Preparing new version

Added the langchain "Tool" functionality by creating a python function and then adding the functionality of that function to the tool by 'func' variable in the 'Tool' function.

.github/workflows/black.yml

@@ -1,10 +1,10 @@
 name: Lint
 
-on: [push, pull_request]
+on: [pull_request]
 
 jobs:
   lint:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
-      - uses: psf/black@stable
+      - uses: psf/black@stable

.github/workflows/mkdocs.yml

@@ -1,7 +1,7 @@
 name: Deploy MkDocs
 
 on:
-  workflow_dispatch:    
+  workflow_dispatch:
   push:
     branches:
       - main
@@ -22,11 +22,23 @@ 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 &&
           sudo apt-get install pngquant &&
-          pip install mkdocs-material
+          pip install mkdocs-material mkdocs-material-extensions pillow cairosvg
 
         env:
           GH_TOKEN: ${{ secrets.GH_TOKEN }}

.github/workflows/tests.yml

@@ -1,6 +1,6 @@
 name: Run Tests
 
-on: [push, pull_request]
+on: [pull_request]
 
 permissions:
   contents: write

.github/workflows/type-checker.yml

@@ -0,0 +1,30 @@
+
+name: Run Type Checks
+
+on: [pull_request]
+
+permissions:
+  contents: write
+
+jobs:
+  type-checker:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+
+      - name: Install Requirements
+        run: |
+          sudo apt-get update &&
+          pip install poetry &&
+          poetry lock &&
+          poetry install
+
+      - name: Run type checks
+        run: poetry run pyright

.gitignore

@@ -5,4 +5,6 @@ dist/
 .env
 assets/*
 .idea
-test.py
+test/
+docs_crew/
+chroma.sqlite3

.pre-commit-config.yaml

@@ -1,11 +1,11 @@
 repos:
-
   - repo: https://github.com/psf/black-pre-commit-mirror
     rev: 23.12.1
     hooks:
       - id: black
         language_version: python3.11
         files: \.(py)$
+        exclude: 'src/crewai/cli/templates/(crew|main)\.py'
 
   - repo: https://github.com/pycqa/isort
     rev: 5.13.2

README.md

@@ -1,71 +1,77 @@
-# crewAI
+<div align="center">
 
-![Logo of crewAI, tow people rowing on a boat](./docs/crewai_logo.png)
+![Logo of crewAI, two people rowing on a boat](./docs/crewai_logo.png)
 
-🤖 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**
 
-- [crewAI](#crewai)
-  - [Why CrewAI?](#why-crewai)
-  - [Getting Started](#getting-started)
-  - [Key Features](#key-features)
-  - [Examples](#examples)
-    - [Code](#code)
-    - [Video](#video)
-      - [Quick Tutorial](#quick-tutorial)
-      - [Trip Planner](#trip-planner)
-      - [Stock Analysis](#stock-analysis)
-  - [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model)
-  - [How CrewAI Compares](#how-crewai-compares)
-  - [Contribution](#contribution)
-    - [Installing Dependencies](#installing-dependencies)
-    - [Virtual Env](#virtual-env)
-    - [Pre-commit hooks](#pre-commit-hooks)
-    - [Running Tests](#running-tests)
-    - [Packaging](#packaging)
-    - [Installing Locally](#installing-locally)
-  - [Hire CrewAI](#hire-crewai)
-  - [License](#license)
+🤖 **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.
+
+<h3>
+
+[Homepage](https://www.crewai.io/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Examples](https://github.com/joaomdmoura/crewai-examples) | [Discord](https://discord.com/invite/X4JWnZnxPb)
+
+</h3>
+
+[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/joaomdmoura/crewAI)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+
+</div>
+
+## Table of contents
+
+- [Why CrewAI?](#why-crewai)
+- [Getting Started](#getting-started)
+- [Key Features](#key-features)
+- [Examples](#examples)
+  - [Quick Tutorial](#quick-tutorial)
+  - [Write Job Descriptions](#write-job-descriptions)
+  - [Trip Planner](#trip-planner)
+  - [Stock Analysis](#stock-analysis)
+- [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model)
+- [How CrewAI Compares](#how-crewai-compares)
+- [Contribution](#contribution)
+- [Hire CrewAI](#hire-crewai)
+- [Telemetry](#telemetry)
+- [License](#license)
 
 ## Why 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.
 
-- 🤖 [Talk with the Docs](https://chatg.pt/DWjSBZn)
-- 📄 [Documentation Wiki](https://joaomdmoura.github.io/crewAI/)
-
 ## Getting Started
 
 To get started with CrewAI, follow these simple steps:
 
-1. **Installation**:
+### 1. Installation
 
 ```shell
 pip install crewai
 ```
 
-The example below also uses duckduckgo, so also install that
+If 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, example bellow uses it:
+
 ```shell
-pip install duckduckgo-search
+pip install 'crewai[tools]'
 ```
 
-2. **Setting Up Your Crew**:
+### 2. Setting Up Your Crew
 
 ```python
 import os
 from crewai import Agent, Task, Crew, Process
+from crewai_tools import SerperDevTool
 
-os.environ["OPENAI_API_KEY"] = "YOUR KEY"
+os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 
-# You can choose to use a local model through Ollama for example. See ./docs/llm-connections.md for more information.
-# from langchain.llms import Ollama
-# ollama_llm = Ollama(model="openhermes")
+# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
 
-# Install duckduckgo-search for this example:
-# !pip install -U duckduckgo-search
+# os.environ["OPENAI_API_BASE"] = 'http://localhost:11434/v1'
+# os.environ["OPENAI_MODEL_NAME"] ='openhermes'  # Adjust based on available model
+# os.environ["OPENAI_API_KEY"] ='sk-111111111111111111111111111111111111111111111111'
 
-from langchain.tools import DuckDuckGoSearchRun
-search_tool = DuckDuckGoSearchRun()
+search_tool = SerperDevTool()
 
 # Define your agents with roles and goals
 researcher = Agent(
@@ -73,37 +79,36 @@ researcher = Agent(
   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.""",
+  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://python.langchain.com/docs/integrations/llms/)
+  # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
   #
-  # Examples:
-  # llm=ollama_llm # was defined above in the file
-  # llm=OpenAI(model_name="gpt-3.5", temperature=0.7)
-  # For the OpenAI model you would need to import
-  # from langchain_openai import OpenAI
+  # 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.
+  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,
-  # (optional) llm=ollama_llm
+  allow_delegation=True
 )
 
 # Create tasks for your agents
 task1 = Task(
   description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-  Identify key trends, breakthrough technologies, and potential industry impacts.
-  Your final answer MUST be a full analysis report""",
+  Identify key trends, breakthrough technologies, and potential industry impacts.""",
+  expected_output="Full analysis report in bullet points",
   agent=researcher
 )
 
@@ -111,8 +116,8 @@ task2 = Task(
   description="""Using the insights provided, develop an engaging blog
   post that highlights the most significant AI advancements.
   Your post should be informative yet accessible, catering to a tech-savvy audience.
-  Make it sound cool, avoid complex words so it doesn't sound like AI.
-  Your final answer MUST be the full blog post of at least 4 paragraphs.""",
+  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
 )
 
@@ -130,47 +135,60 @@ print("######################")
 print(result)
 ```
 
-Currently the only supported process is `Process.sequential`, where one task is executed after the other and the outcome of one is passed as extra content into this next.
-
+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/Processes/).
 
 ## Key Features
 
 - **Role-Based Agent Design**: Customize agents with specific roles, goals, and tools.
 - **Autonomous Inter-Agent Delegation**: Agents can autonomously delegate tasks and inquire amongst themselves, enhancing problem-solving efficiency.
 - **Flexible Task Management**: Define tasks with customizable tools and assign them to agents dynamically.
-- **Processes Driven**: Currently only supports `sequential` task execution but more complex processes like consensual and hierarchical being worked on.
-- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](./docs/llm-connections.md) page for details on configuring you agents' connections to models, even ones running locally!
+- **Processes Driven**: Currently only supports `sequential` task execution and `hierarchical` processes, but more complex processes like consensual and autonomous are being worked on.
+- **Save output as file**: Save the output of individual tasks as a file, so you can use it later.
+- **Parse output as Pydantic or Json**: Parse the output of individual tasks as a Pydantic model or as a Json if you want to.
+- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models, even ones running locally!
 
 ![CrewAI Mind Map](./docs/crewAI-mindmap.png "CrewAI Mind Map")
 
 ## Examples
-You can test different real life examples of AI crews [in the examples repo](https://github.com/joaomdmoura/crewAI-examples?tab=readme-ov-file)
 
-### Code
+You can test different real life examples of AI crews in the [crewAI-examples repo](https://github.com/joaomdmoura/crewAI-examples?tab=readme-ov-file):
+
+- [Landing Page Generator](https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator)
+- [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
 - [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](./docs/how-to/Human-Input-on-Execution.md)
 
-### Video
-#### Quick Tutorial
-[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/0.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
+### Quick Tutorial
 
-#### Trip Planner
-[![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/0.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
+[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/maxresdefault.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
 
-#### Stock Analysis
-[![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/0.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
+### Write Job Descriptions
+
+[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting) or watch a video below:
+
+[![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
+
+### Trip Planner
+
+[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner) or watch a video below:
+
+[![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
+
+### Stock Analysis
+
+[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+
+[![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 
 ## Connecting Your Crew to a Model
 
 crewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
 
-Please refer to the [Connect crewAI to LLMs](./docs/how-to/llm-connections.md) page for details on configuring you agents' connections to models.
+Please 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.
 
 ## How CrewAI Compares
 
-- **Autogen**: While Autogen excels 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.
+- **Autogen**: While Autogen does good in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
 
 - **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
 
@@ -187,12 +205,14 @@ CrewAI is open-source and we welcome contributions. If you're looking to contrib
 - We appreciate your input!
 
