[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-3796":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":26,"readmeContent":27,"aiSummary":28,"trendingCount":16,"starSnapshotCount":16,"syncStatus":29,"lastSyncTime":30,"discoverSource":31},3796,"langchainjs","langchain-ai\u002Flangchainjs","langchain-ai","The agent engineering platform","https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fjavascript\u002Flangchain\u002F",null,"TypeScript",17763,3197,93,185,0,4,30,139,25,103,"MIT License",false,"main",[],"2026-06-06 04:01:42","\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fwww.langchain.com\u002F\">\n    \u003Cpicture>\n      \u003Csource media=\"(prefers-color-scheme: light)\" srcset=\".github\u002Fimages\u002Flogo-light.svg\">\n      \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\".github\u002Fimages\u002Flogo-dark.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![npm](https:\u002F\u002Fimg.shields.io\u002Fnpm\u002Fdm\u002Flangchain) [![License: MIT](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-MIT-yellow.svg)](https:\u002F\u002Fopensource.org\u002Flicenses\u002FMIT) [![Twitter](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Furl\u002Fhttps\u002Ftwitter.com\u002Flangchain_js.svg?style=social&label=Follow%20%40LangChain)](https:\u002F\u002Fx.com\u002Flangchain_js)\n\nLangChain is a framework for building 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\u002Fjavascript\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**Documentation**: To learn more about LangChain, check out [the docs](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fjavascript\u002Flangchain\u002Foverview).\n\nIf you're looking for more advanced customization or agent orchestration, check out [LangGraph.js](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fjavascript\u002Flanggraph\u002Foverview) - our framework for building agents and controllable workflows.\n\nFor an equivalent Python library, check out [LangChain](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain).\n\nTo help you ship LangChain apps to production faster, check out [LangSmith](https:\u002F\u002Fsmith.langchain.com).\n[LangSmith](https:\u002F\u002Fsmith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.\n\n## ⚡️ Quick Install\n\nYou can use npm, pnpm, or yarn to install LangChain.js\n\n`npm install -S langchain` or `pnpm install langchain` or `yarn add langchain`\n\n## 🚀 Why use LangChain?\n\nLangChain helps developers build applications powered by LLMs through a standard interface for agents, models, embeddings, vector stores, and more.\n\nUse LangChain for:\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## 📦 LangChain's ecosystem\n\n- [Deep Agents (JS)](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fjavascript\u002Fdeepagents\u002Foverview) - Build agents that can plan, use subagents, and leverage file systems for complex tasks. A higher-level package built on top of LangChain.\n- [LangSmith](https:\u002F\u002Fwww.langchain.com\u002Flangsmith) - Unified developer platform for building, testing, and monitoring LLM applications. With LangSmith, you can debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and deploy agents with confidence.\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- [LangGraph](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fjavascript\u002Flanggraph\u002Foverview) - 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.\n- [Integrations](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fjavascript\u002Fintegrations\u002Fproviders\u002Foverview) — Chat & embedding models, tools & toolkits, and more\n\n## 🌐 Supported Environments\n\nLangChain.js is written in TypeScript and can be used in:\n\n- Node.js (ESM and CommonJS) - 20.x, 22.x, 24.x\n- Cloudflare Workers\n- Vercel \u002F Next.js (Browser, Serverless and Edge functions)\n- Supabase Edge Functions\n- Browser\n- Deno\n- Bun\n\n## 📖 Additional Resources\n\n- [Getting started](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fjavascript\u002Flangchain\u002Foverview): Installation, setting up the environment, simple examples\n- [Learn](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fjavascript\u002Flearn): Learn about the core concepts of LangChain.\n- [LangChain Forum](https:\u002F\u002Fforum.langchain.com): Connect with the community and share all of your technical questions, ideas, and feedback.\n- [Chat LangChain](https:\u002F\u002Fchat.langchain.com): Ask questions & chat with our documentation.\n\n## 💁 Contributing\n\nAs 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.\n\nFor detailed information on how to contribute, see [`CONTRIBUTING.md`](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchainjs\u002Fblob\u002Fmain\u002FCONTRIBUTING.md).\n\nPlease report any security issues or concerns following our [security guidelines](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002F.github\u002Fblob\u002Fmain\u002FSECURITY.md).\n","LangChain.js 是一个用于构建基于大语言模型（LLM）应用的框架。它支持开发者通过标准化接口连接代理、模型、嵌入、向量存储等组件，实现模块化开发。该框架采用 TypeScript 编写，具有强大的数据源集成能力和模型互操作性，允许用户轻松更换底层模型以适应不断变化的技术需求。此外，其提供的快速原型设计能力使得实验和迭代变得更为便捷。LangChain.js 适用于需要实时数据增强、跨系统集成以及快速开发智能应用的各种场景。",2,"2026-06-06 02:57:08","top_language"]