ab340c6db7794575056ce7bd1b746a84ed7", size = 180239, upload-time = "2026-08-07T20:05:03.756Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/bd/b4/71425e3e38be92611300b9cc5e46a5bf98ab23f5ea8a75b73d02a2f1413c/langgraph_checkpoint-4.1.1-py3-none-any.whl", hash = "sha256:25d29144b082827218e7bc3f1e9b0566a4bb007895cd6cc26f66a8428739f56e", size = 56212, upload-time = "2026-05-22T16:57:37.203Z" },
+    { url = "https://files.pythonhosted.org/packages/05/71/3b475f09bd57d3a5649792c66353312b4432afd843f301739dfcebd157f0/langgraph_checkpoint-4.2.0-py3-none-any.whl", hash = "sha256:0547fd228935a0b758865de3a3d6d7a2537c308895d0f9ab092ce9151b5da942", size = 56833, upload-time = "2026-08-07T20:05:02.655Z" },
 ]
 
 [[package]]

libs/partners/anthropic/langchain_anthropic/_version.py

@@ -1,3 +1,3 @@
 """Version information for `langchain-anthropic`."""
 
-__version__ = "1.5.4"
+__version__ = "1.5.5"

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -36,7 +36,11 @@ from langchain_core.messages import (
     is_data_content_block,
 )
 from langchain_core.messages import content as types
-from langchain_core.messages.ai import InputTokenDetails, UsageMetadata
+from langchain_core.messages.ai import (
+    InputTokenDetails,
+    OutputTokenDetails,
+    UsageMetadata,
+)
 from langchain_core.messages.tool import tool_call_chunk as create_tool_call_chunk
 from langchain_core.output_parsers import (
     JsonOutputKeyToolsParser,
@@ -2718,7 +2722,18 @@ def _create_usage_metadata(anthropic_usage: BaseModel) -> UsageMetadata:
     )
     output_tokens = getattr(anthropic_usage, "output_tokens", 0) or 0
 
-    return UsageMetadata(
+    # Reasoning (thinking) tokens are a decomposition of `output_tokens` (not
+    # additive), reported by Anthropic via `output_tokens_details.thinking_tokens`.
+    # Older models omit `output_tokens_details` entirely, so guard with `getattr`.
+    output_token_details: dict = {
+        "reasoning": getattr(
+            getattr(anthropic_usage, "output_tokens_details", None),
+            "thinking_tokens",
+            None,
+        ),
+    }
+
+    usage_metadata = UsageMetadata(
         input_tokens=input_tokens,
         output_tokens=output_tokens,
         total_tokens=input_tokens + output_tokens,
@@ -2726,3 +2741,13 @@ def _create_usage_metadata(anthropic_usage: BaseModel) -> UsageMetadata:
             **{k: v for k, v in input_token_details.items() if v is not None},
         ),
     )
+
+    filtered_output_token_details = {
+        k: v for k, v in output_token_details.items() if v is not None
+    }
+    if filtered_output_token_details:
+        usage_metadata["output_token_details"] = OutputTokenDetails(
+            **filtered_output_token_details,
+        )
+
+    return usage_metadata

libs/partners/anthropic/langchain_anthropic/data/_profiles.py

@@ -34,13 +34,21 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "video_outputs": False,
         "reasoning_output": True,
         "tool_calling": True,
-        "structured_output": False,
+        "structured_output": True,
         "attachment": True,
         "temperature": False,
         "image_url_inputs": True,
         "pdf_tool_message": True,
         "image_tool_message": True,
         "tool_call_streaming": True,
+        "reasoning_effort_levels": [
+            "low",
+            "medium",
+            "high",
+            "xhigh",
+            "max",
+        ],
+        "reasoning_effort_default": "high",
     },
     "claude-haiku-4-5": {
         "name": "Claude Haiku 4.5 (latest)",
@@ -94,60 +102,6 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "image_tool_message": True,
         "tool_call_streaming": True,
     },
-    "claude-opus-4-1": {
-        "name": "Claude Opus 4.1 (latest)",
-        "status": "deprecated",
-        "release_date": "2025-08-05",
-        "last_updated": "2025-08-05",
-        "open_weights": False,
-        "max_input_tokens": 200000,
-        "max_output_tokens": 32000,
-        "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": True,
-        "image_url_inputs": True,
-        "pdf_tool_message": True,
-        "image_tool_message": True,
-        "tool_call_streaming": True,
-    },
-    "claude-opus-4-1-20250805": {
-        "name": "Claude Opus 4.1",
-        "status": "deprecated",
-        "release_date": "2025-08-05",
-        "last_updated": "2025-08-05",
-        "open_weights": False,
-        "max_input_tokens": 200000,
-        "max_output_tokens": 32000,
-        "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": False,
-        "attachment": True,
-        "temperature": True,
-        "image_url_inputs": True,
-        "pdf_tool_message": True,
-        "image_tool_message": True,
-        "tool_call_streaming": True,
-    },
     "claude-opus-4-5": {
         "name": "Claude Opus 4.5 (latest)",
         "release_date": "2025-11-24",
@@ -450,7 +404,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "video_outputs": False,
         "reasoning_output": True,
         "tool_calling": True,
-        "structured_output": False,
+        "structured_output": True,
         "attachment": True,
         "temperature": False,
         "image_url_inputs": True,

libs/partners/anthropic/langchain_anthropic/data/profile_augmentations.toml

@@ -19,9 +19,6 @@ structured_output = true
 reasoning_effort_levels = ["low", "medium", "high", "max"]
 reasoning_effort_default = "high"
 
-[overrides."claude-opus-4-1"]
-structured_output = true
-
 [overrides."claude-opus-4-5"]
 structured_output = true
 reasoning_effort_levels = ["low", "medium", "high"]
@@ -52,5 +49,11 @@ reasoning_effort_levels = ["low", "medium", "high", "xhigh", "max"]
 reasoning_effort_default = "high"
 
 [overrides."claude-sonnet-5"]
+structured_output = true
+reasoning_effort_levels = ["low", "medium", "high", "xhigh", "max"]
+reasoning_effort_default = "high"
+
+[overrides."claude-fable-5"]
+structured_output = true
 reasoning_effort_levels = ["low", "medium", "high", "xhigh", "max"]
 reasoning_effort_default = "high"

libs/partners/anthropic/langchain_anthropic/middleware/anthropic_tools.py

@@ -240,7 +240,9 @@ class _StateClaudeFileToolMiddleware(AgentMiddleware):
                 Command for state update or string result.
             """
             # Build args dict for handler methods
-            args: dict[str, Any] = {"path": path}
+            # `old_path` is populated for `_handle_rename`, which reads the source
+            # path under that key instead of `path`.
+            args: dict[str, Any] = {"path": path, "old_path": path}
             if file_text is not None:
                 args["file_text"] = file_text
             if old_str is not None:
@@ -742,7 +744,9 @@ class _FilesystemClaudeFileToolMiddleware(AgentMiddleware):
                 Command for message update or string result.
             """
             # Build args dict for handler methods
-            args: dict[str, Any] = {"path": path}
+            # `old_path` is populated for `_handle_rename`, which reads the source
+            # path under that key instead of `path`.
+            args: dict[str, Any] = {"path": path, "old_path": path}
             if file_text is not None:
                 args["file_text"] = file_text
             if old_str is not None:

libs/partners/anthropic/pyproject.toml

@@ -20,11 +20,11 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
 
-version = "1.5.4"
+version = "1.5.5"
 requires-python = ">=3.10.0,<4.0.0"
 dependencies = [
     "anthropic>=0.120.0,<1.0.0",
-    "langchain-core>=1.5.2,<2.0.0",
+    "langchain-core>=1.5.4,<2.0.0",
     "pydantic>=2.7.4,<3.0.0",
 ]
 

libs/partners/anthropic/tests/unit_tests/middleware/test_anthropic_tools.py

@@ -1,13 +1,17 @@
 """Unit tests for Anthropic text editor and memory tool middleware."""
 
+import tempfile
+from pathlib import Path
 from unittest.mock import MagicMock
 
 import pytest
 from langchain_core.messages import SystemMessage, ToolMessage
+from langgraph.prebuilt import ToolRuntime
 from langgraph.types import Command
 
 from langchain_anthropic.middleware.anthropic_tools import (
     AnthropicToolsState,
+    FilesystemClaudeTextEditorMiddleware,
     StateClaudeMemoryMiddleware,
     StateClaudeTextEditorMiddleware,
     _validate_path,
@@ -309,6 +313,85 @@ class TestFileOperations:
         assert files.get("/memories/new.txt") is not None
         assert files["/memories/new.txt"]["content"] == ["line1"]
 
