CrewAI vs LangGraph: A Team of Agents or a Graph You Own

CrewAI vs LangGraph comes down to one question: do you want to describe who does the work, or exactly what happens next?

CrewAI starts from people. You define agents with roles and goals, group them into a crew, and let them split the work. LangGraph starts from control flow. You define a state object, write nodes that update it, and draw the edges between them yourself.

Both are MIT-licensed, both are Python-first, and the gap between them has narrowed. CrewAI added Flows, which give you steps, state and control flow around your crews. So the old line, "CrewAI for multi-agent, LangGraph for control", is only half true now.

• Pick CrewAI if your work really does split into specialists, and you want to get there with less code.
• Pick LangGraph if you need to pause for a human, resume after a crash, or replay a run step by step.
• Two myths to drop first: CrewAI is not built on LangChain, and LangGraph doesn't need LangChain either.

Quick comparison

The same two-step workflow, written both ways

Take one small job: research a topic, then write a draft from the notes. Here's how each framework asks you to think about it.

In CrewAI, you describe a class with two methods and say which one listens to which. The research method is marked @start(), so it runs when the Flow kicks off. The write method is marked @listen(research), so it fires when research finishes and receives research's output as its argument. If research is itself a hard, open-ended task, you put a Crew inside that step, say a searcher and a fact-checker, and store the crew's result in the Flow's state.

In LangGraph, you describe the data first. You declare a state with fields like topic, notes and draft. Each step is a plain function that reads the state and returns the fields it changed. Then you wire it: start goes to research, research goes to write, write goes to end. You compile the graph, attach a checkpointer if you want it to survive restarts, and run it with a thread ID.

A minimal sketch of each (illustrative, helper functions omitted):

# CrewAI Flow
class PostFlow(Flow):
    @start()
    def research(self):
        return research_topic("crewai vs langgraph")

    @listen(research)
    def write(self, notes):
        return write_draft(notes)

# LangGraph StateGraph
builder = StateGraph(State)
builder.add_node("research", research)
builder.add_node("write", write)
builder.add_edge(START, "research")
builder.add_edge("research", "write")
builder.add_edge("write", END)
graph = builder.compile(checkpointer=InMemorySaver())

For two steps, they're nearly the same length. The difference shows up at step five, when you add a branch, a retry, and a person who has to approve the draft. CrewAI answers with a @router and @human_feedback. LangGraph answers with a conditional edge and an interrupt(). Both work. What differs is how much of the run you can inspect and rewind afterwards.

Where CrewAI is stronger

Getting a multi-agent system working with little code. CrewAI's documentation calls Crews "teams of autonomous agents that collaborate to solve specific tasks". You write an agent's role, goal and tools in a few lines, and delegation between agents comes built in. In LangGraph, a team of agents is something you wire up as nodes and edges.

Choosing how much autonomy you want. A Crew runs its tasks either sequentially or hierarchically. The hierarchical process needs a manager, either a manager_llm or a custom manager_agent, which delegates tasks down a chain of command. That's a real feature when the work splits into specialists, and a source of variance when it doesn't.

A readable structure layer. Flows read like event handlers: @start for entry points, @listen for "run after this", @router for branches, and and_ / or_ to wait on several steps. The recommended pattern is to start with a Flow and drop a Crew into the steps that need a team.

Simple persistence. Add @persist to a Flow or a method and state is saved to SQLite by default. You can resume a run under the same ID, or fork from an earlier state into a new branch.

Where LangGraph is stronger

Explicit state you can reason about. LangGraph's graph API makes the state schema the input to every node and edge. Each key can have its own reducer, which decides whether an update replaces the old value or merges with it. When two branches write to the same list, you decide what happens, not the framework.

Persistence as a first-class layer. A checkpointer saves the graph's state at every step, scoped to a thread ID. The docs name InMemorySaver, SqliteSaver and PostgresSaver. The same mechanism gives you conversation continuity, fault tolerance, and time travel, where you replay or fork a run from an earlier checkpoint.

