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Fast and Flexible Multi-Agent Automation Framework
CrewAI is an open-source Python framework with high-level abstractions and low-level APIs for building production-ready multi-agent workflows.
It gives developers autonomous agent collaboration through Crews and precise, event-driven control through Flows.
- CrewAI Crews: Optimize for autonomy and collaborative intelligence with role-based AI agents.
- CrewAI Flows: Build event-driven automations that combine precise workflow control, single LLM calls, and native support for Crews.
With over 100,000 developers certified through our community courses at learn.crewai.com, CrewAI is rapidly becoming the
standard for production-ready agentic automation.
CrewAI AMP Suite
For organizations that need a commercial control plane around CrewAI, CrewAI AMP Suite adds managed deployment, observability, governance, security, and enterprise support.
You can try one part of the suite, the Crew Control Plane, for free.
Crew Control Plane Key Features:
- Tracing & Observability: Monitor and track your AI agents and workflows in real-time, including metrics, logs, and traces.
- Unified Control Plane: A centralized platform for managing, monitoring, and scaling your AI agents and workflows.
- Seamless Integrations: Easily connect with existing enterprise systems, data sources, and cloud infrastructure.
- Advanced Security: Built-in robust security and compliance measures ensuring safe deployment and management.
- Actionable Insights: Real-time analytics and reporting to optimize performance and decision-making.
- 24/7 Support: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
- On-premise and Cloud Deployment Options: Deploy CrewAI AMP on-premise or in the cloud, depending on your security and compliance requirements.
CrewAI AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
intelligent automations.
Table of contents
Build with AI
Using an AI coding agent? Teach it CrewAI best practices in one command:
Claude Code:
/plugin marketplace add crewAIInc/skills
/plugin install crewai-skills@crewai-plugins
/reload-plugins
Four skills that activate automatically when you ask relevant CrewAI questions:
| Skill |
When it runs |
getting-started |
Scaffolding new projects, choosing between LLM.call() / Agent / Crew / Flow, wiring crew.jsonc / main.py |
design-agent |
Configuring agents — role, goal, backstory, tools, LLMs, memory, guardrails |
design-task |
Writing task descriptions, dependencies, structured output (output_pydantic, output_json), human review |
ask-docs |
Querying the live CrewAI docs MCP server for up-to-date API details |
Cursor, Codex, Windsurf, and others (skills.sh):
npx skills add crewaiinc/skills
This installs the official CrewAI Skills — structured instructions that teach coding agents how to scaffold Flows, configure Crews, design agents and tasks, and follow CrewAI patterns.
Why CrewAI?
CrewAI unlocks the true potential of multi-agent automation, delivering speed, flexibility, and control through Crews of AI agents and event-driven Flows:
- Purpose-built architecture: Designed specifically for agent orchestration, with a lightweight Python core and clean primitives for real-world automation.
- High Performance: Optimized for speed and minimal resource usage, enabling faster execution.
- Flexible Low-Level Customization: Complete freedom to customize everything from workflows and system architecture to agent behaviors, internal prompts, and execution logic.
- Ideal for Every Use Case: Proven effective for simple tasks, complex workflows, and production-grade automation.
- Robust Community: Backed by a rapidly growing community of over 100,000 certified developers offering comprehensive support and resources.
CrewAI empowers developers and teams to build intelligent automations that balance simplicity, flexibility, and production-grade control.
Getting Started
Setup and run your first CrewAI agents by following this tutorial.
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:
Crews: Teams of AI agents with true autonomy and agency, working together to accomplish complex tasks through role-based collaboration. Crews enable:
- Natural, autonomous decision-making between agents
- Dynamic task delegation and collaboration
- Specialized roles with defined goals and expertise
- Flexible problem-solving approaches
Flows: Production-ready, event-driven workflows that deliver precise control over complex automations. Flows provide:
- Fine-grained control over execution paths for real-world scenarios
- Secure, consistent state management between tasks
- Clean integration of AI agents with production Python code
- Conditional branching for complex business logic
The true power of CrewAI emerges when combining Crews and Flows. This synergy allows you to:
- Build complex, production-grade applications
- Balance autonomy with precise control
- Handle sophisticated real-world scenarios
- Maintain clean, maintainable code structure
Getting Started with Installation
To get started with CrewAI, follow these simple steps. The full walkthrough lives in the installation guide.
1. Installation
CrewAI requires Python >=3.10 and <3.14. Check your version with:
python3 --version
CrewAI uses UV for dependency management and package handling. If you haven't installed uv yet, install it first.
macOS/Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
If your system doesn't have curl, you can use wget:
wget -qO- https://astral.sh/uv/install.sh | sh
Windows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
If you run into any issues, refer to UV's installation guide.
Then install the CrewAI CLI:
uv tool install crewai
If you encounter a PATH warning, run:
uv tool update-shell
If you encounter the chroma-hnswlib==0.7.6 build error (fatal error C1083: Cannot open include file: 'float.h') on Windows, install Visual Studio Build Tools with Desktop development with C++.
