AI Agents in Practice: LangGraph vs CrewAI

Building AI agents has quickly moved from toy demos to production-grade systems. Today, agents don’t just answer questions—they plan, delegate, coordinate, and recover from errors.

Two popular frameworks in this space are LangGraph and CrewAI. Both aim to simplify agent orchestration, but they take very different design paths.

In this post, we’ll:

• Understand what each framework is optimized for
• Compare their mental models
• Walk through simple code snippets
• End with a clear recommendation on when to choose which

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What Is an AI Agent (Quick Context)

An AI agent is typically:

• A reasoning LLM
• With tools (search, code, APIs)
• Some form of memory or state
• And a control flow (who does what, when)

The hard part isn’t calling an LLM — it’s managing flow, state, and reliability once things get complex.

That’s where LangGraph and CrewAI differ the most.

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LangGraph: State Machines for AI Agents

Mental Model

LangGraph treats an agent system as a graph of states and transitions.

Think:

• Nodes = functions (LLM calls, tools, routers)
• Edges = deterministic or conditional transitions
• State = explicit, typed, and persistent

If you like control, debuggability, and determinism, LangGraph will feel natural.

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Simple LangGraph Example

from langgraph.graph import StateGraph, END
from typing import TypedDict

class AgentState(TypedDict):
    input: str
    output: str

def llm_node(state: AgentState):
    # imagine this calls an LLM
    return {"output": f"Processed: {state['input']}"}

graph = StateGraph(AgentState)

graph.add_node("llm", llm_node)
graph.set_entry_point("llm")
graph.add_edge("llm", END)

app = graph.compile()

result = app.invoke({"input": "Explain AI agents"})
print(result["output"])

What Stands Out

• Explicit state passing
• Clear execution graph
• Easy to add branching, loops, retries
• Production-friendly (logging, tracing, checkpoints)

Downsides

• More boilerplate
• You must think in graphs
• Slower to prototype for simple tasks

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CrewAI: Role-Based Collaboration

Mental Model

CrewAI is inspired by human teams.

You define:

• Agents (roles + personality)
• Tasks
• A crew that executes tasks collaboratively

If LangGraph is a workflow engine, CrewAI is a manager of specialists.

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Simple CrewAI Example

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Researcher",
    goal="Find accurate information",
    backstory="Expert at online research"
)

writer = Agent(
    role="Writer",
    goal="Explain concepts clearly",
    backstory="Writes clear technical blogs"
)

task1 = Task(
    description="Research AI agents",
    agent=researcher
)

task2 = Task(
    description="Write a blog post using the research",
    agent=writer
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[task1, task2]
)

result = crew.kickoff()
print(result)

What Stands Out

• Very intuitive
• Minimal setup
• Great for content, research, and workflows
• Feels “alive” and collaborative

Downsides

• Less deterministic
• Harder to debug deeply
• Less control over execution order and state

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LangGraph vs CrewAI: Side-by-Side

Aspect	LangGraph	CrewAI
Core idea	Graph-based state machine	Role-based collaboration
Control	Very high	Medium
Ease of use	Medium–Hard	Easy
Debugging	Excellent	Moderate
Determinism	Strong	Weaker
Best for	Complex workflows, backend agents	Research, content, automation
Production readiness	High	Improving

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When Things Get Real (Important Insight)

Once your agent:

• Needs retries
• Must branch on LLM output
• Requires long-running state
• Or must be audited/debugged

👉 LangGraph scales better

But if your agent:

• Feels like a team of humans
• Focuses on creativity or research
• Needs fast iteration

👉 CrewAI shines

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Can You Combine Them?

Yes—and this is a powerful pattern.

• Use CrewAI for high-level creative or research tasks
• Embed those agents inside LangGraph nodes
• Let LangGraph handle control flow and failure recovery

This hybrid approach gives you flexibility + reliability.

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Final Recommendation

Choose LangGraph if:

• You are building backend or product-grade agents
• You care about state, retries, and observability
• You think in workflows and systems

Choose CrewAI if:

• You want fast results
• You’re building research, writing, or automation agents
• You prefer a human-like abstraction

My Honest Suggestion

Prototype with CrewAI. Productionize with LangGraph.

That combo gives you speed and safety — and saves you from painful rewrites later.

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