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This PR fixes the documentation issue reported in #39683: corrects `capabilites` to `capabilities` in `README.md`. Fixes #39683 Fixes # --- Read the full contributing guidelines: https://docs.langchain.com/oss/python/contributing/overview > **All contributions must be in English.** See the [language policy](https://docs.langchain.com/oss/python/contributing/overview#language-policy). If you paste a large clearly AI generated description here your PR may be IGNORED or CLOSED! Thank you for contributing to LangChain! Follow these steps to have your pull request considered as ready for review. 1. PR title: Should follow the format: TYPE(SCOPE): DESCRIPTION - Examples: - fix(anthropic): resolve flag parsing error - feat(core): add multi-tenant support - test(openai): update API usage tests - Allowed TYPE and SCOPE values: https://github.com/langchain-ai/langchain/blob/master/.github/workflows/pr_lint.yml#L15-L33 2. PR description: - Write 1-2 sentences that make the change easy to understand: who benefits, what problem they had, and how this solves it. Prefer a simple user story over a long summary. - The `Fixes #xx` line at the top is **required** for external contributions — update the issue number and keep the keyword. This links your PR to the approved issue and auto-closes it on merge. - If there are any breaking changes, please clearly describe them. - If this PR depends on another PR being merged first, please include "Depends on #PR_NUMBER" in the description. ## Release note 3. Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. - We will not consider a PR unless these three are passing in CI. 4. How did you verify your code works? Additional guidelines: - All external PRs must link to an issue or discussion where a solution has been approved by a maintainer, and you must be assigned to that issue. PRs without prior approval will be closed. - PRs should not touch more than one package unless absolutely necessary. - Do not update the `uv.lock` files or add dependencies to `pyproject.toml` files (even optional ones) unless you have explicit permission to do so by a maintainer. ## Social handles (optional) Twitter: @ LinkedIn: https://linkedin.com/in/ Fixes #39683
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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 capabilities 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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