removing logs and preping new version

* Fix Creating-a-Crew-and-kick-it-off.md

- Update deps to include `crewai[tools]`
- Remove invalid `max_inter` arg from Task constructor call

* Update Creating-a-Crew-and-kick-it-off.md

---------

Co-authored-by: João Moura <joaomdmoura@gmail.com>

.circleci/config.yml

@@ -1,27 +0,0 @@
-version: 2.1
-
-jobs:
-  build-and-test:
-    docker:
-      - image: python:3.9.18
-    steps:
-      - checkout
-      - run:
-          name: Install poetry
-          command: pip install poetry
-      - run:
-          name: Install dependencies
-          command: poetry install
-      - run:
-          name: Update PATH and Define Environment Variable at Runtime
-          command: |
-            echo 'export OPENAI_API_KEY=fake-api-key' >> "$BASH_ENV"
-            source "$BASH_ENV"
-      - run:
-          name: Run tests
-          command: poetry run pytest
-
-workflows:
-  build-and-test:
-    jobs:
-      - build-and-test

.github/workflows/black.yml

@@ -0,0 +1,10 @@
+name: Lint
+
+on: [pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: psf/black@stable

.github/workflows/mkdocs.yml

@@ -0,0 +1,47 @@
+name: Deploy MkDocs
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+
+permissions:
+  contents: write
+
+jobs:
+  deploy:
+    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: 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 mkdocs-material-extensions pillow cairosvg
+
+        env:
+          GH_TOKEN: ${{ secrets.GH_TOKEN }}
+
+      - name: Build and deploy MkDocs
+        run: mkdocs gh-deploy --force

.github/workflows/tests.yml

@@ -0,0 +1,32 @@
+name: Run Tests
+
+on: [pull_request]
+
+permissions:
+  contents: write
+
+env:
+  OPENAI_API_KEY: fake-api-key
+
+jobs:
+  deploy:
+    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 tests
+        run: poetry run pytest

.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

@@ -4,4 +4,7 @@ __pycache__
 dist/
 .env
 assets/*
-.idea
+.idea
+test/
+docs_crew/
+chroma.sqlite3

README.md

@@ -1,16 +1,37 @@
-# crewAI
+<div align="center">
 
-![Logo of crewAI, tow people rowing on a boat](./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**
 
-- [Why CrewAI](#why-crewai)
+🤖 **crewAI**: Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
+
+<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)
-- [Local Open Source Models](#local-open-source-models)
-- [CrewAI x AutoGen x ChatDev](#how-crewai-compares)
+  - [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?
@@ -18,106 +39,163 @@
 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://chat.openai.com/g/g-qqTuUWsBY-crewai-assistant)
-- 📄 [Documention Wiki](https://github.com/joaomdmoura/CrewAI/wiki)
-
 ## Getting Started
 
 To get started with CrewAI, follow these simple steps:
 
-1. **Installation**:
+### 1. Installation
 
 ```shell
 pip install crewai
 ```
 
-2. **Setting Up Your Crew**:
+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:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+The example below also uses DuckDuckGo's Search. You can install it with `pip` too:
+
+```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"
+os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
+
+# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
+# osOPENAI_API_BASE='http://localhost:11434/v1'
+# OPENAI_MODEL_NAME='openhermes'  # Adjust based on available model
+# OPENAI_API_KEY=''
+
+# Install duckduckgo-search for this example:
+# !pip install -U duckduckgo-search
+
+from langchain_community.tools import DuckDuckGoSearchRun
+search_tool = DuckDuckGoSearchRun()
 
 # Define your agents with roles and goals
 researcher = Agent(
-  role='Researcher',
-  goal='Discover new insights',
-  backstory="You're a world class researcher working on a major data science company",
+  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
-  # llm=OpenAI(temperature=0.7, model_name="gpt-4"). It uses langchain.chat_models, default is GPT4
+  allow_delegation=False,
+  tools=[search_tool]
+  # You can pass an optional llm attribute specifying what mode you wanna use.
+  # It can be a local model through Ollama / LM Studio or a remote
+  # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
+  #
+  # import os
+  # os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
+  #
+  # OR
+  #
+  # from langchain_openai import ChatOpenAI
+  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
 )
 writer = Agent(
-  role='Writer',
-  goal='Create engaging content',
-  backstory="You're a famous technical writer, specialized on writing data related content",
+  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=False
+  allow_delegation=True
 )
 
 # Create tasks for your agents
-task1 = Task(description='Investigate the latest AI trends', agent=researcher)
-task2 = Task(description='Write a blog post on AI advancements', agent=writer)
+task1 = Task(
+  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
+  Identify key trends, breakthrough technologies, and potential industry impacts.""",
+  expected_output="Full analysis report in bullet points",
+  agent=researcher
+)
+
+task2 = Task(
+  description="""Using the insights provided, develop an engaging 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.""",
+  expected_output="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, # Crew verbose more will let you know what tasks are being worked on, you can set it to 1 or 2 to different logging levels
-  process=Process.sequential # Sequential process will have tasks executed one after the other and the outcome of the previous one is passed as extra content into this next.
+  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.
+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.
+- **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 you agents' connections to models, even ones running locally!
 
