chore(model-profiles): refresh model profile data

release 1.3.0a3

libs/core/langchain_core/language_models/_utils.py

@@ -2,6 +2,7 @@ import re
 from collections.abc import Sequence
 from typing import (
     TYPE_CHECKING,
+    Any,
     Literal,
     TypedDict,
     TypeVar,
@@ -14,6 +15,21 @@ from langchain_core.messages.content import (
 )
 
 
+def _filter_invocation_params_for_tracing(params: dict[str, Any]) -> dict[str, Any]:
+    """Filter out large/inappropriate fields from invocation params for tracing.
+
+    Removes fields like tools, functions, messages, response_format that can be large.
+
+    Args:
+        params: The invocation parameters to filter.
+
+    Returns:
+        The filtered parameters with large fields removed.
+    """
+    excluded_keys = {"tools", "functions", "messages", "response_format"}
+    return {k: v for k, v in params.items() if k not in excluded_keys}
+
+
 def is_openai_data_block(
     block: dict, filter_: Literal["image", "audio", "file"] | None = None
 ) -> bool:

libs/core/langchain_core/language_models/chat_models.py

@@ -25,6 +25,7 @@ from langchain_core.callbacks import (
 )
 from langchain_core.globals import get_llm_cache
 from langchain_core.language_models._utils import (
+    _filter_invocation_params_for_tracing,
     _normalize_messages,
     _update_message_content_to_blocks,
 )
@@ -567,6 +568,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
                 self.tags,
                 inheritable_metadata,
                 self.metadata,
+                langsmith_inheritable_metadata=_filter_invocation_params_for_tracing(
+                    params
+                ),
             )
             (run_manager,) = callback_manager.on_chat_model_start(
                 self._serialized,
@@ -695,6 +699,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
             self.tags,
             inheritable_metadata,
             self.metadata,
+            langsmith_inheritable_metadata=_filter_invocation_params_for_tracing(
+                params
+            ),
         )
         (run_manager,) = await callback_manager.on_chat_model_start(
             self._serialized,
@@ -972,6 +979,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
             self.tags,
             inheritable_metadata,
             self.metadata,
+            langsmith_inheritable_metadata=_filter_invocation_params_for_tracing(
+                params
+            ),
         )
         messages_to_trace = [
             _format_for_tracing(message_list) for message_list in messages
@@ -1095,6 +1105,9 @@ class BaseChatModel(BaseLanguageModel[AIMessage], ABC):
             self.tags,
             inheritable_metadata,
             self.metadata,
+            langsmith_inheritable_metadata=_filter_invocation_params_for_tracing(
+                params
+            ),
         )
 