+    def test_rename_via_tool_dispatch(self) -> None:
+        """End-to-end: renaming through the actual `file_tool` dispatch.
+
+        Regression test for the dispatch building `args` with the source path
+        under `"path"` while `_handle_rename` read `args["old_path"]`, which
+        raised `KeyError` on every rename command.
+        """
+        middleware = StateClaudeTextEditorMiddleware()
+        state: AnthropicToolsState = {
+            "messages": [],
+            "text_editor_files": {
+                "/notes/old.txt": {
+                    "content": ["hello"],
+                    "created_at": "2025-01-01T00:00:00",
+                    "modified_at": "2025-01-01T00:00:00",
+                }
+            },
+        }
+        (file_tool,) = middleware.tools
+
+        result = file_tool.invoke(
+            {
+                "command": "rename",
+                "path": "/notes/old.txt",
+                "new_path": "/notes/new.txt",
+                "runtime": ToolRuntime(
+                    context=None,
+                    state=state,
+                    config={},
+                    stream_writer=lambda _: None,
+                    tool_call_id="tc-1",
+                    store=None,
+                ),
+            }
+        )
+
+        assert isinstance(result, Command)
+        assert result.update is not None
+        files = result.update["text_editor_files"]
+        assert files["/notes/old.txt"] is None
+        assert files["/notes/new.txt"]["content"] == ["hello"]
+        message = result.update["messages"][0]
+        assert isinstance(message, ToolMessage)
+        assert "renamed" in message.content
+
+
+class TestFilesystemRenameViaToolDispatch:
+    """End-to-end tests for filesystem-backed rename through `file_tool`."""
+
+    def test_rename_via_tool_dispatch(self) -> None:
+        """Regression test mirroring `TestFileOperations.test_rename_via_tool_dispatch`
+        for the filesystem-backed middleware, which has its own dispatch closure
+        and `_handle_rename` implementation.
+        """
+        with tempfile.TemporaryDirectory() as root:
+            (Path(root) / "old.txt").write_text("hello")
+            middleware = FilesystemClaudeTextEditorMiddleware(root_path=root)
+            (file_tool,) = middleware.tools
+
+            result = file_tool.invoke(
+                {
+                    "command": "rename",
+                    "path": "/old.txt",
+                    "new_path": "/new.txt",
+                    "runtime": ToolRuntime(
+                        context=None,
+                        state={},
+                        config={},
+                        stream_writer=lambda _: None,
+                        tool_call_id="tc-2",
+                        store=None,
+                    ),
+                }
+            )
+
+            assert isinstance(result, Command)
+            assert not (Path(root) / "old.txt").exists()
+            assert (Path(root) / "new.txt").read_text() == "hello"
+
 
 class TestSystemMessageHandling:
     """Test system message handling in wrap_model_call."""

libs/partners/anthropic/tests/unit_tests/test_chat_models.py

@@ -150,10 +150,6 @@ def test_set_default_max_tokens() -> None:
     llm = ChatAnthropic(model="claude-sonnet-4-5-20250929", anthropic_api_key="test")
     assert llm.max_tokens == 64000
 
-    # Test claude-opus-4-1 models
-    llm = ChatAnthropic(model="claude-opus-4-1-20250805", anthropic_api_key="test")
-    assert llm.max_tokens == 32000
-
     # Test claude-haiku-4-5 models
     llm = ChatAnthropic(model="claude-haiku-4-5-20251001", anthropic_api_key="test")
     assert llm.max_tokens == 64000
@@ -2081,6 +2077,42 @@ def test_usage_metadata_standardization() -> None:
     assert result["total_tokens"] == 0
 
 
+def test_usage_metadata_reasoning_tokens() -> None:
+    """Reasoning tokens should be surfaced via `output_token_details.reasoning`.
+
+    Anthropic reports these through `output_tokens_details.thinking_tokens` as a
+    decomposition of `output_tokens` (not additive).
+    """
+
+    class OutputTokensDetails(BaseModel):
+        thinking_tokens: int = 20
+
+    class UsageWithReasoning(BaseModel):
+        input_tokens: int = 100
+        output_tokens: int = 50
+        output_tokens_details: OutputTokensDetails | None = OutputTokensDetails()
+
+    # Case 1: reasoning tokens present
+    result = _create_usage_metadata(UsageWithReasoning())
+    assert result["input_tokens"] == 100
+    assert result["output_tokens"] == 50
+    assert result["total_tokens"] == 150
+    assert result.get("output_token_details") == {"reasoning": 20}
+
+    # Case 2: output_tokens_details explicitly None
+    result = _create_usage_metadata(UsageWithReasoning(output_tokens_details=None))
+    assert result["output_tokens"] == 50
+    assert "output_token_details" not in result
+
+    # Case 3: output_tokens_details field absent (older models)
+    class UsageNoDetails(BaseModel):
+        input_tokens: int = 100
+        output_tokens: int = 50
+
+    result = _create_usage_metadata(UsageNoDetails())
+    assert "output_token_details" not in result
+
+
 def test_usage_metadata_cache_creation_ttl() -> None:
     """Test _create_usage_metadata with granular cache_creation TTL fields."""
 

libs/partners/anthropic/uv.lock

@@ -343,7 +343,7 @@ name = "exceptiongroup"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" }
 wheels = [
@@ -552,7 +552,7 @@ wheels = [
 
 [[package]]
 name = "langchain"
-version = "1.3.14"
+version = "1.3.15"
 source = { editable = "../../langchain_v1" }
 dependencies = [
     { name = "langchain-core" },
@@ -581,7 +581,7 @@ requires-dist = [
     { name = "langchain-perplexity", marker = "extra == 'perplexity'" },
     { name = "langchain-together", marker = "extra == 'together'" },
     { name = "langchain-xai", marker = "extra == 'xai'" },
-    { name = "langgraph", specifier = ">=1.2.5,<1.3.0" },
+    { name = "langgraph", specifier = ">=1.2.11,<1.3.0" },
     { name = "pydantic", specifier = ">=2.7.4,<3.0.0" },
 ]
 provides-extras = ["community", "anthropic", "openai", "azure-ai", "google-vertexai", "google-genai", "fireworks", "ollama", "together", "mistralai", "huggingface", "groq", "aws", "baseten", "deepseek", "xai", "perplexity", "meta"]
@@ -617,7 +617,7 @@ typing = [
 
 [[package]]
 name = "langchain-anthropic"
-version = "1.5.4"
+version = "1.5.5"
 source = { editable = "." }
 dependencies = [
     { name = "anthropic" },
@@ -690,7 +690,7 @@ typing = [
 
 [[package]]
 name = "langchain-core"
-version = "1.5.3"
+version = "1.5.4"
 source = { editable = "../../core" }
 dependencies = [
     { name = "jsonpatch" },
@@ -808,7 +808,7 @@ typing = [
 
 [[package]]
 name = "langgraph"
-version = "1.2.5"
+version = "1.2.11"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "langchain-core" },
@@ -818,9 +818,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "xxhash" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/77/9d/7c9ebd17b95569122e2d2e641f535cf086c870d66bb8e59be33cdba856b3/langgraph-1.2.5.tar.gz", hash = "sha256:09a3bdec6fdb3228623fc78b6f69a1400d383f66348d0b04d0efb692022cc6ef", size = 712532, upload-time = "2026-06-12T20:30:58.498Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/56/0d/c8e7ee98896659e1b6555db0ab115a9ca899844744645d5d894032bab1d7/langgraph-1.2.11.tar.gz", hash = "sha256:9ecfe11e50d338b34b15cf4d8a442642de103e8ae6971320efba84e4542eb363", size = 725753, upload-time = "2026-08-11T14:00:36.945Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a2/03/187281cf61845c5a9c397ae6cd9cd73bb54b39435e5575a7b83c853e5b76/langgraph-1.2.5-py3-none-any.whl", hash = "sha256:9286bb5def82fc865959c14378fe473518dc097d586225f622f029637a2a4bb9", size = 246150, upload-time = "2026-06-12T20:30:57.018Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/7f/c5c30e4be99ff821029c7ac872a480676bb179c9f3df85ea3f38d13f86d4/langgraph-1.2.11-py3-none-any.whl", hash = "sha256:8bab70de7b2d00b5300fb289bcf38d8b241400f3184c1e95e8ce706fb0e8686b", size = 248854, upload-time = "2026-08-11T14:00:35.494Z" },
 ]
 
 [[package]]

libs/partners/exa/langchain_exa/retrievers.py

@@ -27,12 +27,9 @@ def _get_metadata(result: Any) -> dict[str, Any]:
         "published_date": result.published_date,
         "author": result.author,
     }
-    if getattr(result, "highlights"):
-        metadata["highlights"] = result.highlights
-    if getattr(result, "highlight_scores"):
-        metadata["highlight_scores"] = result.highlight_scores
-    if getattr(result, "summary"):
-        metadata["summary"] = result.summary
+    for attribute in ("highlights", "highlight_scores", "summary"):
+        if value := getattr(result, attribute, None):
+            metadata[attribute] = value
     return metadata
 