-![CrewAI Mind Map](/crewAI-mindmap.png "CrewAI Mind Map")
+![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)
 
-## Local Open Source Models
-crewAI supports integration with local models, thorugh tools such as [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.
+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):
 
-### 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`.
+- [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)
 
-### 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:
+### Quick Tutorial
 
-```python
-from langchain.llms import Ollama
-ollama_openhermes = Ollama(model="agent")
-# 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:
+[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/maxresdefault.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
 
-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
-  verbose=True
-)
-```
+### 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](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.
 
@@ -134,12 +212,14 @@ 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
 ```
@@ -151,21 +231,64 @@ 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 [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.

crewai/agent.py

@@ -1,155 +0,0 @@
-from typing import Any, List, Optional
-
-from langchain.agents import AgentExecutor
-from langchain.agents.format_scratchpad import format_log_to_str
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationSummaryMemory
-from langchain.tools.render import render_text_description
-from langchain_core.runnables.config import RunnableConfig
-from pydantic import BaseModel, Field, InstanceOf, model_validator
-
-from crewai.agents import CacheHandler, CrewAgentOutputParser, ToolsHandler
-from crewai.prompts import Prompts
-
-
-class Agent(BaseModel):
-    """Represents an agent in a system.
-
-    Each agent has a role, a goal, a backstory, and an optional language model (llm).
-    The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.
-
-    Attributes:
-            agent_executor: An instance of the AgentExecutor class.
-            role: The role of the agent.
-            goal: The objective of the agent.
-            backstory: The backstory of the agent.
-            llm: The language model that will run the agent.
-            memory: Whether the agent should have memory or not.
-            verbose: Whether the agent execution should be in verbose mode.
-            allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
-    """
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    role: str = Field(description="Role of the agent")
-    goal: str = Field(description="Objective of the agent")
-    backstory: str = Field(description="Backstory of the agent")
-    llm: Optional[Any] = Field(
-        default_factory=lambda: ChatOpenAI(
-            temperature=0.7,
-            model_name="gpt-4",
-        ),
-        description="Language model that will run the agent.",
-    )
-    memory: bool = Field(
-        default=True, description="Whether the agent should have memory or not"
-    )
-    verbose: bool = Field(
-        default=False, description="Verbose mode for the Agent Execution"
-    )
-    allow_delegation: bool = Field(
-        default=True, description="Allow delegation of tasks to agents"
-    )
-    tools: List[Any] = Field(
-        default_factory=list, description="Tools at agents disposal"
-    )
-    agent_executor: Optional[InstanceOf[AgentExecutor]] = Field(
-        default=None, description="An instance of the AgentExecutor class."
-    )
-    tools_handler: Optional[InstanceOf[ToolsHandler]] = Field(
-        default=None, description="An instance of the ToolsHandler class."
-    )
-    cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
-        default=CacheHandler(), description="An instance of the CacheHandler class."
-    )
-
-    @model_validator(mode="after")
-    def check_agent_executor(self) -> "Agent":
-        if not self.agent_executor:
-            self.set_cache_handler(self.cache_handler)
-        return self
-
-    def execute_task(
-        self, task: str, context: str = None, tools: List[Any] = None
-    ) -> str:
-        """Execute a task with the agent.
-
-        Args:
-            task: Task to execute.
-            context: Context to execute the task in.
-            tools: Tools to use for the task.
-
-        Returns:
-            Output of the agent
-        """
-        if context:
-            task = "\n".join(
-                [task, "\nThis is the context you are working with:", context]
-            )
-
-        tools = tools or self.tools
-        self.agent_executor.tools = tools
-
-        return self.agent_executor.invoke(
-            {
-                "input": task,
-                "tool_names": self.__tools_names(tools),
-                "tools": render_text_description(tools),
-            },
-            RunnableConfig(callbacks=[self.tools_handler]),
-        )["output"]
-
-    def set_cache_handler(self, cache_handler) -> None:
-        self.cache_handler = cache_handler
-        self.tools_handler = ToolsHandler(cache=self.cache_handler)
-        self.__create_agent_executor()
-
-    def __create_agent_executor(self) -> AgentExecutor:
-        """Create an agent executor for the agent.
-
-        Returns:
-            An instance of the AgentExecutor class.
-        """
-        agent_args = {
-            "input": lambda x: x["input"],
-            "tools": lambda x: x["tools"],
-            "tool_names": lambda x: x["tool_names"],
-            "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
-        }
-        executor_args = {
-            "tools": self.tools,
-            "verbose": self.verbose,
-            "handle_parsing_errors": True,
-        }
-
-        if self.memory:
-            summary_memory = ConversationSummaryMemory(
-                llm=self.llm, memory_key="chat_history", input_key="input"
-            )
-            executor_args["memory"] = summary_memory
-            agent_args["chat_history"] = lambda x: x["chat_history"]
-            prompt = Prompts.TASK_EXECUTION_WITH_MEMORY_PROMPT
-        else:
-            prompt = Prompts.TASK_EXECUTION_PROMPT
