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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-model-profiles
Warning
This package is currently in development and the API is subject to change.
CLI tool for updating model profile data in LangChain integration packages.
Quick Install
pip install langchain-model-profiles
🤔 What is this?
langchain-model-profiles is a CLI tool for fetching and updating model capability data from models.dev for use in LangChain integration packages.
LangChain chat models expose a .profile field that provides programmatic access to model capabilities such as context window sizes, supported modalities, tool calling, structured output, and more. This CLI tool helps maintainers keep that data up-to-date.
Data sources
This package is built on top of the excellent work by the models.dev project, an open source initiative that provides model capability data.
LangChain model profiles augment the data from models.dev with some additional fields. We intend to keep this aligned with the upstream project as it evolves.
📖 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.
Usage
Update model profile data for a specific provider:
langchain-profiles refresh --provider anthropic --data-dir ./langchain_anthropic/data
This downloads the latest model data from models.dev, merges it with any augmentations defined in profile_augmentations.toml, and generates a profiles.py file.