 ### Installing Dependencies
+
 ```bash
 poetry lock
 poetry install
 ```
 
 ### Virtual Env
+
 ```bash
 poetry shell
 ```
@@ -204,25 +224,64 @@ pre-commit install
 ```
 
 ### Running Tests
+
 ```bash
 poetry run pytest
 ```
 
+### Running static type checks
+
+```bash
+poetry run pyright
+```
+
 ### Packaging
+
 ```bash
 poetry build
 ```
 
 ### Installing Locally
+
 ```bash
 pip install dist/*.tar.gz
 ```
 
 ## Hire CrewAI
+
 We'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 at [sales@crewai.io](mailto:sales@crewai.io)
+If you are interested on having access to it and hiring weekly hours with our team, feel free to email us at [joao@crewai.com](mailto:joao@crewai.com).
+
+## Telemetry
+
+CrewAI 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 crewAI
+  - So we can understand how many users are using the latest version
+- Version of Python
+  - So we can decide on what versions to better support
+- General OS (e.g. number of CPUs, macOS/Windows/Linux)
+  - So we know what OS we should focus on and if we could build specific OS related features
+- Number of agents and tasks in a crew
+  - So we make sure we are testing internally with similar use cases and educate people on the best practices
+- Crew Process being used
+  - Understand where we should focus our efforts
+- If Agents are using memory or allowing delegation
+  - Understand if we improved the features or maybe even drop them
+- If Tasks are being executed in parallel or sequentially
+  - Understand if we should focus more on parallel execution
+- Language model being used
+  - Improved support on most used languages
+- Roles of agents in a crew
+  - Understand high level use cases so we can build better tools, integrations and examples about it
+- Tools names available
+  - Understand out of the publically available tools, which ones are being used the most so we can improve them
+
+Users can opt-in sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews.
 
 ## License
-CrewAI is released under the MIT License
-
 
+CrewAI is released under the MIT License.

docs/core-concepts/Agent-Tools.md

@@ -1,90 +0,0 @@
-# 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.

docs/core-concepts/Agents.md

@@ -0,0 +1,66 @@
+---
+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>
+    <ul>
+      <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.                                                                                                                                           |
+| **LLM** *(optional)*                 | The language model used by the agent to process and generate text. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified.                                            |
+| **Tools** *(optional)*               | Set of capabilities or functions that the agent can use to perform tasks. Tools can be shared or exclusive to specific agents. It's an attribute that can be set during the initialization of an agent, with a default value of an empty list.  |
+| **Function Calling LLM** *(optional)*  | If passed, this agent will use this LLM to execute function calling for tools instead of relying on the main LLM output.                                                                                                                       |
+| **Max Iter** *(optional)*            | The maximum number of iterations the agent can perform before being forced to give its best answer. Default is `15`.                                                                                                                           |
+| **Max RPM** *(optional)*             | The maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
+| **Verbose** *(optional)*             | Enables detailed logging of the agent's execution for debugging or monitoring purposes when set to True. Default is `False`.                                                                                                                    |
+| **Allow Delegation** *(optional)*    | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`.                                                                                                       |
+| **Step Callback** *(optional)*       | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                                |
+| **Memory** *(optional)*             | Indicates whether the agent should have memory or not, with a default value of False. This impacts the agent's ability to remember past interactions. Default is `False`.                                                                      |
+
+## Creating an Agent
+
+!!! note "Agent Interaction"
+    Agents can interact with each other using crewAI's built-in delegation and communication mechanisms. 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 including all attributes:
+
+```python
+# Example: Creating an agent with all attributes
+from crewai import Agent
+
+agent = Agent(
+  role='Data Analyst',
+  goal='Extract actionable insights',
+  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.""",
+  tools=[my_tool1, my_tool2],  # Optional, defaults to an empty list
+  llm=my_llm,  # Optional
+  function_calling_llm=my_llm,  # Optional
+  max_iter=15,  # Optional
+  max_rpm=None, # Optional
+  verbose=True,  # Optional
+  allow_delegation=True,  # Optional
+  step_callback=my_intermediate_step_callback,  # Optional
+  memory=True  # Optional
+)
+```
+
+## 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.

docs/core-concepts/Collaboration.md

@@ -0,0 +1,38 @@
+---
+title: How Agents Collaborate in CrewAI
+description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.
+---
+
+## 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.
+
+## Enhanced Attributes for Improved Collaboration
+The `Crew` class has been enriched with several attributes to support advanced functionalities:
+
+- **Language Model Management (`manager_llm`, `function_calling_llm`)**: Manages language models for executing tasks and tools, facilitating sophisticated agent-tool interactions. It's important to note that `manager_llm` is mandatory when using a hierarchical process for ensuring proper execution flow.
+- **Process Flow (`process`)**: Defines the execution logic (e.g., sequential, hierarchical) to streamline task distribution and execution.
+- **Verbose Logging (`verbose`)**: Offers detailed logging capabilities for monitoring and debugging purposes. It supports both integer and boolean types to indicate the verbosity level.
+- **Configuration (`config`)**: Allows extensive customization to tailor the crew's behavior according to specific requirements.
+- **Rate Limiting (`max_rpm`)**: Ensures efficient utilization of resources by limiting requests per minute.
+- **Internationalization Support (`language`)**: Facilitates operation in multiple languages, enhancing global usability.
+- **Execution and Output Handling (`full_output`)**: Distinguishes between full and final outputs for nuanced control over task results.
+- **Callback and Telemetry (`step_callback`)**: Integrates callbacks for step-wise execution monitoring and telemetry for performance analytics.
+- **Crew Sharing (`share_crew`)**: Enables sharing of crew information with CrewAI for continuous improvement and training models.
+- **Usage Metrics (`usage_metrics`)**: Store all metrics for the language model (LLM) usage during all tasks' execution, providing insights into operational efficiency and areas for improvement, you can check it after the crew execution.
+
+## 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, with enhanced customization and monitoring features to adapt to various operational needs.
+
+## Example Scenario
+Consider a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The integration of advanced language model management and process flow attributes allows for more sophisticated interactions, such as the writer delegating complex research tasks to the researcher or querying specific information, thereby facilitating a seamless workflow.
+
+## Conclusion
+The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.

docs/core-concepts/Creating-Tasks.md

@@ -1,50 +0,0 @@
-# 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.

docs/core-concepts/Crews.md

@@ -0,0 +1,98 @@
+---
+title: crewAI Crews
+description: Understanding and utilizing crews in the crewAI framework.
+---
+
+## What is a Crew?
+!!! note "Definition of a Crew"
+    A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
+
+## Crew Attributes
+
+| Attribute               | Description                                                  |
+| :---------------------- | :----------------------------------------------------------- |
+| **Tasks**               | A list of tasks assigned to the crew.                        |
+| **Agents**              | A list of agents that are part of the crew.                  |
+| **Process** *(optional)*              | The process flow (e.g., sequential, hierarchical) the crew follows. |
+| **Verbose** *(optional)*              | The verbosity level for logging during execution.            |
+| **Manager LLM** *(optional)*          | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
+| **Function Calling LLM** *(optional)* | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling. |
+| **Config** *(optional)*               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
+| **Max RPM** *(optional)*              | Maximum requests per minute the crew adheres to during execution. |
+| **Language**  *(optional)*            | Language used for the crew, defaults to English.             |
+| **Full Output** *(optional)*          | Whether the crew should return the full output with all tasks outputs or just the final output. |
+| **Step Callback** *(optional)*        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
+| **Share Crew** *(optional)*           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
+
+!!! note "Crew Max RPM"
+		The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
+
+## Creating a Crew
+
+!!! note "Crew Composition"
+    When assembling a crew, you combine agents with complementary roles and tools, assign tasks, and select a process that dictates their execution order and interaction.
+
+### Example: Assembling a Crew
+
+```python
+from crewai import Crew, Agent, Task, Process
+from langchain_community.tools import DuckDuckGoSearchRun
+
+# Define agents with specific roles and tools
+researcher = Agent(
+    role='Senior Research Analyst',
+    goal='Discover innovative AI technologies',
+    tools=[DuckDuckGoSearchRun()]
+)
+
+writer = Agent(
+    role='Content Writer',
+    goal='Write engaging articles on AI discoveries',
+    verbose=True
+)
+
+# Create tasks for the agents
+research_task = Task(
+    description='Identify breakthrough AI technologies',
+    agent=researcher
+)
+write_article_task = Task(
+    description='Draft an article on the latest AI technologies',
+    agent=writer
+)
+
+# Assemble the crew with a sequential process
+my_crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, write_article_task],
+    process=Process.sequential,
+    full_output=True,
+    verbose=True,
+)
+```
+
+## Crew Usage Metrics
+
+After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.
+
+```python
+# Access the crew's usage metrics
+crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
+crew.kickoff()
+print(crew.usage_metrics)
+```
+
+## Crew Execution Process
+
+- **Sequential Process**: Tasks are executed one after another, allowing for a linear flow of work.
+- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. **Note**: A `manager_llm` is required for this process and it's essential for validating the process flow.
+
+### Kicking Off a Crew
+
+Once your crew is assembled, initiate the workflow with the `kickoff()` method. This starts the execution process according to the defined process flow.
+
+```python
+# Start the crew's task execution
+result = my_crew.kickoff()
+print(result)
+```

docs/core-concepts/Defining-Tasks.md

@@ -1,42 +0,0 @@
-# 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. Here’s 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.

docs/core-concepts/Delegation-and-Collaboration.md

@@ -1,26 +0,0 @@
-# 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.

docs/core-concepts/Managing-Processes.md

@@ -1,49 +0,0 @@
-# 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 is the only process currently implemented in CrewAI. It 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.
-- **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.
-- **Hierarchical (WIP)**: Also in the pipeline, this process will introduce a chain of command to task execution, where some agents may have the authority to prioritize tasks or delegate them, akin to a traditional corporate hierarchy.
-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.
-
-
-## Defining a Sequential Process
-
-Creating a sequential process in CrewAI is straightforward and reflects the simplicity of coordinating a team's efforts step by step. In this process the outcome of the previous task is sent into the next one as context that I should use to accomplish it's task
-
-```python
-from crewai import Process
-
-# Define a sequential process
-sequential_process = Process.sequential
-```
-
-# The Magic of Sequential Processes
-
-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.
-
-## 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.
-
-```python
-from crewai import Crew
-
-# Create a crew with a sequential process
-crew = Crew(agents=my_agents, tasks=my_tasks, process=sequential_process)
-```
-
-## 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.
-
-## 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.

docs/core-concepts/Processes.md

@@ -0,0 +1,60 @@
+---
+title: Managing Processes in CrewAI
+description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
+---
+
+## Understanding Processes
+!!! note "Core Concept"
+    In CrewAI, processes orchestrate the execution of tasks by agents, akin to project management in human teams. These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.
+
+## Process Implementations
+
+- **Sequential**: Executes tasks sequentially, ensuring tasks are completed in an orderly progression.
+- **Hierarchical**: Organizes tasks in a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command. Note: A manager language model (`manager_llm`) must be specified in the crew to enable the hierarchical process, allowing for the creation and management of tasks by the manager.
+- **Consensual Process (Planned)**: Currently under consideration for future development, this process type aims for collaborative decision-making among agents on task execution, introducing a more democratic approach to task management within CrewAI. As of now, it is not implemented in the codebase.
+
+## The Role of Processes in Teamwork
+Processes enable individual agents to operate as a cohesive unit, streamlining their efforts to achieve common objectives with efficiency and coherence.
+
+## Assigning Processes to a Crew
+To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. Note: For a hierarchical process, ensure to define `manager_llm` for the manager agent.
+
+```python
+from crewai import Crew
+from crewai.process import Process
+from langchain_openai import ChatOpenAI
+
+# 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
+# Ensure to provide a manager_llm
+crew = Crew(
+    agents=my_agents,
+    tasks=my_tasks,
+    process=Process.hierarchical,
+    manager_llm=ChatOpenAI(model="gpt-4")
+)
+```
+**Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object, and for the hierarchical process, `manager_llm` is also required.
+
+## Sequential Process
+This method mirrors dynamic team workflows, progressing through tasks in a thoughtful and systematic manner. Task execution follows the predefined order in the task list, with the output of one task serving as context for the next.
+
+To customize task context, utilize the `context` parameter in the `Task` class to specify outputs that should be used as context for subsequent tasks.
+
+## Hierarchical Process
+Emulates a corporate hierarchy, crewAI creates a manager automatically for you, requiring the specification of a manager language model (`manager_llm`) for the manager agent. This agent oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities, reviews outputs, and assesses task completion.
+
+## Process Class: Detailed Overview
+The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`, and future `consensual`). This design choice guarantees that only valid processes are utilized within the CrewAI framework.
+
+## Planned Future Processes
+- **Consensual Process**: This collaborative decision-making process among agents on task execution is under consideration but not currently implemented. This future enhancement aims to introduce a more democratic approach to task management within CrewAI.
+
+## Conclusion
+The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. Documentation will be regularly updated to reflect new processes and enhancements, ensuring users have access to the most current and comprehensive information.

docs/core-concepts/Tasks.md

@@ -0,0 +1,227 @@
+---
+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, 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**      | 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. |
+| **Async Execution** *(optional)*      | Indicates whether the task should be executed asynchronously, allowing the crew to continue with the next task without waiting for completion. |
+| **Context**  *(optional)*     | Other tasks that will have their output used as context for this task. If a task is asynchronous, the system will wait for that to finish before using its output as context. |
+| **Output JSON**  *(optional)*     | Takes a pydantic model and returns the output as a JSON object. **Agent LLM needs to be using an OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
+| **Output Pydantic**  *(optional)*     | Takes a pydantic model and returns the output as a pydantic object. **Agent LLM needs to be using an OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
+| **Output File**  *(optional)*     | Takes a file path and saves the output of the task on it. |
+| **Callback**  *(optional)*  | A function to be executed after the task is completed. |
+
+## Creating a Task
+
+This is the simplest 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"
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
+
+from crewai import Agent, Task, Crew
+from crewai_tools import SerperDevTool
+
+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
+)
+
+search_tool = SerperDevTool()
+
+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.
+
+## Referring to Other Tasks
+