-
-        execution_prompt = prompt.partial(
-            goal=self.goal,
-            role=self.role,
-            backstory=self.backstory,
-        )
-
-        bind = self.llm.bind(stop=["\nObservation"])
-        inner_agent = (
-            agent_args
-            | execution_prompt
-            | bind
-            | CrewAgentOutputParser(
-                tools_handler=self.tools_handler, cache=self.cache_handler
-            )
-        )
-        self.agent_executor = AgentExecutor(agent=inner_agent, **executor_args)
-
-    @staticmethod
-    def __tools_names(tools) -> str:
-        return ", ".join([t.name for t in tools])

crewai/agents/__init__.py

@@ -1,3 +0,0 @@
-from .cache_handler import CacheHandler
-from .output_parser import CrewAgentOutputParser
-from .tools_handler import ToolsHandler

crewai/agents/output_parser.py

@@ -1,81 +0,0 @@
-import re
-from typing import Union
-
-from langchain.agents.output_parsers import ReActSingleInputOutputParser
-from langchain_core.agents import AgentAction, AgentFinish
-from langchain_core.exceptions import OutputParserException
-
-from .cache_handler import CacheHandler
-from .tools_handler import ToolsHandler
-
-FINAL_ANSWER_ACTION = "Final Answer:"
-FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
-    "Parsing LLM output produced both a final answer and a parse-able action:"
-)
-
-
-class CrewAgentOutputParser(ReActSingleInputOutputParser):
-    """Parses ReAct-style LLM calls that have a single tool input.
-
-    Expects output to be in one of two formats.
-
-    If the output signals that an action should be taken,
-    should be in the below format. This will result in an AgentAction
-    being returned.
-
-    ```
-    Thought: agent thought here
-    Action: search
-    Action Input: what is the temperature in SF?
-    ```
-
-    If the output signals that a final answer should be given,
-    should be in the below format. This will result in an AgentFinish
-    being returned.
-
-    ```
-    Thought: agent thought here
-    Final Answer: The temperature is 100 degrees
-    ```
-
-    It also prevents tools from being reused in a roll.
-    """
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    tools_handler: ToolsHandler
-    cache: CacheHandler
-
-    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
-        includes_answer = FINAL_ANSWER_ACTION in text
-        regex = (
-            r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
-        )
-        action_match = re.search(regex, text, re.DOTALL)
-        if action_match:
-            if includes_answer:
-                raise OutputParserException(
-                    f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
-                )
-            action = action_match.group(1).strip()
-            action_input = action_match.group(2)
-            tool_input = action_input.strip(" ")
-            tool_input = tool_input.strip('"')
-
-            last_tool_usage = self.tools_handler.last_used_tool
-            if last_tool_usage:
-                usage = {
-                    "tool": action,
-                    "input": tool_input,
-                }
-                if usage == last_tool_usage:
-                    raise OutputParserException(
-                        f"""\nI just used the {action} tool with input {tool_input}. So I already knwo the result of that."""
-                    )
-
-            result = self.cache.read(action, tool_input)
-            if result:
-                return AgentFinish({"output": result}, text)
-
-        return super().parse(text)

crewai/agents/tools_handler.py

@@ -1,42 +0,0 @@
-from typing import Any, Dict
-
-from langchain.callbacks.base import BaseCallbackHandler
-
-from .cache_handler import CacheHandler
-
-
-class ToolsHandler(BaseCallbackHandler):
-    """Callback handler for tool usage."""
-
-    last_used_tool: Dict[str, Any] = {}
-    cache: CacheHandler = None
-
-    def __init__(self, cache: CacheHandler = None, **kwargs: Any):
-        """Initialize the callback handler."""
-        self.cache = cache
-        super().__init__(**kwargs)
-
-    def on_tool_start(
-        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
-    ) -> Any:
-        """Run when tool starts running."""
-        name = serialized.get("name")
-        if name not in ["invalid_tool", "_Exception"]:
-            tools_usage = {
-                "tool": name,
-                "input": input_str,
-            }
-            self.last_used_tool = tools_usage
-
-    def on_tool_end(self, output: str, **kwargs: Any) -> Any:
-        """Run when tool ends running."""
-        if (
-            "is not a valid tool" not in output
-            and "Invalid or incomplete response" not in output
-            and "Invalid Format" not in output
-        ):
-            self.cache.add(
-                tool=self.last_used_tool["tool"],
-                input=self.last_used_tool["input"],
-                output=output,
-            )

crewai/crew.py

@@ -1,122 +0,0 @@
-import json
-from typing import Any, Dict, List, Optional, Union
-
-from pydantic import (
-    BaseModel,
-    Field,
-    InstanceOf,
-    Json,
-    field_validator,
-    model_validator,
-)
-from pydantic_core import PydanticCustomError
-
-from crewai.agent import Agent
-from crewai.agents import CacheHandler
-from crewai.process import Process
-from crewai.task import Task
-from crewai.tools.agent_tools import AgentTools
-
-
-class Crew(BaseModel):
-    """Class that represents a group of agents, how they should work together and their tasks."""
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    tasks: List[Task] = Field(description="List of tasks", default_factory=list)
-    agents: List[Agent] = Field(
-        description="List of agents in this crew.", default_factory=list
-    )
-    process: Process = Field(
-        description="Process that the crew will follow.", default=Process.sequential
-    )
-    verbose: Union[int, bool] = Field(
-        description="Verbose mode for the Agent Execution", default=0
-    )
-    config: Optional[Union[Json, Dict[str, Any]]] = Field(
-        description="Configuration of the crew.", default=None
-    )