 

libs/partners/exa/tests/unit_tests/test_retrievers.py

@@ -0,0 +1,41 @@
+"""Unit tests for the Exa retriever."""
+
+from types import SimpleNamespace
+from typing import Any
+
+from langchain_exa.retrievers import _get_metadata
+
+
+def _make_result(**optional_metadata: Any) -> SimpleNamespace:
+    return SimpleNamespace(
+        title="Example",
+        url="https://example.com",
+        id="result-1",
+        score=0.95,
+        published_date="2024-01-01",
+        author="Author",
+        **optional_metadata,
+    )
+
+
+def test_get_metadata_omits_missing_optional_attributes() -> None:
+    """Test that missing optional result attributes are omitted."""
+    assert _get_metadata(_make_result()) == {
+        "title": "Example",
+        "url": "https://example.com",
+        "id": "result-1",
+        "score": 0.95,
+        "published_date": "2024-01-01",
+        "author": "Author",
+    }
+
+
+def test_get_metadata_includes_available_optional_attributes() -> None:
+    """Test that available optional attributes are retained independently."""
+    metadata = _get_metadata(
+        _make_result(highlights=["Excerpt"], summary="A short summary")
+    )
+
+    assert metadata["highlights"] == ["Excerpt"]
+    assert metadata["summary"] == "A short summary"
+    assert "highlight_scores" not in metadata

libs/partners/fireworks/langchain_fireworks/chat_models.py

@@ -471,8 +471,6 @@ def _convert_chunk_to_message_chunk(
 ) -> BaseMessageChunk:
     choices = chunk.get("choices") or []
     response_metadata: dict[str, Any] = {"model_provider": "fireworks"}
-    if service_tier := chunk.get("service_tier"):
-        response_metadata["service_tier"] = service_tier
     if not choices:
         # Final chunk emitted when `stream_options.include_usage=True`:
         # `choices` is empty and the chunk carries only `usage`.
@@ -1103,6 +1101,8 @@ class ChatFireworks(BaseChatModel):
                 if finish_reason := choice.get("finish_reason"):
                     generation_info["finish_reason"] = finish_reason
                     generation_info["model_name"] = self.model_name
+                    if service_tier := chunk.get("service_tier"):
+                        generation_info["service_tier"] = service_tier
                 logprobs = choice.get("logprobs")
                 if logprobs:
                     generation_info["logprobs"] = logprobs
@@ -1214,6 +1214,8 @@ class ChatFireworks(BaseChatModel):
                 if finish_reason := choice.get("finish_reason"):
                     generation_info["finish_reason"] = finish_reason
                     generation_info["model_name"] = self.model_name
+                    if service_tier := chunk.get("service_tier"):
+                        generation_info["service_tier"] = service_tier
                 logprobs = choice.get("logprobs")
                 if logprobs:
                     generation_info["logprobs"] = logprobs

libs/partners/fireworks/tests/unit_tests/test_chat_models.py

@@ -1668,7 +1668,7 @@ class TestServiceTier:
         assert isinstance(result, AIMessage)
         assert result.response_metadata["service_tier"] == "priority"
 
-    def test_service_tier_echoed_in_stream_chunks(self) -> None:
+    def test_service_tier_echoed_once_in_stream_chunks(self) -> None:
         model = _make_model(service_tier="priority")
         model.client = MagicMock()
         chunks: list[dict[str, Any]] = [
@@ -1676,6 +1676,10 @@ class TestServiceTier:
                 "choices": [{"delta": {"role": "assistant", "content": "hi"}}],
                 "service_tier": "priority",
             },
+            {
+                "choices": [{"delta": {}, "finish_reason": "stop"}],
+                "service_tier": "priority",
+            },
             {
                 "choices": [],
                 "usage": {
@@ -1687,10 +1691,42 @@ class TestServiceTier:
             },
         ]
         model.client.create.return_value = iter(chunks)
-        out = list(model.stream("Hello"))
-        tagged = [c for c in out if c.response_metadata.get("service_tier")]
-        assert tagged
-        assert all(c.response_metadata["service_tier"] == "priority" for c in tagged)
+        output = list(model.stream("Hello"))
+        tagged = [c for c in output if c.response_metadata.get("service_tier")]
+        assert len(tagged) == 1
+        assert tagged[0].response_metadata["service_tier"] == "priority"
+
+        combined = output[0]
+        for chunk in output[1:]:
+            combined += chunk
+        assert combined.response_metadata["service_tier"] == "priority"
+
+    async def test_service_tier_echoed_once_in_async_stream_chunks(self) -> None:
+        model = _make_model(service_tier="priority")
+        model.async_client = MagicMock()
+        chunks: list[dict[str, Any]] = [
+            {
+                "choices": [{"delta": {"role": "assistant", "content": "hi"}}],
+                "service_tier": "priority",
+            },
+            {
+                "choices": [{"delta": {}, "finish_reason": "stop"}],
+                "service_tier": "priority",
+            },
+        ]
+
+        async def _aiter() -> Any:
+            for chunk in chunks:
+                yield chunk
+
+        async def _create(**_kwargs: Any) -> Any:
+            return _aiter()
+
+        model.async_client.create = MagicMock(side_effect=_create)
+        output = [chunk async for chunk in model.astream("Hello")]
+        tagged = [c for c in output if c.response_metadata.get("service_tier")]
+        assert len(tagged) == 1
+        assert tagged[0].response_metadata["service_tier"] == "priority"
 
     def test_service_tier_absent_when_not_in_response(self) -> None:
         model = _make_model()

libs/partners/huggingface/langchain_huggingface/data/_profiles.py

@@ -145,6 +145,28 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "temperature": True,
         "tool_call_streaming": True,
     },
+    "Qwen/Qwen3-235B-A22B-Instruct-2507": {
+        "name": "Qwen3 235B-A22B Instruct 2507",
+        "release_date": "2025-07-21",
+        "last_updated": "2025-07-21",
+        "open_weights": True,
+        "max_input_tokens": 262144,
+        "max_output_tokens": 16384,
+        "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": False,
+        "tool_calling": True,
+        "structured_output": True,
+        "attachment": False,
+        "temperature": True,
+        "tool_call_streaming": True,
+    },
     "Qwen/Qwen3-235B-A22B-Thinking-2507": {
         "name": "Qwen3-235B-A22B-Thinking-2507",
         "release_date": "2025-07-25",
@@ -597,6 +619,50 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "temperature": True,
         "tool_call_streaming": True,
     },
+    "deepseek-ai/DeepSeek-V3": {
+        "name": "DeepSeek-V3",
+        "release_date": "2024-12-26",
+        "last_updated": "2024-12-26",
+        "open_weights": True,
+        "max_input_tokens": 64000,
+        "max_output_tokens": 8192,
+        "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": False,
+        "tool_calling": True,
+        "structured_output": True,
+        "attachment": False,
+        "temperature": True,
+        "tool_call_streaming": True,
+    },
+    "deepseek-ai/DeepSeek-V3.1": {
+        "name": "DeepSeek-V3.1",
+        "release_date": "2025-08-21",
+        "last_updated": "2025-08-21",
+        "open_weights": True,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 8192,
+        "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,
+        "structured_output": True,
+        "attachment": False,
+        "temperature": True,
+        "tool_call_streaming": True,
+    },
     "deepseek-ai/DeepSeek-V3.2": {
         "name": "DeepSeek-V3.2",
         "release_date": "2025-12-01",

libs/partners/huggingface/uv.lock

@@ -1113,7 +1113,7 @@ wheels = [
 
 [[package]]
 name = "langchain"
-version = "1.3.14"
+version = "1.3.15"
 source = { editable = "../../langchain_v1" }
 dependencies = [
     { name = "langchain-core" },
@@ -1142,7 +1142,7 @@ requires-dist = [
     { name = "langchain-perplexity", marker = "extra == 'perplexity'" },
     { name = "langchain-together", marker = "extra == 'together'" },
     { name = "langchain-xai", marker = "extra == 'xai'" },
-    { name = "langgraph", specifier = ">=1.2.5,<1.3.0" },
+    { name = "langgraph", specifier = ">=1.2.11,<1.3.0" },
     { name = "pydantic", specifier = ">=2.7.4,<3.0.0" },
 ]
 provides-extras = ["community", "anthropic", "openai", "azure-ai", "google-vertexai", "google-genai", "fireworks", "ollama", "together", "mistralai", "huggingface", "groq", "aws", "baseten", "deepseek", "xai", "perplexity", "meta"]
@@ -1202,7 +1202,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.5.3"
+version = "1.5.4"
 source = { editable = "../../core" }
 dependencies = [
     { name = "jsonpatch" },
@@ -1392,7 +1392,7 @@ typing = [
 
 [[package]]
 name = "langgraph"
-version = "1.2.5"
+version = "1.2.11"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "langchain-core" },
@@ -1402,9 +1402,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "xxhash" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/77/9d/7c9ebd17b95569122e2d2e641f535cf086c870d66bb8e59be33cdba856b3/langgraph-1.2.5.tar.gz", hash = "sha256:09a3bdec6fdb3228623fc78b6f69a1400d383f66348d0b04d0efb692022cc6ef", size = 712532, upload-time = "2026-06-12T20:30:58.498Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/56/0d/c8e7ee98896659e1b6555db0ab115a9ca899844744645d5d894032bab1d7/langgraph-1.2.11.tar.gz", hash = "sha256:9ecfe11e50d338b34b15cf4d8a442642de103e8ae6971320efba84e4542eb363", size = 725753, upload-time = "2026-08-11T14:00:36.945Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a2/03/187281cf61845c5a9c397ae6cd9cd73bb54b39435e5575a7b83c853e5b76/langgraph-1.2.5-py3-none-any.whl", hash = "sha256:9286bb5def82fc865959c14378fe473518dc097d586225f622f029637a2a4bb9", size = 246150, upload-time = "2026-06-12T20:30:57.018Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/7f/c5c30e4be99ff821029c7ac872a480676bb179c9f3df85ea3f38d13f86d4/langgraph-1.2.11-py3-none-any.whl", hash = "sha256:8bab70de7b2d00b5300fb289bcf38d8b241400f3184c1e95e8ce706fb0e8686b", size = 248854, upload-time = "2026-08-11T14:00:35.494Z" },
 ]
 
 [[package]]
@@ -3407,11 +3407,11 @@ wheels = [
 
 [[package]]
 name = "setuptools"
-version = "81.0.0"
+version = "84.0.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0d/1c/73e719955c59b8e424d015ab450f51c0af856ae46ea2da83eba51cc88de1/setuptools-81.0.0.tar.gz", hash = "sha256:487b53915f52501f0a79ccfd0c02c165ffe06631443a886740b91af4b7a5845a", size = 1198299, upload-time = "2026-02-06T21:10:39.601Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6d/44/f5da03a8ef95d369145c5bb53050e7877c9f3d312e128605fd9504829143/setuptools-84.0.0.tar.gz", hash = "sha256:f4695c21257f0d9b537ec2692c941d02ee143b7cc1276941349a546573b2ef73", size = 1168449, upload-time = "2026-08-08T18:27:58.365Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e1/e3/c164c88b2e5ce7b24d667b9bd83589cf4f3520d97cad01534cd3c4f55fdb/setuptools-81.0.0-py3-none-any.whl", hash = "sha256:fdd925d5c5d9f62e4b74b30d6dd7828ce236fd6ed998a08d81de62ce5a6310d6", size = 1062021, upload-time = "2026-02-06T21:10:37.175Z" },
+    { url = "https://files.pythonhosted.org/packages/95/9c/c510029fc6ef33a6275cd2c5d3cecd6613dfd6aa401d57c54f1c18852ccf/setuptools-84.0.0-py3-none-any.whl", hash = "sha256:51a52592b3b99e102b609654876bd65f19f999935166d1352678931132b0c670", size = 818216, upload-time = "2026-08-08T18:27:56.719Z" },
 ]
 
 [[package]]

libs/partners/openai/langchain_openai/_version.py

@@ -1,3 +1,3 @@
 """Version information for `langchain-openai`."""
 
