Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.

crewAIInc/crewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.

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Logo of CrewAI, two people rowing on a boat

CrewAI

🤖 CrewAI: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.

GitHub Repo stars License: MIT

Table of contents

Why CrewAI?

The power of AI collaboration has too much to offer. CrewAI is a standalone framework, built from the ground up without dependencies on Langchain or other agent frameworks. It's designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions.

Getting Started

Learning Resources

Learn CrewAI through our comprehensive courses:

Understanding Flows and Crews

CrewAI offers two powerful, complementary approaches that work seamlessly together to build sophisticated AI applications:

  1. Crews: Teams of AI agents with true autonomy and agency, working together to accomplish complex tasks through role-based collaboration. Crews enable:
  1. Flows: Production-ready, event-driven workflows that deliver precise control over complex automations. Flows provide:

The true power of CrewAI emerges when combining Crews and Flows. This synergy allows you to:

Getting Started with Installation

To get started with CrewAI, follow these simple steps:

1. Installation

Ensure you have Python >=3.10 <3.13 installed on your system. CrewAI uses UV for dependency management and package handling, offering a seamless setup and execution experience.

First, install CrewAI:

If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command:

pip install 'crewai[tools]'

The command above installs the basic package and also adds extra components which require more dependencies to function.

Troubleshooting Dependencies

If you encounter issues during installation or usage, here are some common solutions:

Common Issues

  1. ModuleNotFoundError: No module named 'tiktoken'
  1. Failed building wheel for tiktoken

2. Setting Up Your Crew with the YAML Configuration

To create a new CrewAI project, run the following CLI (Command Line Interface) command:

crewai create crew <project_name>

This command creates a new project folder with the following structure:

my_project/
├── .gitignore
├── pyproject.toml
├── README.md
├── .env
└── src/
    └── my_project/
        ├── __init__.py
        ├── main.py
        ├── crew.py
        ├── tools/
        │   ├── custom_tool.py
        │   └── __init__.py
        └── config/
            ├── agents.yaml
            └── tasks.yaml

You can now start developing your crew by editing the files in the src/my_project folder. The main.py file is the entry point of the project, the crew.py file is where you define your crew, the agents.yaml file is where you define your agents, and the tasks.yaml file is where you define your tasks.

To customize your project, you can:

Example of a simple crew with a sequential process:

Instantiate your crew:

crewai create crew latest-ai-development

Modify the files as needed to fit your use case:

agents.yaml

# src/my_project/config/agents.yaml
  researcher: 
  role: >
    {topic} Senior Data Researcher
  goal: >
    Uncover cutting-edge developments in {topic}
  backstory: >
    You're a seasoned researcher with a knack for uncovering the latest
    developments in {topic}. Known for your ability to find the most relevant
    information and present it in a clear and concise manner.

  reporting_analyst: 
  role: >
    {topic} Reporting Analyst
  goal: >
    Create detailed reports based on {topic} data analysis and research findings
  backstory: >
    You're a meticulous analyst with a keen eye for detail. You're known for
    your ability to turn complex data into clear and concise reports, making
    it easy for others to understand and act on the information you provide.

tasks.yaml

# src/my_project/config/tasks.yaml
  research_task: 
  description: >
    Conduct a thorough research about {topic}
    Make sure you find any interesting and relevant information given
    the current year is 2025.
  expected_output: >
    A list with 10 bullet points of the most relevant information about {topic}
  agent: researcher

  reporting_task: 
  description: >
    Review the context you got and expand each topic into a full section for a report.
    Make sure the report is detailed and contains any and all relevant information.
  expected_output: >
    A fully fledge reports with the mains topics, each with a full section of information.
    Formatted as markdown without '\`\`\`'
  agent: reporting_analyst
  output_file: report.md

crew.py

# src/my_project/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool

@CrewBase
class LatestAiDevelopmentCrew():
	"""LatestAiDevelopment crew"""

