07fa576de1
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
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
🤔 What is this?
LangChain is the easiest way to start building agents and applications powered by LLMs. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and more. LangChain provides a pre-built agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications.
We recommend you use LangChain if you want to quickly build agents and autonomous applications. Use LangGraph, our low-level agent orchestration framework and runtime, when you have more advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latency.
LangChain agents are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. (You do not need to know LangGraph for basic LangChain agent usage.)
📖 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.