Course Content
CrewAI Multi-Agents
9 sections · 53 lessons
How is CrewAI different from LangChain Agents and LangGraph?
What you need to know
Three levels of abstraction
Think of it as a ladder, from "you write everything" to "the framework decides":
| LangChain agent | LangGraph | CrewAI | |
|---|---|---|---|
| Main unit | one agent with tools | a node in a state graph | an agent with a role |
| Who decides the next step | the model's tool loop | edges you draw | the task list, a manager, or a Flow |
| State | the message history | a typed state object, checkpointed | task outputs, Flow state, memory |
| Multi-agent | you wire it yourself | you wire it, precisely | built in |
| Pause and resume | limited | first-class (checkpointer, interrupts) | Flow persistence with @persist, task replay |
Where CrewAI sits
CrewAI's Crew layer is declarative: you say who the agents are and what each task must produce. You give up some control — the agent decides how many tool calls to make — and you gain speed of building. Its Flow layer closes much of the gap with LangGraph: Flows have typed state, branching with @router and persistence. But LangGraph's model of checkpoints and human interrupts is still more mature for long-running work.
How to choose
- CrewAI when the job naturally reads as "a researcher, an analyst and a writer", the output is a document or a decision, and a run of a minute or two is fine.
- LangGraph when you need exact control over every transition, the process runs for hours or days, it must survive restarts, and it has side effects (payments, emails) that must not repeat.
- A plain LangChain agent (or any single tool-calling loop) when one agent with a few tools is enough.
They also combine: a LangGraph node can call a CrewAI crew, or a CrewAI tool can wrap a LangGraph graph. Section 9 of this course goes deeper on the comparison.
A real-life example
A lending startup builds two systems.
The market brief — "what are other NBFCs charging for two-wheeler loans this month?" — is a research, analyse, write job. They build it as a three-agent CrewAI crew in a day. If a run fails, they simply run it again.
The loan approval pipeline runs for up to three days: collect documents, wait for the customer to upload a missing payslip, run checks, wait for a credit officer's approval, then disburse. It needs to pause for humans, survive server restarts, and never disburse twice. They build it in LangGraph with a checkpointer, and one node calls a CrewAI document-review crew to read the uploaded files.
Same company, same week — the choice followed the shape of the work, not a preference for one library.
Follow-up questions to expect
- "Can CrewAI do what LangGraph does?" — Partly. Flows give event-driven control, routing and persisted state, but LangGraph's checkpointing, time travel and interrupt model are more complete for durable workflows.
- "Which is easier to debug?" — LangGraph, because every edge is explicit. In CrewAI, sequential crews are easy to trace; hierarchical crews and delegation are harder because the model chooses the path.
- "Would you use both?" — Yes, for example LangGraph for the durable outer process and a CrewAI crew inside one node for a research or review step.