LangGraph에서 CrewAI로 옮기기: 엔지니어를 위한 실전 가이드

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

class ResearchState(TypedDict):
    topic: str
    raw_research: str
    summary: str
    formatted_output: str

def research_topic(state: ResearchState) -> dict:
    # Call an LLM or search API
    result = llm.invoke(f"Research the topic: {state['topic']}")
    return {"raw_research": result}

def write_summary(state: ResearchState) -> dict:
    result = llm.invoke(
        f"Summarize this research:\n{state['raw_research']}"
    )
    return {"summary": result}

def format_output(state: ResearchState) -> dict:
    result = llm.invoke(
        f"Format this summary as a polished article section:\n{state['summary']}"
    )
    return {"formatted_output": result}

# Build the graph
graph = StateGraph(ResearchState)
graph.add_node("research", research_topic)
graph.add_node("summarize", write_summary)
graph.add_node("format", format_output)

graph.add_edge(START, "research")
graph.add_edge("research", "summarize")
graph.add_edge("summarize", "format")
graph.add_edge("format", END)

# Compile and run
app = graph.compile()
result = app.invoke({"topic": "quantum computing advances in 2026"})
print(result["formatted_output"])

from crewai import LLM, Agent, Crew, Process, Task
from crewai.flow.flow import Flow, listen, start
from pydantic import BaseModel

llm = LLM(model="openai/gpt-5.2")

class ResearchState(BaseModel):
    topic: str = ""
    raw_research: str = ""
    summary: str = ""
    formatted_output: str = ""

class ResearchFlow(Flow[ResearchState]):
    @start()
    def research_topic(self):
        # Option 1: Direct LLM call
        result = llm.call(f"Research the topic: {self.state.topic}")
        self.state.raw_research = result
        return result

    @listen(research_topic)
    def write_summary(self, research_output):
        # Option 2: A single agent
        summarizer = Agent(
            role="Research Summarizer",
            goal="Produce concise, accurate summaries of research content",
            backstory="You are an expert at distilling complex research into clear, "
            "digestible summaries.",
            llm=llm,
            verbose=True,
        )
        result = summarizer.kickoff(
            f"Summarize this research:\n{self.state.raw_research}"
        )
        self.state.summary = str(result)
        return self.state.summary

    @listen(write_summary)
    def format_output(self, summary_output):
        # Option 3: a complete crew (with one or more agents)
        formatter = Agent(
            role="Content Formatter",
            goal="Transform research summaries into polished, publication-ready article sections",
            backstory="You are a skilled editor with expertise in structuring and "
            "presenting technical content for a general audience.",
            llm=llm,
            verbose=True,
        )
        format_task = Task(
            description=f"Format this summary as a polished article section:\n{self.state.summary}",
            expected_output="A well-structured, polished article section ready for publication.",
            agent=formatter,
        )
        crew = Crew(
            agents=[formatter],
            tasks=[format_task],
            process=Process.sequential,
            verbose=True,
        )
        result = crew.kickoff()
        self.state.formatted_output = str(result)
        return self.state.formatted_output

# Run the flow
flow = ResearchFlow()
flow.state.topic = "quantum computing advances in 2026"
result = flow.kickoff()
print(flow.state.formatted_output)

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

class ContentState(TypedDict):
    input_text: str
    content_type: str
    result: str

def classify_content(state: ContentState) -> dict:
    content_type = llm.invoke(
        f"Classify this content as 'technical', 'creative', or 'business':\n{state['input_text']}"
    )
    return {"content_type": content_type.strip().lower()}

def process_technical(state: ContentState) -> dict:
    result = llm.invoke(f"Process as technical doc:\n{state['input_text']}")
    return {"result": result}

def process_creative(state: ContentState) -> dict:
    result = llm.invoke(f"Process as creative writing:\n{state['input_text']}")
    return {"result": result}

def process_business(state: ContentState) -> dict:
    result = llm.invoke(f"Process as business content:\n{state['input_text']}")
    return {"result": result}

# Routing function
def route_content(state: ContentState) -> Literal["technical", "creative", "business"]:
    return state["content_type"]

# Build the graph
graph = StateGraph(ContentState)
graph.add_node("classify", classify_content)
graph.add_node("technical", process_technical)
graph.add_node("creative", process_creative)
graph.add_node("business", process_business)

graph.add_edge(START, "classify")
graph.add_conditional_edges(
    "classify",
    route_content,
    {
        "technical": "technical",
        "creative": "creative",
        "business": "business",
    }
)
graph.add_edge("technical", END)
graph.add_edge("creative", END)
graph.add_edge("business", END)

app = graph.compile()
result = app.invoke({"input_text": "Explain how TCP handshakes work"})

from crewai import LLM, Agent
from crewai.flow.flow import Flow, listen, router, start
from pydantic import BaseModel

llm = LLM(model="openai/gpt-5.2")

class ContentState(BaseModel):
    input_text: str = ""
    content_type: str = ""
    result: str = ""

class ContentFlow(Flow[ContentState]):
    @start()
    def classify_content(self):
        self.state.content_type = (
            llm.call(
                f"Classify this content as 'technical', 'creative', or 'business':\n"
                f"{self.state.input_text}"
            )
            .strip()
            .lower()
        )
        return self.state.content_type