-__version__ = "1.4.1"
+__version__ = "1.4.3"

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

@@ -165,7 +165,7 @@ def _convert_from_v1_to_chat_completions(message: AIMessage) -> AIMessage:
                 if block_type == "text":
                     # Strip annotations
                     new_content.append({"type": "text", "text": block["text"]})
-                elif block_type in ("reasoning", "tool_call"):
+                elif block_type in ("reasoning", "tool_call", "invalid_tool_call"):
                     pass
                 else:
                     new_content.append(block)

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

@@ -564,6 +564,7 @@ def _handle_openai_bad_request(e: openai.BadRequestError) -> None:
         "context_length_exceeded" in str(e)
         or "Input tokens exceed the configured limit" in e.message
         or "prompt is too long" in e.message
+        or "ContextWindowExceededError" in e.message
     ):
         raise OpenAIContextOverflowError(
             message=e.message, response=e.response, body=e.body
@@ -984,7 +985,11 @@ class BaseChatOpenAI(BaseChatModel):
     """
 
     include_response_headers: bool = False
-    """Whether to include response headers in the output message `response_metadata`."""
+    """Whether to include response headers in the output message `response_metadata`.
+
+    Note: some inference providers return additional metadata (such as served model
+    names) in the response headers. Enable to capture these metadata.
+    """
 
     disabled_params: dict[str, Any] | None = Field(default=None)
     """Parameters of the OpenAI client or `chat.completions` endpoint that should be
@@ -3306,6 +3311,24 @@ class ChatOpenAI(BaseChatOpenAI):  # type: ignore[override]
             )
             ```
 
+        !!! warning "Model name can trigger Responses API routing"
+
+            The choice between the Chat Completions API (`/v1/chat/completions`)
+            and the Responses API (`/v1/responses`) is inferred in part from the
+            model name, independent of `base_url`.
+
+            `use_responses_api` should generally be set explicitly to avoid ambiguity,
+            especially when using OpenAI-compatible providers:
+
+            ```python
+            model = ChatOpenAI(
+                base_url="http://localhost:8000/v1",
+                api_key="EMPTY",
+                model="codex-7b-instruct",
+                use_responses_api=False,
+            )
+            ```
+
     ??? info "`model_kwargs` vs `extra_body`"
 
         Use the correct parameter for different types of API arguments:

libs/partners/openai/pyproject.toml

@@ -20,10 +20,10 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
 
-version = "1.4.1"
+version = "1.4.3"
 requires-python = ">=3.10.0,<4.0.0"
 dependencies = [
-    "langchain-core>=1.5.1,<2.0.0",
+    "langchain-core>=1.5.3,<2.0.0",
     "openai>=2.45.0,<3.0.0",
     "tiktoken>=0.7.0,<1.0.0",
 ]

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

@@ -3643,6 +3643,13 @@ def test_compat_responses_v03_apply_patch_tool_outputs() -> None:
                         "name": "get_weather",
                         "args": {"location": "San Francisco"},
                     },
+                    {
+                        "type": "invalid_tool_call",
+                        "id": "call_234",
+                        "name": "get_weather",
+                        "args": '{"location":',
+                        "error": "Failed to parse tool call arguments as JSON",
+                    },
                     {
                         "type": "text",
                         "text": "Hello, world!",
@@ -3653,11 +3660,29 @@ def test_compat_responses_v03_apply_patch_tool_outputs() -> None:
                 ],
                 id="chatcmpl-123",
                 response_metadata={"model_provider": "openai", "model_name": "gpt-4.1"},
+                invalid_tool_calls=[
+                    InvalidToolCall(
+                        id="call_234",
+                        name="get_weather",
+                        args='{"location":',
+                        error="Failed to parse tool call arguments as JSON",
+                        type="invalid_tool_call",
+                    )
+                ],
             ),
             AIMessage(
                 [{"type": "text", "text": "Hello, world!"}],
                 id="chatcmpl-123",
                 response_metadata={"model_provider": "openai", "model_name": "gpt-4.1"},
+                invalid_tool_calls=[
+                    InvalidToolCall(
+                        id="call_234",
+                        name="get_weather",
+                        args='{"location":',
+                        error="Failed to parse tool call arguments as JSON",
+                        type="invalid_tool_call",
+                    )
+                ],
             ),
         )
     ],
@@ -3668,6 +3693,7 @@ def test_convert_from_v1_to_chat_completions(
     result = _convert_from_v1_to_chat_completions(message_v1)
     assert result == expected
     assert result.tool_calls == message_v1.tool_calls  # tool calls remain cached
+    assert result.invalid_tool_calls == message_v1.invalid_tool_calls
 
     # Check no mutation
     assert message_v1 != result
@@ -4507,6 +4533,31 @@ def test_context_overflow_error_prompt_too_long() -> None:
     assert "prompt is too long" in str(exc_info.value)
 
 
+def test_context_overflow_error_context_window_exceeded() -> None:
+    """Test context overflow error triggered by ContextWindowExceededError."""
+    error_body = {
+        "error": {
+            "message": "ContextWindowExceededError: maximum context length exceeded",
+            "type": "invalid_request_error",
+            "param": "messages",
+            "code": "invalid_request_error",
+        }
+    }
+    bad_request_error = openai.BadRequestError(
+        message=error_body["error"]["message"],
+        response=MagicMock(status_code=400),
+        body=error_body,
+    )
+    llm = ChatOpenAI()
+
+    with patch.object(llm.client, "with_raw_response") as mock_client:
+        mock_client.create.side_effect = bad_request_error
+        with pytest.raises(ContextOverflowError) as exc_info:
+            llm.invoke([HumanMessage(content="test")])
+
+    assert "ContextWindowExceededError" in str(exc_info.value)
+
+
 def test_context_overflow_error_backwards_compatibility() -> None:
     """Test that ContextOverflowError can be caught as BadRequestError."""
     llm = ChatOpenAI()

libs/partners/openai/uv.lock

@@ -601,7 +601,7 @@ wheels = [
 
 [[package]]
 name = "langchain"
-version = "1.3.14"
+version = "1.3.15"
 source = { editable = "../../langchain_v1" }
 dependencies = [
     { name = "langchain-core" },
@@ -630,13 +630,13 @@ requires-dist = [
     { name = "langchain-perplexity", marker = "extra == 'perplexity'" },
     { name = "langchain-together", marker = "extra == 'together'" },
     { name = "langchain-xai", marker = "extra == 'xai'" },
-    { name = "langgraph", specifier = ">=1.2.5,<1.3.0" },
+    { name = "langgraph", specifier = ">=1.2.11,<1.3.0" },
     { name = "pydantic", specifier = ">=2.7.4,<3.0.0" },
 ]
 provides-extras = ["community", "anthropic", "openai", "azure-ai", "google-vertexai", "google-genai", "fireworks", "ollama", "together", "mistralai", "huggingface", "groq", "aws", "baseten", "deepseek", "xai", "perplexity", "meta"]
 
 [package.metadata.requires-dev]
-lint = [{ name = "ruff", specifier = ">=0.15.0,<0.16.0" }]
+lint = [{ name = "ruff", specifier = ">=0.15.0,<0.17.0" }]
 test = [
     { name = "blockbuster", specifier = ">=1.5.26,<1.6.0" },
     { name = "langchain-openai", editable = "." },
@@ -666,7 +666,7 @@ typing = [
 
 [[package]]
 name = "langchain-core"
-version = "1.5.0"
+version = "1.5.4"
 source = { editable = "../../core" }
 dependencies = [
     { name = "jsonpatch" },
@@ -697,9 +697,9 @@ requires-dist = [
 dev = [
     { name = "grandalf", specifier = ">=0.8.0,<1.0.0" },
     { name = "jupyter", specifier = ">=1.0.0,<2.0.0" },
-    { name = "setuptools", specifier = ">=67.6.1,<83.0.0" },
+    { name = "setuptools", specifier = ">=67.6.1,<84.0.0" },
 ]
-lint = [{ name = "ruff", specifier = ">=0.15.0,<0.16.0" }]
+lint = [{ name = "ruff", specifier = ">=0.15.0,<0.17.0" }]
 test = [
     { name = "blockbuster", specifier = ">=1.5.18,<1.6.0" },
     { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
@@ -728,7 +728,7 @@ typing = [
 
