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223 lines
8.6 KiB
Markdown
223 lines
8.6 KiB
Markdown
<div align="center">
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# **crewAI**
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Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
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<h3>
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[Homepage](https://www.crewai.io/) | [Documentation](https://joaomdmoura.github.io/crewAI/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Examples](https://joaomdmoura.github.io/crewAI/#examples-and-tutorials) | [Discord](https://discord.com/invite/X4JWnZnxPb)
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</h3>
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[](https://github.com/joaomdmoura/crewAI)
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[](https://opensource.org/licenses/MIT)
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<!-- [](https://discord.com/invite/X4JWnZnxPb) -->
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</div>
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## Table of contents
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- [Why CrewAI?](#why-crewai)
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- [Getting Started](#getting-started)
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- [Key Features](#key-features)
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- [Examples](#examples)
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- [Quick Tutorial](#quick-tutorial)
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- [Trip Planner](#trip-planner)
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- [Stock Analysis](#stock-analysis)
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- [Connecting Your Crew to any Model](#connecting-your-crew-to-a-model)
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- [How CrewAI Compares](#how-crewai-compares)
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- [Contribution](#contribution)
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- [crewAI Enterprise](#crewai-enterprise)
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- [License](#license)
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## Why crewAI?
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crewAI is designed to enable AI agentsoperate in a cohesive unit, by assuming roles, and sharing goals.
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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 that can be integrate into products and automations.
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## Getting Started
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To get started with CrewAI, follow these simple steps:
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### 1. Installation
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```shell
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pip install crewai
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```
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The example below also uses DuckDuckGo's Search. You can install it with `pip` too:
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```shell
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pip install duckduckgo-search
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```
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### 2. Setting Up Your Crew
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Example for crew using the `sequential` process:
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```python
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import os
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from crewai import Agent, Task, Crew, Process
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os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
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# You can choose to use any major provider and local models as well, check the docs: https://docs.crewai.com/how-to/LLM-Connections/
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from langchain_community.tools import DuckDuckGoSearchRun
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search_tool = DuckDuckGoSearchRun()
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# Define your agents with roles and goals
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researcher = Agent(
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role='Senior Research Analyst',
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goal='Uncover cutting-edge developments in AI ',
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backstory="""You work at a leading tech think tank.
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Your expertise lies in identifying emerging trends.
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You have a knack for dissecting complex data and presenting actionable insights.""",
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verbose=True,
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allow_delegation=False,
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tools=[search_tool]
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)
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writer = Agent(
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role='Tech Content Strategist',
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goal='Craft compelling content about tech',
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backstory="""You are a renowned Content Strategist, known for transforming complex concepts into compelling narratives.""",
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verbose=True,
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allow_delegation=True
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)
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# Create tasks for your agents
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task1 = Task(
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description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
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Identify key trends, breakthrough technologies, and potential industry impacts.""",
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expected_output="A comprehensive full analysis report.",
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agent=researcher
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)
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task2 = Task(
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description="""Using the insights provided, develop an engaging blog
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post that highlights the most significant AI advancements.
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Your post should be informative yet accessible, catering to a tech-savvy audience.
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Make it sound cool, avoid complex words so it doesn't sound like AI.""",
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expected_output="A complete 4 paragraph long blog post that is engaging and informative.",
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agent=writer
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)
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# Instantiate your crew with a sequential process
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crew = Crew(
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agents=[researcher, writer],
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tasks=[task1, task2],
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Process=Process.sequential, # You can use different processes: https://docs.crewai.com/core-concepts/Managing-Processes/
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verbose=2, # You can set it to 1 or 2 to different logging levels
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)
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# Get your crew to work!
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result = crew.kickoff()
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print("######################")
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print(result)
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```
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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/Managing-Processes/).
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## Key Features
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- **Role-Based Agent Design**: Customize agents with specific roles, goals, and tools.
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- **Autonomous Inter-Agent Delegation**: Agents can autonomously delegate tasks and inquire amongst themselves, enhancing problem-solving efficiency.
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- **Flexible Task Management**: Define tasks with customizable tools and assign them to agents dynamically.
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- **Processes Driven**: Currently supports `sequential` and `hierarchical` task execution, but more complex processes like consensual and autonomous are being worked on.
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- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models, even ones running locally!
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## Examples
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You can test different real life examples of AI crews in the [ `Examples repository`](https://github.com/joaomdmoura/crewAI-examples?tab=readme-ov-file).
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### Quick Tutorial
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[](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
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### Trip Planner
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[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner) or watch a video below:
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[](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
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### Stock Analysis
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[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis) or watch a video below:
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[](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
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## Connecting Your Crew to a Model
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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.
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Please refer to the [Connect crewAI to LLMs](https://joaomdmoura.github.io/crewAI/how-to/LLM-Connections/) page for details on configuring you agents' connections to models.
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## How CrewAI Compares
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- **Autogen**: While Autogen excels in creating conversational agents capable of working together, it lacks an inherent concept of process, that ability to assign specific tasks to agents and a lot of the safeguards that created more reliable outcome in crewAI. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
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- **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.
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**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.
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## Contribution
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### Installing Dependencies
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```bash
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poetry lock
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poetry install
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```
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### Virtual Env
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```bash
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poetry shell
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```
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### Pre-commit hooks
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```bash
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pre-commit install
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```
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### Running Tests
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```bash
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poetry run pytest
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```
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### Running static type checks
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```bash
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poetry run pyright
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```
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### Packaging
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```bash
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poetry build
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```
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### Installing Locally
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```bash
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pip install dist/*.tar.gz
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```
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## crewAI Enterprise
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crewAI Inc, is 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.
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If you are interested on having access to it and hiring weekly hours with our team, feel free to email us at [joao@crewai.io](mailto:joao@crewai.com).
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## License
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CrewAI is released under the MIT License.
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