+In crewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output, including multiple 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_ai_task = Task(
+  description='Find and summarize the latest AI news',
+  expected_output='A bullet list summary of the top 5 most important AI news',
+  async_execution=True,
+  agent=research_agent,
+  tools=[search_tool]
+)
+
+research_ops_task = Task(
+  description='Find and summarize the latest AI Ops news',
+  expected_output='A bullet list summary of the top 5 most important AI Ops news',
+  async_execution=True,
+  agent=research_agent,
+  tools=[search_tool]
+)
+
+write_blog_task = Task(
+  description="Write a full blog post about the importance of AI and its latest news",
+  expected_output='Full blog post that is 4 paragraphs long',
+  agent=writer_agent,
+  context=[research_ai_task, research_ops_task]
+)
+
+#...
+```
+
+## Asynchronous Execution
+
+You can define a task to be executed asynchronously. This means that the crew will not wait for it to be completed to continue with the next task. This is useful for tasks that take a long time to be completed, or that are not crucial for the next tasks to be performed.
+
+You can then use the `context` attribute to define in a future task that it should wait for the output of the asynchronous task to be completed.
+
+```python
+#...
+
+list_ideas = Task(
+	description="List of 5 interesting ideas to explore for an article about AI.",
+	expected_output="Bullet point list of 5 ideas for an article.",
+	agent=researcher,
+	async_execution=True # Will be executed asynchronously
+)
+
+list_important_history = Task(
+	description="Research the history of AI and give me the 5 most important events.",
+	expected_output="Bullet point list of 5 important events.",
+	agent=researcher,
+	async_execution=True # Will be executed asynchronously
+)
+
+write_article = Task(
+	description="Write an article about AI, its history, and interesting ideas.",
+	expected_output="A 4 paragraph article about AI.",
+	agent=writer,
+	context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
+)
+
+#...
+```
+
+## Callback Mechanism
+
+The callback function is executed after the task is completed, allowing for actions or notifications to be triggered based on the task's outcome.
+
+```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.raw_output}
+	""")
+
+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
+)
+
+#...
+```
+
+## Accessing a Specific Task Output
+
+Once a crew finishes running, you can access the output of a specific task by using the `output` attribute of the task object:
+
+```python
+# ...
+task1 = 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=[task1, task2, task3],
+	verbose=2
+)
+
+result = crew.kickoff()
+
+# Returns a TaskOutput object with the description and results of the task
+print(f"""
+	Task completed!
+	Task: {task1.output.description}
+	Output: {task1.output.raw_output}
+""")
+```
+
+## Tool Override Mechanism
+
+Specifying tools in a task allows for dynamic adaptation of agent capabilities, emphasizing CrewAI's flexibility.
+
+## Error Handling and Validation Mechanisms
+
+While creating and executing tasks, certain validation mechanisms are in place to ensure the robustness and reliability of task attributes. These include but are not limited to:
+
+- Ensuring only one output type is set per task to maintain clear output expectations.
+- Preventing the manual assignment of the `id` attribute to uphold the integrity of the unique identifier system.
+
+These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
+
+## 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, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/core-concepts/Tools.md

@@ -0,0 +1,231 @@
+---
+title: crewAI Tools
+description: Understanding and leveraging tools within the crewAI framework for agent collaboration and task execution.
+---
+
+## Introduction
+CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers. This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
+
+## What is a Tool?
+!!! note "Definition"
+    A tool in CrewAI is a skill or function that agents can utilize to perform various actions. This includes tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools), enabling everything from simple searches to complex interactions and effective teamwork among agents.
+
+## Key Characteristics of Tools
+
+- **Utility**: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
+- **Integration**: Boosts agent capabilities by seamlessly integrating tools into their workflow.
+- **Customizability**: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
+
+## Using crewAI Tools
+
+To enhance your agents' capabilities with crewAI tools, begin by installing our extra tools package:
+
+```bash
+pip install 'crewai[tools]'
+```
+
+Here's an example demonstrating their use:
+
+```python
+import os
+from crewai import Agent, Task, Crew
+# Importing crewAI tools
+from crewai_tools import (
+    DirectoryReadTool,
+    FileReadTool,
+    SerperDevTool,
+    WebsiteSearchTool
+)
+
+# Set up API keys
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
+os.environ["OPENAI_API_KEY"] = "Your Key"
+
+# Instantiate tools
+docs_tool = DirectoryReadTool(directory='./blog-posts')
+file_tool = FileReadTool()
+search_tool = SerperDevTool()
+web_rag_tool = WebsiteSearchTool()
+
+# Create agents
+researcher = Agent(
+    role='Market Research Analyst',
+    goal='Provide up-to-date market analysis of the AI industry',
+    backstory='An expert analyst with a keen eye for market trends.',
+    tools=[search_tool, web_rag_tool],
+    verbose=True
+)
+
+writer = Agent(
+    role='Content Writer',
+    goal='Craft engaging blog posts about the AI industry',
+    backstory='A skilled writer with a passion for technology.',
+    tools=[docs_tool, file_tool],
+    verbose=True
+)
+
+# Define tasks
+research = Task(
+    description='Research the latest trends in the AI industry and provide a summary.',
+    expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
+    agent=researcher
+)
+
+write = Task(
+    description='Write an engaging blog post about the AI industry, based on the research analyst’s summary. Draw inspiration from the latest blog posts in the directory.',
+    expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
+    agent=writer,
+    output_file='blog-posts/new_post.md'  # The final blog post will be saved here
+)
+
+# Assemble a crew
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research, write],
+    verbose=2
+)
+
+# Execute tasks
+crew.kickoff()
+```
+
+## Available crewAI Tools
+
+Most of the tools in the crewAI toolkit offer the ability to set specific arguments or let them to be more wide open, this is the case for most of the tools, for example:
+
+```python
+from crewai_tools import DirectoryReadTool
+
+# This will allow the agent with this tool to read any directory it wants during it's execution
+tool = DirectoryReadTool()
+
+# OR
+
+# This will allow the agent with this tool to read only the directory specified during it's execution
+toos = DirectoryReadTool(directory='./directory')
+```
+
+Specific per tool docs are coming soon.
+Here is a list of the available tools and their descriptions:
+
+| Tool                        | Description                                                                                   |
+| :-------------------------- | :-------------------------------------------------------------------------------------------- |
+| **CodeDocsSearchTool**      | A RAG tool optimized for searching through code documentation and related technical documents.|
+| **CSVSearchTool**           | A RAG tool designed for searching within CSV files, tailored to handle structured data.       |
+| **DirectorySearchTool**     | A RAG tool for searching within directories, useful for navigating through file systems.      |
+| **DOCXSearchTool**          | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.          |
+| **DirectoryReadTool**       | Facilitates reading and processing of directory structures and their contents.                |
+| **FileReadTool**            | Enables reading and extracting data from files, supporting various file formats.              |
+| **GithubSearchTool**        | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
+| **SeperDevTool**            | A specialized tool for development purposes, with specific functionalities under development. |
+| **TXTSearchTool**           | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.      |
+| **JSONSearchTool**          | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
+| **MDXSearchTool**           | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.      |
+| **PDFSearchTool**           | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
+| **PGSearchTool**            | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
+| **RagTool**                 | A general-purpose RAG tool capable of handling various data sources and types.                 |
+| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.     |
+| **ScrapeWebsiteTool**       | Facilitates scraping entire websites, ideal for comprehensive data collection.                 |
+| **WebsiteSearchTool**       | A RAG tool for searching website content, optimized for web data extraction.                   |
+| **XMLSearchTool**           | A RAG tool designed for searching within XML files, suitable for structured data formats.      |
+| **YoutubeChannelSearchTool**| A RAG tool for searching within YouTube channels, useful for video content analysis.           |
+| **YoutubeVideoSearchTool**  | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.          |
+
+## Creating your own Tools
+!!! example "Custom Tool Creation"
+    Developers can craft custom tools tailored for their agent’s needs or utilize pre-built options:
+
+To create your own crewAI tools you will need to install our extra tools package:
+
+```bash
+pip install 'crewai[tools]'
+```
+
+Once you do that there are two main ways for one to create a crewAI tool:
+
+### Subclassing `BaseTool`
+
+```python
+from crewai_tools import BaseTool
+
+class MyCustomTool(BaseTool):
+    name: str = "Name of my tool"
+    description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
+
+    def _run(self, argument: str) -> str:
+        # Implementation goes here
+        return "Result from custom tool"
+```
+
+Define a new class inheriting from `BaseTool`, specifying `name`, `description`, and the `_run` method for operational logic.
+
+
+### Utilizing the `tool` Decorator
+
+For a simpler approach, create a `Tool` object directly with the required attributes and a functional logic.
+
+```python
+from crewai_tools import tool
+@tool("Name of my tool")
+def my_tool(question: str) -> str:
+    """Clear description for what this tool is useful for, you agent will need this information to use it."""
+    # Function logic here
+```
+
+```python
+import json
+import requests
+from crewai import Agent
+from crewai.tools import tool
+from unstructured.partition.html import partition_html
+
+    # Annotate the function with the tool decorator from crewAI
+@tool("Integration with a given API")
+def integration_tool(argument: str) -> str:
+    """Integration with a given API"""
+    # Code here
+    return resutls # string to be sent back to the agent
+
+# 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=[integration_tool]
+)
+```
+
+## Using LangChain Tools
+!!! info "LangChain Integration"
+    CrewAI seamlessly integrates with LangChain’s comprehensive toolkit for search-based queries and more, here are the available built-in tools that are offered by Langchain [LangChain Toolkit](https://python.langchain.com/docs/integrations/tools/)
+ :
+
+```python
+from crewai import Agent
+from langchain.agents import Tool
+from langchain.utilities import GoogleSerperAPIWrapper
+
+# Setup API keys
+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]
+)
+
+# rest of the code ...
+```
+
+## Conclusion
+Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem.

docs/core-concepts/Understanding-Agents.md

@@ -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.

docs/getting-started.md

@@ -1,95 +0,0 @@
-# Getting Started
-
-To get started with CrewAI, follow these simple steps:
-
-1. **Installation**:
-
-```shell
-pip install crewai
-```
-
-The example below also uses duckduckgo, so also install that
-```shell
-pip install duckduckgo-search
-```
-
-2. **Setting Up Your Crew**:
-
-```python
-import os
-from crewai import Agent, Task, Crew, Process
-
-os.environ["OPENAI_API_KEY"] = "YOUR KEY"
-
-# You can choose to use a local model through Ollama for example. See ./docs/llm-connections.md for more information.
-# from langchain.llms import Ollama
-# ollama_llm = Ollama(model="openhermes")
-
-# Install duckduckgo-search for this example:
-# !pip install -U duckduckgo-search
-
-from langchain.tools import DuckDuckGoSearchRun
-search_tool = DuckDuckGoSearchRun()
-
-# Define your agents with roles and goals
-researcher = Agent(
-  role='Senior Research Analyst',
-  goal='Uncover cutting-edge developments in AI and data science',
-  backstory="""You work at a leading tech think tank.
-  Your expertise lies in identifying emerging trends.
-  You have a knack for dissecting complex data and presenting
-  actionable insights.""",
-  verbose=True,
-  allow_delegation=False,
-  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 of others (https://python.langchain.com/docs/integrations/llms/)
-  #
-  # Examples:
-  # llm=ollama_llm # was defined above in the file
-  # 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,
-  # (optional) llm=ollama_llm
-)
-
-# Create tasks for your agents
-task1 = Task(
-  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-  Identify key trends, breakthrough technologies, and potential industry impacts.
-  Your final answer MUST be a full analysis report""",
-  agent=researcher
-)
-
-task2 = Task(
-  description="""Using the insights provided, develop an engaging blog
-  post that highlights the most significant AI advancements.
-  Your post should be informative yet accessible, catering to a tech-savvy audience.
-  Make it sound cool, avoid complex words so it doesn't sound like AI.
-  Your final answer MUST be the full blog post of at least 4 paragraphs.""",
-  agent=writer
-)
-
-# Instantiate your crew with a sequential process
-crew = Crew(
-  agents=[researcher, writer],
-  tasks=[task1, task2],
-  verbose=2, # You can set it to 1 or 2 to different logging levels
-)
-
-# Get your crew to work!
-result = crew.kickoff()
-
-print("######################")
-print(result)
-```
-
-Currently the only supported process is `Process.sequential`, where one task is executed after the other and the outcome of one is passed as extra content into this next.

docs/how-to/Create-Custom-Tools.md

@@ -0,0 +1,91 @@
+---
+title: Creating your own Tools
+description: Guide on how to create and use custom tools within the crewAI framework.
+---
+
+## Creating your own Tools
+!!! example "Custom Tool Creation"
+    Developers can craft custom tools tailored for their agent’s needs or utilize pre-built options:
+
+To create your own crewAI tools you will need to install our extra tools package:
+
+```bash
+pip install 'crewai[tools]'
+```
+
+Once you do that there are two main ways for one to create a crewAI tool:
+
+### Subclassing `BaseTool`
+
+```python
+from crewai_tools import BaseTool
+
+class MyCustomTool(BaseTool):
+    name: str = "Name of my tool"
+    description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
+
+    def _run(self, argument: str) -> str:
+        # Implementation goes here
+        return "Result from custom tool"
+```
+
+Define a new class inheriting from `BaseTool`, specifying `name`, `description`, and the `_run` method for operational logic.
+
+
+### Utilizing the `tool` Decorator
+
+For a simpler approach, create a `Tool` object directly with the required attributes and a functional logic.
+
+```python
+from crewai_tools import tool
+@tool("Name of my tool")
+def my_tool(question: str) -> str:
+    """Clear description for what this tool is useful for, you agent will need this information to use it."""
+    # Function logic here
+```
+
+```python
+import json
+import requests
+from crewai import Agent
+from crewai.tools import tool
+from unstructured.partition.html import partition_html
+
+    # Annotate the function with the tool decorator from crewAI
+@tool("Integration with a given API")
+def integtation_tool(argument: str) -> str:
+    """Integration with a given API"""
+    # Code here
+    return resutls # string to be sent back to the agent
+
+# 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=[integtation_tool]
+)
+```
+
+
+
+### Using the `Tool` function from langchain
+
+For another simple approach, create a function in python directly with the required attributes and a functional logic.
+
+```python
+def combine(a, b):
+    return a + b
+```
+
+Then you can add that function into the your tool by using 'func' variable in the Tool function.
+
+```python
+from langchain.agents import Tool
+
+math_tool = Tool(
+            name="Math tool",
+            func=math_tool,
+            description="Useful for adding two numbers together, in other words combining them."
+        )
+```

docs/how-to/Creating-a-Crew-and-kick-it-off.md

@@ -1,75 +1,118 @@
-# Get a crew working
+---
+title: Assembling and Activating Your CrewAI Team
+description: A comprehensive guide to creating a dynamic CrewAI team for your projects, with updated functionalities including verbose mode, memory capabilities, and more.
+---
 
-Assembling a Crew in CrewAI is like casting characters for a play. Each agent you create is a cast member with a unique part to play. When your crew is assembled, you'll give the signal, and they'll spring into action, each performing their role in the grand scheme of your project.
+## Introduction
+Embark on your CrewAI journey by setting up your environment and initiating your AI crew with enhanced features. This guide ensures a seamless start, incorporating the latest updates.
 
-# Step 1: Assemble Your Agents
+## Step 0: Installation
+Install CrewAI and any necessary packages for your project.
 
-Start by creating your agents, each with its own role and backstory. These backstories add depth to the agents, influencing how they approach their tasks and interact with one another.
+```shell
+pip install crewai
+pip install 'crewai[tools]'
+```
+
+## Step 1: Assemble Your Agents
+Define your agents with distinct roles, backstories, and now, enhanced capabilities such as verbose mode and memory usage. These elements add depth and guide their task execution and interaction within the crew.
 