	@agent
	def researcher(self) -> Agent:
		return Agent(
			config=self.agents_config['researcher'],
			verbose=True,
			tools=[SerperDevTool()]
		)

	@agent
	def reporting_analyst(self) -> Agent:
		return Agent(
			config=self.agents_config['reporting_analyst'],
			verbose=True
		)

	@task
	def research_task(self) -> Task:
		return Task(
			config=self.tasks_config['research_task'],
		)

	@task
	def reporting_task(self) -> Task:
		return Task(
			config=self.tasks_config['reporting_task'],
			output_file='report.md'
		)

	@crew
	def crew(self) -> Crew:
		"""Creates the LatestAiDevelopment crew"""
		return Crew(
			agents=self.agents, # Automatically created by the @agent decorator
			tasks=self.tasks, # Automatically created by the @task decorator
			process=Process.sequential,
			verbose=True,
		)

main.py

#!/usr/bin/env python
# src/my_project/main.py
import sys
from latest_ai_development.crew import LatestAiDevelopmentCrew

def run():
    """
    Run the crew.
    """
    inputs = {
        'topic': 'AI Agents'
    }
    LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)

3. Running Your Crew

Before running your crew, make sure you have the following keys set as environment variables in your .env file:

Lock the dependencies and install them by using the CLI command but first, navigate to your project directory:

cd my_project
crewai install (Optional)

To run your crew, execute the following command in the root of your project:

or

python src/my_project/main.py

If an error happens due to the usage of poetry, please run the following command to update your crewai package:

You should see the output in the console and the report.md file should be created in the root of your project with the full final report.

In addition to the sequential process, you can use the hierarchical process, which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. See more about the processes here.

Key Features

Note: CrewAI is a standalone framework built from the ground up, without dependencies on Langchain or other agent frameworks.

CrewAI Mind Map

Examples

You can test different real life examples of AI crews in the CrewAI-examples repo:

Quick Tutorial

CrewAI Tutorial

Write Job Descriptions

Check out code for this example or watch a video below:

Jobs postings

Trip Planner

Check out code for this example or watch a video below:

Trip Planner

Stock Analysis

Check out code for this example or watch a video below:

Stock Analysis

Using Crews and Flows Together

CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines. Here's how you can orchestrate multiple Crews within a Flow:

from crewai.flow.flow import Flow, listen, start, router
from crewai import Crew, Agent, Task
from pydantic import BaseModel

# Define structured state for precise control
class MarketState(BaseModel):
  sentiment: str = "neutral"
  confidence: float = 0.0
  recommendations: list = []

class AdvancedAnalysisFlow(Flow[MarketState]):
    @start()
    def fetch_market_data(self):
        # Demonstrate low-level control with structured state
        self.state.sentiment = "analyzing"
        return {"sector": "tech", "timeframe": "1W"}  # These parameters match the task description template

    @listen(fetch_market_data)
    def analyze_with_crew(self, market_data):
        # Show crew agency through specialized roles
        analyst = Agent(
            role="Senior Market Analyst",
            goal="Conduct deep market analysis with expert insight",
            backstory="You're a veteran analyst known for identifying subtle market patterns"
        )
        researcher = Agent(
            role="Data Researcher",
            goal="Gather and validate supporting market data",
            backstory="You excel at finding and correlating multiple data sources"
        )
        
        analysis_task = Task(
            description="Analyze {sector} sector data for the past {timeframe}",
            expected_output="Detailed market analysis with confidence score",
            agent=analyst
        )
        research_task = Task(
            description="Find supporting data to validate the analysis",
            expected_output="Corroborating evidence and potential contradictions",
            agent=researcher
        )
        
        # Demonstrate crew autonomy
        analysis_crew = Crew(
            agents=[analyst, researcher],
            tasks=[analysis_task, research_task],
            process=Process.sequential,
            verbose=True
        )
        return analysis_crew.kickoff(inputs=market_data)  # Pass market_data as named inputs

