CrewAI vs LangGraph: Which Should You Use for AI Agents?

The short answer

Pick CrewAI when you want a small team of role-based agents and a short path to a Python prototype. Pick LangGraph when the agent must pause for a person, branch, retry, or resume after a restart. If you do not want to write Python, skip both and use a workflow tool such as n8n.

CrewAI: a crew of specialists

CrewAI is an open-source Python framework, plus a hosted platform, for multi-agent work. You describe each agent with a role, a goal, and a short backstory, assign tasks, and run them as a crew. The usual shapes are sequential (researcher, then writer, then editor) or hierarchical (a manager agent delegates). That maps cleanly onto jobs people already describe as a team.

You spend your time on the roles and the task wording, not on a control-flow diagram. The path through the work is mostly implied by the crew. Explicit loops, a hard stop for approval, and "continue tomorrow from this exact step" are not the center of the model. CrewAI has grown more explicit control-flow options, but the framework's advantage is still the role-based crew.

LangGraph: a graph with saved state

LangGraph, from the LangChain team, is an open-source library for agent workflows modeled as a graph. Nodes do the work. Edges decide what runs next, including loops back to an earlier node. Every step reads and writes one shared state object, so you can see what the agent believes at each point.

Two features matter in production. You can pause a node until a person approves, rejects, or edits the draft, then continue from that point. And the state can be saved (LangGraph calls this checkpointing) so a crashed or restarted process resumes instead of starting over. That is the right shape for a support draft that waits overnight, a research run that retries a failed fetch, or any agent that must not lose its place. You write more code than you do for a CrewAI crew, and you have to design the state up front.

How to choose

Match the tool to the shape of the job, not to whichever README you saw last.

• Several specialists, one pass, prototype this week: CrewAI.
• Approval gates, retries, or a run that must resume: LangGraph.
• No Python, or operators need to edit the workflow themselves: n8n, Make, or Zapier.
• One coding agent editing a repository: Cursor or Claude Code, not a multi-agent framework.

What we ship instead of locking you in

ShipReadyAgents templates do not require CrewAI or LangGraph. Each one ships as an n8n workflow and as a plain Python version with an AGENTS.md file, and each one keeps a person in the loop before anything is sent, posted, or merged. The Lead Research Agent is the kind of multi-step research people often sketch first as a crew. The Inbox Triage Agent is the kind of job that needs an approval stop, which is the LangGraph-shaped problem, solved in the template without adopting either framework.

Check current docs before you commit

Both projects move quickly, and hosted plans change. Read the current CrewAI and LangGraph docs before you build on either, and check pricing on their sites. This page does not quote plan prices, so it does not go stale on numbers.