updating docs

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João Moura
2024-02-03 22:43:09 -08:00
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name: Deploy MkDocs
on:
workflow_dispatch:
workflow_dispatch:
push:
branches:
- main
@@ -22,6 +22,18 @@ jobs:
with:
python-version: '3.10'
- name: Calculate requirements hash
id: req-hash
run: echo "::set-output name=hash::$(sha256sum requirements-doc.txt | awk '{print $1}')"
- name: Setup cache
uses: actions/cache@v3
with:
key: mkdocs-material-${{ steps.req-hash.outputs.hash }}
path: .cache
restore-keys: |
mkdocs-material-
- name: Install Requirements
run: |
sudo apt-get update &&

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# What is a Tool?
A tool in CrewAI is a function or capability that an agent can utilize to perform actions, gather information, or interact with external systems, behind the scenes tools are [LangChain Tools](https://python.langchain.com/docs/modules/agents/tools/).
These tools can be as straightforward as a search function or as sophisticated as integrations with other chains or APIs.
## Key Characteristics of Tools
- **Utility**: Tools are designed to serve specific purposes, such as searching the web, analyzing data, or generating content.
- **Integration**: Tools can be integrated into agents to extend their capabilities beyond their basic functions.
- **Customizability**: Developers can create custom tools tailored to the specific needs of their agents or use pre-built LangChain ones available in the ecosystem.
# Creating your own Tools
You can easily create your own tool using [LangChain Tool Custom Tool Creation](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
Example:
```python
import json
import requests
from crewai import Agent
from langchain.tools import tool
from unstructured.partition.html import partition_html
class BrowserTools():
@tool("Scrape website content")
def scrape_website(website):
"""Useful to scrape a website content"""
url = f"https://chrome.browserless.io/content?token={config('BROWSERLESS_API_KEY')}"
payload = json.dumps({"url": website})
headers = {
'cache-control': 'no-cache',
'content-type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
elements = partition_html(text=response.text)
content = "\n\n".join([str(el) for el in elements])
# Return only the first 5k characters
return content[:5000]
# Create an agent and assign the scrapping tool
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[BrowserTools().scrape_website]
)
```
# Using Existing Tools
Check [LangChain Integration](https://python.langchain.com/docs/integrations/tools/) for a set of useful existing tools.
To assign a tool to an agent, you'd provide it as part of the agent's properties during initialization.
```python
from crewai import Agent
from langchain.agents import Tool
from langchain.utilities import GoogleSerperAPIWrapper
# Initialize SerpAPI tool with your API key
os.environ["OPENAI_API_KEY"] = "Your Key"
os.environ["SERPER_API_KEY"] = "Your Key"
search = GoogleSerperAPIWrapper()
# Create tool to be used by agent
serper_tool = Tool(
name="Intermediate Answer",
func=search.run,
description="useful for when you need to ask with search",
)
# Create an agent and assign the search tool
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[serper_tool]
)
```
# Tool Interaction
Tools enhance an agent's ability to perform tasks autonomously or in collaboration with other agents. For instance, an agent might use a search tool to gather information, then pass that data to another agent specialized in analysis.
# Conclusion
Tools are vital components that expand the functionality of agents within the CrewAI framework. They enable agents to perform a wide range of actions and collaborate effectively with one another. As you build with CrewAI, consider the array of tools you can leverage to empower your agents and how they can be interwoven to create a robust AI ecosystem.

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---
title: crewAI Agents
description: What are crewAI Agents and how to use them.
---
## What is an Agent?
!!! note "What is an Agent?"
An agent is an **autonomous unit** programmed to:
<ul>
<li class='leading-3'>Perform tasks</li>
<li class='leading-3'>Make decisions</li>
<li class='leading-3'>Communicate with other agents</li>
<br/>
Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.
## Agent Attributes
| Attribute | Description |
| :---------- | :----------------------------------- |
| **Role** | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| **Goal** | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| **Backstory** | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| **Tools** | Set of capabilities or functions that the agent can use to perform tasks. Tools can be shared or exclusive to specific agents. |
| **Verbose** | This allow you to actually see what is going on during the Crew execution. |
| **Allow Delegation** | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. |
## Creating an Agent
!!! note "Agent Interaction"
Agents can interact with each other using the CrewAI's built-in delegation and communication mechanisms.<br/>This allows for dynamic task management and problem-solving within the crew.
To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example:
```python
from crewai import Agent
# Create an agent with a role and a goal
agent = Agent(
role='Data Analyst',
goal='Extract actionable insights',
verbose=True,
backstory="""You're a data analyst at a large company.
You're responsible for analyzing data and providing insights
to the business.
You're currently working on a project to analyze the
performance of our marketing campaigns."""
)
```
## Conclusion
Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

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---
title: How Agents Collaborate in CrewAI
description: Exploring the dynamics of agent collaboration within the CrewAI framework.
---
## Collaboration Fundamentals
!!! note "Core of Agent Interaction"
Collaboration in CrewAI is fundamental, enabling agents to combine their skills, share information, and assist each other in task execution, embodying a truly cooperative ecosystem.
- **Information Sharing**: Ensures all agents are well-informed and can contribute effectively by sharing data and findings.
- **Task Assistance**: Allows agents to seek help from peers with the required expertise for specific tasks.
- **Resource Allocation**: Optimizes task execution through the efficient distribution and sharing of resources among agents.
## Delegation: Dividing to Conquer
Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.
## Implementing Collaboration and Delegation
Setting up a crew involves defining the roles and capabilities of each agent. CrewAI seamlessly manages their interactions, ensuring efficient collaboration and delegation.
## Example Scenario
Imagine a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The writer can delegate research tasks or ask questions to the researcher, facilitating a seamless workflow.
## Conclusion
Collaboration and delegation are pivotal, transforming individual AI agents into a coherent, intelligent crew capable of tackling complex tasks. CrewAI's framework not only simplifies these interactions but enhances their effectiveness, paving the way for sophisticated AI-driven solutions.