-    cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
-        default=CacheHandler(), description="An instance of the CacheHandler class."
-    )
-
-    @classmethod
-    @field_validator("config", mode="before")
-    def check_config_type(cls, v: Union[Json, Dict[str, Any]]):
-        if isinstance(v, Json):
-            return json.loads(v)
-        return v
-
-    @model_validator(mode="after")
-    def check_config(self):
-        if not self.config and not self.tasks and not self.agents:
-            raise PydanticCustomError(
-                "missing_keys", "Either agents and task need to be set or config.", {}
-            )
-
-        if self.config:
-            if not self.config.get("agents") or not self.config.get("tasks"):
-                raise PydanticCustomError(
-                    "missing_keys_in_config", "Config should have agents and tasks", {}
-                )
-
-            self.agents = [Agent(**agent) for agent in self.config["agents"]]
-
-            tasks = []
-            for task in self.config["tasks"]:
-                task_agent = [agt for agt in self.agents if agt.role == task["agent"]][
-                    0
-                ]
-                del task["agent"]
-                tasks.append(Task(**task, agent=task_agent))
-
-            self.tasks = tasks
-
-        if self.agents:
-            for agent in self.agents:
-                agent.set_cache_handler(self.cache_handler)
-        return self
-
-    def kickoff(self) -> str:
-        """Kickoff the crew to work on its tasks.
-
-        Returns:
-            Output of the crew for each task.
-        """
-        for agent in self.agents:
-            agent.cache_handler = self.cache_handler
-
-        if self.process == Process.sequential:
-            return self.__sequential_loop()
-
-    def __sequential_loop(self) -> str:
-        """Loop that executes the sequential process.
-
-        Returns:
-            Output of the crew.
-        """
-        task_outcome = None
-        for task in self.tasks:
-            # Add delegation tools to the task if the agent allows it
-            if task.agent.allow_delegation:
-                tools = AgentTools(agents=self.agents).tools()
-                task.tools += tools
-
-            self.__log("debug", f"Working Agent: {task.agent.role}")
-            self.__log("info", f"Starting Task: {task.description} ...")
-
-            task_outcome = task.execute(task_outcome)
-
-            self.__log("debug", f"Task output: {task_outcome}")
-
-        return task_outcome
-
-    def __log(self, level, message):
-        """Log a message"""
-        level_map = {"debug": 1, "info": 2}
-        verbose_level = (
-            2 if isinstance(self.verbose, bool) and self.verbose else self.verbose
-        )
-        if verbose_level and level_map[level] <= verbose_level:
-            print(message)

crewai/prompts.py

@@ -1,84 +0,0 @@
-"""Prompts for generic agent."""
-
-from textwrap import dedent
-from typing import ClassVar
-
-from langchain.prompts import PromptTemplate
-from pydantic import BaseModel
-
-
-class Prompts(BaseModel):
-    """Prompts for generic agent."""
-
-    TASK_SLICE: ClassVar[str] = dedent(
-        """\
-	Begin! This is VERY important to you, your job depends on it!
-
-	Current Task: {input}"""
-    )
-
-    SCRATCHPAD_SLICE: ClassVar[str] = "\n{agent_scratchpad}"
-
-    MEMORY_SLICE: ClassVar[str] = dedent(
-        """\
-	This is the summary of your work so far:
-	{chat_history}"""
-    )
-
-    ROLE_PLAYING_SLICE: ClassVar[str] = dedent(
-        """\
-	You are {role}.
-	{backstory}
-
-	Your personal goal is: {goal}"""
-    )
-
-    TOOLS_SLICE: ClassVar[str] = dedent(
-        """\
-
-
-	TOOLS:
-	------
-	You have access to the following tools:
-
-	{tools}
-
-	To use a tool, please use the exact following format:
-
-	```
-	Thought: Do I need to use a tool? Yes
-	Action: the action to take, should be one of [{tool_names}], just the name.
-	Action Input: the input to the action
-	Observation: the result of the action
-	```
-
-	When you have a response for your task, or if you do not need to use a tool, you MUST use the format:
-
-	```
-	Thought: Do I need to use a tool? No
-	Final Answer: [your response here]
-	```"""
-    )
-
-    VOTING_SLICE: ClassVar[str] = dedent(
-        """\
-	You are working on a crew with your co-workers and need to decide who will execute the task.
-
-	These are your format instructions:
-	{format_instructions}
-
-	These are your co-workers and their roles:
-	{coworkers}"""
-    )
-
-    TASK_EXECUTION_WITH_MEMORY_PROMPT: ClassVar[str] = PromptTemplate.from_template(
-        ROLE_PLAYING_SLICE + TOOLS_SLICE + MEMORY_SLICE + TASK_SLICE + SCRATCHPAD_SLICE
-    )
-
-    TASK_EXECUTION_PROMPT: ClassVar[str] = PromptTemplate.from_template(
-        ROLE_PLAYING_SLICE + TOOLS_SLICE + TASK_SLICE + SCRATCHPAD_SLICE
-    )
-
-    CONSENSUNS_VOTING_PROMPT: ClassVar[str] = PromptTemplate.from_template(
-        ROLE_PLAYING_SLICE + VOTING_SLICE + TASK_SLICE + SCRATCHPAD_SLICE
-    )

crewai/task.py

@@ -1,39 +0,0 @@
-from typing import Any, List, Optional
-
-from pydantic import BaseModel, Field, model_validator
-
-from crewai.agent import Agent
-
-
-class Task(BaseModel):
-    """Class that represent a task to be executed."""
-
-    description: str = Field(description="Description of the actual task.")
-    agent: Optional[Agent] = Field(
-        description="Agent responsible for the task.", default=None
-    )
-    tools: List[Any] = Field(
-        default_factory=list,
-        description="Tools the agent are limited to use for this task.",
-    )
-
-    @model_validator(mode="after")
-    def check_tools(self):
-        if not self.tools and (self.agent and self.agent.tools):
-            self.tools.extend(self.agent.tools)
-        return self
-
-    def execute(self, context: str = None) -> str:
-        """Execute the task.
-
-        Returns:
-            Output of the task.
-        """
-        if self.agent:
-            return self.agent.execute_task(
-                task=self.description, context=context, tools=self.tools
-            )
-        else:
-            raise Exception(
