From 5f27b546ddc89aa415993b88b63ac0af5672959c Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Wed, 5 Nov 2025 14:22:20 -0500 Subject: [PATCH] chore: update `README.md` with `deepagents` (#33843) --- README.md | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 4e0f63ece3..f27e13dcd5 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@

-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. +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. ```bash pip install langchain @@ -44,7 +44,10 @@ If you're looking for more advanced customization or agent orchestration, check --- -**Documentation**: To learn more about LangChain, check out [the docs](https://docs.langchain.com/oss/python/langchain/overview). +**Documentation**: + +- [docs.langchain.com](https://docs.langchain.com/oss/python/langchain/overview) – Comprehensive documentation, including conceptual overviews and guides +- [reference.langchain.com/python](https://reference.langchain.com/python) – API reference docs for LangChain packages **Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback. @@ -58,7 +61,7 @@ LangChain helps developers build applications powered by LLMs through a standard Use LangChain for: - **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. -- **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. +- **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. ## LangChain ecosystem @@ -66,13 +69,14 @@ While the LangChain framework can be used standalone, it also integrates seamles To improve your LLM application development, pair LangChain with: -- [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab. -- [LangSmith](https://www.langchain.com/langsmith) - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time. -- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio). +- [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) – Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows – and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab. +- [Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview) – List of LangChain integrations, including chat & embedding models, tools & toolkits, and more +- [LangSmith](https://www.langchain.com/langsmith) – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time. +- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in [LangSmith Studio](https://docs.langchain.com/langsmith/studio). +- [Deep Agents](https://github.com/langchain-ai/deepagents) *(new!)* – Build agents that can plan, use subagents, and leverage file systems for complex tasks ## Additional resources -- [API Reference](https://reference.langchain.com/python): Detailed reference on navigating base packages and integrations for LangChain. -- [Integrations](https://docs.langchain.com/oss/python/integrations/providers/overview): List of LangChain integrations, including chat & embedding models, tools & toolkits, and more -- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview): Learn how to contribute to LangChain and find good first issues. -- [Code of Conduct](https://github.com/langchain-ai/langchain/blob/master/.github/CODE_OF_CONDUCT.md): Our community guidelines and standards for participation. +- [API Reference](https://reference.langchain.com/python) – Detailed reference on navigating base packages and integrations for LangChain. +- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) – Learn how to contribute to LangChain projects and find good first issues. +- [Code of Conduct](https://github.com/langchain-ai/langchain/blob/master/.github/CODE_OF_CONDUCT.md) – Our community guidelines and standards for participation.