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83 lines
6.2 KiB
Markdown
83 lines
6.2 KiB
Markdown
<div align="center">
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<a href="https://www.langchain.com/langgraph">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg">
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<source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg">
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<img alt="LangGraph Logo" src=".github/images/logo-dark.svg" width="50%">
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</picture>
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</a>
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</div>
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<div align="center">
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<h3>Low-level orchestration framework for building stateful agents.</h3>
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</div>
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<div align="center">
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<a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langgraph" alt="PyPI - License"></a>
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<a href="https://pypistats.org/packages/langgraph" target="_blank"><img src="https://img.shields.io/pepy/dt/langgraph" alt="PyPI - Downloads"></a>
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<a href="https://pypi.org/project/langgraph/" target="_blank"><img src="https://img.shields.io/pypi/v/langgraph.svg?label=%20" alt="Version"></a>
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<a href="https://x.com/langchain_oss" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
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</div>
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<br>
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Trusted 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.
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```bash
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pip install -U langgraph
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```
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> [!TIP]
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> If you're looking to quickly build agents, check out **[Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview)** — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
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For an equivalent 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).
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## Why use LangGraph?
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LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent:
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- **[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.
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- **[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.
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- **[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.
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- **[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.
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- **[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.
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> [!TIP]
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> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
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## LangGraph ecosystem
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While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
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To improve your LLM application development, pair LangGraph with:
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- [Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
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- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
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- [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.
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- [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).
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---
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## Documentation
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- [docs.langchain.com](https://docs.langchain.com/oss/python/langgraph/overview) – Comprehensive documentation, including conceptual overviews and guides
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- [reference.langchain.com/python/langgraph](https://reference.langchain.com/python/langgraph) – API reference docs for LangGraph packages
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- [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart) – Get started building with LangGraph
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- [Chat LangChain](https://chat.langchain.com/) – Chat with the LangChain documentation and get answers to your questions
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**Discussions**: Visit the [LangChain Forum](https://forum.langchain.com) to connect with the community and share all of your technical questions, ideas, and feedback.
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## Additional resources
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- **[Guides](https://docs.langchain.com/oss/python/learn)** – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
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- **[LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph)** – Learn the basics of LangGraph in our free, structured course.
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- **[Case studies](https://www.langchain.com/built-with-langgraph)** – Hear how industry leaders use LangGraph to ship AI applications at scale.
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- [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview) – Learn how to contribute to LangChain projects and find good first issues.
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- [Code of Conduct](https://github.com/langchain-ai/langchain/?tab=coc-ov-file) – Our community guidelines and standards for participation.
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---
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## Acknowledgements
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LangGraph 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.
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