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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.
81 lines
5.8 KiB
Markdown
81 lines
5.8 KiB
Markdown
<div align="center">
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<a href="https://docs.langchain.com/oss/python/langchain/overview">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg">
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<source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg">
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<img alt="LangChain Logo" src=".github/images/logo-dark.svg" width="50%">
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</picture>
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</a>
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</div>
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<div align="center">
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<h3>The agent engineering platform.</h3>
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</div>
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<div align="center">
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<a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langchain" alt="PyPI - License"></a>
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<a href="https://pypistats.org/packages/langchain" target="_blank"><img src="https://img.shields.io/pepy/dt/langchain" alt="PyPI - Downloads"></a>
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<a href="https://pypi.org/project/langchain/#history" target="_blank"><img src="https://img.shields.io/pypi/v/langchain?label=%20" alt="Version"></a>
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<a href="https://x.com/langchain_oss" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
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</div>
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<br>
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LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
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> [!TIP]
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> Just getting started? Check out **[Deep Agents](http://docs.langchain.com/oss/python/deepagents/)** — a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more.
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## Quickstart
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```bash
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uv add langchain
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```
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```python
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from langchain.chat_models import init_chat_model
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model = init_chat_model("openai:gpt-5.5")
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result = model.invoke("Hello, world!")
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```
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If you're looking for more advanced customization or agent orchestration, check out [LangGraph](https://github.com/langchain-ai/langgraph), our framework for building controllable agent workflows.
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For an equivalent JS/TS library, check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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> [!TIP]
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> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
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## LangChain ecosystem
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While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
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- **[Deep Agents](http://docs.langchain.com/oss/python/deepagents/)** — Build agents that can plan, use subagents, and leverage file systems for complex tasks
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- **[LangGraph](https://docs.langchain.com/oss/python/langgraph/overview)** — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
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- **[Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview)** — Chat & embedding models, tools & toolkits, and more
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- **[LangSmith](https://www.langchain.com/langsmith)** — Agent evals, observability, and debugging for LLM apps
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- **[LangSmith Deployment](https://docs.langchain.com/langsmith/deployments)** — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows
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## Why use LangChain?
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LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
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- **Real-time data augmentation** — Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more
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- **Model interoperability** — Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly — LangChain's abstractions keep you moving without losing momentum
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- **Rapid prototyping** — Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle
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- **Production-ready features** — Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices
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- **Vibrant community and ecosystem** — Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community
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- **Flexible abstraction layers** — Work at the level of abstraction that suits your needs — from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity
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---
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## Resources
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- [Documentation](https://docs.langchain.com/oss/python/langchain/overview) — conceptual overviews and guides
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- [LangChain ecosystem overview](https://docs.langchain.com/oss/python/concepts/products) — how LangChain, LangGraph, and Deep Agents fit together
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- [API reference](https://reference.langchain.com/python) — complete reference for all public classes, functions, and types
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- [Discussions](https://forum.langchain.com/c/oss-product-help-lc-and-lg/langchain/14) — community forum for technical questions, ideas, and feedback
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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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- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) — how to contribute and find good first issues
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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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