LangGraph is a low-level framework and runtime for building reliable, stateful, and controllable AI agent workflows.

LangGraph is open source. Paid costs usually come from model providers and optional LangSmith platform usage.

How is LangGraph different from LangChain?

LangChain is the broader agent framework, while LangGraph focuses on graph-based control, state, persistence, and durable execution.

Who should use LangGraph?

LangGraph is best for developers building production agents that need branching logic, memory, checkpoints, and human-in-the-loop controls.

Does LangGraph support multi-agent systems?

Yes. LangGraph can be used for single-agent, multi-agent, hierarchical, supervisor, and swarm-style agent patterns.

What should teams watch for?

Teams should invest in clear graph design, state management, evaluations, observability, and cost controls before production deployment.

LangGraph

Name: LangGraph
Brand: LangGraph

Low-level orchestration framework for reliable, stateful AI agent workflows.

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What is LangGraph?

TL;DR

LangGraph is a low-level framework for building reliable, stateful, and controllable AI agents with graph-based orchestration, persistence, human approval, memory, and long-running workflows in production systems.

Graph-based runtime for stateful AI agent workflows
Human-in-the-loop controls, interrupts, and checkpoints
Supports single-agent, multi-agent, and hierarchical patterns

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About

LangGraph is LangChain's low-level orchestration framework for building reliable, stateful, and controllable AI agents. It gives developers graph-based primitives for workflows where agents need memory, branching logic, human approval, persistence, and long-running execution. The product is useful when a simple assistant loop is not enough. LangGraph helps teams design single-agent, multi-agent, hierarchical, and human-in-the-loop systems with more control over state transitions, checkpoints, interrupts, and tool usage than typical high-level agent abstractions. LangGraph is open source, but it becomes more powerful when combined with the broader LangSmith platform for observability, evaluation, deployment, and debugging. That combination gives teams a path from local agent experiments to more reliable production agent systems. For FutureStack, LangGraph belongs in AI Agents because production agent builders are actively searching for durable orchestration frameworks. It is best for developers who need reliability, persistence, and control when building agents that do more than answer a single prompt.

Use Cases

Multi-agent Task Delegation

Build controllable agents with LangGraph, including supervisor, hierarchical, and multi-agent workflow patterns.

Automated Testing & Bug Resolution

Use durable workflows and checkpoints to build agent systems that can pause, inspect state, recover, and continue complex tasks.

Human-in-the-loop Agents

Add interrupts and approval steps so people can review sensitive agent actions before execution continues.

Persistent Agent Workflows

Create long-running stateful agents with memory, persistence, branching logic, and durable execution across sessions.

Key Features

Graph-based runtime for stateful AI agent workflows
Human-in-the-loop controls, interrupts, and checkpoints
Supports single-agent, multi-agent, and hierarchical patterns
Works with LangSmith for tracing, evaluation, and deployment

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