         messages_to_trace = [

libs/core/langchain_core/language_models/llms.py

@@ -42,6 +42,7 @@ from langchain_core.callbacks import (
     Callbacks,
 )
 from langchain_core.globals import get_llm_cache
+from langchain_core.language_models._utils import _filter_invocation_params_for_tracing
 from langchain_core.language_models.base import (
     BaseLanguageModel,
     LangSmithParams,
@@ -537,6 +538,9 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                 self.tags,
                 inheritable_metadata,
                 self.metadata,
+                langsmith_inheritable_metadata=_filter_invocation_params_for_tracing(
+                    params
+                ),
             )
             (run_manager,) = callback_manager.on_llm_start(
                 self._serialized,
@@ -607,6 +611,9 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             self.tags,
             inheritable_metadata,
             self.metadata,
+            langsmith_inheritable_metadata=_filter_invocation_params_for_tracing(
+                params
+            ),
         )
         (run_manager,) = await callback_manager.on_llm_start(
             self._serialized,
@@ -950,6 +957,8 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             run_name_list = run_name or cast(
                 "list[str | None]", ([None] * len(prompts))
             )
+            params = self.dict()
+            params["stop"] = stop
             callback_managers = [
                 CallbackManager.configure(
                     callback,
@@ -959,6 +968,9 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                     self.tags,
                     meta,
                     self.metadata,
+                    langsmith_inheritable_metadata=_filter_invocation_params_for_tracing(
+                        params
+                    ),
                 )
                 for callback, tag, meta in zip(
                     callbacks, tags_list, metadata_list, strict=False
@@ -966,6 +978,8 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             ]
         else:
             # We've received a single callbacks arg to apply to all inputs
+            params = self.dict()
+            params["stop"] = stop
             callback_managers = [
                 CallbackManager.configure(
                     cast("Callbacks", callbacks),
@@ -975,12 +989,13 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                     self.tags,
                     cast("dict[str, Any]", metadata),
                     self.metadata,
+                    langsmith_inheritable_metadata=_filter_invocation_params_for_tracing(
+                        params
+                    ),
                 )
             ] * len(prompts)
             run_name_list = [cast("str | None", run_name)] * len(prompts)
         run_ids_list = self._get_run_ids_list(run_id, prompts)
-        params = self.dict()
-        params["stop"] = stop
         options = {"stop": stop}
         (
             existing_prompts,
@@ -1214,6 +1229,8 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             run_name_list = run_name or cast(
                 "list[str | None]", ([None] * len(prompts))
             )
+            params = self.dict()
+            params["stop"] = stop
             callback_managers = [
                 AsyncCallbackManager.configure(
                     callback,
@@ -1223,6 +1240,9 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                     self.tags,
                     meta,
                     self.metadata,
+                    langsmith_inheritable_metadata=_filter_invocation_params_for_tracing(
+                        params
+                    ),
                 )
                 for callback, tag, meta in zip(
                     callbacks, tags_list, metadata_list, strict=False
@@ -1230,6 +1250,8 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             ]
         else:
             # We've received a single callbacks arg to apply to all inputs
+            params = self.dict()
+            params["stop"] = stop
             callback_managers = [
                 AsyncCallbackManager.configure(
                     cast("Callbacks", callbacks),
@@ -1239,12 +1261,13 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                     self.tags,
                     cast("dict[str, Any]", metadata),
                     self.metadata,
+                    langsmith_inheritable_metadata=_filter_invocation_params_for_tracing(
+                        params
+                    ),
                 )
             ] * len(prompts)
             run_name_list = [cast("str | None", run_name)] * len(prompts)
         run_ids_list = self._get_run_ids_list(run_id, prompts)
-        params = self.dict()
-        params["stop"] = stop
         options = {"stop": stop}
         (
             existing_prompts,

libs/core/langchain_core/runnables/config.py

@@ -286,11 +286,17 @@ def ensure_config(config: RunnableConfig | None = None) -> RunnableConfig:
         for k, v in config.items():
             if k not in CONFIG_KEYS and v is not None:
                 empty["configurable"][k] = v
-    if (
-        isinstance(model := empty.get("configurable", {}).get("model"), str)
-        and "model" not in empty["metadata"]
-    ):
-        empty["metadata"]["model"] = model
+    for configurable_key in ("model", "checkpoint_ns"):
+        if (
+            isinstance(
+                configurable_value := empty.get("configurable", {}).get(
+                    configurable_key
+                ),
+                str,
+            )
+            and configurable_key not in empty["metadata"]
+        ):
+            empty["metadata"][configurable_key] = configurable_value
     return empty
 
 

libs/core/langchain_core/version.py

@@ -1,3 +1,3 @@
 """langchain-core version information and utilities."""
 
-VERSION = "1.3.0a2"
+VERSION = "1.3.0a3"

libs/core/pyproject.toml

@@ -21,7 +21,7 @@ classifiers = [
     "Topic :: Software Development :: Libraries :: Python Modules",
 ]
 
-version = "1.3.0a2"
+version = "1.3.0a3"
 requires-python = ">=3.10.0,<4.0.0"
 dependencies = [
     "langsmith>=0.3.45,<1.0.0",

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

@@ -3,7 +3,9 @@
 import uuid
 import warnings
 from collections.abc import AsyncIterator, Iterator
+from contextlib import contextmanager
 from typing import TYPE_CHECKING, Any, Literal
+from unittest.mock import patch
 
 import pytest
 from pydantic import model_validator
@@ -17,8 +19,14 @@ from langchain_core.language_models import (
     FakeListChatModel,
     ParrotFakeChatModel,
 )
-from langchain_core.language_models._utils import _normalize_messages
-from langchain_core.language_models.chat_models import _generate_response_from_error
+from langchain_core.language_models._utils import (
+    _filter_invocation_params_for_tracing,
+    _normalize_messages,
+)
+from langchain_core.language_models.chat_models import (
+    SimpleChatModel,
+    _generate_response_from_error,
+)
 from langchain_core.language_models.fake_chat_models import (
     FakeListChatModelError,
     GenericFakeChatModel,
@@ -37,6 +45,7 @@ from langchain_core.tracers import LogStreamCallbackHandler
 from langchain_core.tracers.base import BaseTracer
 from langchain_core.tracers.context import collect_runs
 from langchain_core.tracers.event_stream import _AstreamEventsCallbackHandler
+from langchain_core.tracers.langchain import LangChainTracer
 from langchain_core.tracers.schemas import Run
 from tests.unit_tests.fake.callbacks import (
     BaseFakeCallbackHandler,
@@ -321,6 +330,20 @@ class FakeTracer(BaseTracer):
         self.traced_run_ids.append(run.id)
 
