1cc4dc7cc9
## 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>
🦜🍎️ LangChain Core
Looking for the JS/TS version? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications.
Quick Install
pip install langchain-core
🤔 What is this?
LangChain Core contains the base abstractions that power the LangChain ecosystem.
These abstractions are designed to be as modular and simple as possible.
The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.
⛰️ Why build on top of LangChain Core?
The LangChain ecosystem is built on top of langchain-core. Some of the benefits:
- Modularity: We've designed Core around abstractions that are independent of each other, and not tied to any specific model provider.
- Stability: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
- Battle-tested: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.
📖 Documentation
For full documentation, see the API reference. For conceptual guides, tutorials, and examples on using LangChain, see the LangChain Docs.
📕 Releases & Versioning
See our Releases and Versioning policies.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.