Build stateful, multi-actor LLM apps as graphs — durable execution, human-in-the-loop, streaming.
You reach for LangGraph the moment a simple agent loop stops being enough — when you need state that survives a crash, a human approving a step mid-run, or a flow that can loop back on itself. Most teams arrive here from plain LangChain and don't leave. If all you want is a quick tool-calling agent, this is more machinery than you need — start lighter and come back when you hit the wall.
LangGraph is a library for building stateful, multi-actor applications with LLMs — agent and multi-agent workflows modeled as graphs with cycles, branching, persistence and first-class streaming.
pip install langgraph
What teams reach for next — and why each earns a place beside LangGraph.
Trace, test and monitor LLM apps in production.
Open-source control plane for running fleets of heterogeneous AI agents as a "company" — bring your own agent, assign goals, org charts, budgets, governance, and an audited ticket system.
Orchestrate role-playing, autonomous AI agents that collaborate on tasks.
Program — don't prompt — your language models. Compile declarative pipelines into optimized prompts.
CLI + agent-skills layer that turns your coding assistant into a Google Cloud agent-lifecycle expert: scaffold ADK projects, run and evaluate them, then deploy and publish to Gemini Enterprise.