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# What is a Task?
A Task in CrewAI is essentially a job or an assignment that an AI agent needs to complete. It's defined by what needs to be done and can include additional information like which agent should do it and what tools they might need.
# Task Properties
- **Description**: A clear, concise statement of what the task entails.
- **Agent**: Optionally, you can specify which agent is responsible for the task. If not, the crew's process will determine who takes it on.
- **Tools**: These are the functions or capabilities the agent can utilize to perform the task. They can be anything from simple actions like 'search' to more complex interactions with other agents or APIs.
# Integrating Tools with Tasks
In CrewAI, tools are functions from the `langchain` toolkit that agents can use to interact with the world. These can be generic utilities or specialized functions designed for specific actions. When you assign tools to a task, they empower the agent to perform its duties more effectively.
## Example of Creating a Task with Tools
```python
from crewai import Task
from langchain.agents import Tool
from langchain.utilities import GoogleSerperAPIWrapper
# Initialize SerpAPI tool with your API key
os.environ["OPENAI_API_KEY"] = "Your Key"
os.environ["SERPER_API_KEY"] = "Your Key"
search = GoogleSerperAPIWrapper()
# Create tool to be used by agent
serper_tool = Tool(
name="Intermediate Answer",
func=search.run,
description="useful for when you need to ask with search",
)
# Create a task with a description and the search tool
task = Task(
description='Find and summarize the latest and most relevant news on AI',
tools=[serper_tool]
)
```
When the task is executed by an agent, the tools specified in the task will override the agent's default tools. This means that for the duration of this task, the agent will use the search tool provided, even if it has other tools assigned to it.
# Tool Override Mechanism
The ability to override an agent's tools with those specified in a task allows for greater flexibility. An agent might generally use a set of standard tools, but for certain tasks, you may want it to use a particular tool that is more suited to the task at hand.
# Conclusion
Creating tasks with the right tools is crucial in CrewAI. It ensures that your agents are not only aware of what they need to do but are also equipped with the right functions to do it effectively. This feature underlines the flexibility and power of the CrewAI system, where tasks can be tailored with specific tools to achieve the best outcome.

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# Overview of a Task
In the CrewAI framework, tasks are the individual assignments that agents are responsible for completing. They are the fundamental units of work that your AI crew will undertake. Understanding how to define and manage tasks is key to leveraging the full potential of CrewAI.
A task in CrewAI encapsulates all the information needed for an agent to execute it, including a description, the agent assigned to it, and any specific tools required. Tasks are designed to be flexible, allowing for both simple and complex actions depending on your needs.
# Properties of a Task
Every task in CrewAI has several properties:
- **Description**: A clear and concise statement of what needs to be done.
- **Agent**: The agent assigned to the task (optional). If no agent is specified, the task can be picked up by any agent based on the process defined.
- **Tools**: A list of tools (optional) that the agent can use to complete the task. These can override the agent's default tools if necessary.
# Creating a Task
Creating a task is straightforward. You define what needs to be done and, optionally, who should do it and what tools they should use. Heres a conceptual guide:
```python
from crewai import Task
# Define a task with a designated agent and specific tools
task = Task(description='Generate monthly sales report', agent=sales_agent, tools=[reporting_tool])
```
# Task Assignment
Tasks can be assigned to agents in several ways:
- Directly, by specifying the agent when creating the task.
- [WIP] Through the Crew's process, which can assign tasks based on agent roles, availability, or other criteria.
# Task Execution
Once a task has been defined and assigned, it's ready to be executed. Execution is typically handled by the Crew object, which manages the workflow and ensures that tasks are completed according to the defined process.
# Task Collaboration
Tasks in CrewAI can be designed to require collaboration between agents. For example, one agent might gather data while another analyzes it. This collaborative approach can be defined within the task properties and managed by the Crew's process.
# Conclusion
Tasks are the driving force behind the actions of agents in CrewAI. By properly defining tasks, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. In the following sections, we will explore how tasks fit into the larger picture of processes and crew management.