 [[package]]
 name = "langchain-openai"
-version = "1.4.1"
+version = "1.4.3"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -777,7 +777,7 @@ requires-dist = [
 
 [package.metadata.requires-dev]
 dev = []
-lint = [{ name = "ruff", specifier = ">=0.13.1,<0.16.0" }]
+lint = [{ name = "ruff", specifier = ">=0.13.1,<0.17.0" }]
 test = [
     { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
     { name = "langchain", editable = "../../langchain_v1" },
@@ -854,17 +854,17 @@ requires-dist = [
 ]
 
 [package.metadata.requires-dev]
-lint = [{ name = "ruff", specifier = ">=0.15.0,<0.16.0" }]
+lint = [{ name = "ruff", specifier = ">=0.15.0,<0.17.0" }]
 test = []
 test-integration = []
 typing = [
-    { name = "mypy", specifier = ">=2.1.0,<2.2.0" },
+    { name = "mypy", specifier = ">=2.1.0,<2.4.0" },
     { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" },
 ]
 
 [[package]]
 name = "langgraph"
-version = "1.2.5"
+version = "1.2.11"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "langchain-core" },
@@ -874,9 +874,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "xxhash" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/77/9d/7c9ebd17b95569122e2d2e641f535cf086c870d66bb8e59be33cdba856b3/langgraph-1.2.5.tar.gz", hash = "sha256:09a3bdec6fdb3228623fc78b6f69a1400d383f66348d0b04d0efb692022cc6ef", size = 712532, upload-time = "2026-06-12T20:30:58.498Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/56/0d/c8e7ee98896659e1b6555db0ab115a9ca899844744645d5d894032bab1d7/langgraph-1.2.11.tar.gz", hash = "sha256:9ecfe11e50d338b34b15cf4d8a442642de103e8ae6971320efba84e4542eb363", size = 725753, upload-time = "2026-08-11T14:00:36.945Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a2/03/187281cf61845c5a9c397ae6cd9cd73bb54b39435e5575a7b83c853e5b76/langgraph-1.2.5-py3-none-any.whl", hash = "sha256:9286bb5def82fc865959c14378fe473518dc097d586225f622f029637a2a4bb9", size = 246150, upload-time = "2026-06-12T20:30:57.018Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/7f/c5c30e4be99ff821029c7ac872a480676bb179c9f3df85ea3f38d13f86d4/langgraph-1.2.11-py3-none-any.whl", hash = "sha256:8bab70de7b2d00b5300fb289bcf38d8b241400f3184c1e95e8ce706fb0e8686b", size = 248854, upload-time = "2026-08-11T14:00:35.494Z" },
 ]
 
 [[package]]

libs/partners/openrouter/langchain_openrouter/chat_models.py

@@ -88,6 +88,8 @@ def _create_stream_generation_info(
 ) -> dict[str, Any]:
     generation_info = {"finish_reason": choice["finish_reason"]}
     generation_info["model_name"] = chunk_dict.get("model") or model_name
+    if provider := chunk_dict.get("provider"):
+        generation_info["provider"] = provider
     if system_fingerprint := chunk_dict.get("system_fingerprint"):
         generation_info["system_fingerprint"] = system_fingerprint
     if native_finish_reason := choice.get("native_finish_reason"):
@@ -616,8 +618,15 @@ class ChatOpenRouter(BaseChatModel):
                 # Usage-only chunk (no choices) — emit with usage_metadata
                 if usage := chunk_dict.get("usage"):
                     usage_metadata = _create_usage_metadata(usage)
+                    response_metadata: dict[str, Any] = {}
+                    if "cost" in usage:
+                        response_metadata["cost"] = usage["cost"]
+                    if "cost_details" in usage:
+                        response_metadata["cost_details"] = usage["cost_details"]
                     usage_chunk = AIMessageChunk(
-                        content="", usage_metadata=usage_metadata
+                        content="",
+                        usage_metadata=usage_metadata,
+                        response_metadata=response_metadata,
                     )
                     generation_chunk = ChatGenerationChunk(message=usage_chunk)
                     if run_manager:
@@ -701,8 +710,15 @@ class ChatOpenRouter(BaseChatModel):
                 # Usage-only chunk (no choices) — emit with usage_metadata
                 if usage := chunk_dict.get("usage"):
                     usage_metadata = _create_usage_metadata(usage)
+                    response_metadata = {}
+                    if "cost" in usage:
+                        response_metadata["cost"] = usage["cost"]
+                    if "cost_details" in usage:
+                        response_metadata["cost_details"] = usage["cost_details"]
                     usage_chunk = AIMessageChunk(
-                        content="", usage_metadata=usage_metadata
+                        content="",
+                        usage_metadata=usage_metadata,
+                        response_metadata=response_metadata,
                     )
                     generation_chunk = ChatGenerationChunk(message=usage_chunk)
                     if run_manager:
@@ -836,6 +852,7 @@ class ChatOpenRouter(BaseChatModel):
         # Extract top-level response metadata
         response_model = response.get("model")
         system_fingerprint = response.get("system_fingerprint")
+        provider = response.get("provider")
 
         for res in choices:
             message = _convert_dict_to_message(res["message"])
@@ -849,6 +866,8 @@ class ChatOpenRouter(BaseChatModel):
                         "cost_details"
                     ]
             if isinstance(message, AIMessage):
+                if provider:
+                    message.response_metadata["provider"] = provider
                 if system_fingerprint:
                     message.response_metadata["system_fingerprint"] = system_fingerprint
                 if native_finish_reason := res.get("native_finish_reason"):

