crewAI

<p align="center"> <a href="https://github.com/crewAIInc/crewAI"> <img src="docs/images/crewai_logo.png" width="600px" alt="Open source Multi-AI Agent orchestration framework"> </a> </p> <p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;"> <a href="https://trendshift.io/repositories/11239" target="_blank"> <img src="https://trendshift.io/api/badge/repositories/11239" alt="crewAIInc%2FcrewAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/> </a> </p> <p align="center"> <a href="https://crewai.com">Homepage</a> · <a href="https://docs.crewai.com">Docs</a> · <a href="https://app.crewai.com">Start Cloud Trial</a> · <a href="https://blog.crewai.com">Blog</a> · <a href="https://community.crewai.com">Forum</a> </p> <p align="center"> <a href="https://github.com/crewAIInc/crewAI"> <img src="https://img.shields.io/github/stars/crewAIInc/crewAI" alt="GitHub Repo stars"> </a> <a href="https://github.com/crewAIInc/crewAI/network/members"> <img src="https://img.shields.io/github/forks/crewAIInc/crewAI" alt="GitHub forks"> </a> <a href="https://github.com/crewAIInc/crewAI/issues"> <img src="https://img.shields.io/github/issues/crewAIInc/crewAI" alt="GitHub issues"> </a> <a href="https://github.com/crewAIInc/crewAI/pulls"> <img src="https://img.shields.io/github/issues-pr/crewAIInc/crewAI" alt="GitHub pull requests"> </a> <a href="https://opensource.org/licenses/MIT"> <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT"> </a> </p> <p align="center"> <a href="https://pypi.org/project/crewai/"> <img src="https://img.shields.io/pypi/v/crewai" alt="PyPI version"> </a> <a href="https://pypi.org/project/crewai/"> <img src="https://img.shields.io/pypi/dm/crewai" alt="PyPI downloads"> </a> <a href="https://twitter.com/crewAIInc"> <img src="https://img.shields.io/twitter/follow/crewAIInc?style=social" alt="Twitter Follow"> </a> </p> ### Fast and Flexible Multi-Agent Automation Framework > CrewAI 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. - **CrewAI Crews**: Optimize for autonomy and collaborative intelligence. - **CrewAI Flows**: The **enterprise and production architecture** for building and deploying multi-agent systems. Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the standard for enterprise-ready AI automation. # CrewAI AMP Suite CrewAI AMP 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](https://app.crewai.com) ## 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 - [Why CrewAI?](#why-crewai) - [Getting Started](#getting-started) - [Key Features](#key-features) - [Understanding Flows and Crews](#understanding-flows-and-crews) - [CrewAI vs LangGraph](#how-crewai-compares) - [Examples](#examples) - [Quick Tutorial](#quick-tutorial) - [Write Job Descriptions](#write-job-descriptions) - [Trip Planner](#trip-planner) - [Stock Analysis](#stock-analysis) - [Using Crews and Flows Together](#using-crews-and-flows-together) - [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model) - [How CrewAI Compares](#how-crewai-compares) - [Frequently Asked Questions (FAQ)](#frequently-asked-questions-faq) - [Contribution](#contribution) - [Telemetry](#telemetry) - [License](#license) ## Why CrewAI? <div align="center" style="margin-bottom: 30px;"> <img src="docs/images/asset.png" alt="CrewAI Logo" width="100%"> </div> 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: - **Standalone Framework**: Built from scratch, independent of LangChain or any other agent framework. - **High Performance**: Optimized for speed and minimal resource usage, enabling faster execution. - **Flexible Low Level Customization**: Complete freedom to customize at both high and low levels - from overall workflows and system architecture to granular agent behaviors, internal prompts, and execution logic. - **Ideal for Every Use Case**: Proven effective for both simple tasks and highly complex, real-world, enterprise-grade scenarios. - **Robust Community**: Backed by a rapidly growing community of over **100,000 certified** developers offering comprehensive support and resources. CrewAI empowers developers and enterprises to confidently build intelligent automations, bridging the gap between simplicity, flexibility, and performance. ## Getting Started Setup and run your first CrewAI agents by following this tutorial. [](https://www.youtube.com/watch?v=-kSOTtYzgEw "CrewAI Getting Started Tutorial") ### Learning Resources Learn CrewAI through our comprehensive courses: - [Multi AI Agent Systems with CrewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/) - Master the fundamentals of multi-agent systems - [Practical Multi AI Agents and Advanced Use Cases](https://www.deeplearning.ai/short-courses/practical-multi-ai-agents-and-advanced-use-cases-with-crewai/) - 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 2. **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](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. First, install CrewAI: ```shell 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: ```shell 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]'` 2. **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: ```shell 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: ```shell crewai create crew latest-ai-development ``` Modify the files as needed to fit your use case: **agents.yaml** ```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** ````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** ```python # 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** ```python #!/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: - An [OpenAI API key](https://platform.openai.com/account/api-keys) (or other LLM API key): `OPENAI_API_KEY=sk-...` - A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE` Lock the dependencies and install them by using the CLI command but first, navigate to your project directory: ```shell cd my_project crewai install (Optional) ``` To run your crew, execute the following command in the root of your project: ```bash crewai run ``` or ```bash 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: ```bash crewai update ``` 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](https://docs.crewai.com/core-concepts/Processes/). ## Key Features 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. - **Standalone & Lean**: Completely independent from other frameworks like LangChain, offering faster execution and lighter resource demands. - **Flexible & Precise**: Easily orchestrate autonomous agents through intuitive [Crews](https://docs.crewai.com/concepts/crews) or precise [Flows](https://docs.crewai.com/concepts/flows), achieving perfect balance for your needs. - **Seamless Integration**: Effortlessly combine Crews (autonomy) and Flows (precision) to create complex, real-world automations. - **Deep Customization**: Tailor every aspect—from high-level workflows down to low-level internal prompts and agent behaviors. - **Reliable Performance**: Consistent results across simple tasks and complex, enterprise-level automations. - **Thriving Community**: Backed by robust documentation and over 100,000 certified developers, providing exceptional support and guidance. Choose CrewAI to easily build powerful, adaptable, and production-ready AI automations. ## Examples You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file): - [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator) - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution) - [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) - [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) ### Quick Tutorial [](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial") ### Write Job Descriptions [Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below: [](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings") ### Trip Planner [Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below: [](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner") ### Stock Analysis [Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below: [](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis") ### 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: ```python 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",