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License\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://pypistats.org/packages/langgraph\" target=\"_blank\"\u003e\u003cimg src=\"https://img.shields.io/pepy/dt/langgraph\" alt=\"PyPI - Downloads\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://pypi.org/project/langgraph/\" target=\"_blank\"\u003e\u003cimg src=\"https://img.shields.io/pypi/v/langgraph.svg?label=%20\" alt=\"Version\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://x.com/langchain\" target=\"_blank\"\u003e\u003cimg src=\"https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social\u0026label=Follow%20%40LangChain\" alt=\"Twitter / X\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\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\nIf you're looking to quickly build agents with LangChain's `create_agent` (built on LangGraph), check out the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).\n\n\u003e [!NOTE]\n\u003e Looking for the JS/TS library? Check out [LangGraph.js](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).\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://docs.langchain.com/oss/python/langgraph/durable-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://docs.langchain.com/oss/python/langgraph/interrupts)** — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.\n- **[Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory)** — 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://www.langchain.com/langsmith)** — 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://docs.langchain.com/langsmith/deployments)** — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.\n\n\u003e [!TIP]\n\u003e For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).\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://github.com/langchain-ai/deepagents) *(new!)* – Build agents that can plan, use subagents, and leverage file systems for complex tasks.\n- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.\n- [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.\n- [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).\n\n---\n\n## Documentation\n\n- [docs.langchain.com](https://docs.langchain.com/oss/python/langgraph/overview) – Comprehensive documentation, including conceptual overviews and guides\n- [reference.langchain.com/python/langgraph](https://reference.langchain.com/python/langgraph) – API reference docs for LangGraph packages\n- [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart) – Get started building with LangGraph\n- [Chat LangChain](https://chat.langchain.com/) – Chat with the LangChain documentation and get answers to your questions\n\n**Discussions**: Visit the [LangChain Forum](https://forum.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://docs.langchain.com/oss/python/learn)** – Quick, actionable code snippets for topics such as streaming, adding memory \u0026 persistence, and design patterns (e.g. branching, subgraphs, etc.).\n- **[LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph)** – Learn the basics of LangGraph in our free, structured course.\n- **[Case studies](https://www.langchain.com/built-with-langgraph)** – Hear how industry leaders use LangGraph to ship AI applications at scale.\n- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) – Learn how to contribute to LangChain projects and find good first issues.\n- [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) – Our community guidelines and standards for participation.\n\n---\n\n## Acknowledgements\n\nLangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.\n","funding_links":[],"categories":["Agent Development Frameworks","**Section 4** : LangChain Features, Usage, and Comparisons","Python","重点关注框架","🧺 Curated catalog","Agents","A01_文本生成_文本对话","Framework","NLP","Graph RAG \u0026 Root Cause Analysis for Logs and Incidents","Table of Open-Source AI Agents Projects","Repos","🤖 AI Agents","LangChain Ecosystem","Agentic Framework","Orchestration","Integrations","🏗️ Frameworks \u0026 Orchestration","Frameworks","Learning","HarmonyOS","2. 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