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# How Agents Collaborate:
In CrewAI, collaboration is the cornerstone of agent interaction. Agents are designed to work together by sharing information, requesting assistance, and combining their skills to complete tasks more efficiently.
- **Information Sharing**: Agents can share findings and data amongst themselves to ensure all members are informed and can contribute effectively.
- **Task Assistance**: If an agent encounters a task that requires additional expertise, it can seek the help of another agent with the necessary skill set.
- **Resource Allocation**: Agents can share or allocate resources such as tools or processing power to optimize task execution.
Collaboration is embedded in the DNA of CrewAI, enabling a dynamic and adaptive approach to problem-solving.
# Delegation: Dividing to Conquer
Delegation is the process by which an agent assigns a task to another agent, or just ask another agent, it's an intelligent decision-making process that enhances the crew's functionality.
By default all agents can delegate work and ask questions, so if you want an agent to work alone make sure to set that option when initializing an Agent, this is useful to prevent deviations if the task is supposed to be straightforward.
## Implementing Collaboration and Delegation
When setting up your crew, you'll define the roles and capabilities of each agent. CrewAI's infrastructure takes care of the rest, managing the complex interplay of agents as they work together.
## Example Scenario:
Imagine a scenario where you have a researcher agent that gathers data and a writer agent that compiles reports. The writer can autonomously ask question or delegate more in depth research work depending on its needs as it tries to complete its task.
# Conclusion
Collaboration and delegation are what transform a collection of AI agents into a unified, intelligent crew. With CrewAI, you have a framework that not only simplifies these interactions but also makes them more effective, paving the way for sophisticated AI systems that can tackle complex, multi-dimensional tasks.

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# Managing Processes in CrewAI
Processes are the heart of CrewAI's workflow management, akin to the way a human team organizes its work. In CrewAI, processes define the sequence and manner in which tasks are executed by agents, mirroring the coordination you'd expect in a well-functioning team of people.
## Understanding Processes
A process in CrewAI can be thought of as the game plan for how your AI agents will handle their workload. Just as a project manager assigns tasks to team members based on their skills and the project timeline, CrewAI processes assign tasks to agents to ensure efficient workflow.
## Process Implementations
- **Sequential (Supported)**: This process ensures tasks are handled one at a time, in a given order, much like a relay race where one runner passes the baton to the next.
- **Hierarchical**: This process introduces a chain of command to task execution. You define the crew and the system assigns a manager to properly coordinate the planning and execution of tasks through delegation and validation of results, akin to a traditional corporate hierarchy.
- **Consensual (WIP)**: Envisioned for a future update, the consensual process will enable agents to make joint decisions on task execution, similar to a team consensus in a meeting before proceeding.
These additional processes, once implemented, will offer more nuanced and sophisticated ways for agents to interact and complete tasks, much like teams in complex organizational structures.
## The Role of Processes in Teamwork
The process you choose for your crew is critical. It's what transforms a group of individual agents into a cohesive unit that can tackle complex projects with the precision and harmony you'd find in a team of skilled humans.
## Assigning Processes to a Crew
To assign a process to a crew, simply set it during the crew's creation. The process will dictate the crew's approach to task execution. Different from the sequential process, you don't need to assign an agent to a task.
```python
from crewai import Crew
from crewai.process import Process
# Create a crew with a sequential process
crew = Crew(agents=my_agents, tasks=my_tasks, process=Process.sequential)
# OR create a crew with a hierarchical process
crew = Crew(agents=my_agents, tasks=my_tasks, process=Process.hierarchical)
```
## Sequential Process
The sequential process is where much of CrewAI's magic happens. It ensures that tasks are approached with the same thoughtful progression that a human team would use, fostering a natural and logical flow of work while passing on task outcome into the next.
## Hierarchical Process
The hierarchical process is tiny bit more magic, as the chain of command and task assignment is hidden from the user. The system will automatically assign a manager the execute the tasks, but the agent will never execute the job by itself. Instead, the manager will plan the steps to execute the task and delegate the work to the agents. The agents will then execute the task and report back to the manager, who will validate the results and pass the task outcome to the next task.
## Conclusion
Processes bring structure and order to the CrewAI ecosystem, allowing agents to collaborate effectively and accomplish goals systematically. As CrewAI evolves, additional process types will be introduced to enhance the framework's versatility, much like a team that grows and adapts over time.

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---
title: Managing Processes in CrewAI
description: An overview of workflow management through processes in CrewAI.
---
## Understanding Processes
!!! note "Core Concept"
Processes in CrewAI orchestrate how tasks are executed by agents, akin to project management in human teams. They ensure tasks are distributed and completed efficiently, according to a predefined game plan.
## Process Implementations
- **Sequential**: Executes tasks one after another, ensuring a linear and orderly progression.
- **Hierarchical**: Implements a chain of command, where tasks are delegated and executed based on a managerial structure.
- **Consensual (WIP)**: Future process type aiming for collaborative decision-making among agents on task execution.
## The Role of Processes in Teamwork
Processes transform individual agents into a unified team, coordinating their efforts to achieve common goals with efficiency and harmony.
## Assigning Processes to a Crew
Specify the process during crew creation to determine the execution strategy:
```python
from crewai import Crew
from crewai.process import Process
# Example: Creating a crew with a sequential process
crew = Crew(agents=my_agents, tasks=my_tasks, process=Process.sequential)
# Example: Creating a crew with a hierarchical process
crew = Crew(agents=my_agents, tasks=my_tasks, process=Process.hierarchical)
```
## Sequential Process
Ensures a natural flow of work, mirroring human team dynamics by progressing through tasks thoughtfully and systematically.
Tasks need to be pre-assigned to agents, and the order of execution is determined by the order of the tasks in the list.
Tasks are executed one after another, ensuring a linear and orderly progression and the output of one task is automatically used as context into the next task.
You can also define specific task's outputs that should be used as context for another task by using the `context` parameter in the `Task` class.
## Hierarchical Process
Mimics a corporate hierarchy, where a manager oversees task execution, planning, delegation, and validation, enhancing task coordination.
In this process tasks don't need to be pre-assigned to agents, the manager will decide which agent will perform each task, review the output and decide if the task is completed or not.
## Conclusion
Processes are vital for structured collaboration within CrewAI, enabling agents to work together systematically. Future updates will introduce new processes, further mimicking the adaptability and complexity of human teamwork.

