## Summary
Enhances the `init_chat_model` function with comprehensive input
validation, improved model inference patterns, and better error handling
to provide a significantly improved user experience.
## Changes Made
- ✅ **Input Validation**: Added comprehensive type and value checking
for all parameters
- ✅ **Enhanced Model Inference**: Improved pattern matching with
case-insensitive support and new model patterns
- ✅ **Better Error Messages**: Detailed error messages with examples and
documentation links
- ✅ **Comprehensive Tests**: Added extensive test coverage for all new
functionality
- ✅ **Documentation**: Enhanced docstrings and examples
## Backward Compatibility
All changes are fully backward compatible. No breaking changes
introduced.
## Testing
- Added 6 new test functions covering input validation, model inference,
and error handling
- All existing tests continue to pass
- Comprehensive parametrized testing for various model patterns
## User Experience Improvements
- Better error messages help users quickly resolve configuration issues
- Enhanced model inference reduces the need to specify providers
explicitly
- Comprehensive input validation catches issues early with helpful
guidance
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
## Summary
Fixes#34247
When using `Annotated[type, Field(description="...")]` syntax with the
`@tool` decorator, field descriptions were being lost during schema
generation. The `_get_annotation_description()` function only checked
for string annotations but not for Pydantic `FieldInfo` objects.
## Changes
- Extended `_get_annotation_description()` to also extract descriptions
from `FieldInfo` objects within `Annotated` types
- Added import for `pydantic.fields.FieldInfo`
- Added unit test to verify `Field(description=...)` is preserved
## Why this approach
The fix is minimal and targeted - it extends the existing description
extraction logic rather than restructuring the schema generation. This
maintains backward compatibility while supporting both annotation
styles:
```python
# Both now work correctly:
topic: Annotated[str, "The research topic"] # existing
topic: Annotated[str, Field(description="...")] # now fixed
```
## Known limitation
This fix only handles `pydantic.fields.FieldInfo` (Pydantic v2). The v1
compatibility layer (`pydantic.v1.fields.FieldInfo`) is a different
class and will not have descriptions extracted. This is intentional:
- Pydantic v1 is deprecated; users should migrate to v2
- The v1 compat layer exists for legacy model migration, not new tool
definitions
- Duck-typing on `description` attribute could match unintended objects
If v1 `Field` support is needed, it can be addressed in a follow-up PR
with explicit handling.
## Testing
- Added `test_tool_field_description_preserved()` covering required and
optional params
- Verified existing `test_tool_annotated_descriptions` still passes
- Lint and type checks pass
---
> [!NOTE]
> This PR was developed with AI agent assistance (Factory/Droid).
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
## Summary
- Fixes issue where Pydantic default values from `args_schema` were not
passed to tool functions when the caller omits optional arguments
- Modified `_parse_input()` in `libs/core/langchain_core/tools/base.py`
to include fields with non-None defaults
- Added unit tests to verify default args behavior for both sync and
async tools
## Problem
When a tool has an `args_schema` with default values:
```python
class SearchArgs(BaseModel):
query: str = Field(..., description="Search query")
page: int = Field(default=1, description="Page number")
size: int = Field(default=10, description="Results per page")
@tool("search", args_schema=SearchArgs)
def search_tool(query: str, page: int, size: int) -> str:
return f"query={query}, page={page}, size={size}"
# This threw: TypeError: search_tool() missing 2 required positional arguments
search_tool.invoke({"query": "test"})
```
The defaults from `args_schema` were being discarded because
`_parse_input()` filtered validated results to only include keys from
the original input.
## Solution
Changed the filtering logic to:
1. Include all fields that were in the original input (validated)
2. Also include fields with non-None defaults from the Pydantic schema
This applies user-defined defaults (like `Field(default=1)`) while
excluding synthetic fields from `*args`/`**kwargs` which have
`default=None`.
## Test plan
- [x] Added `test_tool_args_schema_default_values` - tests sync tool
with defaults
- [x] Added `test_tool_args_schema_default_values_async` - tests async
tool with defaults
- [x] All existing tests pass (150 passed, 4 skipped)
- [x] Lint passes
Fixes#34384
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
## Summary
Fixes#33970
`get_buffer_string` was only checking for the deprecated `function_call`
field in `additional_kwargs`, which modern LLM providers no longer
return. This fix updates the function to check for the modern
`tool_calls` field first, falling back to `function_call` for legacy
compatibility.
## Changes
- Check `AIMessage.tool_calls` first (modern standard)
- Fall back to `additional_kwargs["function_call"]` (legacy support)
- Added 3 unit tests covering tool_calls, empty content, and precedence
behavior
## Testing
```python
# Before fix: tool_calls info was lost
msg = AIMessage(content="Hi", tool_calls=[{"name": "search", ...}])
get_buffer_string([msg]) # "AI: Hi" (no tool info)
# After fix: tool_calls are included
get_buffer_string([msg]) # "AI: Hi[{\"name\": \"search\", ...}]"
```
- All existing `get_buffer_string` tests pass
- Legacy `function_call` behavior preserved
---
> [!NOTE]
> This PR was developed with AI agent assistance (Factory/Droid).
