Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

CrewAI Expert in CrewAI the leading role based multi agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. 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. Expertise Agent persona design Task decomposition Crew orchestration Process selection Memory configuration Flow design Capabilities Agent definitions (role, goal, backstory) Task design and dependencies Crew orchestration Process types (sequential, hierarchical) Memory configuration Tool integration Flows for complex workflows Prerequisites 0: Python proficiency 1: Multi agent concepts 2: Understanding of delegation Required skills: Python 3.10+, crewai package, LLM API access Scope 0: Python only 1: Best for structured workflows 2: Can be verbose for simple cases 3: Flows are newer feature Ecosystem Primary CrewAI framework CrewAI Tools Common integrations OpenAI / Anthropic / Ollama SerperDev (search) FileReadTool, DirectoryReadTool Custom tools Platforms Python applications FastAPI backends Enterprise deployments Patterns Basic Crew with YAML Config Define agents and tasks in YAML (recommended) When to use : Any CrewAI project 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=self.tasks config['writing task']) @crew def crew(self) Crew: return Crew( agents=self.agents, tasks=self.tasks, process=Process.sequential, verbose=True ) main.py crew = ContentCrew() result = crew.crew().kickoff(inputs={"topic": "AI Agents in 2025"}) Hierarchical Process Manager agent delegates to workers When to use : Complex tasks needing coordination 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 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) Memory Configuration Enable agent memory for context When to use : Multi turn or complex workflows from crewai import Crew Memory types: Short term: Within task execution Long term: Across executions Entity: About specific entities crew = Crew( agents=[...], tasks=[...], memory=True, Enable all memory types verbose=True ) Custom memory config from crewai.memory import LongTermMemory, ShortTermMemory crew = Crew( agents=[...], tasks=[...], memory=True, long term memory=LongTermMemory( storage=CustomStorage() Custom backend ), short term memory=ShortTermMemory( storage=CustomStorage() ), embedder={ "provider": "openai", "config": {"model": "text embedding 3 small"} } ) Memory helps agents: Remember previous interactions Build on past work Maintain consistency Flows for Complex Workflows Event driven orchestration with state When to use : Complex, multi stage workflows from crewai.flow.flow import Flow, listen, start, and , or , router class ContentFlow(Flow): State persists across steps model config = {"extra": "allow"} @start() def gather requirements(self): """First step gather inputs.""" self.topic = self.inputs.get("topic", "AI") self.style = self.inputs.get("style", "professional") return {"topic": self.topic} @listen(gather requirements) def research(self, requirements): """Research after requirements gathered.""" research crew = ResearchCrew() result = research crew.crew().kickoff( inputs={"topic": requirements["topic"]} ) self.research = result.raw return result @listen(research) def write content(self, research result): """Write after research complete.""" writing crew = WritingCrew() result = writing crew.crew().kickoff( inputs={ "research": self.research, "style": self.style } ) return result @router(write content) def quality check(self, content): """Route based on quality.""" if self.needs revision(content): return "revise" return "publish" @listen("revise") def revise content(self): """Revision flow.""" Re run writing with feedback pass @listen("publish") def publish content(self): """Final publishing.""" return {"status": "published", "content": self.content} Run flow flow = ContentFlow() result = flow.kickoff(inputs={"topic": "AI Agents"}) Custom Tools Create tools for agents When to use : Agents need external capabilities from crewai.tools import BaseTool from pydantic import BaseModel, Field Method 1: Class based tool class SearchInput(BaseModel): query: str = Field(..., description="Search query") class WebSearchTool(BaseTool): name: str = "web search" description: str = "Search the web for information" args schema: type[BaseModel] = SearchInput def run(self, query: str) str: Implementation results = search api.search(query) return format results(results) Method 2: Function decorator from crewai import tool @tool("Database Query") def query database(sql: str) str: """Execute SQL query and return results.""" return db.execute(sql) Assign tools to agents researcher = Agent( role="Researcher", goal="Find information", backstory="...", tools=[WebSearchTool(), query database] ) Collaboration Delegation Triggers langgraph state machine graph langgraph (Need explicit state management) observability tracing langfuse (Need LLM observability) structured output json schema structured output (Need structured responses) Research and Writing Crew Skills: crewai, structured output Workflow: Observable Agent Team Skills: crewai, langfuse Workflow: Complex Workflow with Flows Skills: crewai, langgraph Workflow: Related Skills Works well with: langgraph , autonomous agents , langfuse , structured output When to Use User mentions or implies: crewai User mentions or implies: multi agent team User mentions or implies: agent roles User mentions or implies: crew of agents User mentions or implies: role based agents User mentions or implies: collaborative agents Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.