Glossary · AI & Agents
LangGraph
Overview
How LangGraph works
LangGraph models an AI application as a graph: nodes are steps (such as calling a model or a tool) and edges decide what happens next, including loops and branches. All nodes share a common state that is updated as the workflow runs. Because it supports cycles, it can handle agents that retry, revise, and iterate, which simple linear chains cannot.
LangGraph vs LangChain
LangChain popularized linear chains for straightforward, step-after-step tasks. LangGraph, from the same team, targets more complex, long-running agents that need durable state, checkpointing, and human-in-the-loop approval at key moments. It pairs with LangSmith for tracing and debugging. In practice, teams reach for LangGraph when an agent must be controllable and reliable, not just quick to build.
When businesses need LangGraph
LangGraph suits agents that take multiple steps, call several tools, or must pause for human review before acting, such as processing a claim or triaging a support case. Our team uses it for AI Agents and Knowledge Assistant (RAG) projects where predictability matters, adding checkpoints and guardrails so an agent behaves consistently in production.
Where we use it
Related Zen in Tech services
How our team puts LangGraph to work in real projects.
FAQ
LangGraph — common questions
LangGraph vs LangChain, what's the difference?
LangChain orchestrates mostly linear sequences of steps, while LangGraph represents workflows as graphs with shared state, loops, and branching. LangGraph is aimed at stateful, multi-step agents that need durability and control; LangChain remains simpler for straightforward pipelines.
When should I use LangGraph?
Use LangGraph when an agent needs multiple steps, retries, branching logic, persistent memory, or human approval before taking actions. For a single prompt or a simple linear flow, a basic chain is usually enough.
Is LangGraph production-ready?
Yes. LangGraph is designed for production, with features like state persistence, checkpointing, and human-in-the-loop control. Teams typically pair it with monitoring and evaluation to ensure agents behave reliably as they scale.
Keep exploring
Related terms
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