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Docs update (#1008)
* Updated Docs: New Getting started section + content update / addition * fixed indentation issue * Minor updates to fix typos --------- Co-authored-by: theCyberTech <the_t3ch@pm.me>
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---
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title: Assembling and Activating Your CrewAI Team
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description: A comprehensive guide to creating a dynamic CrewAI team for your projects, with updated functionalities including verbose mode, memory capabilities, asynchronous execution, output customization, language model configuration, code execution, integration with third-party agents, and improved task management.
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---
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## Introduction
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Embark on your CrewAI journey by setting up your environment and initiating your AI crew with the latest features. This guide ensures a smooth start, incorporating all recent updates for an enhanced experience, including code execution capabilities, integration with third-party agents, and advanced task management.
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## Step 0: Installation
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Install CrewAI and any necessary packages for your project. CrewAI is compatible with Python >=3.10,<=3.13.
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```shell
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pip install crewai
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pip install 'crewai[tools]'
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```
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## Step 1: Assemble Your Agents
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Define your agents with distinct roles, backstories, and enhanced capabilities. The Agent class now supports a wide range of attributes for fine-tuned control over agent behavior and interactions, including code execution and integration with third-party agents.
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```python
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import os
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from langchain.llms import OpenAI
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from crewai import Agent
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from crewai_tools import SerperDevTool, BrowserbaseLoadTool, EXASearchTool
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os.environ["OPENAI_API_KEY"] = "Your OpenAI Key"
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os.environ["SERPER_API_KEY"] = "Your Serper Key"
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os.environ["BROWSERBASE_API_KEY"] = "Your BrowserBase Key"
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os.environ["BROWSERBASE_PROJECT_ID"] = "Your BrowserBase Project Id"
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search_tool = SerperDevTool()
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browser_tool = BrowserbaseLoadTool()
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exa_search_tool = EXASearchTool()
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# Creating a senior researcher agent with advanced configurations
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researcher = Agent(
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role='Senior Researcher',
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goal='Uncover groundbreaking technologies in {topic}',
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backstory=("Driven by curiosity, you're at the forefront of innovation, "
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"eager to explore and share knowledge that could change the world."),
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memory=True,
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verbose=True,
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allow_delegation=False,
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tools=[search_tool, browser_tool],
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allow_code_execution=False, # New attribute for enabling code execution
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max_iter=15, # Maximum number of iterations for task execution
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max_rpm=100, # Maximum requests per minute
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max_execution_time=3600, # Maximum execution time in seconds
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system_template="Your custom system template here", # Custom system template
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prompt_template="Your custom prompt template here", # Custom prompt template
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response_template="Your custom response template here", # Custom response template
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)
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# Creating a writer agent with custom tools and specific configurations
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writer = Agent(
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role='Writer',
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goal='Narrate compelling tech stories about {topic}',
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backstory=("With a flair for simplifying complex topics, you craft engaging "
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"narratives that captivate and educate, bringing new discoveries to light."),
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verbose=True,
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allow_delegation=False,
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memory=True,
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tools=[exa_search_tool],
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function_calling_llm=OpenAI(model_name="gpt-3.5-turbo"), # Separate LLM for function calling
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)
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# Setting a specific manager agent
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manager = Agent(
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role='Manager',
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goal='Ensure the smooth operation and coordination of the team',
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verbose=True,
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backstory=(
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"As a seasoned project manager, you excel in organizing "
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"tasks, managing timelines, and ensuring the team stays on track."
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),
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allow_code_execution=True, # Enable code execution for the manager
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)
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```
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### New Agent Attributes and Features
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1. `allow_code_execution`: Enable or disable code execution capabilities for the agent (default is False).
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2. `max_execution_time`: Set a maximum execution time (in seconds) for the agent to complete a task.
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3. `function_calling_llm`: Specify a separate language model for function calling.
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---
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title: Installing crewAI
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description: A comprehensive guide to installing crewAI and its dependencies, including the latest updates and installation methods.
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---
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# Installing crewAI
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Welcome to crewAI! This guide will walk you through the installation process for crewAI and its dependencies. crewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. Let's get started!
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## Installation
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To install crewAI, you need to have Python >=3.10 and <=3.13 installed on your system:
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```shell
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# Install the main crewAI package
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pip install crewai
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# Install the main crewAI package and the tools package
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# that includes a series of helpful tools for your agents
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pip install 'crewai[tools]'
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```
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---
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title: Starting a New CrewAI Project
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description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods.
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---
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# Starting Your CrewAI Project
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Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
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## Prerequisites
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We assume you have already installed CrewAI. If not, please refer to the [installation guide](https://docs.crewai.com/how-to/Installing-CrewAI/) to install CrewAI and its dependencies.
