abd9d4ce31
Dependabot has been stripping upper/lower bounds from internal `langchain-*` deps in partner `pyproject.toml` files (e.g. #37288 reduced `langchain-core>=1.3.2,<2.0.0` to bare `langchain-core`). Locks down the config so bumps preserve existing specifiers, and restores the bounds it already mangled across the monorepo. ## Changes - Add `versioning-strategy: increase` to every `uv` ecosystem block in `.github/dependabot.yml` so future bumps move the lower bound in place instead of rewriting the constraint. - Ignore workspace-internal packages (`langchain-core`, `langchain`, `langchain-classic`, `langchain-text-splitters`, `langchain-tests`, `langchain-model-profiles`) on every `uv` block — these are editable installs from local paths and their published constraints are hand-curated for release, not Dependabot's to bump. - Restore stripped bounds across all `libs/` packages — runtime `dependencies` and every dep group (`test`, `dev`, `test_integration`, `typing`, `lint`) — to `>=1.4.0,<2.0.0` for `langchain-core` and `>=1.0.0,<2.0.0` for the other internal packages.
🦜️🔗 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.