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Fast and Flexible Multi-Agent Automation FrameworkCrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely independent of LangChain or other agent frameworks. It empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario.
With over 100,000 developers certified through our community courses at learn.crewai.com, CrewAI is rapidly becoming the standard for enterprise-ready AI automation.
CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
You can try one part of the suite the Crew Control Plane for free
Crew Control Plane Key Features:CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient, intelligent automations.
CrewAI unlocks the true potential of multi-agent automation, delivering the best-in-class combination of speed, flexibility, and control with either Crews of AI Agents or Flows of Events:
CrewAI empowers developers and enterprises to confidently build intelligent automations, bridging the gap between simplicity, flexibility, and performance.
Setup and run your first CrewAI agents by following this tutorial.
Learning Resources
Learn CrewAI through our comprehensive courses:
CrewAI offers two powerful, complementary approaches that work seamlessly together to build sophisticated AI applications:
Crews: Teams of AI agents with true autonomy and agency, working together to accomplish complex tasks through role-based collaboration. Crews enable:
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:
To get started with CrewAI, follow these simple steps:
Ensure you have Python >=3.10 <3.14 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 DependenciesIf you encounter issues during installation or usage, here are some common solutions:
ModuleNotFoundError: No module named 'tiktoken'
pip install 'crewai[embeddings]'pip install 'crewai[tools]'Failed building wheel for tiktoken
pip install --upgrade pippip install tiktoken --prefer-binaryTo 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.
src/my_project/config/agents.yaml to define your agents.src/my_project/config/tasks.yaml to define your tasks.src/my_project/crew.py to add your own logic, tools, and specific arguments.src/my_project/main.py to add custom inputs for your agents and tasks..env file.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 from crewai.agents.agent_builder.base_agent import BaseAgent from typing import List @CrewBase class LatestAiDevelopmentCrew(): """LatestAiDevelopment crew""" agents: List[BaseAgent] tasks: List[Task] @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)
Before running your crew, make sure you have the following keys set as environment variables in your .env file:
OPENAI_API_KEY=sk-...SERPER_API_KEY=YOUR_KEY_HERELock 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.
CrewAI stands apart as a lean, standalone, high-performance multi-AI Agent framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
Choose CrewAI to easily build powerful, adaptable, and production-ready AI automations.
You can test different real life examples of AI crews in the CrewAI-examples repo:
Check out code for this example or watch a video below:
Check out code for this example or watch a video below:
Check out code for this example or watch a video below:
Using Crews and Flows TogetherCrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines. CrewAI flows support logical operators like or_ and and_ to combine multiple conditions. This can be used with @start, @listen, or @router decorators to create complex triggering conditions.
or_: Triggers when any of the specified conditions are met.and_Triggers when all of the specified conditions are met.Here's how you can orchestrate multiple Crews within a Flow:
from crewai.flow.flow import Flow, listen, start, router, or_
from crewai import Crew, Agent, Task, Process
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(or_("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:
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 your agents' connections to models.
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).
CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:
pip install dist/*.tar.gz
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.
CrewAI is released under the MIT License.
Frequently Asked Questions (FAQ)A: CrewAI is a standalone, lean, and fast Python framework built specifically for orchestrating autonomous AI agents. Unlike frameworks like LangChain, CrewAI does not rely on external dependencies, making it leaner, faster, and simpler.
Q: How do I install CrewAI?A: Install CrewAI using pip:
For additional tools, use:
pip install 'crewai[tools]'Q: Does CrewAI depend on LangChain?
A: No. CrewAI is built entirely from the ground up, with no dependencies on LangChain or other agent frameworks. This ensures a lean, fast, and flexible experience.
Q: Can CrewAI handle complex use cases?A: Yes. CrewAI excels at both simple and highly complex real-world scenarios, offering deep customization options at both high and low levels, from internal prompts to sophisticated workflow orchestration.
Q: Can I use CrewAI with local AI models?A: Absolutely! CrewAI supports various language models, including local ones. Tools like Ollama and LM Studio allow seamless integration. Check the LLM Connections documentation for more details.
Q: What makes Crews different from Flows?A: Crews provide autonomous agent collaboration, ideal for tasks requiring flexible decision-making and dynamic interaction. Flows offer precise, event-driven control, ideal for managing detailed execution paths and secure state management. You can seamlessly combine both for maximum effectiveness.
Q: How is CrewAI better than LangChain?A: CrewAI provides simpler, more intuitive APIs, faster execution speeds, more reliable and consistent results, robust documentation, and an active community—addressing common criticisms and limitations associated with LangChain.
Q: Is CrewAI open-source?A: Yes, CrewAI is open-source and actively encourages community contributions and collaboration.
Q: Does CrewAI collect data from users?A: CrewAI collects anonymous telemetry data strictly for improvement purposes. Sensitive data such as prompts, tasks, or API responses are never collected unless explicitly enabled by the user.
Q: Where can I find real-world CrewAI examples?A: Check out practical examples in the CrewAI-examples repository, covering use cases like trip planners, stock analysis, and job postings.
Q: How can I contribute to CrewAI?A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
Q: What additional features does CrewAI Enterprise offer?A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
Q: Is CrewAI Enterprise available for cloud and on-premise deployments?A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
Q: Can I try CrewAI Enterprise for free?A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the Crew Control Plane for free.
Q: Does CrewAI support fine-tuning or training custom models?A: Yes, CrewAI can integrate with custom-trained or fine-tuned models, allowing you to enhance your agents with domain-specific knowledge and accuracy.
Q: Can CrewAI agents interact with external tools and APIs?A: Absolutely! CrewAI agents can easily integrate with external tools, APIs, and databases, empowering them to leverage real-world data and resources.
Q: Is CrewAI suitable for production environments?A: Yes, CrewAI is explicitly designed with production-grade standards, ensuring reliability, stability, and scalability for enterprise deployments.
Q: How scalable is CrewAI?A: CrewAI is highly scalable, supporting simple automations and large-scale enterprise workflows involving numerous agents and complex tasks simultaneously.
Q: Does CrewAI offer debugging and monitoring tools?A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
Q: What programming languages does CrewAI support?A: CrewAI is primarily Python-based but easily integrates with services and APIs written in any programming language through its flexible API integration capabilities.
Q: Does CrewAI offer educational resources for beginners?A: Yes, CrewAI provides extensive beginner-friendly tutorials, courses, and documentation through learn.crewai.com, supporting developers at all skill levels.
Q: Can CrewAI automate human-in-the-loop workflows?A: Yes, CrewAI fully supports human-in-the-loop workflows, allowing seamless collaboration between human experts and AI agents for enhanced decision-making.
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