-                f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, either consensual or hierarchical."
-            )

crewai/tools/agent_tools.py

@@ -1,72 +0,0 @@
-from textwrap import dedent
-from typing import List
-
-from langchain.tools import Tool
-from pydantic import BaseModel, Field
-
-from crewai.agent import Agent
-
-
-class AgentTools(BaseModel):
-    """Tools for generic agent."""
-
-    agents: List[Agent] = Field(description="List of agents in this crew.")
-
-    def tools(self):
-        return [
-            Tool.from_function(
-                func=self.delegate_work,
-                name="Delegate work to co-worker",
-                description=dedent(
-                    f"""Useful to delegate a specific task to one of the
-				following co-workers: [{', '.join([agent.role for agent in self.agents])}].
-				The input to this tool should be a pipe (|) separated text of length
-				three, representing the role you want to delegate it to, the task and
-				information necessary. For example, `coworker|task|information`.
-				"""
-                ),
-            ),
-            Tool.from_function(
-                func=self.ask_question,
-                name="Ask question to co-worker",
-                description=dedent(
-                    f"""Useful to ask a question, opinion or take from on
-				of the following co-workers: [{', '.join([agent.role for agent in self.agents])}].
-				The input to this tool should be a pipe (|) separated text of length
-				three, representing the role you want to ask it to, the question and
-				information necessary. For example, `coworker|question|information`.
-				"""
-                ),
-            ),
-        ]
-
-    def delegate_work(self, command):
-        """Useful to delegate a specific task to a coworker."""
-        return self.__execute(command)
-
-    def ask_question(self, command):
-        """Useful to ask a question, opinion or take from a coworker."""
-        return self.__execute(command)
-
-    def __execute(self, command):
-        """Execute the command."""
-        try:
-            agent, task, information = command.split("|")
-        except ValueError:
-            return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|information`."
-
-        if not agent or not task or not information:
-            return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|question|information`."
-
-        agent = [
-            available_agent
-            for available_agent in self.agents
-            if available_agent.role == agent
-        ]
-
-        if len(agent) == 0:
-            return f"\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: {', '.join([agent.role for agent in self.agents])}."
-
-        agent = agent[0]
-        result = agent.execute_task(task, information)
-        return result

docs/core-concepts/Agents.md

@@ -0,0 +1,65 @@
+---
+title: crewAI Agents
+description: What are crewAI Agents and how to use them.
+---
+
+## What is an Agent?
+!!! note "What is an Agent?"
+    An agent is an **autonomous unit** programmed to:
+    <ul>
+      <li class='leading-3'>Perform tasks</li>
+      <li class='leading-3'>Make decisions</li>
+      <li class='leading-3'>Communicate with other agents</li>
+      <br/>
+    Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.
+
+## Agent Attributes
+
+| Attribute           | Description                          |
+| :------------------ | :----------------------------------- |
+| **Role**            | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.  |
+| **Goal**            | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
+| **Backstory**       | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
+| **LLM**             | The language model used by the agent to process and generate text. Defaults to using OpenAI's GPT-4 (`ChatOpenAI`), unless another model is specified through the environment variable "OPENAI_MODEL_NAME". |
+| **Tools**           | 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. |
+| **Function Calling LLM** | The language model used by this agent to call functions. It is an optional field and, if not provided, the behavior of defaulting to the main `llm` is implicit. |
+| **Max Iter**        | The maximum number of iterations the agent can perform before being forced to give its best answer. Default is `15`. |
+| **Max RPM**         | The maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified. |
+| **Verbose**         | Enables detailed logging of the agent's execution for debugging or monitoring purposes when set to True. Default is `False` |
+| **Allow Delegation**| Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. |
+| **Step Callback**   | 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**          | 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 the CrewAI's built-in delegation and communication mechanisms.<br/>This allows for dynamic task management and problem-solving within the crew.
+
+To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example 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
+  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 +1,37 @@
+---
+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.
+- **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.
+- **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.
+
+## 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/Crews.md

@@ -0,0 +1,80 @@
+---
+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**          | The process flow (e.g., sequential, hierarchical) the crew follows. |
+| **Verbose**          | The verbosity level for logging during execution.            |
+| **Manager LLM**      | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
+| **Function Calling LLM** | The language model used by all agents in the crew for calling functions. If none is passed, the main LLM for each agent will be used. |
+| **Config**           | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
+| **Max RPM**          | Maximum requests per minute the crew adheres to during execution. |
+| **Language**         | Language used for the crew, defaults to English.             |
