crewai

What this skill does

# 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"