LangGraph

AI Agents

LangChain

FreemiumOpen SourceAPI

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Stateful agent orchestration framework by LangChain: graph-based workflows, durable memory and human-in-the-loop, with the LangGraph Platform for one-click deployment

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Our Verdict

Highly Recommended

The production standard for stateful agent orchestration

When agent demos need to become dependable systems, teams keep landing on LangGraph. Its graph model demands more upfront thinking than role-based frameworks, but that investment buys precise state control, durable execution and real human-in-the-loop — the three things production agents actually need. With 1.0 stability and the deployment platform, it is the safest bet for serious agent engineering today.

Best for

•Engineering teams shipping stateful agents to production
•Workflows needing checkpoints, retries and human approval
•Teams already invested in the LangChain/LangSmith ecosystem

Consider alternatives if

•You want the fastest role-based prototyping (→ CrewAI)
•You build in TypeScript first (→ Mastra)

Try LangGraph

Supported Platforms

Web AppWindowsmacOSLinuxAPI

Available platforms include Web App, Windows, macOS, Linux, and API.

Key Features

Graph-based orchestration: model agent steps as nodes and edges with full control over state

Durable execution and checkpointing: workflows resume exactly where they failed

First-class human-in-the-loop: interrupt, inspect and resume agent runs

LangGraph Platform: one-click deployment with built-in LangSmith observability

Python and JavaScript/TypeScript SDKs with feature parity

Battle-tested in production at companies like Uber, LinkedIn and Klarna

Pricing

free

Open-source framework is free (MIT license): build and self-host stateful agents, paying only your LLM providers; LangSmith Developer tier is free for solo use

paid

LangGraph Platform deployment via LangSmith Plus at $39/seat/month plus usage-based node execution fees; Enterprise plans with custom pricing

Use Cases

Complex stateful agents with branching, loops and retries

Long-running workflows that must survive failures

Approval flows requiring human review mid-run

Production multi-agent systems with strict observability needs

Pros

Finest-grained control of any mainstream agent framework

Durable execution and checkpointing are genuinely production-grade

Huge ecosystem via LangChain integrations and LangSmith tooling

Proven at scale by large enterprises

Cons

Steeper learning curve than role-based frameworks like CrewAI

Graph boilerplate feels heavy for simple single-agent tasks

Usage-based platform billing can be hard to forecast

Conventions are tied to the broader LangChain ecosystem

Latest Update

2026: after the stable 1.0 release across Python and JS, LangGraph doubles down on durable execution, node caching and the LangGraph Platform, cementing its position as the production standard for stateful agents