[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-2098":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":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":44,"readmeContent":45,"aiSummary":46,"trendingCount":16,"starSnapshotCount":16,"syncStatus":47,"lastSyncTime":48,"discoverSource":49},2098,"langgraph","langchain-ai\u002Flanggraph","langchain-ai","Build resilient agents.","https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flanggraph\u002F",null,"Python",34451,5786,159,330,0,88,572,2655,445,45,"MIT License",false,"main",true,[27,28,29,30,31,32,33,34,35,36,5,37,38,39,40,41,42,43],"agents","ai","ai-agents","chatgpt","deepagents","enterprise","framework","gemini","generative-ai","langchain","llm","multiagent","open-source","openai","pydantic","python","rag","2026-06-12 02:00:37","\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fwww.langchain.com\u002Flanggraph\">\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=\"LangGraph 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>Low-level orchestration framework for building stateful agents.\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\u002Flanggraph\" alt=\"PyPI - License\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypistats.org\u002Fpackages\u002Flanggraph\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpepy\u002Fdt\u002Flanggraph\" alt=\"PyPI - Downloads\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Flanggraph\u002F\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Flanggraph.svg?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\nTrusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.\n\n```bash\npip install -U langgraph\n```\n\n> [!TIP]\n> If you're looking to quickly build agents, check out **[Deep Agents](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Fdeepagents\u002Foverview)** — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.\n\nFor an equivalent JS\u002FTS library, check out [LangGraph.js](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraphjs) and the [JS docs](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fjavascript\u002Flanggraph\u002Foverview).\n\n## Why use LangGraph?\n\nLangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent:\n\n- **[Durable execution](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flanggraph\u002Fdurable-execution)** — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.\n- **[Human-in-the-loop](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flanggraph\u002Finterrupts)** — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.\n- **[Comprehensive memory](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flanggraph\u002Fmemory)** — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.\n- **[Debugging with LangSmith](https:\u002F\u002Fwww.langchain.com\u002Flangsmith)** — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.\n- **[Production-ready deployment](https:\u002F\u002Fdocs.langchain.com\u002Flangsmith\u002Fdeployments)** — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.\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## LangGraph ecosystem\n\nWhile LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.\n\nTo improve your LLM application development, pair LangGraph with:\n\n- [Deep Agents](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Fdeepagents\u002Foverview) – Build agents that can plan, use subagents, and leverage file systems for complex tasks.\n- [LangChain](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flangchain\u002Foverview) – Provides integrations and composable components to streamline LLM application development.\n- [LangSmith](https:\u002F\u002Fwww.langchain.com\u002Flangsmith) – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.\n- [LangSmith Deployment](https:\u002F\u002Fdocs.langchain.com\u002Flangsmith\u002Fdeployments) – 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:\u002F\u002Fdocs.langchain.com\u002Flangsmith\u002Fstudio).\n\n---\n\n## Documentation\n\n- [docs.langchain.com](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flanggraph\u002Foverview) – Comprehensive documentation, including conceptual overviews and guides\n- [reference.langchain.com\u002Fpython\u002Flanggraph](https:\u002F\u002Freference.langchain.com\u002Fpython\u002Flanggraph) – API reference docs for LangGraph packages\n- [LangGraph Quickstart](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flanggraph\u002Fquickstart) – Get started building with LangGraph\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- **[Guides](https:\u002F\u002Fdocs.langchain.com\u002Foss\u002Fpython\u002Flearn)** – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).\n- **[LangChain Academy](https:\u002F\u002Facademy.langchain.com\u002Fcourses\u002Fintro-to-langgraph)** – Learn the basics of LangGraph in our free, structured course.\n- **[Case studies](https:\u002F\u002Fwww.langchain.com\u002Fbuilt-with-langgraph)** – Hear how industry leaders use LangGraph to ship AI applications at scale.\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\n---\n\n## Acknowledgements\n\nLangGraph is inspired by [Pregel](https:\u002F\u002Fresearch.google\u002Fpubs\u002Fpub37252\u002F) and [Apache Beam](https:\u002F\u002Fbeam.apache.org\u002F). The public interface draws inspiration from [NetworkX](https:\u002F\u002Fnetworkx.org\u002Fdocumentation\u002Flatest\u002F). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.\n","LangGraph 是一个用于构建、管理和部署长时间运行且具有状态的代理的低级编排框架。它支持构建持久执行的代理，这些代理能够在失败后恢复，并且可以持续运行很长时间；同时提供了人机交互功能，允许在执行过程中随时检查和修改代理状态；还具备全面的记忆功能，包括短期工作记忆和长期持久记忆，以支持复杂的任务处理。此外，通过 LangSmith 提供了强大的调试工具，帮助开发者深入了解代理的行为。该框架适用于需要创建复杂、持久且可管理的人工智能代理的企业级应用场景。",2,"2026-06-11 02:48:05","top_all"]