CrewAI vs LangGraph: Which Agent Framework Should You Choose?

Choosing an agent framework is one of the most important decisions you'll make when building AI applications. The two leading options in 2026 are CrewAI and LangGraph—both capable, both different.

This guide compares them across architecture, features, use cases, and helps you decide which is right for your project.

Quick Comparison

Feature	CrewAI	LangGraph
Architecture	Role-based agents + Flows	Graph-based state machines
Learning Curve	Easier	Steeper
Flexibility	Opinionated	Very flexible
State Management	Built-in (Flows)	Checkpointing
Multi-Agent	Native (Crews)	Manual coordination
Enterprise Ready	Yes ($99/mo+)	Yes (LangSmith)
Language	Python	Python (TS beta)

Architecture Deep Dive

CrewAI: Roles and Flows

CrewAI uses a dual architecture:

• Crews — Teams of role-based agents that collaborate
• Flows — State-driven workflows that control execution

# CrewAI example
from crewai import Agent, Task, Crew

# Define agents by role
researcher = Agent(
    role="Research Analyst",
    goal="Find accurate information",
    backstory="You are an expert researcher..."
)

writer = Agent(
    role="Content Writer", 
    goal="Create engaging content",
    backstory="You are a skilled writer..."
)

# Define tasks
research_task = Task(
    description="Research the topic: {topic}",
    agent=researcher
)

write_task = Task(
    description="Write an article based on research",
    agent=writer
)

# Crew coordinates agents
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task]
)

result = crew.kickoff(inputs={"topic": "AI agents"})

Pros: Intuitive mental model, easy multi-agent setup, handles coordination automatically.

Cons: Less control over exact flow, opinionated structure.

LangGraph: Graph-Based State Machines

LangGraph represents workflows as graphs where nodes are functions and edges define transitions:

# LangGraph example
from langgraph.graph import StateGraph, END
from typing import TypedDict

class AgentState(TypedDict):
    messages: list
    research: str
    draft: str

def research_node(state):
    # Do research
    return {"research": "findings..."}

def write_node(state):
    # Write based on research
    return {"draft": "article..."}

def should_continue(state):
    if state["draft"]:
        return END
    return "write"

# Build graph
builder = StateGraph(AgentState)
builder.add_node("research", research_node)
builder.add_node("write", write_node)
builder.add_edge("research", "write")
builder.add_conditional_edges("write", should_continue)

graph = builder.compile()
result = graph.invoke({"messages": []})

Pros: Full control, explicit state transitions, great for complex logic.

Cons: More boilerplate, steeper learning curve, manual agent coordination.

State Management

CrewAI Flows

from crewai.flow.flow import Flow, start, listen

class SupportFlow(Flow):
    @start()
    def receive_ticket(self):
        self.state["ticket"] = self.inputs["ticket"]
        return analyze_ticket(self.state["ticket"])
    
    @listen(receive_ticket)
    def route_ticket(self, analysis):
        self.state["analysis"] = analysis
        if analysis.priority == "high":
            return self.escalate()
        return self.auto_respond()
    
    # State persists through the flow

LangGraph Checkpointing

from langgraph.checkpoint.sqlite import SqliteSaver

# Persistent checkpointing
memory = SqliteSaver.from_conn_string("./checkpoints.db")
graph = builder.compile(checkpointer=memory)

# State persists across calls
config = {"configurable": {"thread_id": "conversation_123"}}
graph.invoke({"messages": [msg1]}, config)
# Later...
graph.invoke({"messages": [msg2]}, config)  # Continues from checkpoint

Multi-Agent Coordination

CrewAI Multi-Agent

# Natural multi-agent setup
crew = Crew(
    agents=[analyst, researcher, writer, editor],
    tasks=[analyze, research, write, edit],
    process=Process.hierarchical,  # Manager coordinates
    manager_llm=ChatOpenAI(model="gpt-4")
)

LangGraph Multi-Agent

# Manual coordination required
def supervisor_node(state):
    # Decide which agent to call
    next_agent = decide_next_agent(state)
    return {"next": next_agent}

def researcher_node(state):
    result = researcher.run(state["task"])
    return {"research": result}

def writer_node(state):
    result = writer.run(state["research"])
    return {"draft": result}

# Wire it all up manually
builder.add_node("supervisor", supervisor_node)
builder.add_node("researcher", researcher_node)
builder.add_node("writer", writer_node)
builder.add_conditional_edges("supervisor", route_to_agent)

When to Choose CrewAI

• Multi-agent systems — Native support, less boilerplate
• Role-based workflows — Researcher → Writer → Editor patterns
• Faster prototyping — Get something working quickly
• Less technical teams — More intuitive concepts
• Enterprise needs — CrewAI Enterprise has built-in observability

When to Choose LangGraph

• Complex control flow — Loops, conditionals, state machines
• LangChain ecosystem — Already using LangChain tools
• Fine-grained control — Need exact behavior specification
• Custom persistence — Specific checkpointing requirements
• TypeScript support — LangGraph has TS beta

The Cross-Framework Problem

Here's the thing both frameworks share: state doesn't transfer out.

If you build with CrewAI and later want to switch to LangGraph:

• Your workflows don't transfer
• Your state is locked in
• Your agent history is gone

This is why framework-agnostic infrastructure matters. Your workflows should be portable.

# Works with either framework
import agentmemo

# Store workflow (framework-agnostic)
agentmemo.workflows.create({
    "name": "customer-support",
    "definition": workflow_markdown,  # Plain text, not framework-specific
    "framework_hint": "crewai"  # Optional
})

# Later, read from LangGraph
workflow = agentmemo.workflows.get("customer-support")
# Works fine - it's just markdown

Recommendation

If you need...	Choose...
Quick multi-agent prototype	CrewAI
Complex state machine logic	LangGraph
Role-based agent teams	CrewAI
LangChain integration	LangGraph
Enterprise observability	Either (with respective tools)
TypeScript	LangGraph
Framework portability	Add AgentMemo to either