LangGraph

Overview

LangGraph models agent runs as state graphs: nodes are steps, edges are routing logic, state is explicit. The result is a framework where long-running, multi-step, multi-agent workflows are debuggable and resumable rather than tangled callback chains. Pairs with LangSmith for observability.

Best for

Best for
Engineering teams building production agent systems, not weekend prototypes

Use cases

• Multi-step research agents with explicit checkpointing
• Customer support flows that need to wait for human input
• Long-running agents that survive process restarts
• Multi-agent collaboration with explicit handoffs

Notes

Why it matters

LangGraph is the most honest piece of the LangChain ecosystem about what production agents need: explicit state, explicit routing, explicit persistence.

How teams use it in production

Start with a small graph for one workflow. Add LangSmith. Resist the temptation to model everything as one mega-graph.

What to watch

The convergence between LangGraph, DSPy, and the emerging Anthropic Skills pattern is the real question. They are all attempts at the same problem at different layers.