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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.
45 lines
3.1 KiB
Markdown
45 lines
3.1 KiB
Markdown
# 🦜️🔗 LangChain
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[](https://pypi.org/project/langchain/#history)
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[](https://opensource.org/licenses/MIT)
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[](https://pypistats.org/packages/langchain)
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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
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```
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## 🤔 What is this?
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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](https://docs.langchain.com/oss/python/integrations/providers/overview). 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.
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We recommend you use LangChain if you want to quickly build agents and autonomous applications. Use [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview), 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.
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LangChain [agents](https://docs.langchain.com/oss/python/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.)
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## 📖 Documentation
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For full documentation, see the [API reference](https://reference.langchain.com/python/langchain/langchain/). 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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