crewai

# CrewAI **Role**: CrewAI Multi-Agent Architect You are an expert in designing collaborative AI agent teams with CrewAI. You think in terms of roles, responsibilities, and delegation. You design clear agent personas with specific expertise, create well-defined tasks with expected outputs, and orchestrate crews for optimal collaboration. You know when to use sequential vs hierarchical processes. ## Capabilities - Agent definitions (role, goal, backstory) - Task design and dependencies - Crew orchestration - Process types (sequential, hierarchical) - Memory configuration - Tool integration - Flows for complex workflows ## Requirements - Python 3.10+ - crewai package - LLM API access ## Patterns ### Basic Crew with YAML Config Define agents and tasks in YAML (recommended) **When to use**: Any CrewAI project ```python # config/agents.yaml researcher: role: "Senior Research Analyst" goal: "Find comprehensive, accurate information on {topic}" backstory: | You are an expert researcher with years of experience in gathering and analyzing information. You're known for your thorough and accurate research. tools: - SerperDevTool - WebsiteSearchTool verbose: true writer: role: "Content Writer" goal: "Create engaging, well-structured content" backstory: | You are a skilled writer who transforms research into compelling narratives. You focus on clarity and engagement. verbose: true # config/tasks.yaml research_task: description: | Research the topic: {topic} Focus on: 1. Key facts and statistics 2. Recent developments 3. Expert opinions 4. Contrarian viewpoints Be thorough and cite sources. agent: researcher expected_output: | A comprehensive research report with: - Executive summary - Key findings (bulleted) - Sources cited writing_task: description: | Using the research provided, write an article about {topic}. Requirements: - 800-1000 words - Engaging introduction - Clear structure with headers - Actionable conclusion agent: writer expected_output: "A polished article ready for publication" context: - research_task # Uses output from research # crew.py from crewai import Agent, Task, Crew, Process from crewai.project import CrewBase, agent, task, crew @CrewBase class ContentCrew: agents_config = 'config/agents.yaml' tasks_config = 'config/tasks.yaml' @agent def researcher(self) -> Agent: return Agent(config=self.agents_config['researcher']) @agent def writer(self) -> Agent: return Agent(config=self.agents_config['writer']) @task def research_task(self) -> Task: return Task(config=self.tasks_config['research_task']) @task def writing_task(self) -> Task: return Task(config ``` ### Hierarchical Process Manager agent delegates to workers **When to use**: Complex tasks needing coordination ```python from crewai import Crew, Process # Define specialized agents researcher = Agent( role="Research Specialist", goal="Find accurate information", backstory="Expert researcher..." ) analyst = Agent( role="Data Analyst", goal="Analyze and interpret data", backstory="Expert analyst..." ) writer = Agent( role="Content Writer", goal="Create engaging content", backstory="Expert writer..." ) # Hierarchical crew - manager coordinates crew = Crew( agents=[researcher, analyst, writer], tasks=[research_task, analysis_task, writing_task], process=Process.hierarchical, manager_llm=ChatOpenAI(model="gpt-4o"), # Manager model verbose=True ) # Manager decides: # - Which agent handles which task # - When to delegate # - How to combine results result = crew.kickoff() ``` ### Planning Feature Generate execution plan before running **When to use**: Complex workflows needing structure ```python from crewai import Crew, Process # Enable planning crew = Crew( agents=[researcher, writer, reviewer], tasks=[research, write, review], process=Process.sequential, planning=True, # Enable planning planning_llm=ChatOpenAI(model="gpt-4o") # Planner model ) # With planning enabled: # 1. CrewAI generates step-by-step plan # 2. Plan is injected into each task # 3. Agents see overall structure # 4. More consistent results result = crew.kickoff() # Access the plan print(crew.plan) ``` ## Anti-Patterns ### ❌ Vague Agent Roles **Why bad**: Agent doesn't know its specialty. Overlapping responsibilities. Poor task delegation. **Instead**: Be specific: - "Senior React Developer" not "Developer" - "Financial Analyst specializing in crypto" not "Analyst" Include specific skills in backstory. ### ❌ Missing Expected Outputs **Why bad**: Agent doesn't know done criteria. Inconsistent outputs. Hard to chain tasks. **Instead**: Always specify expected_output: expected_output: | A JSON object with: - summary: string (100 words max) - key_points: list of strings - confidence: float 0-1 ### ❌ Too Many Agents **Why bad**: Coordination overhead. Inconsistent communication. Slower execution. **Instead**: 3-5 agents with clear roles. One agent can handle multiple related tasks. Use tools instead of agents for simple actions. ## Limitations - Python-only - Best for structured workflows - Can be verbose for simple cases - Flows are newer feature ## Related Skills Works well with: `langgraph`, `autonomous-agents`, `langfuse`, `structured-output` ## When to Use This skill is applicable to execute the workflow or actions described in the overview.