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---
title: crewAI Tasks
description: Overview and management of tasks within the crewAI framework.
---
## Overview of a Task
!!! note "What is a Task?"
In the CrewAI framework, tasks are individual assignments that agents complete. They encapsulate necessary information for execution, including a description, assigned agent, and required tools, offering flexibility for various action complexities.
Tasks in CrewAI can be designed to require collaboration between agents. For example, one agent might gather data while another analyzes it. This collaborative approach can be defined within the task properties and managed by the Crew's process.
## Task Attributes
| Attribute | Description |
| :---------- | :----------------------------------- |
| **Description** | A clear, concise statement of what the task entails. |
| **Agent** | Optionally, you can specify which agent is responsible for the task. If not, the crew's process will determine who takes it on. |
| **Expected Output** *(optional)* | Clear and detailed definition of expected output for the task. |
| **Tools** *(optional)* | These are the functions or capabilities the agent can utilize to perform the task. They can be anything from simple actions like 'search' to more complex interactions with other agents or APIs. |
| **Context** *(optional)* | Other tasks that will have their output used as context for this task. |
| **Callback** *(optional)* | A function to be executed after the task is completed. |
## Creating a Task
This is the simpliest example for creating a task, it involves defining its scope and agent, but there are optional attributes that can provide a lot of flexibility:
```python
from crewai import Task
task = Task(
description='Find and summarize the latest and most relevant news on AI',
agent=sales_agent
)
```
!!! note "Task Assignment"
Tasks can be assigned directly by specifying an `agent` to them, or they can be assigned in run time if you are using the `hierarchical` through CrewAI's process, considering roles, availability, or other criteria.
## Integrating Tools with Tasks
Tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools) enhance task performance, allowing agents to interact more effectively with their environment. Assigning specific tools to tasks can tailor agent capabilities to particular needs.
## Creating a Task with Tools
```python
import os
os.environ["OPENAI_API_KEY"] = "Your Key"
from crewai import Agent, Task, Crew
from langchain.agents import Tool
from langchain_community.tools import DuckDuckGoSearchRun
research_agent = Agent(
role='Researcher',
goal='Find and summarize the latest AI news',
backstory="""You're a researcher at a large company.
You're responsible for analyzing data and providing insights
to the business."""
verbose=True
)
# Install duckduckgo-search for this example:
# !pip install -U duckduckgo-search
search_tool = DuckDuckGoSearchRun()
task = Task(
description='Find and summarize the latest AI news',
expected_output='A bullet list summary of the top 5 most important AI news',
agent=research_agent,
tools=[search_tool]
)
crew = Crew(
agents=[research_agent],
tasks=[task],
verbose=2
)
result = crew.kickoff()
print(result)
```
This demonstrates how tasks with specific tools can override an agent's default set for tailored task execution.
## Refering other Tasks
In crewAI the output of one task is automatically relayed into the next one, but you can specifically define what tasks output should be used as context for another task.
This is useful when you have a task that depends on the output of another task that is not performed immediately after it. This is done through the `context` attribute of the task:
```python
# ...
research_task = Task(
description='Find and summarize the latest AI news',
expected_output='A bullet list summary of the top 5 most important AI news',
agent=research_agent,
tools=[search_tool]
)
write_blog_task = Task(
description="Write a full blog post about the importante of AI and it's latest news",
expected_output='Full blog post that is 4 paragraphs long',
agent=writer_agent,
context=[research_task]
)
#...
```
## Callback Mechanism
You can define a callback function that will be executed after the task is completed. This is useful for tasks that need to trigger some side effect after they are completed, while the crew is still running.
```python
# ...
def callback_function(output: TaskOutput):
# Do something after the task is completed
# Example: Send an email to the manager
print(f"""
Task completed!
Task: {output.description}
Output: {output.result}
""")
research_task = Task(
description='Find and summarize the latest AI news',
expected_output='A bullet list summary of the top 5 most important AI news',
agent=research_agent,
tools=[search_tool],
callback=callback_function
)
#...
```
## Tool Override Mechanism
Specifying tools in a task allows for dynamic adaptation of agent capabilities, emphasizing CrewAI's flexibility.
## Conclusion
Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit.
Equipping tasks with appropriate tools is crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments.

