Guide
CrewAI vs LangGraph is usually the first real decision a team makes when an agent project moves past a prototype. Both are open-source Python frameworks for building agents, both can run several agents together, and both are used in production. They differ in what you describe when you build: CrewAI asks you to describe a team, and LangGraph asks you to describe a graph.
That difference decides how much of the run you control, how you pause and resume it, and how you work out what went wrong. This guide compares the two on those points so you can choose before you build.
| CrewAI | LangGraph | |
|---|---|---|
| What you define | Agents with a role, goal and backstory, and tasks for them | A state schema, nodes that update it, and edges between nodes |
| Who decides the path | The agents, within the process you pick | Your edges and routing functions |
| Fixed control flow | Flows: @start, @listen, @router | The graph itself, with conditional edges |
| Saved state and resume | @persist on a Flow saves its state | A checkpointer, keyed by thread_id |
| Human approval | human_input on a task; @human_feedback in a Flow | interrupt() in a node, resumed with Command(resume=...) |
| Runaway runs | max_iter on each agent, which forces a final answer | recursion_limit, which raises GraphRecursionError |
| Relation to LangChain | Standalone; does not depend on it | From the LangChain team, works with its components |
In CrewAI you build a crew. Each agent has a role, a goal and a backstory, and each task has a description, an expected output and usually an assigned agent. You then choose how the crew works through the tasks.
With Process.sequential, tasks run in order and each output feeds the next. With Process.hierarchical, a manager, set with manager_llm or manager_agent, decides which agent handles what. Either way, the agents decide much of how each task gets done.
That is quick to start with, and it reads like the way people already describe the work: a researcher, a writer, a reviewer. The cost is that the path through a run is chosen by models, so two runs of the same input can take different routes.
Flows are CrewAI's answer to that. A Flow is a Python class with a typed state, where @start marks the entry point, @listen runs a method when another finishes and @router returns a label that picks the next branch, as set out in the CrewAI Flows documentation. A Flow step can kick off a whole crew, so a team of agents sits inside a path you control.
Adding @persist to a Flow saves its state to SQLite so it can recover on restart, and @human_feedback pauses a step for a person to approve or reject it. If you are comparing CrewAI with LangGraph for production work, compare Flows, not crews alone.
In LangGraph you build a StateGraph. You define the state as a typed schema, add nodes, which are functions that read the state and return updates, and connect them with edges. Conditional edges call a routing function that picks the next node from the state.
Nothing moves unless an edge says so. A model can still decide things inside a node, such as which tool to call, but the shape of the run is code you wrote and can read. That is more work up front than describing a crew, and it is also why LangGraph runs are easier to reason about step by step.
Compile the graph with a checkpointer, such as SqliteSaver or PostgresSaver, and LangGraph saves a checkpoint of the state at every step under a thread_id, as the LangGraph persistence docs describe. The same thread_id resumes that run, and a new one starts fresh. The LangGraph checkpointer guide covers choosing and running one.
The practical differences show up in three places.
LangGraph gives you control by default and lets you hand parts of it to a model. CrewAI gives the agents room by default and lets you take control back with Flows. If your process has to follow the same steps every time, such as a compliance check or a fixed approval chain, LangGraph's graph expresses that directly.
If the work is open-ended, research or drafting where the useful path is not known in advance, a CrewAI crew expresses it with less code.
A LangGraph checkpointer saves state at every step, which is what lets a run stop, wait and continue later, even after an error. CrewAI Flows save state with @persist. Both work; the difference is that resuming is central to how LangGraph is designed, while in CrewAI it is something you add to a Flow.
LangGraph's interrupt() pauses a node anywhere in your code and needs a checkpointer and a thread_id to resume (LangGraph interrupts docs). In CrewAI, human_input=True on a task asks a person to review the agent's final answer, and @human_feedback adds an approve or reject step to a Flow. If people must approve actions in the middle of a run, test that path early in whichever framework you choose.
The way a framework fails matters as much as the way it builds, because that is where the time goes once an agent is live.
CrewAI failures tend to come from agent freedom: an agent that loops on a task until max_iter forces a final answer, which can look like a normal result, a manager in a hierarchical crew that delegates to the wrong agent, or output that does not match the expected output the next task needs. Debugging means reading what each agent decided and why. The CrewAI debugging guide walks through those cases.
LangGraph failures tend to come from the graph: a conditional edge that keeps routing back to the same node until the run hits recursion_limit and raises GraphRecursionError, a state update that overwrites a value another node needed, or a missing checkpointer that makes an interrupt impossible to resume. Debugging means reading state at each checkpoint. The LangGraph debugging guide covers those.
Say a LangGraph run starts raising GraphRecursionError overnight, or a CrewAI crew starts returning a final answer that is really a forced stop. With FleetHelp, the agent itself sends the error and what it was doing to our support fleet over Telegram, and gets back a tested fix, usually in under 60 seconds, so the team starts the morning with a cause instead of a log to read.
FleetHelp never touches your infrastructure; your agent or your team applies the fix. See FleetHelp support for CrewAI and LangGraph agents.
Neither framework removes the need to test what the agent does before it ships; the AI agent testing guide applies to both.
Choose LangGraph when:
Choose CrewAI when:
If your team is split, prototype the hardest step in both. An afternoon on the one part you are least sure of tells you more than any comparison table, including this one.
FleetHelp is not an agent framework, and it does not choose one for you. It supports agents built on either: when a CrewAI crew or a LangGraph graph breaks and the cause is not obvious, your agents can ask.
With FleetHelp's managed support, your agents message our support fleet directly over Telegram. Ours diagnose the failure and reply with a tested fix, usually in under 60 seconds, any hour.
FleetHelp has no access to your infrastructure and cannot deploy, restart, roll back or change anything you run. Plans start at $99 a month; see pricing and subscribe.
CrewAI starts from a team of agents, and LangGraph starts from a graph of steps you define. CrewAI's role-based agents are given tasks and decide much of the path themselves, with Flows added when you need fixed control. LangGraph's explicit graph of nodes and edges over a shared state means you define every step and branch the run can take.
No, CrewAI does not depend on LangChain. Its README calls it a standalone Python framework with its own primitives, and LangChain is not among its dependencies. LangGraph comes from the LangChain team and works well with LangChain components, although a LangGraph node can be any Python function.
Neither is better in general; it depends on how much of the run you need to control. LangGraph suits workflows that need exact control, pausing for approval and resuming from a saved checkpoint. CrewAI suits work that maps to roles and tasks, and its Flows add fixed control and saved state when a crew needs to sit inside a production pipeline.
Yes, because both run ordinary Python. A LangGraph node can call a CrewAI crew, or a CrewAI Flow step can invoke a compiled LangGraph graph. It adds two sets of concepts to debug, so do it only when one part of the system clearly fits each framework.