 ```python
-from crewai import Agent
+import os
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
+os.environ["OPENAI_API_KEY"] = "Your Key"
 
-# Create a researcher agent
+from crewai import Agent
+from crewai_tools import SerperDevTool
+search_tool = SerperDevTool()
+
+# Creating a senior researcher agent with memory and verbose mode
 researcher = Agent(
   role='Senior Researcher',
-  goal='Discover groundbreaking technologies',
+  goal='Uncover groundbreaking technologies in {topic}',
   verbose=True,
-  backstory='A curious mind fascinated by cutting-edge innovation and the potential to change the world, you know everything about tech.'
+  memory=True,
+  backstory=(
+    "Driven by curiosity, you're at the forefront of"
+    "innovation, eager to explore and share knowledge that could change"
+    "the world."
+  ),
+  tools=[search_tool],
+  allow_delegation=True
 )
 
-# Create a writer agent
+# Creating a writer agent with custom tools and delegation capability
 writer = Agent(
   role='Writer',
-  goal='Craft compelling stories about tech discoveries',
+  goal='Narrate compelling tech stories about {topic}',
   verbose=True,
-  backstory='A creative soul who translates complex tech jargon into engaging narratives for the masses, you write using simple words in a friendly and inviting tone that does not sounds like AI.'
+  memory=True,
+  backstory=(
+    "With a flair for simplifying complex topics, you craft"
+    "engaging narratives that captivate and educate, bringing new"
+    "discoveries to light in an accessible manner."
+  ),
+  tools=[search_tool],
+  allow_delegation=False
 )
 ```
 
-# Step 2: Define the Tasks
-
-Outline the tasks that your agents need to tackle. These tasks are their missions, the specific objectives they need to achieve.
+## Step 2: Define the Tasks
+Detail the specific objectives for your agents, including new features for asynchronous execution and output customization. These tasks ensure a targeted approach to their roles.
 
 ```python
 from crewai import Task
 
-# Task for the researcher
+# Research task
 research_task = Task(
-  description='Identify the next big trend in AI',
-  agent=researcher  # Assigning the task to the researcher
+  description=(
+    "Identify the next big trend in {topic}."
+    "Focus on identifying pros and cons and the overall narrative."
+    "Your final report should clearly articulate the key points"
+    "its market opportunities, and potential risks."
+  ),
+  expected_output='A comprehensive 3 paragraphs long report on the latest AI trends.',
+  tools=[search_tool],
+  agent=researcher,
 )
 
-# Task for the writer
+# Writing task with language model configuration
 write_task = Task(
-  description='Write an article on AI advancements leveraging the research made.',
-  agent=writer  # Assigning the task to the writer
+  description=(
+    "Compose an insightful article on {topic}."
+    "Focus on the latest trends and how it's impacting the industry."
+    "This article should be easy to understand, engaging, and positive."
+  ),
+  expected_output='A 4 paragraph article on {topic} advancements formatted as markdown.',
+  tools=[search_tool],
+  agent=writer,
+  async_execution=False,
+  output_file='new-blog-post.md'  # Example of output customization
 )
 ```
 
-# Step 3: Form the Crew
-
-Bring your agents together into a crew. This is where you define the process they'll follow to complete their tasks.
+## Step 3: Form the Crew
+Combine your agents into a crew, setting the workflow process they'll follow to accomplish the tasks, now with the option to configure language models for enhanced interaction.
 
 ```python
 from crewai import Crew, Process
 
-# Instantiate your crew
-tech_crew = Crew(
+# Forming the tech-focused crew with enhanced configurations
+crew = Crew(
   agents=[researcher, writer],
   tasks=[research_task, write_task],
-  process=Process.sequential  # Tasks will be executed one after the other
+  process=Process.sequential  # Optional: Sequential task execution is default
 )
 ```
 
-# Step 4: Kick It Off
-
-With the crew formed and the stage set, it's time to start the show. Kick off the process and watch as your agents collaborate to achieve their goals.
+## Step 4: Kick It Off
+Initiate the process with your enhanced crew ready. Observe as your agents collaborate, leveraging their new capabilities for a successful project outcome. You can also pass the inputs that will be interpolated into the agents and tasks.
 
