Why I chose LangGraph over CrewAI for multi-agent orchestration

I needed to solve a real problem: orchestrating six different AI agents across my real estate and engineering businesses without relying on expensive SaaS platforms.

Each business in my Load Bearing Empire requires different agent behaviors. My property management service needs agents that handle tenant inquiries and maintenance scheduling. My structural engineering consultancy needs agents that process RFIs and coordinate with project teams. My lead generation service needs agents that qualify prospects and book appointments.

CrewAI looked appealing at first. The framework promises simple multi-agent workflows with minimal setup. But when I dug deeper, I found limitations that would hurt my infrastructure-first approach.

The control problem

CrewAI abstracts away too much of the orchestration logic. You define agents and tasks, but the framework decides how they interact. Here's a typical CrewAI setup:

from crewai import Crew, Agent, Task

agent1 = Agent(role="researcher", goal="Find information")
agent2 = Agent(role="writer", goal="Create content")

task = Task(description="Research and write about topic")
crew = Crew(agents=[agent1, agent2], tasks=[task])

result = crew.kickoff()

This works for simple cases. But what happens when you need conditional logic? What if Agent A should only talk to Agent B under specific circumstances? What if you need to implement custom retry logic or error handling between agents?

CrewAI gives you limited options. You're stuck with their predefined interaction patterns.

LangGraph's state-first approach

LangGraph treats multi-agent orchestration as a state management problem. You define a graph where each node represents a decision point or action. Agents become functions that modify shared state.

Here's how I structure my property management workflow:

from langgraph.graph import StateGraph
from typing import TypedDict

class AgentState(TypedDict):
    inquiry_type: str
    tenant_id: str
    priority_level: int
    assigned_agent: str
    response_ready: bool

def classify_inquiry(state: AgentState) -> AgentState:
    # Classification logic here
    return {"inquiry_type": "maintenance", "priority_level": 2}

def route_to_agent(state: AgentState) -> str:
    if state["priority_level"] > 3:
        return "emergency_agent"
    return "standard_agent"

workflow = StateGraph(AgentState)
workflow.add_node("classifier", classify_inquiry)
workflow.add_node("emergency_agent", handle_emergency)
workflow.add_node("standard_agent", handle_standard)
workflow.add_conditional_edges("classifier", route_to_agent)

You control exactly how agents interact. You decide when state gets passed between nodes. You implement your own routing logic.

Performance on limited resources

I run everything on a single Vultr VPS with 8GB RAM. Resource efficiency matters.

CrewAI spawns separate processes for each agent by default. With six businesses running different agent combinations, this quickly consumes memory. I measured average memory usage of 400-600MB per CrewAI crew during peak loads.

LangGraph agents share the same Python process. State gets passed as dictionaries between functions. My entire multi-agent system uses 150-200MB of memory during the same workloads.

The difference compounds when you're running multiple workflows simultaneously.

Debugging and observability

When something breaks in CrewAI, you get limited visibility into the agent interactions. The framework handles message passing internally. You see the final result, but troubleshooting intermediate steps requires diving into CrewAI's logging system.

LangGraph workflows are just Python functions. You can add print statements, logging, or custom monitoring at any step:

def maintenance_agent(state: AgentState) -> AgentState:
    print(f"Processing ticket {state['tenant_id']} - Priority {state['priority_level']}")

    # Your agent logic here
    response = generate_response(state)

    # Log the result
    logger.info(f"Response generated: {len(response)} characters")

    return {"response_ready": True, "response": response}

You own the entire execution path. No black box abstractions.

The infrastructure angle

CrewAI encourages you to use their cloud platform for advanced features like agent memory and task persistence. This goes against my philosophy of owning your infrastructure.

LangGraph integrates cleanly with any database. I store agent state in my existing Supabase instance. Workflow history, agent performance metrics, and business logic all live in systems I control.

Here's my state persistence setup:

def save_state(state: AgentState, workflow_id: str):
    supabase.table("agent_workflows").insert({
        "workflow_id": workflow_id,
        "state": state,
        "timestamp": datetime.utcnow()
    }).execute()

def load_state(workflow_id: str) -> AgentState:
    result = supabase.table("agent_workflows").select("*").eq("workflow_id", workflow_id).execute()
    return result.data[0]["state"]

No vendor lock-in. No monthly fees for basic functionality.

The bottom line

LangGraph requires more upfront thinking about your workflows. You write more code than CrewAI's declarative approach. But you get precise control over agent interactions, better resource utilization, and complete ownership of your orchestration logic.

For my use case - multiple businesses running on shared infrastructure with custom requirements - LangGraph was the clear choice.

If you're building multi-agent systems and want to maintain control over your infrastructure, start with LangGraph's state management approach. Your future self will thank you when you need to customize agent behavior beyond what frameworks allow.