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---
title: crewAI Tools
description: Understanding and leveraging tools within the crewAI framework.
---
## What is a Tool?
!!! note "Definition"
A tool in CrewAI, is a skill, something Agents can use perform tasks, right now those can be tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools), those are basically functions that an agent can utilize for various actions, from simple searches to complex interactions with external systems.
## Key Characteristics of Tools
- **Utility**: Designed for specific tasks such as web searching, data analysis, or content generation.
- **Integration**: Enhance agent capabilities by integrating tools directly into their workflow.
- **Customizability**: Offers the flexibility to develop custom tools or use existing ones from LangChain's ecosystem.
## Creating your own Tools
!!! example "Custom Tool Creation"
Developers can craft custom tools tailored for their agents needs or utilize pre-built options. Heres how to create one:
```python
import json
import requests
from crewai import Agent
from langchain.tools import tool
from unstructured.partition.html import partition_html
class BrowserTools():
# Anotate the fuction with the tool decorator from LangChain
@tool("Scrape website content")
def scrape_website(website):
# Write logic for the tool.
# In this case a function to scrape website content
url = f"https://chrome.browserless.io/content?token={config('BROWSERLESS_API_KEY')}"
payload = json.dumps({"url": website})
headers = {'cache-control': 'no-cache', 'content-type': 'application/json'}
response = requests.request("POST", url, headers=headers, data=payload)
elements = partition_html(text=response.text)
content = "\n\n".join([str(el) for el in elements])
return content[:5000]
# Assign the scraping tool to an agent
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[BrowserTools().scrape_website]
)
```
## Using LangChain Tools
!!! info "LangChain Integration"
CrewAI seamlessly integrates with LangChains comprehensive toolkit. Assigning an existing tool to an agent is straightforward:
```python
from crewai import Agent
from langchain.agents import Tool
from langchain.utilities import GoogleSerperAPIWrapper
import os
# Setup API keys
os.environ["OPENAI_API_KEY"] = "Your Key"
os.environ["SERPER_API_KEY"] = "Your Key"
search = GoogleSerperAPIWrapper()
# Create and assign the search tool to an agent
serper_tool = Tool(
name="Intermediate Answer",
func=search.run,
description="Useful for search-based queries",
)
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[serper_tool]
)
```
## Conclusion
Tools are crucial for extending the capabilities of CrewAI agents, allowing them to undertake a diverse array of tasks and collaborate efficiently. When building your AI solutions with CrewAI, consider both custom and existing tools to empower your agents and foster a dynamic AI ecosystem.

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@@ -1,41 +0,0 @@
# What is an Agent?
In CrewAI, an agent is an autonomous unit programmed to perform tasks, make decisions, and communicate with other agents. Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.
# Key Properties of an Agent
- **Role**: Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.
- **Goal**: The individual objective that the agent aims to achieve. It guides the agent's decision-making process.
- **Backstory**: Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.
- **Tools**: A set of capabilities or functions that the agent can use to perform tasks. Tools can be shared or exclusive to specific agents.
- **Verbose**: This allow you to actually see what is going on during the Crew execution.
- **Allow Delegation**: Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent.
# Agent Lifecycle
1. **Initialization**: An agent is created with a defined role, goal, backstory, and set of tools.
2. **Task Assignment**: The agent is assigned tasks either directly or through the crew's process management.
3. **Execution**: The agent performs the task using its available tools and in accordance with its role and goal.
4. **Collaboration**: Throughout the execution, the agent can communicate with other agents to delegate, inquire, or assist.
# Creating an Agent
To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example:
```python
from crewai import Agent
# Create an agent with a role and a goal
agent = Agent(
role='Data Analyst',
goal='Extract actionable insights',
verbose=True,
backstory="You'er a data analyst at a large company. I am responsible for analyzing data and providing insights to the business. I am currently working on a project to analyze the performance of our marketing campaigns. I have been asked to provide insights on how to improve the performance of our marketing campaigns."
)
```
# Agent Interaction
Agents can interact with each other using the CrewAI's built-in delegation and communication mechanisms. This allows for dynamic task management and problem-solving within the crew.
# Conclusion
Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