 ```python
-# Begin the task execution
-tech_crew.kickoff()
+# Starting the task execution process with enhanced feedback
+result = crew.kickoff(inputs={'topic': 'AI in healthcare'})
+print(result)
 ```
 
-# Conclusion
-
-Creating a crew and setting it into motion is a straightforward process in CrewAI. With each agent playing their part and a clear set of tasks, your AI ensemble is ready to take on any challenge. Remember, the richness of their backstories and the clarity of their goals will greatly enhance their performance and the outcomes of their collaboration.
+## Conclusion
+Building and activating a crew in CrewAI has evolved with new functionalities. By incorporating verbose mode, memory capabilities, asynchronous task execution, output customization, and language model configuration, your AI team is more equipped than ever to tackle challenges efficiently. The depth of agent backstories and the precision of their objectives enrich collaboration, leading to successful project outcomes.

docs/how-to/Customizing-Agents.md

@@ -1,66 +1,83 @@
-# Customizable Attributes
+---
+title: Customizing Agents in CrewAI
+description: A comprehensive guide to tailoring agents for specific roles, tasks, and advanced customizations within the CrewAI framework.
+---
 
-Customizing your AI agents is a cornerstone of creating an effective CrewAI team. Each agent can be tailored to fit the unique needs of your project, allowing for a dynamic and versatile AI workforce.
+## Customizable Attributes
+Crafting an efficient CrewAI team hinges on the ability to tailor your AI agents dynamically to meet the unique requirements of any project. This section covers the foundational attributes you can customize.
 
-When you initialize an Agent, you can set various attributes that define its behavior and role within the Crew:
+### Key Attributes for Customization
+- **Role**: Specifies the agent's job within the crew, such as 'Analyst' or 'Customer Service Rep'.
+- **Goal**: Defines what the agent aims to achieve, in alignment with its role and the overarching objectives of the crew.
+- **Backstory**: Provides depth to the agent's persona, enriching its motivations and engagements within the crew.
+- **Tools**: Represents the capabilities or methods the agent uses to perform tasks, from simple functions to intricate integrations.
 
-- **Role**: The job title or function of the agent within your crew. This can be anything from 'Analyst' to 'Customer Service Rep'.
-- **Goal**: What the agent is aiming to achieve. Goals should be aligned with the agent's role and the overall objectives of the crew.
-- **Backstory**: A narrative that provides depth to the agent's character. This could include previous experience, motivations, or anything that adds context to their role.
-- **Tools**: The abilities or methods the agent uses to complete tasks. This could be as simple as a 'search' function or as complex as a custom-built analysis tool.
+## Advanced Customization Options
+Beyond the basic attributes, CrewAI allows for deeper customization to enhance an agent's behavior and capabilities significantly.
 
-# Understanding Tools in CrewAI
+### Language Model Customization
+Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`), offering advanced control over their processing and decision-making abilities.
+By default crewAI agents are ReAct agents, but by setting the `function_calling_llm` you can turn them into a function calling agents.
 
-Tools in CrewAI are functions that empower agents to interact with the world around them. These can range from generic utilities like a search function to more complex ones like integrating with an external API. The integration with LangChain allows you to utilize a suite of ready-to-use tools such as [Google Serper](https://python.langchain.com/docs/integrations/tools/google_serper), which enables agents to perform web searches and gather data.
+### Enabling Memory for Agents
+CrewAI supports memory for agents, enabling them to remember past interactions. This feature is critical for tasks requiring awareness of previous contexts or decisions.
 
-# Customizing Agents and Tools
+## Performance and Debugging Settings
+Adjusting an agent's performance and monitoring its operations are crucial for efficient task execution.
 
-You can customize an agent by passing parameters when creating an instance. Each parameter tweaks how the agent behaves and interacts within the crew.
+### Verbose Mode and RPM Limit
+- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization. Specifically, it provides insights into agent execution processes, aiding in the optimization of performance.
+- **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`), controlling the agent's query frequency to external services.
 
-Customizing an agent's tools is particularly important. Tools define what an agent can do and how it interacts with tasks. For instance, if a task requires data analysis, assigning an agent with data-related tools would be optimal.
+### Maximum Iterations for Task Execution
+The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. The default value is set to 15, providing a balance between thoroughness and efficiency. Once the agent approaches this number it will try it's best to give a good answer.
 
-When initializing your agents, you can equip them with a set of tools that enable them to perform their roles more effectively:
+## Customizing Agents and Tools
+Agents are customized by defining their attributes and tools during initialization. Tools are critical for an agent's functionality, enabling them to perform specialized tasks. In this example we will use the crewAI tools package to create a tool for a research analyst agent.
 
+```shell
+pip install 'crewai[tools]'
+```
+
+### Example: Assigning Tools to an Agent
 ```python
+import os
 from crewai import Agent
-from langchain.agents import Tool
-from langchain.utilities import GoogleSerperAPIWrapper
+from crewai_tools import SerperDevTool
 
-# Initialize SerpAPI tool with your API key
+# Set API keys for tool initialization
 os.environ["OPENAI_API_KEY"] = "Your Key"
 os.environ["SERPER_API_KEY"] = "Your Key"
 
-search = GoogleSerperAPIWrapper()
+# Initialize a search tool
+search_tool = SerperDevTool()
 
-# 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
+# Initialize the agent with advanced options
 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]
+  tools=[search_tool],
+  memory=True,
+  verbose=True,
+  max_rpm=10, # Optional: Limit requests to 10 per minute, preventing API abuse
+  max_iter=5, # Optional: Limit task iterations to 5 before the agent tries to give its best answer
+  allow_delegation=False
 )
 ```
 
 ## Delegation and Autonomy
+Controlling an agent's ability to delegate tasks or ask questions is vital for tailoring its autonomy and collaborative dynamics within the crewAI framework. By default, the `allow_delegation` attribute is set to `True`, enabling agents to seek assistance or delegate tasks as needed. This default behavior promotes collaborative problem-solving and efficiency within the crewAI ecosystem.
 
-One of the most powerful aspects of CrewAI agents is their ability to delegate tasks to one another. Each agent by default can delegate work or ask question to anyone in the crew, but you can disable that by setting `allow_delegation` to `false`, this is particularly useful for straightforward agents that should execute their tasks in isolation.
-
+### Example: Disabling Delegation for an Agent
 ```python
 agent = Agent(
   role='Content Writer',
-  goal='Write the most amazing content related to market trends an business.',
-  backstory='An expert writer with many years of experience in market trends, stocks and all business related things.',
+  goal='Write engaging content on market trends',
+  backstory='A seasoned writer with expertise in market analysis.',
   allow_delegation=False
 )
 ```
 
 ## Conclusion
-
-Customization is what makes CrewAI powerful. By adjusting the attributes of each agent, you can ensure that your AI team is well-equipped to handle the challenges you set for them. Remember, the more thought you put into your agents' roles, goals, backstories, and tools, the more nuanced and effective their interactions and task execution will be.
+Customizing agents in CrewAI by setting their roles, goals, backstories, and tools, alongside advanced options like language model customization, memory, performance settings, and delegation preferences, equips a nuanced and capable AI team ready for complex challenges.

docs/how-to/Hierarchical.md

@@ -0,0 +1,61 @@
+---
+title: Implementing the Hierarchical Process in CrewAI
+description: A comprehensive guide to understanding and applying the hierarchical process within your CrewAI projects, updated to reflect the latest coding practices and functionalities.
+---
+
+## Introduction
+The hierarchical process in CrewAI introduces a structured approach to task management, simulating traditional organizational hierarchies for efficient task delegation and execution. This systematic workflow enhances project outcomes by ensuring tasks are handled with optimal efficiency and accuracy.
+
+!!! note "Complexity and Efficiency"
+    The hierarchical process is designed to leverage advanced models like GPT-4, optimizing token usage while handling complex tasks with greater efficiency.
+
+## Hierarchical Process Overview
+By default, tasks in CrewAI are managed through a sequential process. However, adopting a hierarchical approach allows for a clear hierarchy in task management, where a 'manager' agent coordinates the workflow, delegates tasks, and validates outcomes for streamlined and effective execution. This manager agent is automatically created by crewAI so you don't need to worry about it.
+
+### Key Features
+- **Task Delegation**: A manager agent allocates tasks among crew members based on their roles and capabilities.
+- **Result Validation**: The manager evaluates outcomes to ensure they meet the required standards.
+- **Efficient Workflow**: Emulates corporate structures, providing an organized approach to task management.
+
+## Implementing the Hierarchical Process
+To utilize the hierarchical process, it's essential to explicitly set the process attribute to `Process.hierarchical`, as the default behavior is `Process.sequential`. Define a crew with a designated manager and establish a clear chain of command.
+
+!!! note "Tools and Agent Assignment"
+    Assign tools at the agent level to facilitate task delegation and execution by the designated agents under the manager's guidance.
+
+!!! note "Manager LLM Requirement"
+    Configuring the `manager_llm` parameter is crucial for the hierarchical process. The system requires a manager LLM to be set up for proper function, ensuring tailored decision-making.
+
+```python
+from langchain_openai import ChatOpenAI
+from crewai import Crew, Process, Agent
+
+# Agents are defined with an optional tools parameter
+researcher = Agent(
+    role='Researcher',
+    goal='Conduct in-depth analysis',
+    # tools=[]  # This can be optionally specified; defaults to an empty list
+)
+writer = Agent(
+    role='Writer',
+    goal='Create engaging content',
+    # tools=[]  # Optionally specify tools; defaults to an empty list
+)
+
+# Establishing the crew with a hierarchical process
+project_crew = Crew(
+    tasks=[...],  # Tasks to be delegated and executed under the manager's supervision
+    agents=[researcher, writer],
+    manager_llm=ChatOpenAI(temperature=0, model="gpt-4"),  # Mandatory for hierarchical process
+    process=Process.hierarchical  # Specifies the hierarchical management approach
+)
+```
+
+### Workflow in Action
+1. **Task Assignment**: The manager assigns tasks strategically, considering each agent's capabilities.
+2. **Execution and Review**: Agents complete their tasks, with the manager ensuring quality standards.
+3. **Sequential Task Progression**: Despite being a hierarchical process, tasks follow a logical order for smooth progression, facilitated by the manager's oversight.
+
+
+## Conclusion
+Adopting the hierarchical process in crewAI, with the correct configurations and understanding of the system's capabilities, facilitates an organized and efficient approach to project management.

docs/how-to/Human-Input-on-Execution.md

@@ -1,63 +1,81 @@
-# Human Input on Execution
+---
+title: Human Input on Execution
+description: Comprehensive guide on integrating CrewAI with human input during execution in complex decision-making processes or when needed help during complex tasks. 
+---
 
-Human inputs is important in many agent execution use cases, humans are AGI so they can can be prompted to step in and provide extra details ins necessary.
-Using it with crewAI is pretty straightforward and you can do it through a LangChain Tool. 
-Check [LangChain Integration](https://python.langchain.com/docs/integrations/tools/human_tools) for more details:
+# Human Input in Agent Execution
 
-Example:
+Human input plays a pivotal role in several agent execution scenarios, enabling agents to seek additional information or clarification when necessary. This capability is invaluable in complex decision-making processes or when agents need more details to complete a task effectively.
+
+## Using Human Input with CrewAI
+
+Incorporating human input with CrewAI is straightforward, enhancing the agent's ability to make informed decisions. While the documentation previously mentioned using a "LangChain Tool" and a specific "DuckDuckGoSearchRun" tool from `langchain_community.tools`, it's important to clarify that the integration of such tools should align with the actual capabilities and configurations defined within your `Agent` class setup.
+
+### Example:
+
+```shell
+pip install crewai
+pip install 'crewai[tools]'
+```
 
 ```python
 import os
-from crewai import Agent, Task, Crew, Process
-from langchain.tools import DuckDuckGoSearchRun
+from crewai import Agent, Task, Crew
+from crewai_tools import SerperDevTool
+
 from langchain.agents import load_tools
 
-search_tool = DuckDuckGoSearchRun()
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
+os.environ["OPENAI_API_KEY"] = "Your Key"
+
 