    @router(analyze_with_crew)
    def determine_next_steps(self):
        # Show flow control with conditional routing
        if self.state.confidence > 0.8:
            return "high_confidence"
        elif self.state.confidence > 0.5:
            return "medium_confidence"
        return "low_confidence"

    @listen("high_confidence")
    def execute_strategy(self):
        # Demonstrate complex decision making
        strategy_crew = Crew(
            agents=[
                Agent(role="Strategy Expert",
                      goal="Develop optimal market strategy")
            ],
            tasks=[
                Task(description="Create detailed strategy based on analysis",
                     expected_output="Step-by-step action plan")
            ]
        )
        return strategy_crew.kickoff()

    @listen("medium_confidence", "low_confidence")
    def request_additional_analysis(self):
        self.state.recommendations.append("Gather more data")
        return "Additional analysis required"

This example demonstrates how to:

  1. Use Python code for basic data operations
  2. Create and execute Crews as steps in your workflow
  3. Use Flow decorators to manage the sequence of operations
  4. Implement conditional branching based on Crew results

Connecting Your Crew to a Model

CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.

Please refer to the Connect CrewAI to LLMs page for details on configuring you agents' connections to models.

How CrewAI Compares

CrewAI's Advantage: CrewAI combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control.

P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example (see comparison) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example (detailed analysis).

Contribution

CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:

Installing Dependencies

Virtual Env

Pre-commit hooks

Running Tests

Running static type checks

Packaging

Installing Locally

pip install dist/*.tar.gz

Telemetry

CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.

It's pivotal to understand that NO data is collected concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the share_crew feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. Users can disable telemetry by setting the environment variable OTEL_SDK_DISABLED to true.

Data collected includes:

Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the share_crew attribute to True on their Crews. Enabling share_crew results in the collection of detailed crew and task execution data, including goal, backstory, context, and output of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.

License

CrewAI is released under the MIT License.

Frequently Asked Questions (FAQ)

Q: What is CrewAI?

A: CrewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. It enables agents to work together seamlessly, tackling complex tasks through collaborative intelligence.

Q: How do I install CrewAI?

A: You can install CrewAI using pip:

For additional tools, use:

pip install 'crewai[tools]'

Q: Can I use CrewAI with local models?

A: Yes, CrewAI supports various LLMs, including local models. You can configure your agents to use local models via tools like Ollama & LM Studio. Check the LLM Connections documentation for more details.

Q: What are the key features of CrewAI?

A: Key features include role-based agent design, autonomous inter-agent delegation, flexible task management, process-driven execution, output saving as files, and compatibility with both open-source and proprietary models.

Q: How does CrewAI compare to other AI orchestration tools?

A: CrewAI is designed with production in mind, offering flexibility similar to Autogen's conversational agents and structured processes like ChatDev, but with more adaptability for real-world applications.

Q: Is CrewAI open-source?

A: Yes, CrewAI is open-source and welcomes contributions from the community.

Q: Does CrewAI collect any data?

A: CrewAI uses anonymous telemetry to collect usage data for improvement purposes. No sensitive data (like prompts, task descriptions, or API calls) is collected. Users can opt-in to share more detailed data by setting share_crew=True on their Crews.

Q: Where can I find examples of CrewAI in action?

A: You can find various real-life examples in the CrewAI-examples repository, including trip planners, stock analysis tools, and more.

Q: What is the difference between Crews and Flows?

A: Crews and Flows serve different but complementary purposes in CrewAI. Crews are teams of AI agents working together to accomplish specific tasks through role-based collaboration, delivering accurate and predictable results. Flows, on the other hand, are event-driven workflows that can orchestrate both Crews and regular Python code, allowing you to build complex automation pipelines with secure state management and conditional execution paths.

Q: How can I contribute to CrewAI?

A: Contributions are welcome! You can fork the repository, create a new branch for your feature, add your improvement, and send a pull request. Check the Contribution section in the README for more details.

Summary:

Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. - crewAIInc/crewAI

Source: crewAIInc/crewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.