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

@@ -390,7 +390,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "video_outputs": False,
         "reasoning_output": True,
         "tool_calling": True,
-        "structured_output": True,
+        "structured_output": False,
         "attachment": True,
         "temperature": True,
         "tool_call_streaming": True,
@@ -781,6 +781,28 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "temperature": True,
         "tool_call_streaming": True,
     },
+    "bytedance-seed/seed-2.0-code": {
+        "name": "Seed 2.0 Code",
+        "release_date": "2026-02-14",
+        "last_updated": "2026-02-14",
+        "open_weights": False,
+        "max_input_tokens": 262144,
+        "max_output_tokens": 131072,
+        "text_inputs": True,
+        "image_inputs": True,
+        "audio_inputs": False,
+        "video_inputs": True,
+        "text_outputs": True,
+        "image_outputs": False,
+        "audio_outputs": False,
+        "video_outputs": False,
+        "reasoning_output": True,
+        "tool_calling": True,
+        "structured_output": True,
+        "attachment": True,
+        "temperature": True,
+        "tool_call_streaming": True,
+    },
     "bytedance-seed/seed-2.0-lite": {
         "name": "Seed-2.0-Lite",
         "release_date": "2026-03-10",
@@ -1227,7 +1249,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2026-07-31",
         "open_weights": True,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 65536,
+        "max_output_tokens": 384000,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -1249,7 +1271,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2026-04-24",
         "open_weights": True,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 384000,
+        "max_output_tokens": 393216,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -1408,7 +1430,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2025-12-17",
         "open_weights": False,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 65535,
+        "max_output_tokens": 65536,
         "text_inputs": True,
         "image_inputs": True,
         "audio_inputs": True,
@@ -1812,7 +1834,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2026-04-02",
         "open_weights": True,
         "max_input_tokens": 262144,
-        "max_output_tokens": 16384,
+        "max_output_tokens": 262144,
         "text_inputs": True,
         "image_inputs": True,
         "audio_inputs": False,
@@ -2070,11 +2092,33 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "temperature": True,
         "tool_call_streaming": True,
     },
-    "inclusionai/ling-3.0-flash:free": {
-        "name": "Ling-3.0-flash (free)",
+    "inclusionai/ling-3.0-flash": {
+        "name": "Ling-3.0-flash",
         "release_date": "2026-07-23",
         "last_updated": "2026-07-23",
-        "open_weights": False,
+        "open_weights": True,
+        "max_input_tokens": 262144,
+        "max_output_tokens": 32768,
+        "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,
+        "structured_output": False,
+        "attachment": False,
+        "temperature": True,
+        "tool_call_streaming": True,
+    },
+    "inclusionai/ling-3.0-tiny:free": {
+        "name": "Ling 3.0 Tiny (free)",
+        "release_date": "2026-08-06",
+        "last_updated": "2026-08-06",
+        "open_weights": True,
         "max_input_tokens": 262144,
         "max_output_tokens": 32768,
         "text_inputs": True,
@@ -2180,13 +2224,35 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "temperature": True,
         "tool_call_streaming": True,
     },
+    "liquid/lfm-2.5-2.6b:free": {
+        "name": "LFM2.5-2.6B (free)",
+        "release_date": "2026-08-11",
+        "last_updated": "2026-08-11",
+        "open_weights": True,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 32768,
+        "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,
+        "structured_output": True,
+        "attachment": False,
+        "temperature": True,
+        "tool_call_streaming": True,
+    },
     "mancer/weaver": {
         "name": "Weaver (alpha)",
         "release_date": "2023-08-02",
         "last_updated": "2023-08-02",
         "open_weights": False,
         "max_input_tokens": 8000,
-        "max_output_tokens": 2000,
+        "max_output_tokens": 6000,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -2400,6 +2466,28 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "temperature": True,
         "tool_call_streaming": True,
     },
+    "meta/muse-glimmer-30b": {
+        "name": "Muse Glimmer 30B",
+        "release_date": "2026-08-10",
+        "last_updated": "2026-08-10",
+        "open_weights": True,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "text_inputs": True,
+        "image_inputs": True,
+        "audio_inputs": False,
+        "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": True,
+        "tool_call_streaming": True,
+    },
     "meta/muse-spark-1.1": {
         "name": "Muse Spark 1.1",
         "release_date": "2026-04-08",
@@ -2423,6 +2511,29 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "temperature": True,
         "tool_call_streaming": True,
     },
+    "meta/muse-spark-1.2": {
+        "name": "Muse Spark 1.2",
+        "release_date": "2026-08-05",
+        "last_updated": "2026-08-05",
+        "open_weights": False,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 1048576,
+        "text_inputs": True,
+        "image_inputs": True,
+        "audio_inputs": True,
+        "pdf_inputs": True,
+        "video_inputs": True,
+        "text_outputs": True,
+        "image_outputs": False,
+        "audio_outputs": False,
+        "video_outputs": False,
+        "reasoning_output": True,
+        "tool_calling": True,
+        "structured_output": True,
+        "attachment": True,
+        "temperature": True,
+        "tool_call_streaming": True,
+    },
     "microsoft/phi-4": {
         "name": "Phi 4",
         "release_date": "2025-01-10",
@@ -3191,7 +3302,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "text_inputs": True,
         "image_inputs": True,
         "audio_inputs": False,
-        "video_inputs": False,
+        "video_inputs": True,
         "text_outputs": True,
         "image_outputs": False,
         "audio_outputs": False,
@@ -3385,7 +3496,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2025-12-15",
         "open_weights": True,
         "max_input_tokens": 262144,
-        "max_output_tokens": 262144,
+        "max_output_tokens": 228000,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -3555,6 +3666,50 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "temperature": True,
         "tool_call_streaming": True,
     },
+    "nvidia/nemotron-3.5-lightning": {
+        "name": "Nemotron 3.5 Lightning 30B A3B",
+        "release_date": "2026-08-11",
+        "last_updated": "2026-08-11",
+        "open_weights": True,
+        "max_input_tokens": 262144,
+        "max_output_tokens": 262144,
+        "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": False,
+        "structured_output": True,
+        "attachment": False,
+        "temperature": True,
+        "tool_call_streaming": True,
+    },
+    "nvidia/nemotron-3.5-lightning:free": {
+        "name": "Nemotron 3.5 Lightning (free)",
+        "release_date": "2026-08-11",
+        "last_updated": "2026-08-11",
+        "open_weights": True,
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 65536,
+        "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,
+        "structured_output": False,
+        "attachment": False,
+        "temperature": True,
+        "tool_call_streaming": True,
+    },
     "nvidia/nemotron-nano-12b-v2-vl:free": {
         "name": "Nemotron Nano 12B 2 VL (free)",
         "release_date": "2025-10-28",
@@ -4216,7 +4371,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2025-12-10",
         "open_weights": False,
         "max_input_tokens": 128000,
-        "max_output_tokens": 16384,
+        "max_output_tokens": 32000,
         "text_inputs": True,
         "image_inputs": True,
         "audio_inputs": False,
@@ -4278,29 +4433,6 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "temperature": False,
         "tool_call_streaming": True,
     },
-    "openai/gpt-5.3-chat": {
-        "name": "GPT-5.3 Chat",
-        "release_date": "2026-03-03",
-        "last_updated": "2026-03-03",
-        "open_weights": False,
-        "max_input_tokens": 128000,
-        "max_output_tokens": 16384,
-        "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": False,
-        "tool_calling": True,
-        "structured_output": True,
-        "attachment": True,
-        "temperature": False,
-        "tool_call_streaming": True,
-    },
     "openai/gpt-5.3-codex": {
         "name": "GPT-5.3 Codex",
         "release_date": "2026-02-05",
@@ -5453,7 +5585,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2025-04-28",
         "open_weights": True,
         "max_input_tokens": 131072,
-        "max_output_tokens": 8192,
+        "max_output_tokens": 16384,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -5497,7 +5629,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2025-07-21",
         "open_weights": True,
         "max_input_tokens": 262144,
-        "max_output_tokens": 32768,
+        "max_output_tokens": 16384,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -5673,7 +5805,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2025-04",
         "open_weights": True,
         "max_input_tokens": 262144,
-        "max_output_tokens": 32768,
+        "max_output_tokens": 262144,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -5713,8 +5845,8 @@ _PROFILES: dict[str, dict[str, Any]] = {
     },
     "qwen/qwen3-coder-next": {
         "name": "Qwen3 Coder Next",
-        "release_date": "2026-02-04",
-        "last_updated": "2026-02-04",
+        "release_date": "2026-02-03",
+        "last_updated": "2026-02-03",
         "open_weights": True,
         "max_input_tokens": 262144,
         "max_output_tokens": 262144,
@@ -5827,7 +5959,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2025-09",
         "open_weights": True,
         "max_input_tokens": 262144,
-        "max_output_tokens": 32768,
+        "max_output_tokens": 262144,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -6003,7 +6135,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2026-02-23",
         "open_weights": True,
         "max_input_tokens": 262144,
-        "max_output_tokens": 65536,
+        "max_output_tokens": 81920,
         "text_inputs": True,
         "image_inputs": True,
         "audio_inputs": False,
@@ -6069,7 +6201,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2026-02-15",
         "open_weights": True,
         "max_input_tokens": 262144,
-        "max_output_tokens": 65536,
+        "max_output_tokens": 262144,
         "text_inputs": True,
         "image_inputs": True,
         "audio_inputs": False,
@@ -6179,7 +6311,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2026-04-22",
         "open_weights": True,
         "max_input_tokens": 262144,
-        "max_output_tokens": 131072,
+        "max_output_tokens": 262144,
         "text_inputs": True,
         "image_inputs": True,
         "audio_inputs": False,
@@ -6481,6 +6613,29 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "temperature": False,
         "tool_call_streaming": True,
     },
+    "sakana/sakana-namazu": {
+        "name": "Sakana Namazu",
+        "release_date": "2026-08-11",
+        "last_updated": "2026-08-11",
+        "open_weights": False,
+        "max_input_tokens": 262144,
+        "max_output_tokens": 65536,
+        "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,
+        "tool_call_streaming": True,
+    },
     "sao10k/l3-lunaris-8b": {
         "name": "Llama 3 8B Lunaris",
         "release_date": "2024-08-13",
@@ -6751,7 +6906,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2026-07-15",
         "open_weights": True,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 1048576,
+        "max_output_tokens": 262144,
         "text_inputs": True,
         "image_inputs": True,
         "audio_inputs": True,
@@ -6784,7 +6939,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "video_outputs": False,
         "reasoning_output": True,
         "tool_calling": True,
-        "structured_output": False,
+        "structured_output": True,
         "attachment": True,
         "temperature": True,
         "tool_call_streaming": True,
@@ -6795,7 +6950,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2023-07-22",
         "open_weights": True,
         "max_input_tokens": 6144,
-        "max_output_tokens": 2048,
+        "max_output_tokens": 6144,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -6816,8 +6971,30 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "release_date": "2026-01-27",
         "last_updated": "2026-01-27",
         "open_weights": False,
-        "max_input_tokens": 128000,
-        "max_output_tokens": 128000,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "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,
+        "structured_output": True,
+        "attachment": False,
+        "temperature": True,
+        "tool_call_streaming": True,
+    },
+    "upstage/solar-pro4": {
+        "name": "Solar Pro 4",
+        "release_date": "2026-08-10",
+        "last_updated": "2026-08-10",
+        "open_weights": False,
+        "max_input_tokens": 524288,
+        "max_output_tokens": 131072,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -7218,7 +7395,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2026-04-07",
         "open_weights": True,
         "max_input_tokens": 204800,
-        "max_output_tokens": 128000,
+        "max_output_tokens": 131072,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -7240,7 +7417,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2026-06-13",
         "open_weights": True,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 262144,
+        "max_output_tokens": 131072,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -7376,7 +7553,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "last_updated": "2026-08-01",
         "open_weights": False,
         "max_input_tokens": 1048576,
-        "max_output_tokens": 65536,
+        "max_output_tokens": 262144,
         "text_inputs": True,
         "image_inputs": False,
         "audio_inputs": False,
@@ -7448,7 +7625,7 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "text_inputs": True,
         "image_inputs": True,
         "audio_inputs": False,
-        "video_inputs": False,
+        "video_inputs": True,
         "text_outputs": True,
         "image_outputs": False,
         "audio_outputs": False,

libs/partners/openrouter/tests/integration_tests/test_chat_models.py

@@ -23,13 +23,36 @@ def test_basic_invoke() -> None:
 
 
 def test_streaming() -> None:
-    """Test streaming."""
+    """Test streaming.
+
+    Also asserts that OpenRouter spend survives streaming (regression test for
+    #39333). With `stream_usage` enabled (the default), the final usage-only
+    chunk (`choices: []`) must surface `cost` in `response_metadata`, matching
+    the non-streaming path. Requires a funded OpenRouter account — a `:free`
+    model returns `cost: 0`.
+    """
     model = ChatOpenRouter(model="openai/gpt-4o-mini", temperature=0)
     full: BaseMessageChunk | None = None
     for chunk in model.stream("Say 'hello' and nothing else."):
         full = chunk if full is None else full + chunk
     assert isinstance(full, AIMessageChunk)
     assert full.content
+    assert full.response_metadata["cost"] > 0
+
+
+async def test_astreaming() -> None:
+    """Test async streaming (sister to `test_streaming`).
+
+    Covers `_astream`, which surfaces `cost` on the usage-only chunk the same
+    way `_stream` does (#39333).
+    """
+    model = ChatOpenRouter(model="openai/gpt-4o-mini", temperature=0)
+    full: BaseMessageChunk | None = None
+    async for chunk in model.astream("Say 'hello' and nothing else."):
+        full = chunk if full is None else full + chunk
+    assert isinstance(full, AIMessageChunk)
+    assert full.content
+    assert full.response_metadata["cost"] > 0
 
 
 def test_tool_calling() -> None:

libs/partners/openrouter/tests/unit_tests/test_chat_models.py

@@ -29,6 +29,7 @@ from langchain_openrouter.chat_models import (
     _convert_file_block_to_openrouter,
     _convert_message_to_dict,
     _convert_video_block_to_openrouter,
+    _create_stream_generation_info,
     _create_usage_metadata,
     _format_message_content,
 )
@@ -82,6 +83,7 @@ _SIMPLE_RESPONSE_DICT: dict[str, Any] = {
     "model": MODEL_NAME,
     "object": "chat.completion",
     "created": 1700000000.0,
+    "provider": "Anthropic",
 }
 