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@@ -39,9 +39,9 @@ local_expert = Agent(
## Open AI Compatible API Endpoints
In the context of integrating various language models with CrewAI, the flexibility to switch between different API endpoints is a crucial feature. By utilizing environment variables for configuration details such as `OPENAI_API_BASE_URL`, `OPENAI_API_KEY`, and `MODEL_NAME`, you can easily transition between different APIs or models. For instance, if you want to switch from using the standard OpenAI GPT model to a custom or alternative version, simply update the values of these environment variables.
In the context of integrating various language models with CrewAI, the flexibility to switch between different API endpoints is a crucial feature. By utilizing environment variables for configuration details such as `OPENAI_API_BASE`, `OPENAI_API_KEY`, and `MODEL_NAME`, you can easily transition between different APIs or models. For instance, if you want to switch from using the standard OpenAI GPT model to a custom or alternative version, simply update the values of these environment variables.
The `OPENAI_API_BASE_URL` variable allows you to define the base URL of the API to connect to, while `OPENAI_API_KEY` is used for authentication purposes. Lastly, the `MODEL_NAME` variable specifies the particular language model to be used, such as "gpt-3.5-turbo" or any other available model.
The `OPENAI_API_BASE` variable allows you to define the base URL of the API to connect to, while `OPENAI_API_KEY` is used for authentication purposes. Lastly, the `MODEL_NAME` variable specifies the particular language model to be used, such as "gpt-3.5-turbo" or any other available model.
This method offers an easy way to adapt the system to different models or plataforms, be it for testing, scaling, or accessing different features available on various platforms. By centralizing the configuration in environment variables, the process becomes streamlined, reducing the need for extensive code modifications when switching between APIs or models.
@@ -52,7 +52,7 @@ from langchain.chat_models.openai import ChatOpenAI
load_dotenv()
defalut_llm = ChatOpenAI(openai_api_base=os.environ.get("OPENAI_API_BASE_URL", "https://api.openai.com/v1"),
defalut_llm = ChatOpenAI(openai_api_base=os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
openai_api_key=os.environ.get("OPENAI_API_KEY", "NA"),
model_name=os.environ.get("MODEL_NAME", "gpt-3.5-turbo"))
@@ -66,19 +66,19 @@ example_agent = Agent(
```
The following sections show examples of the configuration settings for various OpenAI API compatible applications and services. We have included links to relavant documentation for the various application and services.
The following sections show examples of the configuration settings for various OpenAI API compatible applications and services. We have included links to relavant documentation for the various application and services.
### Open AI
OpenAI is the default LLM that will be used if you do not specify a value for the `llm` argument when creating an agent. It will also use default values for the `OPENAI_API_BASE_URL` and `MODEL_NAME`. So the only value you need to set when using the OpenAI endpoint is the API key that from your account.
OpenAI is the default LLM that will be used if you do not specify a value for the `llm` argument when creating an agent. It will also use default values for the `OPENAI_API_BASE` and `MODEL_NAME`. So the only value you need to set when using the OpenAI endpoint is the API key that from your account.
```sh
# Required
OPENAI_API_KEY="sk-..."
# Optional
OPENAI_API_BASE_URL=https://api.openai.com/v1
OPENAI_API_BASE=https://api.openai.com/v1
MODEL_NAME="gpt-3.5-turbo"
```
@@ -93,7 +93,7 @@ FastChat is an open platform for training, serving, and evaluating large languag
Configuration settings:
```sh
# Required
OPENAI_API_BASE_URL="http://localhost:8001/v1"
OPENAI_API_BASE="http://localhost:8001/v1"
OPENAI_API_KEY=NA
MODEL_NAME='oh-2.5m7b-q51'
```
@@ -109,7 +109,7 @@ Discover, download, and run local LLMs
Configuration settings:
```sh
# Required
OPENAI_API_BASE_URL="http://localhost:8000/v1"
OPENAI_API_BASE="http://localhost:8000/v1"
OPENAI_API_KEY=NA
MODEL_NAME=NA
@@ -118,7 +118,7 @@ MODEL_NAME=NA
### Mistral API
Mistral AI's API endpoints
Mistral AI's API endpoints
[Mistral AI](https://mistral.ai/)

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@@ -1,36 +1,93 @@
<img src='./crewai_logo.png' width='250'/>
<img src='./crew_only_logo.png' width='250' class='mb-10'/>
# Welcome to crewAI Documentation
🤖 Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
# crewAI Documentation
<p align="center">
<img src='./crewAI-mindmap.png' />
</p>
Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
## Core Concepts
- [Understanding Agents](./core-concepts/Understanding-Agents.md)
- [Creating Tasks](./core-concepts/Creating-Tasks.md)
- [Defining Tasks](./core-concepts/Defining-Tasks.md)
- [Managing Processes](./core-concepts/Managing-Processes.md)
- [Collaboration and Delegation](./core-concepts/Delegation-and-Collaboration.md)
- [Agent Tools](./core-concepts/Agent-Tools.md)
## How-To Guides
- [Connecting to LLMs](./how-to/LLM-Connections.md)
- [Customizing Agents](./how-to/Customizing-Agents.md)
- [Creating a Crew and kick it off](./how-to/Creating-a-Crew-and-kick-it-off.md)
- [Human Input on Execution](./how-to/Human-Input-on-Execution.md)
## Examples and Tutorials
You can test different real life examples of AI crews [in the examples repo](https://github.com/joaomdmoura/crewAI-examples)
- [Trip Planner](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
- [Stock Analysis](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis)
- [Landing Page Generator](https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator)
- [Having Human input on the execution](./how-to/Human-Input-on-Execution.md)
## API Reference
- [Agent API](#agent-api)[WIP]
- [Task API](#task-api)[WIP]
- [Crew API](#crew-api)[WIP]
- [Process API](#process-api)[WIP]
<div style="display:flex; margin:0 auto; justify-content: center;">
<div style="width:25%">
<h2>Core Concepts</h2>
<ul>
<li>
<a href="./core-concepts/Agents">
Agents
</a>
</li>
<li>
<a href="./core-concepts/Tasks">
Tasks
</a>
</li>
<li>
<a href="./core-concepts/Tools">
Tools
</a>
</li>
<li>
<a href="./core-concepts/Processes">
Processes
</a>
</li>
<!-- <li>
<a href="./core-concepts/Managing-Processes">
Managing Processes
</a>
</li>
<li>
<a href="./core-concepts/Delegation-and-Collaboration">
Collaboration and Delegation
</a>
</li>
<li>
<a href="./core-concepts/Agent-Tools">
Agent Tools
</a>
</li> -->
</ul>
</div>
<div style="width:30%">
<h2>How-To Guides</h2>
<ul>
<li>
<a href="./how-to/LLM-Connections">
Connecting to LLMs
</a>
</li>
<li>
<a href="./how-to/Customizing-Agents">
Customizing Agents
</a>
</li>
<li>
<a href="./how-to/Creating-a-Crew-and-kick-it-off">
Creating a Crew and kick it off
</a>
</li>
<li>
<a href="./how-to/Human-Input-on-Execution">
Human Input on Execution
</a>
</li>
</ul>
</div>
<div style="width:30%">
<h2>Examples</h2>
<ul>
<li>
<a href="https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner">
Trip Planner Crew
</a>
</li>
<li>
<a href="https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis">
Stock Analysis
</a>
</li>
<li>
<a href="https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator">
Landing Page Generator
</a>
</li>
</ul>
</div>
</div>