 # Loading Human Tools
 human_tools = load_tools(["human"])
+search_tool = SerperDevTool()
 
-# Define your agents with roles and goals
+# Define your agents with roles, goals, and tools
 researcher = Agent(
   role='Senior Research Analyst',
-  goal='Uncover cutting-edge developments in AI and data science in',
-  backstory="""You are a Senior Research Analyst at a leading tech think tank.
-  Your expertise lies in identifying emerging trends and technologies in AI and
-  data science. You have a knack for dissecting complex data and presenting
-  actionable insights.""",
+  goal='Uncover cutting-edge developments in AI and data science',
+  backstory=(
+    "You are a Senior Research Analyst at a leading tech think tank."
+    "Your expertise lies in identifying emerging trends and technologies in AI and data science."
+    "You have a knack for dissecting complex data and presenting actionable insights."
+  ),
   verbose=True,
   allow_delegation=False,
-  # Passing human tools to the agent
-  tools=[search_tool]+human_tools
+  tools=[search_tool]+human_tools # Passing human tools to the agent
 )
 writer = Agent(
   role='Tech Content Strategist',
   goal='Craft compelling content on tech advancements',
-  backstory="""You are a renowned Tech Content Strategist, known for your insightful
-  and engaging articles on technology and innovation. With a deep understanding of
-  the tech industry, you transform complex concepts into compelling narratives.""",
+  backstory=(
+    "You are a renowned Tech Content Strategist, known for your insightful and engaging articles on technology and innovation."
+    "With a deep understanding of the tech industry, you transform complex concepts into compelling narratives."
+  ),
   verbose=True,
   allow_delegation=True
 )
 
 # Create tasks for your agents
-# Being explicit on the task to ask for human feedback.
 task1 = Task(
-  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-  Identify key trends, breakthrough technologies, and potential industry impacts.
-  Compile your findings in a detailed report. 
-  Make sure to check with the human if the draft is good before returning your Final Answer.
-  Your final answer MUST be a full analysis report""",
-  agent=researcher
+  description=(
+    "Conduct a comprehensive analysis of the latest advancements in AI in 2024."
+    "Identify key trends, breakthrough technologies, and potential industry impacts."
+    "Compile your findings in a detailed report."
+    "Make sure to check with a human if the draft is good before finalizing your answer."
+  ),
+  expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
+  agent=researcher,
 )
 
 task2 = Task(
-  description="""Using the insights from the researcher's report, develop an engaging blog
-  post that highlights the most significant AI advancements.
-  Your post should be informative yet accessible, catering to a tech-savvy audience.
-  Aim for a narrative that captures the essence of these breakthroughs and their
-  implications for the future. 
-  Your final answer MUST be the full blog post of at least 3 paragraphs.""",
+  description=(
+    "Using the insights from the researcher's report, develop an engaging blog post that highlights the most significant AI advancements."
+    "Your post should be informative yet accessible, catering to a tech-savvy audience."
+    "Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
+  ),
+  expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2024',
   agent=writer
 )
 
@@ -73,4 +91,4 @@ result = crew.kickoff()
 
 print("######################")
 print(result)
-```
+```

docs/how-to/LLM-Connections.md

@@ -1,192 +1,109 @@
-# Connect CrewAI to LLMs
+---
+title: Connect CrewAI to LLMs
+description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs), including detailed class attributes and methods.
+---
 
-There are different types of connections.
-Ollama is the recommended way to connect to local LLMs.
-Azure uses a slightly different API and therefore has it's own connection object.
+## Connect CrewAI to LLMs
+!!! note "Default LLM"
+    By default, CrewAI uses OpenAI's GPT-4 model for language processing. However, you can configure your agents to use a different model or API. This guide will show you how to connect your agents to different LLMs through environment variables and direct instantiation.
 
-crewAI is compatible with any of the LangChain LLM components. See this page for more information: https://python.langchain.com/docs/integrations/llms/
+CrewAI offers flexibility in connecting to various LLMs, including local models via [Ollama](https://ollama.ai) and different APIs like Azure. It's compatible with all [LangChain LLM](https://python.langchain.com/docs/integrations/llms/) components, enabling diverse integrations for tailored AI solutions.
 
-## Ollama
+## CrewAI Agent Overview
+The `Agent` class is the cornerstone for implementing AI solutions in CrewAI. Here's an updated overview reflecting the latest codebase changes:
 
-crewAI supports integration with local models thorugh [Ollama](https://ollama.ai/) for enhanced flexibility and customization. This allows you to utilize your own models, which can be particularly useful for specialized tasks or data privacy concerns. We will conver other options for using local models in later sections. However, ollama is the recommended tool to use to host local models when possible.
-
-### Setting Up Ollama
-
-- **Install Ollama**: Ensure that Ollama is properly installed in your environment. Follow the installation guide provided by Ollama for detailed instructions.
-- **Configure Ollama**: Set up Ollama to work with your local model. You will probably need to [tweak the model using a Modelfile](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md). I'd recommend adding `Observation` as a stop word and playing with `top_p` and `temperature`.
-
-### Integrating Ollama with CrewAI
-- Instantiate Ollama Model: Create an instance of the Ollama model. You can specify the model and the base URL during instantiation. For example:
+- **Attributes**:
+    - `role`: Defines the agent's role within the solution.
+    - `goal`: Specifies the agent's objective.
+    - `backstory`: Provides a background story to the agent.
+    - `llm`: Indicates the Large Language Model the agent uses.
+    - `function_calling_llm` *Optinal*: Will turn the ReAct crewAI agent into a function calling agent.
+    - `max_iter`: Maximum number of iterations for an agent to execute a task, default is 15.
+    - `memory`: Enables the agent to retain information during the execution.
+    - `max_rpm`: Sets the maximum number of requests per minute.
+    - `verbose`: Enables detailed logging of the agent's execution.
+    - `allow_delegation`: Allows the agent to delegate tasks to other agents, default is `True`.
+    - `tools`: Specifies the tools available to the agent for task execution.
+    - `step_callback`: Provides a callback function to be executed after each step.
 
 ```python
-from langchain.llms import Ollama
-ollama_openhermes = Ollama(model="openhermes")
-# Pass Ollama Model to Agents: When creating your agents within the CrewAI framework, you can pass the Ollama model as an argument to the Agent constructor. For instance:
+# Required
+os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"
 
-local_expert = Agent(
-  role='Local Expert at this city',
-  goal='Provide the BEST insights about the selected city',
-  backstory="""A knowledgeable local guide with extensive information
-  about the city, it's attractions and customs""",
-  tools=[
-    SearchTools.search_internet,
-    BrowserTools.scrape_and_summarize_website,
-  ],
-  llm=ollama_openhermes, # Ollama model passed here
+# Agent will automatically use the model defined in the environment variable
+example_agent = Agent(
+  role='Local Expert',
+  goal='Provide insights about the city',
+  backstory="A knowledgeable local guide.",
   verbose=True
 )
 ```
 
-## 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. 
-
-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. 
-
-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.
-
-
-```python
-from dotenv import load_dotenv
-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"),
-                        openai_api_key=os.environ.get("OPENAI_API_KEY", "NA"),
-                        model_name=os.environ.get("MODEL_NAME", "gpt-3.5-turbo"))
-
-# Create an agent and assign the LLM
-example_agent = Agent(
-  role='Example Agent',
-  goal='Show how to assign a custom configured LLM',
-  backstory='You hang out in the docs section of GitHub repos.',
-  llm=default_llm
-)
+## Ollama Integration
+Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, set the appropriate environment variables as shown below. Note: Detailed Ollama setup is beyond this document's scope, but general guidance is provided.
 