Human review that can wait for days. Calling interrupt() inside a node saves state and pauses the graph indefinitely. You resume by invoking the graph again on the same thread with a Command carrying the person's answer. One detail catches people out: the whole node restarts from the top on resume, not from the interrupt line. So keep side effects, like sending an email, after the interrupt or in a separate node.

Two ways to write it. Beyond StateGraph, LangGraph has a functional API that adds persistence, memory, human-in-the-loop and streaming to ordinary functions, with plain if statements and loops instead of a drawn graph. It also has a JavaScript version, which matters if your team ships in TypeScript.

Deployment, as named today

Both companies sell a managed runtime on top of the open-source library, and the names have moved around.

CrewAI AMP (Agent Management Platform) is CrewAI's managed platform. You deploy a Crew or a Flow, call it over a REST API, and watch execution traces and logs.

LangSmith Deployment is what LangChain now calls the product previously sold as LangGraph Platform. The rename happened in October 2025, so older tutorials still use the old name. Its documentation lists cloud, hybrid, self-hosted, and standalone server options. On the Agent Server, you don't configure checkpointers yourself.

One surprise: LangChain describes LangSmith Deployment as framework-agnostic and lists CrewAI among the frameworks it can host. So choosing a framework doesn't lock in your hosting as tightly as it first appears.

Neither is needed to run the open-source library. Both repos, crewAIInc/crewAI and langchain-ai/langgraph, are MIT-licensed, so self-hosting on your own servers is always an option.

How to decide

1. Can you name the roles? A researcher, a writer, a reviewer. Start with CrewAI, wrapped in a Flow.
2. Can you draw the flowchart? Boxes, arrows, one approval gate. Start with LangGraph.
3. Does a person need to approve steps, sometimes hours later? LangGraph's interrupt-and-resume is the more mature answer.
4. Is this a prototype to test an idea? CrewAI gets you there with less code.
5. Already on LangChain? LangGraph fits in without a new dependency tree. You still don't need LangChain to use it.
6. Comparing more options? See CrewAI alternatives, CrewAI vs AutoGen, and LangChain alternatives. LangChain vs LlamaIndex covers where LangGraph sits in its own ecosystem.

And if every step runs in a fixed order with no decisions, you may not need either. A few functions calling a model will be easier to debug.

Most people asking this want a working workflow

Developers compare frameworks. A lot of people searching "crewai or langgraph" aren't there yet. They saw an agent workflow on X or YouTube that does their exact job, a research-then-draft pipeline or a lead-qualifying agent, and they want it running. The framework is a detail they only hit because the setup is the only way in.

Taku is built for that step. It's an AI-native desktop workspace where you mirror an AI app, skill, or workflow someone already got working, run it, remix it for your own files, and keep it. It isn't an agent framework and doesn't compete with CrewAI or LangGraph. If you're writing the agent yourself, they're the right tools. Taku is in Beta, and the Mac app is available now. The free app library shows what mirroring looks like.

FAQ

Is CrewAI built on LangChain?

No. Its GitHub README calls it a standalone Python framework with its own primitives for agents, tasks, crews, flows, tools and orchestration.

Can I use LangGraph without LangChain?

Yes. LangGraph is built by LangChain Inc, but its own docs say you don't need LangChain to use it.

What's the difference between CrewAI Flows and LangGraph?

Both define structure and state around LLM steps. Flows use decorators (@start, @listen, @router) on class methods, and Crews can sit inside a step. LangGraph uses an explicit StateGraph with nodes, edges and reducers, plus checkpointers for replay and resume.

Which is better for human-in-the-loop?

Both support it. CrewAI Flows have @human_feedback. LangGraph's interrupt() pauses on a durable checkpoint and resumes on the same thread, which suits approvals that wait a long time.

What happened to LangGraph Platform?

It was renamed LangSmith Deployment in October 2025. LangGraph itself is still the open-source library.

Are both open source?

Yes. Both GitHub repositories are under the MIT licence. Their managed platforms, CrewAI AMP and LangSmith Deployment, are separate commercial products.