 
+class LangChainTracerRunCollector:
+    def __init__(self) -> None:
+        self.tracer = LangChainTracer()
+        self.runs: list[Run] = []
+
+    @contextmanager
+    def tracing_callback(self) -> Iterator[LangChainTracer]:
+        def collect_tracer_run(_: LangChainTracer, run: Run) -> None:
+            self.runs.append(run)
+
+        with patch.object(LangChainTracer, "_persist_run", new=collect_tracer_run):
+            yield self.tracer
+
+
 def test_pass_run_id() -> None:
     llm = FakeListChatModel(responses=["a", "b", "c"])
     cb = FakeTracer()
@@ -1390,3 +1413,100 @@ def test_generate_response_from_error_handles_streaming_response_failure() -> No
     assert metadata["body"] is None
     assert metadata["headers"] == {"content-type": "application/json"}
     assert metadata["status_code"] == 400
+
+
+def test_filter_invocation_params_for_tracing() -> None:
+    """Test that large fields are filtered from invocation params for tracing."""
+    params = {
+        "temperature": 0.7,
+        "tools": [{"name": "test_tool"}],
+        "functions": [{"name": "test_function"}],
+        "messages": [{"role": "system", "content": "test"}],
+        "response_format": {"type": "json_object"},
+    }
+    filtered = _filter_invocation_params_for_tracing(params)
+
+    # Should include temperature
+    assert "temperature" in filtered
+    assert filtered["temperature"] == 0.7
+
+    # Should exclude these large fields
+    assert "tools" not in filtered
+    assert "functions" not in filtered
+    assert "messages" not in filtered
+    assert "response_format" not in filtered
+
+
+class FakeChatModelWithInvocationParams(SimpleChatModel):
+    """Fake chat model with invocation params for testing tracing."""
+
+    temperature: float = 0.7
+
+    @property
+    @override
+    def _llm_type(self) -> str:
+        return "fake-chat-model-with-invocation-params"
+
+    @property
+    @override
+    def _identifying_params(self) -> dict[str, Any]:
+        return {
+            "temperature": self.temperature,
+            "tools": [{"name": "test_tool"}],
+            "functions": [{"name": "test_function"}],
+            "messages": [{"role": "system", "content": "test"}],
+            "response_format": {"type": "json_object"},
+        }
+
+    @override
+    def _call(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> str:
+        return "test response"
+
+
+def test_invocation_params_passed_to_tracer_metadata() -> None:
+    """Test that invocation params are passed to tracer metadata."""
+    llm = FakeChatModelWithInvocationParams()
+    collector = LangChainTracerRunCollector()
+
+    with collector.tracing_callback() as tracer:
+        llm.invoke([HumanMessage(content="Hello")], config={"callbacks": [tracer]})
+
+    assert len(collector.runs) == 1
+    run = collector.runs[0]
+
+    key = "LANGSMITH_LANGGRAPH_API_VARIANT"
+
+    if key in run.extra["metadata"]:
+        del run.extra["metadata"][key]
+
+    assert run.extra == {
+        "batch_size": 1,
+        "invocation_params": {
+            "_type": "fake-chat-model-with-invocation-params",
+            "functions": [{"name": "test_function"}],
+            "messages": [{"content": "test", "role": "system"}],
+            "response_format": {"type": "json_object"},
+            "stop": None,
+            "temperature": 0.7,
+            "tools": [{"name": "test_tool"}],
+        },
+        "metadata": {
+            "_type": "fake-chat-model-with-invocation-params",
+            "ls_integration": "langchain_chat_model",
+            "ls_model_type": "chat",
+            "ls_provider": "fakechatmodelwithinvocationparams",
+            "ls_temperature": 0.7,
+            "revision_id": run.extra["metadata"]["revision_id"],
+            "stop": None,
+            "temperature": 0.7,
+        },
+        "options": {"stop": None},
+        "runtime": run.extra["runtime"],
+    }
+    assert run.metadata == run.extra["metadata"]