+### Setting Up Ollama
+- **Environment Variables Configuration**: To integrate Ollama, set the following environment variables:
+```sh
+OPENAI_API_BASE='http://localhost:11434/v1'
+OPENAI_MODEL_NAME='openhermes'  # Adjust based on available model
+OPENAI_API_KEY=''
 ```
 
-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 Compatible API Endpoints
+Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, and Mistral AI.
 
+### Configuration Examples
+#### FastChat
 ```sh
-# Required
-OPENAI_API_KEY="sk-..."
-
-# Optional
-OPENAI_API_BASE_URL=https://api.openai.com/v1
-MODEL_NAME="gpt-3.5-turbo"
-```
-
-### FastChat
-
-FastChat is an open platform for training, serving, and evaluating large language model based chatbots.
-
-[GitHub](https://github.com/lm-sys/FastChat)
-
-[API Documentation](https://github.com/lm-sys/FastChat?tab=readme-ov-file#api)
-
-Configuration settings:
-```sh
-# Required
-OPENAI_API_BASE_URL="http://localhost:8001/v1"
+OPENAI_API_BASE="http://localhost:8001/v1"
+OPENAI_MODEL_NAME='oh-2.5m7b-q51'
 OPENAI_API_KEY=NA
-MODEL_NAME='oh-2.5m7b-q51'
 ```
 
-### LM Studio
-
-Discover, download, and run local LLMs
-
-[lmstudio.ai](https://lmstudio.ai/)
-
-
-
-Configuration settings:
+#### LM Studio
 ```sh
-# Required
-OPENAI_API_BASE_URL="http://localhost:8000/v1"
-
+OPENAI_API_BASE="http://localhost:8000/v1"
+OPENAI_MODEL_NAME=NA
 OPENAI_API_KEY=NA
-MODEL_NAME=NA
 ```
 
-
-### Mistral API
-
-Mistral AI's API endpoints 
-
-[Mistral AI](https://mistral.ai/)
-
-[Documentation](https://docs.mistral.ai/)
-
+#### Mistral API
 ```sh
 OPENAI_API_KEY=your-mistral-api-key
 OPENAI_API_BASE=https://api.mistral.ai/v1
-MODEL_NAME="mistral-small" # Check documentation for available models
+OPENAI_MODEL_NAME="mistral-small"
 ```
 
-
-
-### text-gen-web-ui
-
-A Gradio web UI for Large Language Models.
-
-[GitHub](https://github.com/oobabooga/text-generation-webui)
-
-[API Documentation](https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API)
-
-Configuration settings:
-
-```sh
-# Required
-API_BASE_URL=http://localhost:5000
-OPENAI_API_KEY=NA
-MODEL_NAME=NA
-```
-
-## Other Inference API Endpoints
-
-Other platforms offer inference APIs such as Anthropic, Azure, and HuggingFace to name a few. Unfortunately, the APIs on the following platforms are not compatible with the OpenAI API specification. So, the following platforms will require a slightly different configuration than the examples in the previous section.
-
-### Azure Open AI
-
-Azure hosted OpenAI API endpoints have their own LLM component that needs to be imported from `langchain_openai`.
-
-For more information, check out the langchain documenation for [Azure OpenAI](https://python.langchain.com/docs/integrations/llms/azure_openai).
-
-```python
-from dotenv import load_dotenv
-from langchain_openai import AzureChatOpenAI
-
-load_dotenv()
-
-default_llm = AzureChatOpenAI(
-    openai_api_version=os.environ.get("AZURE_OPENAI_VERSION", "2023-07-01-preview"),
-    azure_deployment=os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt35"),
-    azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT", "https://<your-endpoint>.openai.azure.com/"),
-    api_key=os.environ.get("AZURE_OPENAI_KEY")
-)
-
-# Create an agent and assign the LLM
-example_agent = Agent(
-  role='Example Agent',
-  goal='Show how to assign a custom configured LLM',
-  backstory='You hang out in the docs section of GitHub repos.',
-  llm=default_llm
-)
-
-```
-
-
-Configuration settings:
+### Azure Open AI Configuration
+For Azure OpenAI API integration, set the following environment variables:
 ```sh
 AZURE_OPENAI_VERSION="2022-12-01"
 AZURE_OPENAI_DEPLOYMENT=""
 AZURE_OPENAI_ENDPOINT=""
 AZURE_OPENAI_KEY=""
 ```
+
+### Example Agent with Azure LLM
+```python
+from dotenv import load_dotenv
+from crewai import Agent
+from langchain_openai import AzureChatOpenAI
+
+load_dotenv()
+
+azure_llm = AzureChatOpenAI(
+    azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
+    api_key=os.environ.get("AZURE_OPENAI_KEY")
+)
+
+azure_agent = Agent(
+  role='Example Agent',
+  goal='Demonstrate custom LLM configuration',
+  backstory='A diligent explorer of GitHub docs.',
+  llm=azure_llm
+)
+```
+
+## Conclusion
+Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.

docs/how-to/Sequential.md

@@ -0,0 +1,60 @@
+---
+title: Using the Sequential Processes in crewAI
+description: A comprehensive guide to utilizing the sequential processe for task execution in crewAI projects.
+---
+
+## Introduction
+CrewAI offers a flexible framework for executing tasks in a structured manner, supporting both sequential and hierarchical processes. This guide outlines how to effectively implement these processes to ensure efficient task execution and project completion.
+
+## Sequential Process Overview
+The sequential process ensures tasks are executed one after the other, following a linear progression. This approach is ideal for projects requiring tasks to be completed in a specific order.
+
+### Key Features
+- **Linear Task Flow**: Ensures orderly progression by handling tasks in a predetermined sequence.
+- **Simplicity**: Best suited for projects with clear, step-by-step tasks.
+- **Easy Monitoring**: Facilitates easy tracking of task completion and project progress.
+
+
+## Implementing the Sequential Process
+Assemble your crew and define tasks in the order they need to be executed.
+
+```python
+from crewai import Crew, Process, Agent, Task
+
+# Define your agents
+researcher = Agent(
+  role='Researcher',
+  goal='Conduct foundational research',
+  backstory='An experienced researcher with a passion for uncovering insights'
+)
+analyst = Agent(
+  role='Data Analyst',
+  goal='Analyze research findings',
+  backstory='A meticulous analyst with a knack for uncovering patterns'
+)
+writer = Agent(
+  role='Writer',
+  goal='Draft the final report',
+  backstory='A skilled writer with a talent for crafting compelling narratives'
+)
+
+# Define the tasks in sequence
+research_task = Task(description='Gather relevant data...', agent=researcher)
+analysis_task = Task(description='Analyze the data...', agent=analyst)
+writing_task = Task(description='Compose the report...', agent=writer)
+
+# Form the crew with a sequential process
+report_crew = Crew(
+  agents=[researcher, analyst, writer],
+  tasks=[research_task, analysis_task, writing_task],
+  process=Process.sequential
+)
+```
+
+### Workflow in Action
+1. **Initial Task**: In a sequential process, the first agent completes their task and signals completion.
+2. **Subsequent Tasks**: Agents pick up their tasks based on the process type, with outcomes of preceding tasks or manager directives guiding their execution.
+3. **Completion**: The process concludes once the final task is executed, leading to project completion.
+
+## Conclusion
+The sequential process in CrewAI provides a clear, straightforward path for task execution. It's particularly suited for projects requiring a logical progression of tasks, ensuring each step is completed before the next begins, thereby facilitating a cohesive final product.

docs/index.md

@@ -1,36 +1,118 @@
-<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/Crews">
+                    Crews
+                </a>
+            </li>
+        </ul>
+    </div>
+    <div style="width:30%">
+        <h2>How-To Guides</h2>
+        <ul>
+            <li>
+                <a href="./how-to/Creating-a-Crew-and-kick-it-off">
+                    Getting Started
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/Create-Custom-Tools">
+                    Create Custom Tools
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/Sequential">
+                    Using Sequential Process
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/Hierarchical">
+                    Using Hierarchical Process
+                </a>
+            </li>
+            <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/Human-Input-on-Execution">
+                    Human Input on Execution
+                </a>
+            </li>
+        </ul>
+    </div>
+    <div style="width:30%">
+        <h2>Examples</h2>
+        <ul>
+            <li>
+                <a target='_blank' href="https://github.com/joaomdmoura/crewAI-examples/tree/main/prep-for-a-meeting">
+                    Prepare for meetings
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner">
+                    Trip Planner Crew
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post">
+                    Create Instagram Post
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis">
+                    Stock Analysis
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/joaomdmoura/crewAI-examples/tree/main/game-builder-crew">
+                    Game Generator
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/joaomdmoura/crewAI-examples/tree/main/CrewAI-LangGraph">
+                    Drafting emails with LangGraph
+                </a>
+            </li>
+            <li>
+                <a target='_blank' href="https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator">
+                    Landing Page Generator
+                </a>
+            </li>
+        </ul>
+    </div>
+</div>

docs/postcss.config.js

@@ -0,0 +1,3 @@
+module.exports = {
+  plugins: [require('tailwindcss'), require('autoprefixer')]
+}

docs/stylesheets/extra.css

@@ -0,0 +1,3 @@
+.md-typeset .admonition-title {
+  margin-bottom: 10px;
+}

docs/stylesheets/output.css

@@ -0,0 +1,565 @@
+/*
+! tailwindcss v3.4.1 | MIT License | https://tailwindcss.com
+*/
+
+/*
+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)
+*/
+
+*,
+::before,
+::after {
+  box-sizing: border-box;
+  /* 1 */
+  border-width: 0;
+  /* 2 */
+  border-style: solid;
+  /* 2 */
+  border-color: #e5e7eb;
+  /* 2 */
+}
+
+::before,
+::after {
+  --tw-content: '';
+}
+
+/*
+1. Use a consistent sensible line-height in all browsers.
+2. Prevent adjustments of font size after orientation changes in iOS.
+3. Use a more readable tab size.
+4. Use the user's configured `sans` font-family by default.
+5. Use the user's configured `sans` font-feature-settings by default.
+6. Use the user's configured `sans` font-variation-settings by default.
+7. Disable tap highlights on iOS
+*/
+
+html,
+:host {
+  line-height: 1.5;
+  /* 1 */
+  -webkit-text-size-adjust: 100%;
+  /* 2 */
+  -moz-tab-size: 4;
+  /* 3 */
+  -o-tab-size: 4;
+     tab-size: 4;
+  /* 3 */
+  font-family: ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
+  /* 4 */
+  font-feature-settings: normal;
+  /* 5 */
+  font-variation-settings: normal;
+  /* 6 */
+  -webkit-tap-highlight-color: transparent;
+  /* 7 */
+}
+
+/*
+1. Remove the margin in all browsers.
+2. Inherit line-height from `html` so users can set them as a class directly on the `html` element.
+*/
+
+body {
+  margin: 0;
+  /* 1 */
+  line-height: inherit;
+  /* 2 */
+}
+
+/*
+1. Add the correct height in Firefox.
+2. Correct the inheritance of border color in Firefox. (https://bugzilla.mozilla.org/show_bug.cgi?id=190655)
+3. Ensure horizontal rules are visible by default.
+*/
+
+hr {
+  height: 0;
+  /* 1 */
+  color: inherit;
+  /* 2 */
+  border-top-width: 1px;
+  /* 3 */
+}
+
+/*
+Add the correct text decoration in Chrome, Edge, and Safari.
+*/
+
+abbr:where([title]) {
+  -webkit-text-decoration: underline dotted;
+          text-decoration: underline dotted;
+}
+
+/*
+Remove the default font size and weight for headings.
+*/
+
+h1,
+h2,
+h3,
+h4,
+h5,
+h6 {
+  font-size: inherit;
+  font-weight: inherit;
+}
+
+/*
+Reset links to optimize for opt-in styling instead of opt-out.
+*/
+
+a {
+  color: inherit;
+  text-decoration: inherit;
+}
+
+/*
+Add the correct font weight in Edge and Safari.
+*/
+
+b,
+strong {
+  font-weight: bolder;
+}
+
+/*
+1. Use the user's configured `mono` font-family by default.
+2. Use the user's configured `mono` font-feature-settings by default.
+3. Use the user's configured `mono` font-variation-settings by default.
+4. Correct the odd `em` font sizing in all browsers.
+*/
+
+code,
+kbd,
+samp,
+pre {
+  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+  /* 1 */
+  font-feature-settings: normal;
+  /* 2 */
+  font-variation-settings: normal;
+  /* 3 */
+  font-size: 1em;
+  /* 4 */
+}
+
+/*
+Add the correct font size in all browsers.
+*/
+
+small {
+  font-size: 80%;
+}
+
+/*
+Prevent `sub` and `sup` elements from affecting the line height in all browsers.
+*/
+
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+
+sub {
+  bottom: -0.25em;
+}
+
+sup {
+  top: -0.5em;
+}
+
+/*
+1. Remove text indentation from table contents in Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=999088, https://bugs.webkit.org/show_bug.cgi?id=201297)
+2. Correct table border color inheritance in all Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=935729, https://bugs.webkit.org/show_bug.cgi?id=195016)