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module.exports = {
plugins: [require('tailwindcss'), require('autoprefixer')]
}

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.md-typeset .admonition-title {
margin-bottom: 10px;
}

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/*
! tailwindcss v3.4.1 | MIT License | https://tailwindcss.com
*/
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1. Prevent padding and border from affecting element width. (https://github.com/mozdevs/cssremedy/issues/4)
2. Allow adding a border to an element by just adding a border-width. (https://github.com/tailwindcss/tailwindcss/pull/116)
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1. Change the font styles in all browsers.
2. Remove the margin in Firefox and Safari.
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1. Correct the inability to style clickable types in iOS and Safari.
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/* 1 */
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Use the modern Firefox focus style for all focusable elements.
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Remove the additional `:invalid` styles in Firefox. (https://github.com/mozilla/gecko-dev/blob/2f9eacd9d3d995c937b4251a5557d95d494c9be1/layout/style/res/forms.css#L728-L737)
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Correct the cursor style of increment and decrement buttons in Safari.
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1. Correct the odd appearance in Chrome and Safari.
2. Correct the outline style in Safari.
*/
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1. Correct the inability to style clickable types in iOS and Safari.
2. Change font properties to `inherit` in Safari.
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This can trigger a poorly considered lint error in some tools but is included by design.
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}
.m-auto {
margin: auto;
}
.my-3 {
margin-top: 0.75rem;
margin-bottom: 0.75rem;
}
.mb-3 {
margin-bottom: 0.75rem;
}
.mt-5 {
margin-top: 1.25rem;
}
.mb-5 {
margin-bottom: 1.25rem;
}
.mr-10 {
margin-right: 2.5rem;
}
.ml-10 {
margin-left: 2.5rem;
}
.mb-10 {
margin-bottom: 2.5rem;
}
.hidden {
display: none;
}
.w-1\/2 {
width: 50%;
}
.w-1\/5 {
width: 20%;
}
.w-2\/3 {
width: 66.666667%;
}
.transform {
transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y));
}
.rounded {
border-radius: 0.25rem;
}
.border-2 {
border-width: 2px;
}
.border-solid {
border-style: solid;
}
.border-red-500 {
--tw-border-opacity: 1;
border-color: rgb(239 68 68 / var(--tw-border-opacity));
}
.border-red-800 {
--tw-border-opacity: 1;
border-color: rgb(153 27 27 / var(--tw-border-opacity));
}
.bg-red-500 {
--tw-bg-opacity: 1;
background-color: rgb(239 68 68 / var(--tw-bg-opacity));
}
.bg-red-300 {
--tw-bg-opacity: 1;
background-color: rgb(252 165 165 / var(--tw-bg-opacity));
}
.bg-red-400 {
--tw-bg-opacity: 1;
background-color: rgb(248 113 113 / var(--tw-bg-opacity));
}
.p-5 {
padding: 1.25rem;
}
.p-2 {
padding: 0.5rem;
}
.p-0 {
padding: 0px;
}
.px-2 {
padding-left: 0.5rem;
padding-right: 0.5rem;
}
.py-1 {
padding-top: 0.25rem;
padding-bottom: 0.25rem;
}
.font-bold {
font-weight: 700;
}
.leading-10 {
line-height: 2.5rem;
}
.leading-3 {
line-height: .75rem;
}
.text-white {
--tw-text-opacity: 1;
color: rgb(255 255 255 / var(--tw-text-opacity));
}
.text-red-900 {
--tw-text-opacity: 1;
color: rgb(127 29 29 / var(--tw-text-opacity));
}
.text-red-800 {
--tw-text-opacity: 1;
color: rgb(153 27 27 / var(--tw-text-opacity));
}
.shadow-sm {
--tw-shadow: 0 1px 2px 0 rgb(0 0 0 / 0.05);
--tw-shadow-colored: 0 1px 2px 0 var(--tw-shadow-color);
box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow);
}
.shadow-md {
--tw-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1);
--tw-shadow-colored: 0 4px 6px -1px var(--tw-shadow-color), 0 2px 4px -2px var(--tw-shadow-color);
box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow);
}
.shadow-lg {
--tw-shadow: 0 10px 15px -3px rgb(0 0 0 / 0.1), 0 4px 6px -4px rgb(0 0 0 / 0.1);
--tw-shadow-colored: 0 10px 15px -3px var(--tw-shadow-color), 0 4px 6px -4px var(--tw-shadow-color);
box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow);
}
.transition {
transition-property: color, background-color, border-color, text-decoration-color, fill, stroke, opacity, box-shadow, transform, filter, -webkit-backdrop-filter;
transition-property: color, background-color, border-color, text-decoration-color, fill, stroke, opacity, box-shadow, transform, filter, backdrop-filter;
transition-property: color, background-color, border-color, text-decoration-color, fill, stroke, opacity, box-shadow, transform, filter, backdrop-filter, -webkit-backdrop-filter;
transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
transition-duration: 150ms;
}

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@@ -0,0 +1,3 @@
@import 'tailwindcss/base';
@import 'tailwindcss/components';
@import 'tailwindcss/utilities';