 _TOOL_RESPONSE_DICT: dict[str, Any] = {
@@ -1908,6 +1910,30 @@ class TestCreateChatResult:
             == "openrouter"
         )
 
+    def test_provider_in_response_metadata(self) -> None:
+        """Test that upstream provider is surfaced in response_metadata."""
+        model = _make_model()
+        result = model._create_chat_result(_SIMPLE_RESPONSE_DICT)
+        msg = result.generations[0].message
+        assert isinstance(msg, AIMessage)
+        assert msg.response_metadata["provider"] == "Anthropic"
+
+    def test_provider_absent_when_not_returned(self) -> None:
+        """Test that provider is not in response_metadata when API omits it."""
+        model = _make_model()
+        response: dict[str, Any] = {
+            "choices": [
+                {
+                    "message": {"role": "assistant", "content": "Hello!"},
+                    "finish_reason": "stop",
+                }
+            ],
+        }
+        result = model._create_chat_result(response)
+        msg = result.generations[0].message
+        assert isinstance(msg, AIMessage)
+        assert "provider" not in msg.response_metadata
+
     def test_reasoning_from_response(self) -> None:
         """Test that reasoning content is extracted from response."""
         model = _make_model()
@@ -2152,6 +2178,7 @@ class TestCreateChatResult:
         assert isinstance(msg, AIMessage)
         assert "system_fingerprint" not in msg.response_metadata
         assert "native_finish_reason" not in msg.response_metadata
+        assert "provider" not in msg.response_metadata
         assert "model" not in msg.response_metadata
         assert result.llm_output is not None
         assert "id" not in result.llm_output
@@ -2278,6 +2305,27 @@ class TestStreamingChunks:
         assert isinstance(message_chunk, AIMessageChunk)
         assert message_chunk.response_metadata.get("model_provider") == "openrouter"
 
+    def test_provider_in_stream_generation_info(self) -> None:
+        """Test that upstream provider is included in stream generation_info."""
+        chunk_dict: dict[str, Any] = {
+            "id": "gen-stream",
+            "model": MODEL_NAME,
+            "provider": "Anthropic",
+        }
+        choice: dict[str, Any] = {"finish_reason": "stop"}
+        gen_info = _create_stream_generation_info(chunk_dict, choice, MODEL_NAME)
+        assert gen_info["provider"] == "Anthropic"
+
+    def test_provider_absent_from_stream_generation_info(self) -> None:
+        """Test that provider is omitted from generation_info when not in chunk."""
+        chunk_dict: dict[str, Any] = {
+            "id": "gen-stream",
+            "model": MODEL_NAME,
+        }
+        choice: dict[str, Any] = {"finish_reason": "stop"}
+        gen_info = _create_stream_generation_info(chunk_dict, choice, MODEL_NAME)
+        assert "provider" not in gen_info
+
     def test_chunk_without_reasoning(self) -> None:
         """Test that chunk without reasoning fields works correctly."""
         chunk: dict[str, Any] = {"choices": [{"delta": {"content": "Hello"}}]}

libs/partners/qdrant/uv.lock

@@ -259,7 +259,7 @@ name = "coloredlogs"
 version = "15.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "humanfriendly", marker = "python_full_version < '3.13'" },
+    { name = "humanfriendly" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cc/c7/eed8f27100517e8c0e6b923d5f0845d0cb99763da6fdee00478f91db7325/coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0", size = 278520, upload-time = "2021-06-11T10:22:45.202Z" }
 wheels = [
@@ -271,7 +271,7 @@ name = "exceptiongroup"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" }
 wheels = [
@@ -292,17 +292,17 @@ name = "fastembed"
 version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "huggingface-hub", marker = "python_full_version < '3.13'" },
-    { name = "loguru", marker = "python_full_version < '3.13'" },
-    { name = "mmh3", marker = "python_full_version < '3.13'" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "huggingface-hub" },
+    { name = "loguru" },
+    { name = "mmh3" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or python_full_version >= '3.13'" },
     { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' and python_full_version < '3.13'" },
-    { name = "onnxruntime", marker = "python_full_version < '3.13'" },
-    { name = "pillow", marker = "python_full_version < '3.13'" },
-    { name = "py-rust-stemmers", marker = "python_full_version < '3.13'" },
-    { name = "requests", marker = "python_full_version < '3.13'" },
-    { name = "tokenizers", marker = "python_full_version < '3.13'" },
-    { name = "tqdm", marker = "python_full_version < '3.13'" },
+    { name = "onnxruntime" },
+    { name = "pillow" },
+    { name = "py-rust-stemmers" },
+    { name = "requests" },
+    { name = "tokenizers" },
+    { name = "tqdm" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/26/25/58865e36b6e8a9a0d0ff905b5601aa30db97956327c0df42ec4ed6accc21/fastembed-0.8.0.tar.gz", hash = "sha256:75966edfa8b006ee78514c726bd7f6a50721dadc89305279052be9db72fd53e8", size = 75115, upload-time = "2026-03-23T16:34:41.699Z" }
 wheels = [
@@ -420,15 +420,15 @@ wheels = [
 
 [[package]]
 name = "h2"
-version = "4.3.0"
+version = "4.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "hpack" },
     { name = "hyperframe" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/1d/17/afa56379f94ad0fe8defd37d6eb3f89a25404ffc71d4d848893d270325fc/h2-4.3.0.tar.gz", hash = "sha256:6c59efe4323fa18b47a632221a1888bd7fde6249819beda254aeca909f221bf1", size = 2152026, upload-time = "2025-08-23T18:12:19.778Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e7/85/7c366e69d84c17bb778fe41419e1fbcce3033d5b7ce29bbffff0a98b859f/h2-4.4.1.tar.gz", hash = "sha256:4e866ffb1a869ae14dd9b5e6beb5c24a13da0495ad72b65925ded182521c1516", size = 2157281, upload-time = "2026-08-03T11:45:09.509Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/69/b2/119f6e6dcbd96f9069ce9a2665e0146588dc9f88f29549711853645e736a/h2-4.3.0-py3-none-any.whl", hash = "sha256:c438f029a25f7945c69e0ccf0fb951dc3f73a5f6412981daee861431b70e2bdd", size = 61779, upload-time = "2025-08-23T18:12:17.779Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/22/e85faf23bd72a92d1921e37d674ca56eb298a3c8be31fdecef0ff2b3aaac/h2-4.4.1-py3-none-any.whl", hash = "sha256:0e25f1462b23c9cb82d9eb02e28bc706dac2a68cb457c6a0d74d63c8a2a5d0e6", size = 62636, upload-time = "2026-08-03T11:44:59.164Z" },
 ]
 
 [[package]]
@@ -448,11 +448,11 @@ wheels = [
 
 [[package]]
 name = "hpack"
-version = "4.1.0"
+version = "4.2.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/2c/48/71de9ed269fdae9c8057e5a4c0aa7402e8bb16f2c6e90b3aa53327b113f8/hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca", size = 51276, upload-time = "2025-01-22T21:44:58.347Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/5b/fcabf6028144a8723726318b07a32c2f3314acdff6265743cf08a344b18e/hpack-4.2.0.tar.gz", hash = "sha256:0895cfa3b5531fc65fe439c05eb65144f123bf7a394fcaa56aa423548d8e45c0", size = 51300, upload-time = "2026-06-23T18:34:46.667Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/71/b4/4a9fcfb2aef6ba44d9073ecd301443aa00b3dac95de5619f2a7de7ec8a91/hpack-4.2.0-py3-none-any.whl", hash = "sha256:858ac0b02280fa582b5080d68db0899c62a80375e0e5413a74970c5e518b6986", size = 34246, upload-time = "2026-06-23T18:34:45.472Z" },
 ]
 