    @router(classify_content)
    def route_content(self, classification):
        if classification == "technical":
            return "process_technical"
        elif classification == "creative":
            return "process_creative"
        else:
            return "process_business"

    @listen("process_technical")
    def handle_technical(self):
        agent = Agent(
            role="Technical Writer",
            goal="Produce clear, accurate technical documentation",
            backstory="You are an expert technical writer who specializes in "
            "explaining complex technical concepts precisely.",
            llm=llm,
            verbose=True,
        )
        self.state.result = str(
            agent.kickoff(f"Process as technical doc:\n{self.state.input_text}")
        )

    @listen("process_creative")
    def handle_creative(self):
        agent = Agent(
            role="Creative Writer",
            goal="Craft engaging and imaginative creative content",
            backstory="You are a talented creative writer with a flair for "
            "compelling storytelling and vivid expression.",
            llm=llm,
            verbose=True,
        )
        self.state.result = str(
            agent.kickoff(f"Process as creative writing:\n{self.state.input_text}")
        )

    @listen("process_business")
    def handle_business(self):
        agent = Agent(
            role="Business Writer",
            goal="Produce professional, results-oriented business content",
            backstory="You are an experienced business writer who communicates "
            "strategy and value clearly to professional audiences.",
            llm=llm,
            verbose=True,
        )
        self.state.result = str(
            agent.kickoff(f"Process as business content:\n{self.state.input_text}")
        )

flow = ContentFlow()
flow.state.input_text = "Explain how TCP handshakes work"
flow.kickoff()
print(flow.state.result)

from crewai import Agent, Task, Crew
from crewai.flow.flow import Flow, listen, start
from pydantic import BaseModel

class ArticleState(BaseModel):
    topic: str = ""
    research: str = ""
    draft: str = ""
    final_article: str = ""

class ArticleFlow(Flow[ArticleState]):

    @start()
    def run_research_crew(self):
        """A full Crew of agents handles research."""
        researcher = Agent(
            role="Senior Research Analyst",
            goal=f"Produce comprehensive research on: {self.state.topic}",
            backstory="You're a veteran analyst known for thorough, "
                       "well-sourced research reports.",
            llm="gpt-4o"
        )

        research_task = Task(
            description=f"Research '{self.state.topic}' thoroughly. "
                        "Cover key trends, data points, and expert opinions.",
            expected_output="A detailed research brief with sources.",
            agent=researcher
        )

        crew = Crew(agents=[researcher], tasks=[research_task])
        result = crew.kickoff()
        self.state.research = result.raw
        return result.raw

    @listen(run_research_crew)
    def run_writing_crew(self, research_output):
        """A different Crew handles writing."""
        writer = Agent(
            role="Technical Writer",
            goal="Write a compelling article based on provided research.",
            backstory="You turn complex research into engaging, clear prose.",
            llm="gpt-4o"
        )

        editor = Agent(
            role="Senior Editor",
            goal="Review and polish articles for publication quality.",
            backstory="20 years of editorial experience at top tech publications.",
            llm="gpt-4o"
        )

        write_task = Task(
            description=f"Write an article based on this research:\n{self.state.research}",
            expected_output="A well-structured draft article.",
            agent=writer
        )

        edit_task = Task(
            description="Review, fact-check, and polish the draft article.",
            expected_output="A publication-ready article.",
            agent=editor
        )

        crew = Crew(agents=[writer, editor], tasks=[write_task, edit_task])
        result = crew.kickoff()
        self.state.final_article = result.raw
        return result.raw

# Run the full pipeline
flow = ArticleFlow()
flow.state.topic = "The Future of Edge AI"
flow.kickoff()
print(flow.state.final_article)

from crewai import LLM
from crewai.flow.flow import Flow, and_, listen, start
from pydantic import BaseModel

llm = LLM(model="openai/gpt-5.2")

class AnalysisState(BaseModel):
    topic: str = ""
    market_data: str = ""
    tech_analysis: str = ""
    competitor_intel: str = ""
    final_report: str = ""

class ParallelAnalysisFlow(Flow[AnalysisState]):
    @start()
    def start_method(self):
        pass

    @listen(start_method)
    def gather_market_data(self):
        # Your agentic or deterministic code
        pass

    @listen(start_method)
    def run_tech_analysis(self):
        # Your agentic or deterministic code
        pass

    @listen(start_method)
    def gather_competitor_intel(self):
        # Your agentic or deterministic code
        pass

    @listen(and_(gather_market_data, run_tech_analysis, gather_competitor_intel))
    def synthesize_report(self):
        # Your agentic or deterministic code
        pass

flow = ParallelAnalysisFlow()
flow.state.topic = "AI-powered developer tools"
flow.kickoff()

pip install crewai
crewai create flow my_first_flow
cd my_first_flow

crewai run