LangGraph vs CrewAI: Choosing the Right Agent Framework
An honest comparison of LangGraph and CrewAI: when to use each, where they break, and how to run either in production.
Two of the most popular Python frameworks for building AI agents are LangGraph and CrewAI. They attack the same problem, orchestrating LLM-powered agents, from opposite directions.
Philosophy
CrewAI inverts control. You declare who works here (agents with roles) and what done means (tasks), and the framework decides the call order, the delegation, the handoffs. It is declarative, and the unit of thought is a team.
LangGraph hands you the call order as a first-class object. You define nodes, edges, and the state that flows between them, and nothing happens that you did not draw. It is imperative, and the unit of thought is a state machine.
Neither is better in the abstract. The right choice depends on whether your problem is naturally a team or naturally a graph.
When to Use CrewAI
CrewAI shines when the problem decomposes into roles with clear handoffs:
Its real strength is time-to-working-system. You can go from idea to running multi-agent crew in an afternoon, and the YAML configuration lets you iterate on agent definitions without touching code.
agents:
- role: "Research Analyst"
goal: "Find accurate, up-to-date information"
backstory: "Expert researcher with attention to detail"
tools: [search, scrape]The tradeoff is control. The framework decides how agents collaborate, and when a run goes sideways, the delegation logic you did not write is the thing you have to debug.
When to Use LangGraph
LangGraph earns its complexity when you need the execution path to be explicit:
Every transition is code you wrote and can step through. Conditional edges, cycles, and parallelism are all first-class.
graph = StateGraph(AgentState)
graph.add_node("analyze", analyze_fn)
graph.add_node("decide", decide_fn)
graph.add_conditional_edges("analyze", route_fn, {
"needs_more_data": "search",
"ready": "decide"
})The tradeoff is volume. A workflow that is 20 lines of CrewAI YAML might be 100 lines of LangGraph, and you own every one of them.
How They Compare in Practice
From building equivalent workflows on both:
| Metric | CrewAI | LangGraph |
|---|---|---|
| Setup time | ~30 min | ~2 hours |
| Lines of code (simple workflow) | ~50 | ~150 |
| Execution overhead | Moderate | Low |
| Debugging | Opaque delegation | Explicit transitions |
| State management | Automatic | Explicit |
The debugging row is the one that matters at 2 a.m. CrewAI hides the control flow, which is wonderful until it is the control flow that is broken. LangGraph makes you write it, which is tedious until it is the thing you need to read.
Deploying Either on Maritime
Maritime's built-in templates cover agent frameworks like OpenClaw, Hermes, and ZeroClaw. There is no dedicated CrewAI or LangGraph template, but anything that runs in a container deploys the same way: add a Dockerfile to your repo and point the CLI at it.
# Works for either framework. The repo just needs a Dockerfile
maritime create my-crew --repo https://github.com/you/my-crew
maritime create my-graph --repo https://github.com/you/my-graphBoth get the same deployment: an API endpoint, encrypted secrets, triggers, and the sleep/wake lifecycle. The platform does not care which framework is inside the container.
Our Recommendation
Start with CrewAI if you are building your first agent system or validating an idea. The lower barrier to entry is worth more than control you do not need yet.
Move to LangGraph when the workflow develops branching that role delegation cannot express, or when you find yourself debugging the framework instead of your logic.
Both are production-ready. Both run on Maritime as containers. Pick the one that matches the shape of your problem.