# langgraphjs **Repository Path**: lifutian66/langgraphjs ## Basic Information - **Project Name**: langgraphjs - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-07-14 - **Last Updated**: 2026-07-14 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
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Low-level orchestration framework for building stateful agents.

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LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks. ```bash npm install @langchain/langgraph @langchain/core ``` > [!TIP] > If you're looking to quickly build agents, check out **[Deep Agents](https://docs.langchain.com/oss/javascript/deepagents/overview)** — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks. For an equivalent Python library, check out [LangGraph](https://github.com/langchain-ai/langgraph) and the [Python docs](https://docs.langchain.com/oss/python/langgraph/overview). ## Why use LangGraph? LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent: - **[Durable execution](https://docs.langchain.com/oss/javascript/langgraph/durable-execution)** — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off. - **[Human-in-the-loop](https://docs.langchain.com/oss/javascript/langgraph/interrupts)** — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution. - **[Comprehensive memory](https://docs.langchain.com/oss/javascript/langgraph/memory)** — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions. - **[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. - **[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. > [!TIP] > For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home). ## LangGraph’s ecosystem While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with: - [Deep Agents (JS)](https://docs.langchain.com/oss/javascript/deepagents/overview) — Build agents that can plan, use subagents, and leverage file systems for complex tasks. A higher-level package built on top of LangGraph. - [LangChain](https://docs.langchain.com/oss/javascript/langchain/overview) – Provides integrations and composable components to streamline LLM application development. - [LangSmith](http://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. ## Additional resources - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback. - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course. - [Streaming Cookbook](https://github.com/langchain-ai/streaming-cookbook): Documentation and examples around LangGraphs's streaming capabilities. - [API Reference](https://reference.langchain.com/javascript/langchain-langgraph): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components. - [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications. ## Acknowledgements 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.