تقييم حالات الاستخدام لـ CrewAI

# Example: Research Crew for market analysis
from crewai import Agent, Crew, Process, Task

# Create specialized agents
researcher = Agent(
    role="Market Research Specialist",
    goal="Find comprehensive market data on emerging technologies",
    backstory="You are an expert at discovering market trends and gathering data."
)

analyst = Agent(
    role="Market Analyst",
    goal="Analyze market data and identify key opportunities",
    backstory="You excel at interpreting market data and spotting valuable insights."
)

# Define their tasks
research_task = Task(
    description="Research the current market landscape for AI-powered healthcare solutions",
    expected_output="Comprehensive market data including key players, market size, and growth trends",
    agent=researcher
)

analysis_task = Task(
    description="Analyze the market data and identify the top 3 investment opportunities",
    expected_output="Analysis report with 3 recommended investment opportunities and rationale",
    agent=analyst,
    context=[research_task]
)

# Create the crew
market_analysis_crew = Crew(
    agents=[researcher, analyst],
    tasks=[research_task, analysis_task],
    process=Process.sequential,
    verbose=True
)

# Run the crew
result = market_analysis_crew.kickoff()

# Example: Customer Support Flow with structured processing
from crewai.flow.flow import Flow, listen, router, start
from pydantic import BaseModel
from typing import List, Dict

# Define structured state
class SupportTicketState(BaseModel):
    ticket_id: str = ""
    customer_name: str = ""
    issue_description: str = ""
    category: str = ""
    priority: str = "medium"
    resolution: str = ""
    satisfaction_score: int = 0

class CustomerSupportFlow(Flow[SupportTicketState]):
    @start()
    def receive_ticket(self):
        self.state.ticket_id = "TKT-12345"
        self.state.customer_name = "Alex Johnson"
        self.state.issue_description = "Unable to access premium features after payment"
        return "Ticket received"

    @listen(receive_ticket)
    def categorize_ticket(self, _):
        from crewai import LLM
        llm = LLM(model="openai/gpt-4o-mini")

        prompt = f"""
        Categorize the following customer support issue into one of these categories:
        - Billing
        - Account Access
        - Technical Issue
        - Feature Request
        - Other

        Issue: {self.state.issue_description}

        Return only the category name.
        """

        self.state.category = llm.call(prompt).strip()
        return self.state.category

    @router(categorize_ticket)
    def route_by_category(self, category):
        return category.lower().replace(" ", "_")

    @listen("billing")
    def handle_billing_issue(self):
        self.state.priority = "high"
        return "Billing issue handled"

    @listen("account_access")
    def handle_access_issue(self):
        self.state.priority = "high"
        return "Access issue handled"

    @listen("billing", "account_access", "technical_issue", "feature_request", "other")
    def resolve_ticket(self, resolution_info):
        self.state.resolution = f"Issue resolved: {resolution_info}"
        return self.state.resolution

# Run the flow
support_flow = CustomerSupportFlow()
result = support_flow.kickoff()

# Example: Content Production Pipeline combining Crews and Flows
from crewai.flow.flow import Flow, listen, start
from crewai import Agent, Crew, Process, Task
from pydantic import BaseModel
from typing import List, Dict

class ContentState(BaseModel):
    topic: str = ""
    target_audience: str = ""
    content_type: str = ""
    outline: Dict = {}
    draft_content: str = ""
    final_content: str = ""
    seo_score: int = 0

class ContentProductionFlow(Flow[ContentState]):
    @start()
    def initialize_project(self):
        self.state.topic = "Sustainable Investing"
        self.state.target_audience = "Millennial Investors"
        self.state.content_type = "Blog Post"
        return "Project initialized"

    @listen(initialize_project)
    def create_outline(self, _):
        researcher = Agent(
            role="Content Researcher",
            goal=f"Research {self.state.topic} for {self.state.target_audience}",
            backstory="You are an expert researcher with deep knowledge of content creation."
        )

        outliner = Agent(
            role="Content Strategist",
            goal=f"Create an engaging outline for a {self.state.content_type}",
            backstory="You excel at structuring content for maximum engagement."
        )

        research_task = Task(
            description=f"Research {self.state.topic} focusing on what would interest {self.state.target_audience}",
            expected_output="Comprehensive research notes with key points and statistics",
            agent=researcher
        )

        outline_task = Task(
            description=f"Create an outline for a {self.state.content_type} about {self.state.topic}",
            expected_output="Detailed content outline with sections and key points",
            agent=outliner,
            context=[research_task]
        )

        outline_crew = Crew(
            agents=[researcher, outliner],
            tasks=[research_task, outline_task],
            process=Process.sequential,
            verbose=True
        )

        result = outline_crew.kickoff()

        import json
        try:
            self.state.outline = json.loads(result.raw)
        except:
            self.state.outline = {"sections": result.raw}

        return "Outline created"

    @listen(create_outline)
    def write_content(self, _):
        writer = Agent(
            role="Content Writer",
            goal=f"Write engaging content for {self.state.target_audience}",
            backstory="You are a skilled writer who creates compelling content."
        )

        editor = Agent(
            role="Content Editor",
            goal="Ensure content is polished, accurate, and engaging",
            backstory="You have a keen eye for detail and a talent for improving content."
        )

        writing_task = Task(
            description=f"Write a {self.state.content_type} about {self.state.topic} following this outline: {self.state.outline}",
            expected_output="Complete draft content in markdown format",
            agent=writer
        )

        editing_task = Task(
            description="Edit and improve the draft content for clarity, engagement, and accuracy",
            expected_output="Polished final content in markdown format",
            agent=editor,
            context=[writing_task]
        )

        writing_crew = Crew(
            agents=[writer, editor],
            tasks=[writing_task, editing_task],
            process=Process.sequential,
            verbose=True
        )

        result = writing_crew.kickoff()
        self.state.final_content = result.raw

        return "Content created"

    @listen(write_content)
    def optimize_for_seo(self, _):
        from crewai import LLM
        llm = LLM(model="openai/gpt-4o-mini")

        prompt = f"""
        Analyze this content for SEO effectiveness for the keyword "{self.state.topic}".
        Rate it on a scale of 1-100 and provide 3 specific recommendations for improvement.

        Content: {self.state.final_content[:1000]}... (truncated for brevity)

        Format your response as JSON with the following structure:
        {{
            "score": 85,
            "recommendations": [
                "Recommendation 1",
                "Recommendation 2",
                "Recommendation 3"
            ]
        }}
        """

        seo_analysis = llm.call(prompt)

        import json
        try:
            analysis = json.loads(seo_analysis)
            self.state.seo_score = analysis.get("score", 0)
            return analysis
        except:
            self.state.seo_score = 50
            return {"score": 50, "recommendations": ["Unable to parse SEO analysis"]}

# Run the flow
content_flow = ContentProductionFlow()
result = content_flow.kickoff()