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Basic Information

LangGraph.js is the JavaScript implementation of LangGraph, a low-level orchestration framework for building controllable AI agents. The repository provides libraries, prebuilt components, examples, and a CLI to construct agent graphs that maintain long-term memory, run multi-step workflows, and incorporate human-in-the-loop approvals and moderation checks. It targets developers who need extensible, low-level primitives rather than rigid high-level abstractions, enabling custom agent architectures and multi-agent systems. The README shows usage such as creating ReAct agents, integrating schema-validated tools, and connecting chat LLMs. The project emphasizes production-readiness with token-by-token streaming and streaming of intermediate steps for visibility. The package is distributed via npm and integrates with LangChain tooling and an optional LangGraph Platform for deployment, scaling, and visual prototyping.

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Features
Key features include low-level, extensible primitives for building agent graphs and composing bespoke agents. The repo provides prebuilt agent templates such as ReAct, Memory, Research, and Retrieval along with templates and tutorials. It supports long-term memory and checkpointing for persistent context, human-in-the-loop and moderation checkpoints to steer agent behavior, and schema-validated tool integrations. First-class streaming offers token-by-token output and intermediate step streaming to observe reasoning in real time. Developer tooling includes a full-stack quickstart CLI that scaffolds chat apps with frontend and package manager choices, API reference documentation, and how-to guides. Integrations with LangChain products like LangSmith and an optional LangGraph Platform add observability, evals, deployment APIs, threads, cron jobs, autoscaling, and visual debugging in LangGraph Studio.
Use Cases
LangGraph.js helps developers build reliable, controllable, and scalable AI agents by supplying the core orchestration primitives and production-oriented features needed for complex workflows. Persistence and long-term memory keep agents on task across long-running sessions. Streaming of tokens and intermediate reasoning improves transparency for debugging and end-user interaction. Human-in-the-loop controls and moderation hooks enable safer, governed behavior in production. Prebuilt agent types, templates, tutorials, and a CLI accelerate development of full-stack chat applications. Integration paths to LangChain tooling and the LangGraph Platform simplify evaluation, observability, and deployment at scale by providing APIs for memory, threads, cron jobs, and autoscaling. The README cites production adoption by companies such as Klarna, Elastic, Uber, and Replit, illustrating real-world applicability.

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