+| **Full Output**      | Whether the crew should return the full output with all tasks outputs or just the final output. |
+| **Step Callback**    | 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**       | 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'
+)
+
+# 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 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.
+
+### 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/Processes.md

@@ -0,0 +1,49 @@
+---
+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, the manager for delegation is automatically created by crewAI.
+- **Consensual (Planned)**: A future process type aiming for collaborative decision-making among agents on task execution, introducing a more democratic approach to task management within CrewAI.
+
+## 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
+Specify the process type upon crew creation to set the execution strategy:
+
+```python
+from crewai import Crew
+from crewai.process import Process
+
+# Example: Creating a crew with a sequential process
+crew = Crew(agents=my_agents, tasks=my_tasks, process=Process.sequential)
+
+# Example: Creating a crew with a hierarchical process
+crew = Crew(agents=my_agents, tasks=my_tasks, process=Process.hierarchical)
+```
+**Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object.
+
+## 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. A "manager" agent is automatically created so it oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents, 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` and `hierarchical`). This design choice guarantees that only valid processes are utilized within the CrewAI framework.
+
+## Planned Future Processes
+- **Consensual Process**: A collaborative decision-making process among agents on task execution is planned but not currently implemented. This future enhancement will 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 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 +1,221 @@
+---
+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** *(optional)*      | Clear and detailed definition of expected output for the task.  |
+| **Tools** *(optional)*   | These are the functions or capabilities the agent can utilize to perform the task. They can be anything from simple actions like 'search' to more complex interactions with other agents or APIs. |
+| **Async Execution** *(optional)*      | If the task should be executed asynchronously. This indicates that the crew will not wait for the task to be completed to continue with the next task. |
+| **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 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 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"
+
+from crewai import Agent, Task, Crew
+from langchain.agents import Tool
+from langchain_community.tools import DuckDuckGoSearchRun
+
+research_agent = Agent(
+    role='Researcher',
+    goal='Find and summarize the latest AI news',
+    backstory="""You're a researcher at a large company.
+    You're responsible for analyzing data and providing insights
+    to the business."""
+    verbose=True
+)
+
+# Install duckduckgo-search for this example:
+# !pip install -U duckduckgo-search
+search_tool = DuckDuckGoSearchRun()
+
+task = Task(
+  description='Find and summarize the latest AI news',
+  expected_output='A bullet list summary of the top 5 most important AI news',
+  agent=research_agent,
+  tools=[search_tool]
+)
+
+crew = Crew(
+    agents=[research_agent],
+    tasks=[task],
+    verbose=2
+)
+
+result = crew.kickoff()
+print(result)
+```
+
+This demonstrates how tasks with specific tools can override an agent's default set for tailored task execution.
+
+## 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 should be used as context for another task.
+
+This is useful when you have a task that depends on the output of another task that is not performed immediately after it. This is done through the `context` attribute of the task:
+
+```python
+# ...
+
+research_task = Task(
+  description='Find and summarize the latest AI news',
+  expected_output='A bullet list summary of the top 5 most important AI news',
+  agent=research_agent,
+  tools=[search_tool]
+)
+
+write_blog_task = Task(
+  description="Write a full blog post about the importance of AI and its latest news",
+  expected_output='Full blog post that is 4 paragraphs long',
+  agent=writer_agent,
+  context=[research_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
+
+You can define a callback function that will be executed after the task is completed. This is useful for tasks that need to trigger some side effect after they are completed, while the crew is still running.
+
+```python
+# ...
+
+def callback_function(output: TaskOutput):
+	# Do something after the task is completed
+	# Example: Send an email to the manager
+	print(f"""
+		Task completed!
+		Task: {output.description}
+		Output: {output.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 and following robust validation practices is 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 +1,232 @@
+---
+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**: Designed for various tasks such as web searching, data analysis, content generation, and agent collaboration.
+- **Integration**: Enhances agent capabilities by integrating tools directly into their workflow.
+- **Customizability**: Offers flexibility to develop custom tools or use existing ones, catering to specific agent needs.
+
+## Using crewAI Tools
+
+crewAI comes with a series to built-in tools that can be used to extend the capabilities of your agents. Start by installing our extra tools package:
+
+```bash
+pip install 'crewai[tools]'
+```
+
+Here is an example on how to use them:
+
+```python
+import os
+from crewai import Agent, Task, Crew
+# Importing some of the crewAI tools
+from crewai_tools import (
+    DirectoryReadTool,
+    FileReadTool,
+    SeperDevTool,
+    WebsiteSearchTool
+)
+
+# get a free account in serper.dev
+os.environ["SERPER_API_KEY"] = "Your Key"
+os.environ["OPENAI_API_KEY"] = "Your Key"
+
+# Instantiate tools
+# Assumes this ./blog-posts exists with existing blog posts on it
+docs_tools = DirectoryReadTool(directory='./blog-posts')