 [[package]]
@@ -493,14 +493,14 @@ name = "huggingface-hub"
 version = "0.35.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "filelock", marker = "python_full_version < '3.13'" },
-    { name = "fsspec", marker = "python_full_version < '3.13'" },
-    { name = "hf-xet", marker = "(python_full_version < '3.13' and platform_machine == 'aarch64') or (python_full_version < '3.13' and platform_machine == 'amd64') or (python_full_version < '3.13' and platform_machine == 'arm64') or (python_full_version < '3.13' and platform_machine == 'x86_64')" },
-    { name = "packaging", marker = "python_full_version < '3.13'" },
-    { name = "pyyaml", marker = "python_full_version < '3.13'" },
-    { name = "requests", marker = "python_full_version < '3.13'" },
-    { name = "tqdm", marker = "python_full_version < '3.13'" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "filelock" },
+    { name = "fsspec" },
+    { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" },
+    { name = "packaging" },
+    { name = "pyyaml" },
+    { name = "requests" },
+    { name = "tqdm" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/10/7e/a0a97de7c73671863ca6b3f61fa12518caf35db37825e43d63a70956738c/huggingface_hub-0.35.3.tar.gz", hash = "sha256:350932eaa5cc6a4747efae85126ee220e4ef1b54e29d31c3b45c5612ddf0b32a", size = 461798, upload-time = "2025-09-29T14:29:58.625Z" }
 wheels = [
@@ -512,7 +512,7 @@ name = "humanfriendly"
 version = "10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pyreadline3", marker = "python_full_version < '3.13' and sys_platform == 'win32'" },
+    { name = "pyreadline3", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cc/3f/2c29224acb2e2df4d2046e4c73ee2662023c58ff5b113c4c1adac0886c43/humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc", size = 360702, upload-time = "2021-09-17T21:40:43.31Z" }
 wheels = [
@@ -569,7 +569,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.5.0"
+version = "1.5.3"
 source = { editable = "../../core" }
 dependencies = [
     { name = "jsonpatch" },
@@ -600,9 +600,9 @@ requires-dist = [
 dev = [
     { name = "grandalf", specifier = ">=0.8.0,<1.0.0" },
     { name = "jupyter", specifier = ">=1.0.0,<2.0.0" },
-    { name = "setuptools", specifier = ">=67.6.1,<83.0.0" },
+    { name = "setuptools", specifier = ">=67.6.1,<84.0.0" },
 ]
-lint = [{ name = "ruff", specifier = ">=0.15.0,<0.16.0" }]
+lint = [{ name = "ruff", specifier = ">=0.15.0,<0.17.0" }]
 test = [
     { name = "blockbuster", specifier = ">=1.5.18,<1.6.0" },
     { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
@@ -745,11 +745,11 @@ requires-dist = [
 ]
 
 [package.metadata.requires-dev]
-lint = [{ name = "ruff", specifier = ">=0.15.0,<0.16.0" }]
+lint = [{ name = "ruff", specifier = ">=0.15.0,<0.17.0" }]
 test = []
 test-integration = []
 typing = [
-    { name = "mypy", specifier = ">=2.1.0,<2.2.0" },
+    { name = "mypy", specifier = ">=2.1.0,<2.4.0" },
     { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" },
 ]
 
@@ -864,8 +864,8 @@ name = "loguru"
 version = "0.7.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "python_full_version < '3.13' and sys_platform == 'win32'" },
-    { name = "win32-setctime", marker = "python_full_version < '3.13' and sys_platform == 'win32'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "win32-setctime", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3a/05/a1dae3dffd1116099471c643b8924f5aa6524411dc6c63fdae648c4f1aca/loguru-0.7.3.tar.gz", hash = "sha256:19480589e77d47b8d85b2c827ad95d49bf31b0dcde16593892eb51dd18706eb6", size = 63559, upload-time = "2024-12-06T11:20:56.608Z" }
 wheels = [
@@ -1244,13 +1244,13 @@ name = "onnxruntime"
 version = "1.23.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "coloredlogs", marker = "python_full_version < '3.13'" },
-    { name = "flatbuffers", marker = "python_full_version < '3.13'" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "coloredlogs" },
+    { name = "flatbuffers" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or python_full_version >= '3.13'" },
     { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' and python_full_version < '3.13'" },
-    { name = "packaging", marker = "python_full_version < '3.13'" },
-    { name = "protobuf", marker = "python_full_version < '3.13'" },
-    { name = "sympy", marker = "python_full_version < '3.13'" },
+    { name = "packaging" },
+    { name = "protobuf" },
+    { name = "sympy" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/4e/28/4c76b7feca063d47880e76bee235e829bcc4adb87cc26ecff248ece31f17/onnxruntime-1.23.0-cp310-cp310-macosx_13_0_arm64.whl", hash = "sha256:009bf5ecad107a7f11af8214fcff19e844214887b38c6673bd63a25af2f6121f", size = 17078761, upload-time = "2025-09-25T19:16:41.541Z" },
@@ -2137,7 +2137,7 @@ name = "sympy"
 version = "1.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "mpmath", marker = "python_full_version < '3.13'" },
+    { name = "mpmath" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" }
 wheels = [
@@ -2170,7 +2170,7 @@ name = "tokenizers"
 version = "0.22.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "huggingface-hub", marker = "python_full_version < '3.13'" },
+    { name = "huggingface-hub" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/1c/46/fb6854cec3278fbfa4a75b50232c77622bc517ac886156e6afbfa4d8fc6e/tokenizers-0.22.1.tar.gz", hash = "sha256:61de6522785310a309b3407bac22d99c4db5dba349935e99e4d15ea2226af2d9", size = 363123, upload-time = "2025-09-19T09:49:23.424Z" }
 wheels = [
@@ -2234,7 +2234,7 @@ name = "tqdm"
 version = "4.67.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "python_full_version < '3.13' and sys_platform == 'win32'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737, upload-time = "2024-11-24T20:12:22.481Z" }
 wheels = [

libs/text-splitters/langchain_text_splitters/json.py

@@ -127,11 +127,24 @@ class RecursiveJsonSplitter:
 
         Returns:
             A list of JSON chunks.
+
+        Raises:
+            TypeError: If `json_data` is not a dict and cannot be converted to
+                one. `None` returns an empty list rather than raising. A
+                top-level list is only accepted when `convert_lists` is `True`.
         """
+        is_list_input = isinstance(json_data, list)
+
         if convert_lists:
-            chunks = self._json_split(self._list_to_dict_preprocessing(json_data))
-        else:
-            chunks = self._json_split(json_data)
+            json_data = self._list_to_dict_preprocessing(json_data)
+
+        if json_data is not None and not isinstance(json_data, dict):
+            msg = f"json_data must be a dict, got {type(json_data).__name__}."
+            if is_list_input and not convert_lists:
+                msg += " Top-level lists can be split by passing convert_lists=True."
+            raise TypeError(msg)
+
+        chunks = self._json_split(json_data)
 
         # Remove the last chunk if it's empty
         if not chunks[-1]:

libs/text-splitters/tests/unit_tests/test_text_splitters.py

@@ -3504,6 +3504,65 @@ def test_split_json_empty_dict_value_in_large_payload() -> None:
     assert found_empty, "Empty dict value was lost during splitting"
 
 
+@pytest.mark.parametrize(
+    ("data", "type_name"),
+    [
+        ([{"a": 1}, {"b": 2}], "list"),
+        ("hello world", "str"),
+        (123, "int"),
+        (True, "bool"),
+    ],
+)
+def test_split_json_non_dict_input_raises(data: object, type_name: str) -> None:
+    """Non-dict, non-convertible top-level input raises TypeError, not silent []."""
+    splitter = RecursiveJsonSplitter(max_chunk_size=50)
+
+    with pytest.raises(TypeError, match=type_name):
+        splitter.split_json(data)  # ty: ignore[invalid-argument-type]
+
+
+def test_split_json_list_without_convert_lists_error_mentions_escape_hatch() -> None:
+    """The error for a bare list should point users at convert_lists=True."""
+    splitter = RecursiveJsonSplitter(max_chunk_size=50)
+
+    with pytest.raises(TypeError, match="convert_lists=True"):
+        splitter.split_json([{"a": 1}, {"b": 2}])  # ty: ignore[invalid-argument-type]
+
+
+def test_split_json_list_with_convert_lists_still_works() -> None:
+    """Regression guard: convert_lists=True must keep working for top-level lists."""
+    splitter = RecursiveJsonSplitter(max_chunk_size=50)
+
+    data = [{"a": 1}, {"b": 2}]
+    chunks = splitter.split_json(data, convert_lists=True)  # ty: ignore[invalid-argument-type]
+
+    assert chunks == [{"0": {"a": 1}, "1": {"b": 2}}]
+
+
+def test_split_json_none_input_returns_empty_list() -> None:
+    """Regression guard: None stays a no-op (matches split_json({}) -> [])."""
+    splitter = RecursiveJsonSplitter(max_chunk_size=50)
+
+    assert splitter.split_json(None) == []  # ty: ignore[invalid-argument-type]
+
+
+def test_split_json_none_input_returns_empty_list_with_convert_lists() -> None:
+    """Regression guard: None stays a no-op even when convert_lists=True."""
+    splitter = RecursiveJsonSplitter(max_chunk_size=50)
+
+    assert splitter.split_json(None, convert_lists=True) == []  # ty: ignore[invalid-argument-type]
+
+
+def test_split_json_convert_lists_true_non_list_no_misleading_hint() -> None:
+    """The convert_lists=True hint shouldn't appear when it wouldn't help."""
+    splitter = RecursiveJsonSplitter(max_chunk_size=50)
+
+    with pytest.raises(TypeError, match="str") as exc_info:
+        splitter.split_json("hello world", convert_lists=True)  # ty: ignore[invalid-argument-type]
+
+    assert "convert_lists" not in str(exc_info.value)
+
+
 def test_powershell_code_splitter_short_code() -> None:
     splitter = RecursiveCharacterTextSplitter.from_language(
         Language.POWERSHELL, chunk_size=60, chunk_overlap=0