---
name: crewai-8e3a079c85ef
description: "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. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents."
source: vibeship-spawner-skills (Apache 2.0)
---

# 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`


---
Source attribution: Dicklesworthstone
Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/templates-davila7/cli-tool/components/skills/ai-research/crewai/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License (with OpenAI/Anthropic Rider)

Copyright (c) 2026 Jeffrey Emanuel

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

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This rider is part of the "conditions" of this License. In the event of any
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Notwithstanding any other provision of this License, no rights are granted to
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For purposes of this rider, "use" includes, without limitation: copying,
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This rider applies to the Software and all Derivative Works. As a condition of
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Any breach of this rider automatically and immediately terminates the
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: Dicklesworthstone
Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/templates-davila7/cli-tool/components/skills/ai-research/crewai/SKILL.md
Attribution basis: Source repository record

Fields of work: Development & Engineering, Product Management, Project & Program Management
Roles: Growth Product Manager, ML / AI Engineer, Product Manager, Project Manager, Technical Product Manager
Specialties: Specialty not specified
Tools: Crewai
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 222538f972a50da3eab2ffccd0597cf70bcdd7e28540ec698d2c8b4db50a4db5