LangGraph vs CrewAI: Choosing the Right Agent Framework

The two most popular frameworks for building AI agents are LangGraph and CrewAI. They solve the same fundamental problem — orchestrating LLM-powered agents — but with very different philosophies.

Philosophy

CrewAI thinks in terms of teams. You define agents with roles, assign them tasks, and let the framework handle delegation and collaboration. It's declarative and role-based.

LangGraph thinks in terms of graphs. You define nodes (functions), edges (transitions), and state that flows through the graph. It's imperative and state-machine-based.

Neither philosophy is objectively better. The right choice depends on your use case.

When to Use CrewAI

CrewAI excels when your problem naturally decomposes into roles and tasks:

• Research workflows — One agent searches, another analyzes, a third summarizes
• Content pipelines — Writer, editor, fact-checker working in sequence
• Customer support — Triage agent routes to specialized agents
• Data processing — Collector, transformer, loader with clear handoffs

CrewAI's strength is speed of development. You can go from idea to working multi-agent system in an afternoon. The YAML-based configuration makes it easy to iterate on agent definitions without changing code.

agents:
  - role: "Research Analyst"
    goal: "Find accurate, up-to-date information"
    backstory: "Expert researcher with attention to detail"
    tools: [search, scrape]

The tradeoff: CrewAI gives you less control over execution flow. The framework decides how agents collaborate, which can be opaque when debugging.

When to Use LangGraph

LangGraph excels when you need precise control over execution:

• Complex branching logic — Different paths based on intermediate results
• Human-in-the-loop — Pausing execution for human review at specific nodes
• Stateful conversations — Long-running interactions with persistent memory
• Custom orchestration — Non-linear workflows that don't fit a simple pipeline

LangGraph's graph model gives you full visibility and control over every state transition. You can add conditional edges, cycles, and parallel branches.

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: More code and more complexity. Simple workflows that would take 20 lines in CrewAI might take 100 in LangGraph.

Performance Comparison

In our testing with equivalent workflows:

Deploying Both on Maritime

Maritime supports both frameworks with zero configuration changes. The platform auto-detects your framework from project structure:

• CrewAI — Detected via pyproject.toml with crewai dependency + agents.yaml
• LangGraph — Detected via langgraph dependency + graph definition

Both get the same deployment experience: push code, get an endpoint, configure triggers. The sleep/wake lifecycle works identically regardless of framework.

# CrewAI project

# LangGraph project cd my-graph && maritime deploy ```

Our Recommendation

Start with CrewAI if you're building your first agent system or prototyping. The lower barrier to entry lets you validate your idea faster.

Move to LangGraph when you need fine-grained control over execution flow, or when your workflow has complex branching that CrewAI's delegation model can't express.

Both are production-ready. Both work on Maritime. Pick the one that matches how you think about your problem.