CrewAI Multi-Agent Orchestrator

What's Changed

Features

• Surface tool failures instead of reporting them as success
• Emit FlowFailedEvent when a flow execution fails
• Implement progressive disclosure for skills

Documentation

• Update snapshot and changelog for v1.15.8

Contributors

@github-actions[bot], @joaomdmoura, @lorenzejay, @lucasgomide

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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
• Why CrewAI?
• Getting Started
• Key Features
• Understanding Flows and Crews
• Examples
• Quick Tutorial
• Write Job Descriptions
• Trip Planner
• Stock Analysis
• Using Crews and Flows Together
• Connecting Your Crew to a Model
• When to Use CrewAI
• Contribution
• Telemetry
• License
• Frequently Asked Questions (FAQ)

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.py / 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?

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<img src="docs/images/asset.png" alt="CrewAI Logo" width="100%">

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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.

CrewAI Getting Started Tutorial

###

Learning Resources

Learn CrewAI through our comprehensive courses:

• Multi AI Agent Systems with CrewAI - Master the fundamentals of multi-agent systems
• Practical Multi AI Agents and Advanced Use Cases - Deep dive into advanced implementations

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:

• Natural, autonomous decision-making between agents
• Dynamic task delegation and collaboration
• Specialized roles with defined goals and expertise
• Flexible problem-solving approaches

1. 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:

1. Installation

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:

uv pip 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:

uv 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'

• Install tiktoken explicitly: uv pip install 'crewai[embeddings]'
• If using embedchain or other tools: uv pip install 'crewai[tools]'

1. Failed building wheel for tiktoken

• Ensure Rust compiler is installed (see installation steps above)
• For Windows: Verify Visual C++ Build Tools are installed
• Try upgrading pip: uv pip install --upgrade pip
• If issues persist, use a pre-built wheel: uv pip install tiktoken --prefer-binary

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:

• Modify src/my_project/config/agents.yaml to define your agents.
• Modify src/my_project/config/tasks.yaml to define your tasks.
• Modify src/my_project/crew.py to add your own logic, tools, and specific arguments.
• Modify src/my_project/main.py to add custom inputs for your agents and tasks.
• Add your environment variables into the .env file.

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
from crewai.agents.agent_builder.base_agent import BaseAgent
from typing import List

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