9
docs/tailwind.config.js Normal file
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@@ -0,0 +1,9 @@
/** @type {import('tailwindcss').Config} */
module.exports = {
content: ["./**/*.md"],
theme: {
extend: {},
},
plugins: [],
}

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@@ -1,9 +1,154 @@
site_name: crewAI Documentation
site_name: crewAI
site_author: crewAI, Inc
site_description: Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
repo_name: crewAI
repo_url: https://github.com/joaomdmoura/crewai/
site_url: https://crewai.com
edit_uri: edit/main/docs/
copyright: Copyright &copy; 2024 crewAI, Inc
markdown_extensions:
- abbr
- admonition
- pymdownx.details
- attr_list
- def_list
- footnotes
- md_in_html
- toc:
permalink: true
- pymdownx.arithmatex:
generic: true
- pymdownx.betterem:
smart_enable: all
- pymdownx.caret
- pymdownx.emoji:
emoji_generator: !!python/name:material.extensions.emoji.to_svg
emoji_index: !!python/name:material.extensions.emoji.twemoji
- pymdownx.highlight:
anchor_linenums: true
line_spans: __span
pygments_lang_class: true
- pymdownx.inlinehilite
- pymdownx.keys
- pymdownx.magiclink:
normalize_issue_symbols: true
repo_url_shorthand: true
user: joaomdmoura
repo: crewAI
- pymdownx.mark
- pymdownx.smartsymbols
- pymdownx.snippets:
auto_append:
- includes/mkdocs.md
- pymdownx.superfences:
custom_fences:
- name: mermaid
class: mermaid
format: !!python/name:pymdownx.superfences.fence_code_format
- pymdownx.tabbed:
alternate_style: true
combine_header_slug: true
slugify: !!python/object/apply:pymdownx.slugs.slugify
kwds:
case: lower
- pymdownx.tasklist:
custom_checkbox: true
- pymdownx.tilde
theme:
name: material
language: en
icon:
repo: fontawesome/brands/github
edit: material/pencil
view: material/eye
admonition:
note: octicons/light-bulb-16
abstract: octicons/checklist-16
info: octicons/info-16
tip: octicons/squirrel-16
success: octicons/check-16
question: octicons/question-16
warning: octicons/alert-16
failure: octicons/x-circle-16
danger: octicons/zap-16
bug: octicons/bug-16
example: octicons/beaker-16
quote: octicons/quote-16
palette:
scheme: default
primary: red
accent: red
- scheme: default
primary: red
accent: red
toggle:
icon: material/brightness-7
name: Switch to dark mode
- scheme: slate
primary: red
accent: red
toggle:
icon: material/brightness-4
name: Switch to light mode
features:
- navigation.tabs
- announce.dismiss
- content.action.edit
- content.action.view
- content.code.annotate
- content.code.copy
- content.code.select
- content.tabs.link
- content.tooltips
- header.autohide
- navigation.footer
- navigation.indexes
# - navigation.prune
# - navigation.sections
# - navigation.tabs
- search.suggest
- navigation.instant
- navigation.instant.progress
- navigation.instant.prefetch
- navigation.tracking
# - navigation.expand
- navigation.path
- navigation.top
- toc.follow
- toc.integrate
- search.highlight
- search.share
nav:
- Home: '/'
- Core Concepts:
- Agents: 'core-concepts/Agents.md'
- Tasks: 'core-concepts/Tasks.md'
- Tools: 'core-concepts/Tools.md'
- Processes: 'core-concepts/Processes.md'
- Collaboration: 'core-concepts/Collaboration.md'
- How to Guides:
- Using Sequential Process: 'how-to/Sequential.md'
- Using Hierarchical Process: 'how-to/Hierarchical.md'
- Connecting to any LLM: 'how-to/LLM-Connections.md'
- Customizing Agents: 'how-to/Customizing-Agents.md'
- Human Input on Execution: 'how-to/Human-Input-on-Execution.md'
- Examples:
- Example 1: 'examples/Example-1.md'
- Example 2: 'examples/Example-2.md'
- Example 3: 'examples/Example-3.md'
extra_css:
- stylesheets/output.css
- stylesheets/extra.css
plugins:
- social
extra:
analytics:
provider: google
property: G-N3Q505TMQ6
social:
- icon: fontawesome/brands/twitter
link: https://twitter.com/joaomdmoura
- icon: fontawesome/brands/github
link: https://github.com/joaomdmoura/crewAI

1080
poetry.lock generated

File diff suppressed because it is too large Load Diff

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@@ -23,19 +23,24 @@ openai = "^1.7.1"
langchain-openai = "^0.0.2"
pyright = "1.1.333"
black = {git = "https://github.com/psf/black.git", rev = "stable"}
mkdocs-material = {extras = ["imaging"], version = "^9.5.7"}
[tool.poetry.group.dev.dependencies]
isort = "^5.13.2"
black = "^24.1"
autoflake = "^2.2.1"
pre-commit = "^3.6.0"
mkdocs = "^1.4.3"
mkdocs-material = "^9.5.3"
mkdocstrings = "^0.22.0"
mkdocstrings-python = "^1.1.2"
[tool.isort]
profile = "black"
known_first_party = ["crewai"]
[tool.poetry.group.test.dependencies]
pytest = "^7.4"
pytest-vcr = "^1.0.2"