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

@@ -13,6 +13,7 @@ from langchain_core.language_models import (
     BaseLLM,
     FakeListLLM,
 )
+from langchain_core.language_models._utils import _filter_invocation_params_for_tracing
 from langchain_core.outputs import Generation, GenerationChunk, LLMResult
 from langchain_core.tracers.context import collect_runs
 from tests.unit_tests.fake.callbacks import (
@@ -284,3 +285,94 @@ def test_get_ls_params() -> None:
 
     ls_params = llm._get_ls_params(stop=["stop"])
     assert ls_params["ls_stop"] == ["stop"]
+
+
+def test_filter_invocation_params_for_tracing() -> None:
+    """Test that large fields are filtered from invocation params for tracing."""
+    params = {
+        "temperature": 0.7,
+        "tools": [{"name": "test_tool"}],
+        "functions": [{"name": "test_function"}],
+        "messages": [{"role": "system", "content": "test"}],
+        "response_format": {"type": "json_object"},
+    }
+    filtered = _filter_invocation_params_for_tracing(params)
+
+    # Should include temperature
+    assert "temperature" in filtered
+    assert filtered["temperature"] == 0.7
+
+    # Should exclude these large fields
+    assert "tools" not in filtered
+    assert "functions" not in filtered
+    assert "messages" not in filtered
+    assert "response_format" not in filtered
+
+
+class FakeLLMWithInvocationParams(BaseLLM):
+    """Fake LLM with invocation params for testing tracing."""
+
+    temperature: float = 0.7
+
+    @property
+    @override
+    def _llm_type(self) -> str:
+        return "fake-llm-with-invocation-params"
+
+    @property
+    @override
+    def _identifying_params(self) -> dict[str, Any]:
+        return {
+            "temperature": self.temperature,
+            "tools": [{"name": "test_tool"}],
+            "functions": [{"name": "test_function"}],
+            "messages": [{"role": "system", "content": "test"}],
+            "response_format": {"type": "json_object"},
+        }
+
+    @override
+    def _generate(
+        self,
+        prompts: list[str],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        generations = [[Generation(text="test response")]]
+        return LLMResult(generations=generations)
+
+    @override
+    async def _agenerate(
+        self,
+        prompts: list[str],
+        stop: list[str] | None = None,
+        run_manager: AsyncCallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        generations = [[Generation(text="test response")]]
+        return LLMResult(generations=generations)
+
+
+async def test_llm_invocation_params_filtered_in_stream() -> None:
+    """Test that invocation params are filtered when streaming."""
+
+    # Create a custom LLM that supports streaming
+    class FakeStreamingLLM(FakeLLMWithInvocationParams):
+        @override
+        def _stream(
+            self,
+            prompt: str,
+            stop: list[str] | None = None,
+            run_manager: CallbackManagerForLLMRun | None = None,
+            **kwargs: Any,
+        ) -> Iterator[GenerationChunk]:
+            yield GenerationChunk(text="test ")
+
+    streaming_llm = FakeStreamingLLM()
+
+    with collect_runs() as cb:
+        list(streaming_llm.stream("Hello", config={"callbacks": [cb]}))
+        assert len(cb.traced_runs) == 1
+        run = cb.traced_runs[0]
+        # Verify the run was traced
+        assert run.extra is not None

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

@@ -95,7 +95,11 @@ def test_ensure_config_copies_model_to_metadata() -> None:
         }
     )
 
-    assert config["metadata"] == {"nooverride": 18, "model": "gpt-4o"}
+    assert config["metadata"] == {
+        "nooverride": 18,
+        "model": "gpt-4o",
+        "checkpoint_ns": "ns-1",
+    }
     assert config["configurable"] == {
         "thread_id": "th-123",
         "checkpoint_id": "ckpt-1",
@@ -157,6 +161,21 @@ def test_ensure_config_copies_top_level_model_to_metadata() -> None:
     assert config["configurable"] == {"model": "gpt-4o"}
 
 
+def test_ensure_config_copies_top_level_checkpoint_ns_to_metadata() -> None:
+    config = ensure_config(
+        cast(
+            "RunnableConfig",
+            {
+                "checkpoint_ns": "ns-1",
+                "metadata": {"nooverride": 18},
+            },
+        )
+    )
+
+    assert config["metadata"] == {"nooverride": 18, "checkpoint_ns": "ns-1"}
+    assert config["configurable"] == {"checkpoint_ns": "ns-1"}
+
+
 def test_get_langsmith_inheritable_metadata_from_config_uses_previous_copy_rules() -> (
     None
 ):

libs/core/uv.lock

@@ -995,7 +995,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "1.3.0a2"
+version = "1.3.0a3"
 source = { editable = "." }
 dependencies = [
     { name = "jsonpatch" },
@@ -1134,7 +1134,7 @@ typing = [
 