+file_read_tool = FileReadTool()
+search_tool = SeperDevTool()
+website_rag = 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, website_rag],
+    verbose=True
+)
+
+writer = Agent(
+    role='Content Writer',
+    goal='Write amazing, super engaging blog post about the AI industry',
+    backstory='A skilled writer with a passion for technology.',
+    tools=[docs_tools, file_read_tool],
+    verbose=True
+)
+
+# Create  tasks
+research = Task(
+    description='Research the AI industry and provide a summary of the latest most trending matters and developments.',
+    expected_output='A summary of the top 3 latest most trending matters and developments in the AI industry with you unique take on why they matter.',
+    agent=researcher
+)
+
+write = Task(
+    description='Write an engaging blog post about the AI industry, using the summary provided by the research analyst. Read the latest blog posts in the directory to get inspiration.',
+    expected_output='A 4 paragraph blog post formatted as markdown with proper subtitles about the latest trends that is engaging and informative and funny, avoid complex words and make it easy to read.',
+    agent=writer,
+    output_file='blog-posts/new_post.md' # The final blog post will be written here
+)
+
+
+# Create a crew
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research, write],
+    verbose=2
+)
+
+# Execute the 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
+        pass
+```
+
+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 LangChain Tools
+!!! info "LangChain Integration"
+    CrewAI seamlessly integrates with LangChain’s comprehensive toolkit for search-based queries and more:
+
+```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 crucial for extending the capabilities of CrewAI agents, allowing them to undertake a diverse array of tasks and collaborate efficiently. When building your AI solutions with CrewAI, consider both custom and existing tools to empower your agents and foster a dynamic AI ecosystem.

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

@@ -0,0 +1,113 @@
+---
+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.
+---
+
+## 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 0: Installation
+Install CrewAI and any necessary packages for your project. The `duckduckgo-search` package is highlighted here for enhanced search capabilities.
+
+```shell
+pip install crewai
+pip install crewai[tools]
+pip install duckduckgo-search
+```
+
+## 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
+import os
+os.environ["OPENAI_API_KEY"] = "Your Key"
+
+from crewai import Agent
+from langchain_community.tools import DuckDuckGoSearchRun
+search_tool = DuckDuckGoSearchRun()
+
+# Topic for the crew run
+topic = 'AI in healthcare'
+
+# Creating a senior researcher agent with memory and verbose mode
+researcher = Agent(
+  role='Senior Researcher',
+  goal=f'Uncover groundbreaking technologies in {topic}',
+  verbose=True,
+  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
+)
+
+# Creating a writer agent with custom tools and delegation capability
+writer = Agent(
+  role='Writer',
+  goal=f'Narrate compelling tech stories about {topic}',
+  verbose=True,
+  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
+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
+
+# Research task
+research_task = Task(
+  description=f"""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,
+)
+
+# Writing task with language model configuration
+write_task = Task(
+  description=f"""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=f'A 4 paragraph article on {topic} advancements fromated 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
+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
+
+# Forming the tech-focused crew with enhanced configurations
+crew = Crew(
+  agents=[researcher, writer],
+  tasks=[research_task, write_task],
+  process=Process.sequential  # Optional: Sequential task execution is default
+)
+```
+
+## 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.
+
+```python
+# Starting the task execution process with enhanced feedback
+result = crew.kickoff()
+print(result)
+```
+
+## 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

@@ -0,0 +1,82 @@
+---
+title: Customizing Agents in CrewAI
+description: A comprehensive guide to tailoring agents for specific roles, tasks, and advanced customizations within the CrewAI framework.
+---
+
+## 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.
+
+### 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.
+
+## Advanced Customization Options
+Beyond the basic attributes, CrewAI allows for deeper customization to enhance an agent's behavior and capabilities significantly.
+
+### 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.
+
+### 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.
+
+## Performance and Debugging Settings
+Adjusting an agent's performance and monitoring its operations are crucial for efficient task execution.
+
+### Verbose Mode and RPM Limit
+- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization.
+- **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`), controlling the agent's query frequency to external services.
+
+### 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.
+
+## 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 crewai_tools import SeperDevTool
+
+# Set API keys for tool initialization
+os.environ["OPENAI_API_KEY"] = "Your Key"
+os.environ["SERPER_API_KEY"] = "Your Key"
+
+# Initialize a search tool
+search_tool = SeperDevTool()
+
+# 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],
+  memory=True,
+  verbose=True,
+  max_rpm=10, # Optinal: Limit requests to 10 per minute, preventing API abuse
+  max_iter=5, # Optional: Limit task iterations to 5 before the agent tried to gives 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.
+
+### Example: Disabling Delegation for an Agent
+```python
+agent = Agent(
+  role='Content Writer',
+  goal='Write engaging content on market trends',
+  backstory='A seasoned writer with expertise in market analysis.',
+  allow_delegation=False
+)
+```
+
+## Conclusion
