[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-288":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":45,"readmeContent":46,"aiSummary":47,"trendingCount":16,"starSnapshotCount":16,"syncStatus":48,"lastSyncTime":49,"discoverSource":50},288,"langchain","langchain-ai\u002Flangchain","langchain-ai","The agent engineering platform.","https:\u002F\u002Fdocs.langchain.com\u002Flangchain\u002F",null,"Python",139478,23114,891,314,0,90,584,2541,454,120,"MIT License",false,"master",[26,27,28,29,30,31,32,33,34,35,5,36,37,38,39,40,41,42,43,44],"agents","ai","ai-agents","anthropic","chatgpt","deepagents","enterprise","framework","gemini","generative-ai","langgraph","llm","multiagent","open-source","openai","pydantic","python","rag","typescript","2026-06-17 04:00:02","\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flangchain\u002Foverview\">\n    \u003Cpicture>\n      \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\".github\u002Fimages\u002Flogo-dark.svg\">\n      \u003Csource media=\"(prefers-color-scheme: light)\" srcset=\".github\u002Fimages\u002Flogo-light.svg\">\n      \u003Cimg alt=\"LangChain Logo\" src=\".github\u002Fimages\u002Flogo-dark.svg\" width=\"50%\">\n    \u003C\u002Fpicture>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003Cdiv align=\"center\">\n  \u003Ch3>The agent engineering platform.\u003C\u002Fh3>\n\u003C\u002Fdiv>\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fopensource.org\u002Flicenses\u002FMIT\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fl\u002Flangchain\" alt=\"PyPI - License\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypistats.org\u002Fpackages\u002Flangchain\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpepy\u002Fdt\u002Flangchain\" alt=\"PyPI - Downloads\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Flangchain\u002F#history\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Flangchain?label=%20\" alt=\"Version\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fx.com\u002Flangchain_oss\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Furl\u002Fhttps\u002Ftwitter.com\u002Flangchain_oss.svg?style=social&label=Follow%20%40LangChain\" alt=\"Twitter \u002F X\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003Cbr>\n\nLangChain 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.\n\n> [!TIP]\n> Just getting started? Check out **[Deep Agents](http:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Fdeepagents\u002F)** — 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.\n\n## Quickstart\n\n```bash\npip install langchain\n# or\nuv add langchain\n```\n\n```python\nfrom langchain.chat_models import init_chat_model\n\nmodel = init_chat_model(\"openai:gpt-5.4\")\nresult = model.invoke(\"Hello, world!\")\n```\n\nIf you're looking for more advanced customization or agent orchestration, check out [LangGraph](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flanggraph\u002Foverview), our framework for building controllable agent workflows.\n\nFor an equivalent JS\u002FTS library, check out [LangChain.js](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchainjs).\n\n> [!TIP]\n> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https:\u002F\u002Fdocs.langchain.com\u002Flangsmith\u002Fhome).\n\n## LangChain ecosystem\n\nWhile 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.\n\n- **[Deep Agents](http:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Fdeepagents\u002F)** — Build agents that can plan, use subagents, and leverage file systems for complex tasks\n- **[LangGraph](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flanggraph\u002Foverview)** — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework\n- **[Integrations](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Fintegrations\u002Fproviders\u002Foverview)** — Chat & embedding models, tools & toolkits, and more\n- **[LangSmith](https:\u002F\u002Fwww.langchain.com\u002Flangsmith)** — Agent evals, observability, and debugging for LLM apps\n- **[LangSmith Deployment](https:\u002F\u002Fdocs.langchain.com\u002Flangsmith\u002Fdeployments)** — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows\n\n## Why use LangChain?\n\nLangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.\n\n- **Real-time data augmentation** — Easily connect LLMs to diverse data sources and external\u002Finternal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more\n- **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\n- **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\n- **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\n- **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\n- **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\n\n---\n\n## Documentation\n\n- [docs.langchain.com](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flangchain\u002Foverview) – Comprehensive documentation, including conceptual overviews and guides\n- [reference.langchain.com\u002Fpython](https:\u002F\u002Freference.langchain.com\u002Fpython) – API reference docs for LangChain packages\n- [Chat LangChain](https:\u002F\u002Fchat.langchain.com\u002F) – Chat with the LangChain documentation and get answers to your questions\n\n**Discussions**: Visit the [LangChain Forum](https:\u002F\u002Fforum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.\n\n## Additional resources\n\n- [Contributing Guide](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Fcontributing\u002Foverview) – Learn how to contribute to LangChain projects and find good first issues.\n- [Code of Conduct](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\u002F?tab=coc-ov-file) – Our community guidelines and standards for participation.\n- [LangChain Academy](https:\u002F\u002Facademy.langchain.com\u002F) – Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.\n","LangChain 是一个用于构建代理和基于大语言模型（LLM）应用程序的框架。它支持将可互操作的组件和第三方集成链接起来，简化AI应用开发过程，并且能够随着底层技术的发展而保持灵活性。核心功能包括对多种AI代理模式的支持、强大的定制化能力以及通过LangGraph实现的复杂任务处理机制。该平台适合需要快速搭建并迭代AI解决方案的企业和个人开发者使用，特别是在需要整合不同AI服务或构建多代理系统时。",2,"2026-06-17 02:33:16","top_all"]