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CI lint jobs use `uv run --all-groups` for all tools, but ruff doesn't need dependency resolution — only mypy does. By splitting into `UV_RUN_LINT` (ruff) and `UV_RUN_TYPE` (mypy), the CI-facing targets run ruff with `--group lint` only, giving fast-fail feedback before mypy triggers the full environment sync. For packages where source code only conditionally imports heavy deps (text-splitters, huggingface), `lint_package` also overrides `UV_RUN_TYPE` to `--group lint --group typing`, skipping the ~3.5GB `test_integration` download entirely. `lint_tests` keeps `--all-groups` since test code legitimately imports those deps. Additionally, `lint_imports.sh` was inconsistently wired — most packages had the script but weren't calling it. ## Changes **Makefile optimization** - Introduce `UV_RUN_LINT` and `UV_RUN_TYPE` Make variables, both defaulting to `uv run --all-groups`. For `lint_package` and `lint_tests`, `UV_RUN_LINT` is overridden to `uv run --group lint` so ruff runs instantly without syncing heavy deps - For `text-splitters` and `huggingface`, override `UV_RUN_TYPE` on `lint_package` to `uv run --group lint --group typing` — mypy runs without downloading torch, CUDA, spacy, etc. **mypy config for lean groups** - Add `transformers` and `transformers.*` to `ignore_missing_imports` in `text-splitters` pyproject.toml (conditional `try/except` import, same treatment as existing `konlpy`/`nltk` entries) - Add `torch`, `torch.*`, `langchain_community`, `langchain_community.*` to `ignore_missing_imports` in `huggingface` pyproject.toml - Add dual `# type: ignore[unreachable, unused-ignore]` in `text-splitters/base.py` to handle the `PreTrainedTokenizerBase` isinstance check that behaves differently depending on whether transformers is installed **lint_imports.sh consistency** - Add `./scripts/lint_imports.sh` to the lint recipe in every package that wasn't calling it (standard-tests, model-profiles, all 15 partners), and create the script for the two packages missing it entirely (`model-profiles`, `openrouter`) - Update all `lint_imports.sh` scripts to allow `from langchain.agents` and `from langchain.tools` imports (legitimate v1 middleware dependencies used by `langchain-anthropic` and `langchain-openai`)
🦜🍎️ 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.