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README installation examples now use `uv add` consistently, matching the repo's `uv`-based Python workflow. The top-level README also gets a cleaner quickstart and resource section with current links for docs, community, learning, and contribution guidance. ## Changes - Replaced `pip install` snippets with `uv add` across package quick install docs, including the Hugging Face extras and `sentence-transformers` upgrade examples. - Updated the top-level quickstart to show only `uv add langchain` and refreshed the example model to `openai:gpt-5.5`. - Pointed the LangGraph orchestration link at the LangGraph GitHub repository. - Consolidated top-level documentation and additional-resource links under a single `Resources` section covering docs, ecosystem overview, API reference, discussions, Academy, contributing, and the Code of Conduct. - Added LangChain Academy and Code of Conduct links to package README resource sections.
53 lines
2.9 KiB
Markdown
53 lines
2.9 KiB
Markdown
# 🦜🍎️ LangChain Core
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[](https://pypi.org/project/langchain-core/#history)
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[](https://opensource.org/licenses/MIT)
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[](https://pypistats.org/packages/langchain-core)
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[](https://x.com/langchain_oss)
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Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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To help you ship LangChain apps to production faster, check out [LangSmith](https://www.langchain.com/langsmith).
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[LangSmith](https://www.langchain.com/langsmith) is a unified developer platform for building, testing, and monitoring LLM applications.
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## Quick Install
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```bash
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uv add langchain-core
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```
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## 🤔 What is this?
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LangChain Core contains the base abstractions that power the LangChain ecosystem.
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These abstractions are designed to be as modular and simple as possible.
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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.
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## ⛰️ Why build on top of LangChain Core?
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The LangChain ecosystem is built on top of `langchain-core`. Some of the benefits:
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- **Modularity**: We've designed Core around abstractions that are independent of each other, and not tied to any specific model provider.
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- **Stability**: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
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- **Battle-tested**: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.
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## 📖 Documentation
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For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_core/). For conceptual guides, tutorials, and examples on using LangChain, see the [LangChain Docs](https://docs.langchain.com/oss/python/langchain/overview). You can also chat with the docs using [Chat LangChain](https://chat.langchain.com).
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## 📕 Releases & Versioning
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See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
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## 💁 Contributing
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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.
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For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).
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## Resources
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- [LangChain Academy](https://academy.langchain.com/) — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
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- [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) — community guidelines and standards
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