Verify the install:
uv tool list
You should see something like:
crewai v0.102.0
- crewai
To upgrade the global CLI later:
uv tool install crewai --upgrade
This upgrades the global crewai CLI tool only. To upgrade the crewai version inside a project's virtual environment, see Upgrading CrewAI in a project.
2. Setting Up Your Crew
crewai create crew creates a JSON-first crew project. Agents live in agents/*.jsonc, tasks and crew-level settings live in crew.jsonc, and crewai run loads that JSON definition directly.
crewai create crew <project_name>
This command creates a new project folder with the following structure:
my_project/
├── .gitignore
├── .env
├── agents/
│ └── researcher.jsonc
├── crew.jsonc
├── knowledge/
├── pyproject.toml
├── README.md
├── skills/
└── tools/
If you need the older Python/YAML scaffold with crew.py, config/agents.yaml, and config/tasks.yaml, run:
crewai create crew <project_name> --classic
See Using Annotations for the classic pattern.
To customize your project, you can:
- Modify
agents/*.jsonc to define each agent's role, goal, backstory, LLM, tools, and behavior.
- Modify
crew.jsonc to define tasks, process, and input defaults.
- Add custom tools in
tools/ and reference them as "custom:<name>".
- Add optional knowledge files in
knowledge/ and skill files in skills/.
- Add your environment variables into the
.env file.
Use {placeholder} values in agent and task text, then set defaults in crew.jsonc under inputs. When you run crewai run, the CLI prompts for any missing values.
Example of a simple crew with a sequential process:
crewai create crew latest-ai-development
cd latest_ai_development
Then edit the generated files:
agents/researcher.jsonc
{
"role": "{topic} Senior Data Researcher",
"goal": "Uncover cutting-edge developments in {topic}",
"backstory": "You're a seasoned researcher who finds relevant information and presents it clearly.",
"llm": "openai/gpt-4o",
"tools": ["SerperDevTool"],
"settings": {
"verbose": true
}
}
agents/reporting_analyst.jsonc
{
"role": "{topic} Reporting Analyst",
"goal": "Create detailed reports based on {topic} data analysis and research findings",
"backstory": "You're a meticulous analyst who turns complex data into clear, concise reports.",
"llm": "openai/gpt-4o",
"settings": {
"verbose": true
}
}
crew.jsonc
{
"name": "Latest AI Development",
"agents": ["researcher", "reporting_analyst"],
"tasks": [
{
"name": "research_task",
"description": "Conduct thorough research about {topic}. Find recent, relevant information.",
"expected_output": "A list with 10 bullet points of the most relevant information about {topic}.",
"agent": "researcher"
},
{
"name": "reporting_task",
"description": "Review the research and expand each topic into a full section for a report.",
"expected_output": "A markdown report with the main topics, each with a full section of information. No fenced code blocks around the whole document.",
"agent": "reporting_analyst",
"context": ["research_task"],
"output_file": "output/report.md",
"markdown": true
}
],
"process": "sequential",
"verbose": true,
"inputs": {
"topic": "AI Agents"
}
}
3. Running Your Crew
Before running your crew, set the required keys in your .env file:
- Your model provider API key — see LLM setup
- A Serper.dev API key if you use web search:
SERPER_API_KEY=YOUR_KEY_HERE
Then install dependencies and run from the project directory:
crewai install
crewai run
If you need additional packages, use uv add <package-name>.
You should see the output in the console, and output/report.md should be created in the project root.
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.
For a Flow-first walkthrough, see the Quickstart.
Key Features
CrewAI gives developers a practical foundation for building agentic systems that move from prototype to production: autonomous collaboration where it helps, explicit workflow control where it matters, and Python-native customization throughout.
- Crews for autonomy: Model teams of specialized AI agents with roles, goals, tools, and tasks.
- Flows for control: Build event-driven workflows with state, branching, routing, and production logic.
- Seamless integration: Combine Crews and Flows to create complex, real-world automations.
- Python-native customization: Customize prompts, tools, execution paths, state, and integrations without fighting the framework.
- Agent-ready capabilities: Use tools, memory, knowledge, checkpointing, async execution, and MCP/A2A support for more capable production agents.
- Production-ready patterns: Add deterministic steps, human input, structured outputs, and checkpointing as your system grows.
- Thriving community: Backed by robust documentation and over 100,000 certified developers, providing exceptional support and guidance.
Choose CrewAI to build powerful, adaptable, and production-ready AI automations.
Examples
You can test different real life examples of AI crews in the CrewAI-examples repo:
Quick Tutorial
Write Job Descriptions
Check out code for this example or watch a video below:
Trip Planner
Check out code for this example or watch a video below:
Stock Analysis
Check out code for this example or watch a video below:
Using Crews and Flows Together
CrewAI'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}",
CrewAI 是一个开源的 Python 框架，用于编排角色扮演式的自主 AI 智能体。这些智能体可以组成 Crews 团队协作，也可以以事件驱动的 Flows 方式运行，从而自动完成复杂任务。