 [[package]]
 name = "langchain-text-splitters"
-version = "1.1.1"
+version = "1.1.2"
 source = { directory = "../text-splitters" }
 dependencies = [
     { name = "langchain-core" },

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

@@ -441,6 +441,31 @@ _PROFILES: dict[str, dict[str, Any]] = {
         "image_tool_message": True,
         "structured_output": False,
     },
+    "claude-opus-4-7": {
+        "name": "Claude Opus 4.7",
+        "release_date": "2026-04-16",
+        "last_updated": "2026-04-16",
+        "open_weights": False,
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 128000,
+        "text_inputs": True,
+        "image_inputs": True,
+        "audio_inputs": False,
+        "pdf_inputs": True,
+        "video_inputs": False,
+        "text_outputs": True,
+        "image_outputs": False,
+        "audio_outputs": False,
+        "video_outputs": False,
+        "reasoning_output": True,
+        "tool_calling": True,
+        "attachment": True,
+        "temperature": False,
+        "image_url_inputs": True,
+        "pdf_tool_message": True,
+        "image_tool_message": True,
+        "structured_output": False,
+    },
     "claude-sonnet-4-0": {
         "name": "Claude Sonnet 4 (latest)",
         "release_date": "2025-05-22",

libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py

@@ -65,6 +65,12 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
     @model_validator(mode="after")
     def validate_environment(self) -> Self:
         """Validate that api key and python package exists in environment."""
+        for field_name in ("model", "repo_id"):
+            value = getattr(self, field_name)
+            if value and value.startswith(("http://", "https://")):
+                msg = f"`{field_name}` must be a HuggingFace repo ID, not a URL."
+                raise ValueError(msg)
+
         huggingfacehub_api_token = self.huggingfacehub_api_token or os.getenv(
             "HF_TOKEN"
         )

libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py

@@ -5,6 +5,7 @@ import logging
 import os
 from collections.abc import AsyncIterator, Iterator, Mapping
 from typing import Any
+from urllib.parse import urlparse
 
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
@@ -23,8 +24,12 @@ def _is_huggingface_hosted_url(url: str | None) -> bool:
     """True if url is HF-hosted (huggingface.co or hf.space)."""
     if not url:
         return False
-    url_lower = url.lower().strip()
-    return "huggingface.co" in url_lower or "hf.space" in url_lower
+    hostname = (urlparse(url).hostname or "").lower()
+    return (
+        hostname == "huggingface.co"
+        or hostname == "hf.space"
+        or hostname.endswith((".huggingface.co", ".hf.space"))
+    )
 
 
 VALID_TASKS = (
@@ -220,6 +225,13 @@ class HuggingFaceEndpoint(LLM):
         endpoint_url = values.get("endpoint_url")
         repo_id = values.get("repo_id")
 
+        if repo_id and repo_id.startswith(("http://", "https://")):
+            msg = (
+                "`repo_id` must be a HuggingFace repo ID, not a URL. "
+                "Use `endpoint_url` for direct endpoints."
+            )
+            raise ValueError(msg)
+
         if sum([bool(model), bool(endpoint_url), bool(repo_id)]) > 1:
             msg = (
                 "Please specify either a `model` OR an `endpoint_url` OR a `repo_id`,"

libs/partners/huggingface/pyproject.toml

@@ -20,10 +20,10 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
 
-version = "1.2.1"
+version = "1.2.2"
 requires-python = ">=3.10.0,<4.0.0"
 dependencies = [
-    "langchain-core>=1.2.21,<2.0.0",
+    "langchain-core>=1.2.31,<2.0.0",
     "tokenizers>=0.19.1,<1.0.0",
     "huggingface-hub>=0.33.4,<2.0.0",
 ]

libs/partners/huggingface/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.10.0, <4.0.0"
 resolution-markers = [
     "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'",
@@ -613,6 +613,7 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7d/ed/6bfa4109fcb23a58819600392564fea69cdc6551ffd5e69ccf1d52a40cbc/greenlet-3.2.4-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:8c68325b0d0acf8d91dde4e6f930967dd52a5302cd4062932a6b2e7c2969f47c", size = 271061, upload-time = "2025-08-07T13:17:15.373Z" },
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+version = "1.2.2"
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libs/partners/openrouter/langchain_openrouter/data/_profiles.py

@@ -167,6 +167,28 @@ _PROFILES: dict[str, dict[str, Any]] = {
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+        "pdf_inputs": True,
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