+Customizing agents in CrewAI by setting their roles, goals, backstories, and tools, alongside advanced options like language model customization, memory, and performance settings, equips a nuanced and capable AI team ready for complex challenges.

docs/how-to/Hierarchical.md

@@ -0,0 +1,60 @@
+---
+title: Implementing the Hierarchical Process in CrewAI
+description: Understanding and applying the hierarchical process within your CrewAI projects, with updates reflecting the latest coding practices.
+---
+
+## Introduction
+The hierarchical process in CrewAI introduces a structured approach to managing tasks, mimicking traditional organizational hierarchies for efficient task delegation and execution. This ensures a systematic workflow that enhances project outcomes.
+
+!!! 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
+Tasks within this process are managed through a clear hierarchy, where a 'manager' agent coordinates the workflow, delegates tasks, and validates outcomes, ensuring a streamlined and effective execution process.
+
+### Key Features
+- **Task Delegation**: A manager agent is responsible for allocating tasks among crew members based on their roles and capabilities.
+- **Result Validation**: The manager evaluates the outcomes to ensure they meet the required standards before moving forward.
+- **Efficient Workflow**: Emulates corporate structures, offering an organized and familiar approach to task management.
+
+## Implementing the Hierarchical Process
+To adopt the hierarchical process, define a crew with a designated manager and establish a clear chain of command for task execution. This structure is crucial for maintaining an orderly and efficient workflow.
+
+!!! note "Tools and Agent Assignment"
+    Tools should be assigned at the agent level, not the task level, to facilitate task delegation and execution by the designated agents under the manager's guidance.
+
+!!! note "Manager LLM Configuration"
+    A manager LLM is automatically assigned to the crew, eliminating the need for manual definition. However, configuring the `manager_llm` parameter is necessary to tailor the manager's decision-making process.
+
+```python
+from langchain_openai import ChatOpenAI
+from crewai import Crew, Process, Agent
+
+# Agents are defined without specifying a manager explicitly
+researcher = Agent(
+	role='Researcher',
+	goal='Conduct in-depth analysis',
+	# tools = [...]
+)
+writer = Agent(
+	role='Writer',
+	goal='Create engaging content',
+	# tools = [...]
+)
+
+# 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"),  # Defines the manager's decision-making engine
+	process=Process.hierarchical  # Specifies the hierarchical management approach
+)
+```
+
+### Workflow in Action
+1. **Task Assignment**: The manager strategically assigns tasks, considering each agent's role and skills.
+2. **Execution and Review**: Agents complete their tasks, followed by a thorough review by the manager to ensure quality standards.
+3. **Sequential Task Progression**: The manager ensures tasks are completed in a logical order, facilitating smooth project progression.
+
+## Conclusion
+Adopting the hierarchical process in CrewAI facilitates a well-organized and efficient approach to project management. By structuring tasks and delegations within a clear hierarchy, it enhances both productivity and quality control, making it an ideal strategy for managing complex projects.

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

@@ -0,0 +1,77 @@
+# Human Input in Agent Execution
+
+Human input is crucial in numerous agent execution scenarios, enabling agents to request additional information or clarification when necessary. This feature is particularly useful in complex decision-making processes or when agents require further 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:
+
+```python
+import os
+from crewai import Agent, Task, Crew, Process
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain.agents import load_tools
+
+search_tool = DuckDuckGoSearchRun()
+
+# Loading Human Tools
+human_tools = load_tools(["human"])
+
+# 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 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,
+  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.""",
+  verbose=True,
+  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.
+  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.""",
+  expected_output='A compelling 3 paragraphs blog post formated as markdown about the latest AI advancements in 2024',
+  agent=writer
+)
+
+# Instantiate your crew with a sequential process
+crew = Crew(
+  agents=[researcher, writer],
+  tasks=[task1, task2],
+  verbose=2
+)
+
+# Get your crew to work!
+result = crew.kickoff()
+
+print("######################")
+print(result)
+```

docs/how-to/LLM-Connections.md

@@ -0,0 +1,102 @@
+---
+title: Connect CrewAI to LLMs
+description: Guide on integrating CrewAI with various Large Language Models (LLMs).
+---
+
+## 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 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.
+
+## CrewAI Agent Overview
+The `Agent` class in CrewAI is central to implementing AI solutions. Here's a brief overview:
+
+- **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.
+
+### Example Changing OpenAI's GPT model
+```python
+# Required
+os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"
+
+# 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
+)
+```
+
+## 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=''
+```
+
+## 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
+OPENAI_API_BASE="http://localhost:8001/v1"
+OPENAI_MODEL_NAME='oh-2.5m7b-q51'
+OPENAI_API_KEY=NA
+```
+
+#### LM Studio
+```sh
+OPENAI_API_BASE="http://localhost:8000/v1"
+OPENAI_MODEL_NAME=NA
+OPENAI_API_KEY=NA
+```
+
+#### Mistral API
+```sh
+OPENAI_API_KEY=your-mistral-api-key
+OPENAI_API_BASE=https://api.mistral.ai/v1
+OPENAI_MODEL_NAME="mistral-small"
+```
+
+### 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

@@ -0,0 +1,113 @@
+<img src='./crew_only_logo.png' width='250' class='mb-10'/>
+
+# 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.
+
+<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/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: [],
+}
+