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## Creating a New Project
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To create a new project, run the following CLI command:
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```shell
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$ crewai create <project_name>
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```
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This command will create a new project folder with the following structure:
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```shell
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my_project/
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├── .gitignore
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├── pyproject.toml
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├── README.md
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└── src/
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└── my_project/
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├── __init__.py
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├── main.py
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├── crew.py
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├── tools/
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│ ├── custom_tool.py
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│ └── __init__.py
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└── config/
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├── agents.yaml
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└── tasks.yaml
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```
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You can now start developing your project by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of your project, and the `crew.py` file is where you define your agents and tasks.
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## Customizing Your Project
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To customize your project, you can:
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- Modify `src/my_project/config/agents.yaml` to define your agents.
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- Modify `src/my_project/config/tasks.yaml` to define your tasks.
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- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
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- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
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- Add your environment variables into the `.env` file.
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### Example: Defining Agents and Tasks
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#### agents.yaml
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```yaml
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researcher:
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role: >
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Job Candidate Researcher
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goal: >
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Find potential candidates for the job
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backstory: >
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You are adept at finding the right candidates by exploring various online
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resources. Your skill in identifying suitable candidates ensures the best
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match for job positions.
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```
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#### tasks.yaml
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```yaml
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research_candidates_task:
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description: >
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Conduct thorough research to find potential candidates for the specified job.
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Utilize various online resources and databases to gather a comprehensive list of potential candidates.
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Ensure that the candidates meet the job requirements provided.
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Job Requirements:
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{job_requirements}
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expected_output: >
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A list of 10 potential candidates with their contact information and brief profiles highlighting their suitability.
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agent: researcher # THIS NEEDS TO MATCH THE AGENT NAME IN THE AGENTS.YAML FILE AND THE AGENT DEFINED IN THE Crew.PY FILE
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context: # THESE NEED TO MATCH THE TASK NAMES DEFINED ABOVE AND THE TASKS.YAML FILE AND THE TASK DEFINED IN THE Crew.PY FILE
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- researcher
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```
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### Referencing Variables:
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Your defined functions with the same name will be used. For example, you can reference the agent for specific tasks from task.yaml file. Ensure your annotated agent and function name is the same otherwise your task wont recognize the reference properly.
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#### Example References
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agent.yaml
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```yaml
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email_summarizer:
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role: >
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Email Summarizer
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goal: >
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Summarize emails into a concise and clear summary
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backstory: >
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You will create a 5 bullet point summary of the report
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llm: mixtal_llm
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```
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task.yaml
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```yaml
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email_summarizer_task:
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description: >
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Summarize the email into a 5 bullet point summary
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expected_output: >
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A 5 bullet point summary of the email
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agent: email_summarizer
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context:
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- reporting_task
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- research_task
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```
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Use the annotations are used to properly reference the agent and task in the crew.py file.
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### Annotations include:
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* @agent
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* @task
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* @crew
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* @llm
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* @tool
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* @callback
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* @output_json
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* @output_pydantic
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* @cache_handler
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crew.py
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```py
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...
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@llm
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def mixtal_llm(self):
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return ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
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@agent
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def email_summarizer(self) -> Agent:
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return Agent(
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config=self.agents_config["email_summarizer"],
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)
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## ...other tasks defined
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@task
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def email_summarizer_task(self) -> Task:
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return Task(
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config=self.tasks_config["email_summarizer_task"],
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)
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...
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```
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## Installing Dependencies
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To install the dependencies for your project, you can use Poetry. First, navigate to your project directory:
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```shell
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$ cd my_project
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$ poetry lock
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$ poetry install
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```
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This will install the dependencies specified in the `pyproject.toml` file.
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## Interpolating Variables
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Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{variable}` will be replaced by the value of the variable in the `main.py` file.
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#### agents.yaml
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```yaml
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research_task:
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description: >
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Conduct a thorough research about the customer and competitors in the context
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of {customer_domain}.
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Make sure you find any interesting and relevant information given the
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current year is 2024.
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expected_output: >
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A complete report on the customer and their customers and competitors,
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including their demographics, preferences, market positioning and audience engagement.
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```
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#### main.py
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```python
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# main.py
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def run():
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inputs = {
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"customer_domain": "crewai.com"
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}
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MyProjectCrew(inputs).crew().kickoff(inputs=inputs)
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```
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## Running Your Project
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To run your project, use the following command:
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```shell
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$ poetry run my_project
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```
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This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
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## Deploying Your Project
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The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.
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