LangGraph

LangGraph

A low-level orchestration framework for building long-running, stateful and controllable AI agents.

Professional overview

LangGraph is useful when a simple prompt chain is not enough. It lets developers model agent workflows as graphs with state, branching, persistence, human-in-the-loop and controlled execution.

Problem it solves

It turns one-off model calls into orchestrated, traceable and reusable task flows for applications that need multiple steps, tool calls, state and human checkpoints.

Best-fit developer scenarios

• Stateful agents
• Multi-step workflows
• Human-in-the-loop systems
• Production agent orchestration

When not to use it

• Very simple one-shot Q&A
• Production flows without state, logs or error handling
• High-risk decisions fully delegated to models

Recommended usage workflow

1. Break the task into inputs, nodes, tools and outputs 2. Validate a minimal workflow before adding complexity 3. Add logs, human approval and retry behavior 4. Expand to longer flows or more agents only after stability is proven

Getting started

1. Install LangGraph in a Python or JavaScript project. 2. Define nodes for model calls, tools, routing and business logic. 3. Represent transitions explicitly instead of hiding workflow logic in prompts. 4. Add persistence and human review for long-running or risky tasks.

Risks and review checklist

• Avoid hiding all business logic inside prompts
• Record intermediate state and tool results
• Add approval or rollback when workflows write to external systems