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LangChain is a general-purpose framework for building LLM applications and provides abstractions for prompts, models, tools, and chains. LangGraph is a separate library built on top of LangChain's primitives that adds graph-based workflow orchestration, persistent state, and deterministic control flow specifically for building AI agents. You can use LangGraph without LangChain, but they integrate deeply when used together.
Yes, LangGraph is open source (MIT licensed) and free to self-host. The commercial LangGraph Platform — which provides managed deployment, autoscaling, and LangGraph Studio — offers a free Developer tier, a Plus tier starting at $20/month plus usage, and custom Enterprise pricing for large-scale deployments.
Yes. LangGraph supports multiple multi-agent patterns including supervisor architectures (a router agent dispatches to specialist subagents), hierarchical topologies (nested subgraphs), and swarm patterns. Subgraphs can be composed and reused, and agents can share state or communicate through message-passing.
Yes. Human-in-the-loop is a first-class primitive. You can configure interrupts at any node so the agent pauses, persists its state to a checkpointer, and resumes only after a human provides approval, edits the state, or supplies additional input.
LangGraph has official SDKs in Python (langgraph) and JavaScript/TypeScript (@langchain/langgraph). Both expose the same core concepts — StateGraph, nodes, edges, checkpointers — and produce functionally equivalent agent behaviors, allowing full-stack teams to share architectural patterns across backend and frontend.
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Last verified March 2026