🤖 crewAI: Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
- Why CrewAI?
- Getting Started
- Key Features
- Examples
- Connecting Your Crew to a Model
- How CrewAI Compares
- Contribution
- Telemetry
- License
The power of AI collaboration has too much to offer. CrewAI is 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.
To get started with CrewAI, follow these simple steps:
pip install crewaiIf 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]'. This command installs the basic package and also adds extra components which require more dependencies to function."
pip install 'crewai[tools]'import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
# It can be a local model through Ollama / LM Studio or a remote
# model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
# Define your agents with roles and goals
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI and data science',
backstory="""You work at a leading tech think tank.
Your expertise lies in identifying emerging trends.
You have a knack for dissecting complex data and presenting actionable insights.""",
verbose=True,
allow_delegation=False,
# You can pass an optional llm attribute specifying what model you wanna use.
# llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
tools=[SerperDevTool()]
)
writer = Agent(
role='Tech Content Strategist',
goal='Craft compelling content on tech advancements',
backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
You transform complex concepts into compelling narratives.""",
verbose=True,
allow_delegation=True
)
# Create tasks for your agents
task1 = Task(
description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
Identify key trends, breakthrough technologies, and potential industry impacts.""",
expected_output="Full analysis report in bullet points",
agent=researcher
)
task2 = Task(
description="""Using the insights provided, develop an engaging blog
post that highlights the most significant AI advancements.
Your post should be informative yet accessible, catering to a tech-savvy audience.
Make it sound cool, avoid complex words so it doesn't sound like AI.""",
expected_output="Full blog post of at least 4 paragraphs",
agent=writer
)
# Instantiate your crew with a sequential process
crew = Crew(
agents=[researcher, writer],
tasks=[task1, task2],
verbose=True,
process = Process.sequential
)
# Get your crew to work!
result = crew.kickoff()
print("######################")
print(result)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.
- Role-Based Agent Design: Customize agents with specific roles, goals, and tools.
- Autonomous Inter-Agent Delegation: Agents can autonomously delegate tasks and inquire amongst themselves, enhancing problem-solving efficiency.
- Flexible Task Management: Define tasks with customizable tools and assign them to agents dynamically.
- Processes Driven: Currently only supports
sequentialtask execution andhierarchicalprocesses, but more complex processes like consensual and autonomous are being worked on. - Save output as file: Save the output of individual tasks as a file, so you can use it later.
- Parse output as Pydantic or Json: Parse the output of individual tasks as a Pydantic model or as a Json if you want to.
- Works with Open Source Models: Run your crew using Open AI or open source models refer to the Connect crewAI to LLMs page for details on configuring your agents' connections to models, even ones running locally!
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:
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.
CrewAI's Advantage: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.
-
Autogen: While Autogen does good in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
-
ChatDev: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:
- Fork the repository.
- Create a new branch for your feature.
- Add your feature or improvement.
- Send a pull request.
- We appreciate your input!
poetry lock
poetry installpoetry shellpre-commit installpoetry run pytestpoetry run mypypoetry buildpip install dist/*.tar.gzCrewAI 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. We don't offer a way to disable it now, but we will in the future.
Data collected includes:
- Version of crewAI
- So we can understand how many users are using the latest version
- Version of Python
- So we can decide on what versions to better support
- General OS (e.g. number of CPUs, macOS/Windows/Linux)
- So we know what OS we should focus on and if we could build specific OS related features
- Number of agents and tasks in a crew
- So we make sure we are testing internally with similar use cases and educate people on the best practices
- Crew Process being used
- Understand where we should focus our efforts
- If Agents are using memory or allowing delegation
- Understand if we improved the features or maybe even drop them
- If Tasks are being executed in parallel or sequentially
- Understand if we should focus more on parallel execution
- Language model being used
- Improved support on most used languages
- Roles of agents in a crew
- Understand high level use cases so we can build better tools, integrations and examples about it
- Tools names available
- Understand out of the publically available tools, which ones are being used the most so we can improve them
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.
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.
A: You can install CrewAI using pip:
pip install crewaiFor additional tools, use:
pip install 'crewai[tools]'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.
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.
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.
A: Yes, CrewAI is open-source and welcomes contributions from the community.
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.
A: You can find various real-life examples in the crewAI-examples repository, including trip planners, stock analysis tools, and more.
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.