+3. Remove gaps between table borders by default.
+*/
+
+table {
+  text-indent: 0;
+  /* 1 */
+  border-color: inherit;
+  /* 2 */
+  border-collapse: collapse;
+  /* 3 */
+}
+
+/*
+1. Change the font styles in all browsers.
+2. Remove the margin in Firefox and Safari.
+3. Remove default padding in all browsers.
+*/
+
+button,
+input,
+optgroup,
+select,
+textarea {
+  font-family: inherit;
+  /* 1 */
+  font-feature-settings: inherit;
+  /* 1 */
+  font-variation-settings: inherit;
+  /* 1 */
+  font-size: 100%;
+  /* 1 */
+  font-weight: inherit;
+  /* 1 */
+  line-height: inherit;
+  /* 1 */
+  color: inherit;
+  /* 1 */
+  margin: 0;
+  /* 2 */
+  padding: 0;
+  /* 3 */
+}
+
+/*
+Remove the inheritance of text transform in Edge and Firefox.
+*/
+
+button,
+select {
+  text-transform: none;
+}
+
+/*
+1. Correct the inability to style clickable types in iOS and Safari.
+2. Remove default button styles.
+*/
+
+button,
+[type='button'],
+[type='reset'],
+[type='submit'] {
+  -webkit-appearance: button;
+  /* 1 */
+  background-color: transparent;
+  /* 2 */
+  background-image: none;
+  /* 2 */
+}
+
+/*
+Use the modern Firefox focus style for all focusable elements.
+*/
+
+:-moz-focusring {
+  outline: auto;
+}
+
+/*
+Remove the additional `:invalid` styles in Firefox. (https://github.com/mozilla/gecko-dev/blob/2f9eacd9d3d995c937b4251a5557d95d494c9be1/layout/style/res/forms.css#L728-L737)
+*/
+
+:-moz-ui-invalid {
+  box-shadow: none;
+}
+
+/*
+Add the correct vertical alignment in Chrome and Firefox.
+*/
+
+progress {
+  vertical-align: baseline;
+}
+
+/*
+Correct the cursor style of increment and decrement buttons in Safari.
+*/
+
+::-webkit-inner-spin-button,
+::-webkit-outer-spin-button {
+  height: auto;
+}
+
+/*
+1. Correct the odd appearance in Chrome and Safari.
+2. Correct the outline style in Safari.
+*/
+
+[type='search'] {
+  -webkit-appearance: textfield;
+  /* 1 */
+  outline-offset: -2px;
+  /* 2 */
+}
+
+/*
+Remove the inner padding in Chrome and Safari on macOS.
+*/
+
+::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+
+/*
+1. Correct the inability to style clickable types in iOS and Safari.
+2. Change font properties to `inherit` in Safari.
+*/
+
+::-webkit-file-upload-button {
+  -webkit-appearance: button;
+  /* 1 */
+  font: inherit;
+  /* 2 */
+}
+
+/*
+Add the correct display in Chrome and Safari.
+*/
+
+summary {
+  display: list-item;
+}
+
+/*
+Removes the default spacing and border for appropriate elements.
+*/
+
+blockquote,
+dl,
+dd,
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+hr,
+figure,
+p,
+pre {
+  margin: 0;
+}
+
+fieldset {
+  margin: 0;
+  padding: 0;
+}
+
+legend {
+  padding: 0;
+}
+
+ol,
+ul,
+menu {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+}
+
+/*
+Reset default styling for dialogs.
+*/
+
+dialog {
+  padding: 0;
+}
+
+/*
+Prevent resizing textareas horizontally by default.
+*/
+
+textarea {
+  resize: vertical;
+}
+
+/*
+1. Reset the default placeholder opacity in Firefox. (https://github.com/tailwindlabs/tailwindcss/issues/3300)
+2. Set the default placeholder color to the user's configured gray 400 color.
+*/
+
+input::-moz-placeholder, textarea::-moz-placeholder {
+  opacity: 1;
+  /* 1 */
+  color: #9ca3af;
+  /* 2 */
+}
+
+input::placeholder,
+textarea::placeholder {
+  opacity: 1;
+  /* 1 */
+  color: #9ca3af;
+  /* 2 */
+}
+
+/*
+Set the default cursor for buttons.
+*/
+
+button,
+[role="button"] {
+  cursor: pointer;
+}
+
+/*
+Make sure disabled buttons don't get the pointer cursor.
+*/
+
+:disabled {
+  cursor: default;
+}
+
+/*
+1. Make replaced elements `display: block` by default. (https://github.com/mozdevs/cssremedy/issues/14)
+2. Add `vertical-align: middle` to align replaced elements more sensibly by default. (https://github.com/jensimmons/cssremedy/issues/14#issuecomment-634934210)
+   This can trigger a poorly considered lint error in some tools but is included by design.
+*/
+
+img,
+svg,
+video,
+canvas,
+audio,
+iframe,
+embed,
+object {
+  display: block;
+  /* 1 */
+  vertical-align: middle;
+  /* 2 */
+}
+
+/*
+Constrain images and videos to the parent width and preserve their intrinsic aspect ratio. (https://github.com/mozdevs/cssremedy/issues/14)
+*/
+
+img,
+video {
+  max-width: 100%;
+  height: auto;
+}
+
+/* Make elements with the HTML hidden attribute stay hidden by default */
+
+[hidden] {
+  display: none;
+}
+
+*, ::before, ::after {
+  --tw-border-spacing-x: 0;
+  --tw-border-spacing-y: 0;
+  --tw-translate-x: 0;
+  --tw-translate-y: 0;
+  --tw-rotate: 0;
+  --tw-skew-x: 0;
+  --tw-skew-y: 0;
+  --tw-scale-x: 1;
+  --tw-scale-y: 1;
+  --tw-pan-x:  ;
+  --tw-pan-y:  ;
+  --tw-pinch-zoom:  ;
+  --tw-scroll-snap-strictness: proximity;
+  --tw-gradient-from-position:  ;
+  --tw-gradient-via-position:  ;
+  --tw-gradient-to-position:  ;
+  --tw-ordinal:  ;
+  --tw-slashed-zero:  ;
+  --tw-numeric-figure:  ;
+  --tw-numeric-spacing:  ;
+  --tw-numeric-fraction:  ;
+  --tw-ring-inset:  ;
+  --tw-ring-offset-width: 0px;
+  --tw-ring-offset-color: #fff;
+  --tw-ring-color: rgb(59 130 246 / 0.5);
+  --tw-ring-offset-shadow: 0 0 #0000;
+  --tw-ring-shadow: 0 0 #0000;
+  --tw-shadow: 0 0 #0000;
+  --tw-shadow-colored: 0 0 #0000;
+  --tw-blur:  ;
+  --tw-brightness:  ;
+  --tw-contrast:  ;
+  --tw-grayscale:  ;
+  --tw-hue-rotate:  ;
+  --tw-invert:  ;
+  --tw-saturate:  ;
+  --tw-sepia:  ;
+  --tw-drop-shadow:  ;
+  --tw-backdrop-blur:  ;
+  --tw-backdrop-brightness:  ;
+  --tw-backdrop-contrast:  ;
+  --tw-backdrop-grayscale:  ;
+  --tw-backdrop-hue-rotate:  ;
+  --tw-backdrop-invert:  ;
+  --tw-backdrop-opacity:  ;
+  --tw-backdrop-saturate:  ;
+  --tw-backdrop-sepia:  ;
+}
+
+::backdrop {
+  --tw-border-spacing-x: 0;
+  --tw-border-spacing-y: 0;
+  --tw-translate-x: 0;
+  --tw-translate-y: 0;
+  --tw-rotate: 0;
+  --tw-skew-x: 0;
+  --tw-skew-y: 0;
+  --tw-scale-x: 1;
+  --tw-scale-y: 1;
+  --tw-pan-x:  ;
+  --tw-pan-y:  ;
+  --tw-pinch-zoom:  ;
+  --tw-scroll-snap-strictness: proximity;
+  --tw-gradient-from-position:  ;
+  --tw-gradient-via-position:  ;
+  --tw-gradient-to-position:  ;
+  --tw-ordinal:  ;
+  --tw-slashed-zero:  ;
+  --tw-numeric-figure:  ;
+  --tw-numeric-spacing:  ;
+  --tw-numeric-fraction:  ;
+  --tw-ring-inset:  ;
+  --tw-ring-offset-width: 0px;
+  --tw-ring-offset-color: #fff;
+  --tw-ring-color: rgb(59 130 246 / 0.5);
+  --tw-ring-offset-shadow: 0 0 #0000;
+  --tw-ring-shadow: 0 0 #0000;
+  --tw-shadow: 0 0 #0000;
+  --tw-shadow-colored: 0 0 #0000;
+  --tw-blur:  ;
+  --tw-brightness:  ;
+  --tw-contrast:  ;
+  --tw-grayscale:  ;
+  --tw-hue-rotate:  ;
+  --tw-invert:  ;
+  --tw-saturate:  ;
+  --tw-sepia:  ;
+  --tw-drop-shadow:  ;
+  --tw-backdrop-blur:  ;
+  --tw-backdrop-brightness:  ;
+  --tw-backdrop-contrast:  ;
+  --tw-backdrop-grayscale:  ;
+  --tw-backdrop-hue-rotate:  ;
+  --tw-backdrop-invert:  ;
+  --tw-backdrop-opacity:  ;
+  --tw-backdrop-saturate:  ;
+  --tw-backdrop-sepia:  ;
+}
+
+.mb-10 {
+  margin-bottom: 2.5rem;
+}
+
+.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));
+}
+
+.leading-3 {
+  line-height: .75rem;
+}
+
+.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;
+}

docs/stylesheets/tailwind.css

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

docs/tailwind.config.js

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

docs/telemetry/Telemetry.md

@@ -0,0 +1,27 @@
+---
+title: Telemetry
+description: Understanding the telemetry data collected by CrewAI and how it contributes to the enhancement of the library.
+---
+## Telemetry
+
+CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users.
+
+It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy.
+
+### Data Collected Includes:
+- **Version of CrewAI**: Assessing the adoption rate of our latest version helps us understand user needs and guide our updates.
+- **Python Version**: Identifying the Python versions our users operate with assists in prioritizing our support efforts for these versions.
+- **General OS Information**: Details like the number of CPUs and the operating system type (macOS, Windows, Linux) enable us to focus our development on the most used operating systems and explore the potential for OS-specific features.
+- **Number of Agents and Tasks in a Crew**: Ensures our internal testing mirrors real-world scenarios, helping us guide users towards best practices.
+- **Crew Process Utilization**: Understanding how crews are utilized aids in directing our development focus.
+- **Memory and Delegation Use by Agents**: Insights into how these features are used help evaluate their effectiveness and future.
+- **Task Execution Mode**: Knowing whether tasks are executed in parallel or sequentially influences our emphasis on enhancing parallel execution capabilities.
+- **Language Model Utilization**: Supports our goal to improve support for the most popular languages among our users.
+- **Roles of Agents within a Crew**: Understanding the various roles agents play aids in crafting better tools, integrations, and examples.
+- **Tool Usage**: Identifying which tools are most frequently used allows us to prioritize improvements in those areas.
+
+### Opt-In Further Telemetry Sharing
+Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. This opt-in approach respects user privacy and aligns with data protection standards by ensuring users have control over their data sharing preferences. Enabling `share_crew` results in the collection of detailed `crew` and `task` execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
+
+### Updates and Revisions
+We are committed to maintaining the accuracy and transparency of our documentation. Regular reviews and updates are performed to ensure our documentation accurately reflects the latest developments of our codebase and telemetry practices. Users are encouraged to review this section for the most current information on our data collection practices and how they contribute to the improvement of CrewAI.

docs/tools/CSVSearchTool.md

@@ -0,0 +1,37 @@
+# CSVSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+
+This tool is used to perform a RAG (Retrieval-Augmented Generation) search within a CSV file's content. It allows users to semantically search for queries in the content of a specified CSV file. This feature is particularly useful for extracting information from large CSV datasets where traditional search methods might be inefficient. All tools with "Search" in their name, including CSVSearchTool, are RAG tools designed for searching different sources of data.
+
+## Installation
+
+Install the crewai_tools package
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+
+```python
+from crewai_tools import CSVSearchTool
+
+# Initialize the tool with a specific CSV file. This setup allows the agent to only search the given CSV file.
+tool = CSVSearchTool(csv='path/to/your/csvfile.csv')
+
+# OR
+
+# Initialize the tool without a specific CSV file. Agent  will need to provide the CSV path at runtime.
+tool = CSVSearchTool()
+```
+
+## Arguments
+
+- `csv` : The path to the CSV file you want to search. This is a mandatory argument if the tool was initialized without a specific CSV file; otherwise, it is optional.

docs/tools/DOCXSearchTool.md

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+# DOCXSearchTool
+
+!!! note "Depend on OpenAI"
+    All RAG tools at the moment can only use openAI to generate embeddings, we are working on adding support for other providers.
+
+!!! note "Experimental"
+    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+
+## Description
+The DOCXSearchTool is a RAG tool designed for semantic searching within DOCX documents. It enables users to effectively search and extract relevant information from DOCX files using query-based searches. This tool is invaluable for data analysis, information management, and research tasks, streamlining the process of finding specific information within large document collections.
+
+## Installation
+Install the crewai_tools package by running the following command in your terminal:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+The following example demonstrates initializing the DOCXSearchTool to search within any DOCX file's content or with a specific DOCX file path.
+
+```python
+from crewai_tools import DOCXSearchTool
+
+# Initialize the tool to search within any DOCX file's content
+tool = DOCXSearchTool()
+
+# OR
+
+# Initialize the tool with a specific DOCX file, so the agent can only search the content of the specified DOCX file
+tool = DOCXSearchTool(docx='path/to/your/document.docx')
+```
+
+## Arguments
+- `docx`: An optional file path to a specific DOCX document you wish to search. If not provided during initialization, the tool allows for later specification of any DOCX file's content path for searching.