LangGraph vs CrewAI
LangGraph is a state machine. CrewAI is a team of specialists. LangGraph vs CrewAI is a real choice about composition, and a narrow one. Both leave the operating layer to you.
LangGraph vs CrewAI is a composition choice
People comparing LangGraph vs CrewAI are choosing how to express an agent in code. LangGraph is a graph of nodes and edges over shared state. You see every transition and you can reason about the run as a state machine. CrewAI is roles and tasks. You see a team of specialists and you reason about who is responsible for which job.
Both are frameworks. Both will get you a demo. Neither decides who the agent is, whose credentials it uses, what it is allowed to do, or how you prove afterwards what it did. LangGraph vs CrewAI does not contain a winner on those questions, because both leave them to you. Kodeus is not a third column in that library comparison. It drafts the application from a description and then operates it.
Use this page to pick a composition style if you are staying in framework land. Use LangGraph alternatives or CrewAI alternatives if one of those names is the thing you are trying to leave.
Side by side
The differences that show up in the first week are about how you write. The differences that show up in the first security review are about what you still have to build, and those match.
| LangGraph | CrewAI | Kodeus | |
|---|---|---|---|
| You express | Nodes, edges and shared state. | Roles, tasks and a crew. | The outcome. The platform drafts the application. |
| You are comfortable when | You want a state machine you can step through. | You think in specialists. | Running it safely is the hard part, not the control flow. |
| Identity | Yours to add. | Yours to add. | Authenticated identity on every request. |
| Credentials | Yours to store. | Yours to store. | Encrypted per user, with revocation. |
| Policy | Prompts, unless you build a check. | Prompts, unless you build a check. | Guardrails that can block, redact, escalate or abort. |
| Trace | Yours to log. | Yours to log. | Structured calls, results and refusals. |
| Where it runs | Wherever you host the process. | Wherever you host the process. | Local, your VPC, Kodeus Cloud, or airgapped. Same policy engine. |
How to choose without a bake-off that wastes a month
If your team argues in diagrams and wants a failed edge to be obvious, LangGraph will feel native. If your team argues in job descriptions and wants to add a specialist without redrawing a graph, CrewAI will feel native. Pick on that, then budget the operational work as a separate project. Pretending the library includes it is how timelines slip.
If that separate project is the thing you do not want to staff, stop the LangGraph vs CrewAI trial. Describe one workflow to Kodeus. We will show the tools, the identity and the approval boundary. You will not keep your graph or your crew. You will keep the outcome, and the runtime will hold the controls a reviewer asks about.
A few practical ties. Both frameworks can call tools, including MCP, if you integrate them. Kodeus puts MCP servers in the spec and records the call. Both can be wrapped in your own auth. Kodeus refuses to be a bring-your-own-agent host, so wrapping is not the offer. Models on Kodeus are chosen per agent and swapped in config. You bring keys and pay the provider.
What the comparison should change in your plan
Write down which sentence is true. "We need a clearer way to compose the agent." Then LangGraph vs CrewAI is the whole project, and you still owe yourself a vault and a trace. "We need this in front of users, inside our network, with a record." Then the framework decision is optional, and AI agent infrastructure is the page that lists what has to exist on day one.
Either way, do not run the bake-off on a toy task and call it production. Use a workflow that has a credential, a person who must approve something, and a record someone will ask for. That is the only LangGraph vs CrewAI test that predicts the next quarter.
Stop the bake-off if the gap is operational
Bring a workflow that has a credential and an approval. We will show the runtime, not another library.
Frequently asked questions
What is the difference in LangGraph vs CrewAI?
LangGraph expresses a run as a graph of nodes and edges over shared state. CrewAI expresses it as roles and tasks that several agents carry out. One is a state machine. The other is a team.
Which should I pick?
Pick LangGraph if you want every transition explicit and you like to step through a run. Pick CrewAI if you think in specialists with jobs. Pick neither as your production platform. Both leave identity, credentials, memory, policy and tracing to you.
Does either one deploy an agent for me?
No. They are libraries for composing behaviour. Deployment, tenancy, secrets and audit are separate projects you still own.
Where does Kodeus sit in LangGraph vs CrewAI?
Beside that choice, not inside it. You describe the outcome. Kodeus drafts the application and operates it. You are not choosing Kodeus as a third graph library.
Is one easier to observe?
A graph is easier to read as a plan. A crew is easier to read as a cast. Neither records the tool call, the result, the refusal and the identity unless you build that.
Can I use MCP tools with either framework?
Yes, if you wire them. On Kodeus, tools attach over MCP as part of the spec, and the call is traced.
What should a comparison include that most posts skip?
Who the agent acts as, where credentials live, what happens when a tool should be refused, and what you can show a reviewer the next day.
How do I go deeper on each side?
LangGraph alternatives and CrewAI alternatives each take one side. This page is only the comparison.