0392b6bae4
`BaseTool.args_schema` is documented as accepting a Pydantic v1 model, but several code paths assumed v2 and raised when handed a v1 schema (e.g. an `AttributeError` from calling `model_json_schema()`/`model_fields` on a v1 model). This affected anyone using a v1 `args_schema`, and anyone composing runnables whose input/output schema is a v1 model. This PR makes the tool/runnable schema-derivation code version-agnostic. ## Type contract `TypeBaseModel` (and `PydanticBaseModel`) now include `pydantic.v1.BaseModel`, so the type honestly reflects what tools and runnables already accept at runtime. The public schema accessors (`Runnable.get_input_schema`/`get_output_schema` and the `input_schema`/`output_schema` properties) return `TypeBaseModel`. ## Version-agnostic helpers Added to `langchain_core.utils.pydantic`, each dispatching on the model's Pydantic version so callers don't have to: - `model_json_schema(model)` — JSON schema for either version. - `model_validate(model, obj)` — validation for either version. - `get_fields(model)` — field map for either version (existing helper, now used consistently). Internally, direct `.model_json_schema()` / `.model_fields` calls are replaced with these helpers (or with `get_input_jsonschema()` / `get_output_jsonschema()`). ## Behavior change worth a close look When deriving a schema from a v1 model (in `RunnableParallel`, `RunnableAssign`, and `RunnableSequence` output schemas), a **required** v1 field is now correctly carried over as required. Previously the v1 path read the field's `default` — which is `None` for a required v1 field — and silently turned required fields into optional/nullable ones; `default_factory` fields were dropped entirely. The new `_get_schema_field_definition` helper translates a v1 `ModelField` faithfully (required → `...`, factory preserved) and dispatches explicitly on the field type. --------- Co-authored-by: Mason Daugherty <mason@langchain.dev> 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. You can also chat with the docs using Chat LangChain.
📕 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.