Package: crewai
crewAI🤖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.Homepage|Documentation|Chat with Docs|Examples|DiscordTable of contentsWhy CrewAI?Getting StartedKey FeaturesExamplesQuick TutorialWrite Job DescriptionsTrip PlannerStock AnalysisConnecting Your Crew to a ModelHow CrewAI ComparesContributionHire CrewAITelemetryLicenseWhy CrewAI?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.Getting StartedTo get started with CrewAI, follow these simple steps:1. InstallationpipinstallcrewaiIf you want to also install crewai-tools, which is a package with tools that can be used by the agents, but more dependencies, you can install it with:pipinstall'crewai[tools]'The example below also uses DuckDuckGo's Search. You can install it withpiptoo:pipinstallduckduckgo-search2. Setting Up Your CrewimportosfromcrewaiimportAgent,Task,Crew,Processos.environ["OPENAI_API_KEY"]="YOUR_API_KEY"# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.# osOPENAI_API_BASE='http://localhost:11434/v1'# OPENAI_MODEL_NAME='openhermes'  # Adjust based on available model# OPENAI_API_KEY=''# Install duckduckgo-search for this example:# !pip install -U duckduckgo-searchfromlangchain_community.toolsimportDuckDuckGoSearchRunsearch_tool=DuckDuckGoSearchRun()# Define your agents with roles and goalsresearcher=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,tools=[search_tool]# You can pass an optional llm attribute specifying what mode you wanna use.# 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/)## import os# os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'## OR## from langchain_openai import ChatOpenAI# llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7))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 agentstask1=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 blogpost 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 processcrew=Crew(agents=[researcher,writer],tasks=[task1,task2],verbose=2,# You can set it to 1 or 2 to different logging levels)# 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.Key FeaturesRole-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 supportssequentialtask 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 theConnect crewAI to LLMspage for details on configuring you agents' connections to models, even ones running locally!ExamplesYou can test different real life examples of AI crews in thecrewAI-examples repo:Landing Page GeneratorHaving Human input on the executionTrip PlannerStock AnalysisQuick TutorialWrite Job DescriptionsCheck out code for this exampleor watch a video below:Trip PlannerCheck out code for this exampleor watch a video below:Stock AnalysisCheck out code for this exampleor watch a video below:Connecting Your Crew to a ModelcrewAI 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 theConnect crewAI to LLMspage for details on configuring you agents' connections to models.How CrewAI ComparesAutogen: 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'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.ContributionCrewAI 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!Installing Dependenciespoetrylock
poetryinstallVirtual EnvpoetryshellPre-commit hookspre-commitinstallRunning TestspoetryrunpytestRunning static type checkspoetryrunpyrightPackagingpoetrybuildInstalling Locallypipinstalldist/*.tar.gzHire CrewAIWe're a company developing crewAI and crewAI Enterprise, we for a limited time are offer consulting with selected customers, to get them early access to our enterprise solution
If you are interested on having access to it and hiring weekly hours with our team, feel free to email us atjoao@crewai.com.TelemetryCrewAI 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.There is NO data being collected on the prompts, tasks descriptions agents backstories or goals nor tools usage, no API calls, nor responses nor any data that is being processed by the agents, nor any secrets and env vars.Data collected includes:Version of crewAISo we can understand how many users are using the latest versionVersion of PythonSo we can decide on what versions to better supportGeneral 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 featuresNumber of agents and tasks in a crewSo we make sure we are testing internally with similar use cases and educate people on the best practicesCrew Process being usedUnderstand where we should focus our effortsIf Agents are using memory or allowing delegationUnderstand if we improved the features or maybe even drop themIf Tasks are being executed in parallel or sequentiallyUnderstand if we should focus more on parallel executionLanguage model being usedImproved support on most used languagesRoles of agents in a crewUnderstand high level use cases so we can build better tools, integrations and examples about itTools names availableUnderstand out of the publically available tools, which ones are being used the most so we can improve themUsers can opt-in sharing the complete telemetry data by setting theshare_crewattribute toTrueon their Crews.LicenseCrewAI is released under the MIT License.