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Adds [PEP 702](https://peps.python.org/pep-0702/) `__deprecated__`
attribute support to the `@deprecated` decorator, enabling IDE and type
checker integration for deprecation warnings.
---
PEP 702 introduced the `__deprecated__` attribute convention, which type
checkers (Pyright, mypy) and IDEs (VS Code with Pylance, PyCharm) can
use to surface deprecations directly in the editor. This PR sets
`__deprecated__` on all objects decorated with `@deprecated`.
With this change, developers using supported IDEs will see:
- **Strikethrough text** on deprecated symbols
- **Hover messages** showing the deprecation reason and suggested
alternative
- **Diagnostic warnings** during type checking (e.g., `pyright`, `mypy`)
### References
- [PEP 702 – Marking deprecations using the type
system](https://peps.python.org/pep-0702/)
- [`typing.deprecated`
specification](https://typing.python.org/en/latest/spec/directives.html#deprecated)
Adds automatic tool call counting to tracing by means of a new
`store_tool_call_count_in_run()`, which calls on newly added
`count_tool_calls_in_run()`.
Runs on successful LLM completion. Does not run on errored runs.
### Description:
earlier we have to use like below:
```python
from langchain_core.messages import trim_messages
from langchain_core.messages.utils import count_tokens_approximately
trim_messages(..., token_counter=count_tokens_approximately)
```
Now can be used as like this also
```python
from langchain_core.messages import trim_messages
trim_messages(..., token_counter="approximate")
```
- [x] **Added tests**
- [x] **Lint and test**: Run this as I made change in langchain/core, uv
run --group test pytest tests/unit_tests/messages/test_utils.py -v
<img width="1006" height="66" alt="image"
src="https://github.com/user-attachments/assets/c6938c29-a781-4e7f-871b-8e888ee764b7"
/>
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Adds `usage_metadata` (token counts, etc.) to the run metadata in
`LangChainTracer`.
When an LLM run ends, usage metadata is extracted from all generations
and aggregated using the existing `add_usage` helper, then stored in
`run.extra["metadata"]["usage_metadata"]`.
The original data in outputs remains unchanged.
Also, see #34415
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
ref https://github.com/langchain-ai/langchainjs/pull/9665
Fixes trace persistence for iterator/generator inputs (like
`RunnableGenerator`) where the full input isn't available at chain
start. Instead of POSTing a run with incomplete inputs on start and
PATCHing later, this defers the POST until chain end when inputs are
fully realized.
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Replace direct `__annotations__` access with `get_type_hints()` in
`_convert_any_typed_dicts_to_pydantic` to handle [PEP
649](https://peps.python.org/pep-0649/) deferred annotations in Python
3.14:
> [`Changed in version 3.14: Annotations are now lazily evaluated by
default`](https://docs.python.org/3/reference/compound_stmts.html#annotations)
Before:
```python
class MyTool(TypedDict):
name: str
MyTool.__annotations__ # {'name': 'str'} - string, not type
issubclass('str', ...) # TypeError: arg 1 must be a class
```
After:
```python
get_type_hints(MyTool) # {'name': <class 'str'>} - actual type
```
Fixes#34291
Just a small fix of some broken hyperlinks in the documentation of the
function `langchain_openai/chat_models/base.py#with_structured_output`
and a rephrase of the reference to supported models.
Co-authored-by: Thomas Reuhl <thomas.reuhl@telekom.de>
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Issues with combining flex and nano
```shell
FAILED tests/integration_tests/chat_models/test_base.py::test_openai_invoke - openai.InternalServerError: Error code: 500 - {'error': {'message': 'The server had an error while processing your request. Sorry about that!', 'type': 'server_error', 'param': None, 'code': None}}
FAILED tests/integration_tests/chat_models/test_base.py::test_stream - openai.InternalServerError: Error code: 500 - {'error': {'message': 'The server had an error processing your request. Sorry about that! You can retry your request, or contact us through our help center at help.openai.com if you keep seeing this error. (Please include the request ID req_e726769d95994fd4bccbe55680a35f59 in your email.)', 'type': 'server_error', 'param': None, 'code': None}}
FAILED tests/integration_tests/chat_models/test_base.py::test_flex_usage_responses[False] - openai.InternalServerError: Error code: 500 - {'error': {'message': 'An error occurred while processing your request. You can retry your request, or contact us through our help center at help.openai.com if the error persists. Please include the request ID req_935316418319494d8682e4adcd67ab47 in your message.', 'type': 'server_error', 'param': None, 'code': 'server_error'}}
FAILED tests/integration_tests/chat_models/test_base.py::test_flex_usage_responses[True] - openai.APIError: An error occurred while processing your request. You can retry your request, or contact us through our help center at help.openai.com if the error persists. Please include the request ID req_f3c164d0d1f045a5a0f5965ab5c253bf in your message.
```
