Fix merge conflict

* Almost working!

* It fully works but not clean enought

* Working but not clean engouth

* Everything is workign

* WIP. Working on adding and & or to flows. In the middle of setting up template for flow as well

* template working

* Everything is working

* More changes and todos

* Add more support for @start

* Router working now

* minor tweak to

* minor tweak to conditions and event handling

* Update logs

* Too trigger happy with cleanup

* Added in Thiago fix

* Flow passing results again

* Working on docs.

* made more progress updates on docs

* Finished talking about controlling flows

* add flow output

* fixed flow output section

* add crews to flows section is looking good now

* more flow doc changes

* Update docs and add more examples

* drop visualizer

* save visualizer

* pyvis is beginning to work

* pyvis working

* it is working

* regular methods and triggers working. Need to work on router next.

* properly identifying router and router children nodes. Need to fix color

* children router working. Need to support loops

* curving cycles but need to add curve conditionals

* everythin is showing up properly need to fix curves

* all working. needs to be cleaned up

* adjust padding

* drop lib

* clean up prior to PR

* incorporate joao feedback

* final tweaks for joao

.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

.editorconfig

@@ -0,0 +1,14 @@
+# .editorconfig
+root = true
+
+# All files
+[*]
+charset = utf-8
+end_of_line = lf
+insert_final_newline = true
+trim_trailing_whitespace = true
+
+# Python files
+[*.py]
+indent_style = space
+indent_size = 2

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,116 @@
+name: Bug report
+description: Create a report to help us improve CrewAI
+title: "[BUG]"
+labels: ["bug"]
+assignees: []
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: Provide a clear and concise description of what the bug is.
+    validations:
+      required: true
+  - type: textarea
+    id: steps-to-reproduce
+    attributes:
+      label: Steps to Reproduce
+      description: Provide a step-by-step process to reproduce the behavior.
+      placeholder: |
+        1. Go to '...'
+        2. Click on '....'
+        3. Scroll down to '....'
+        4. See error
+    validations:
+      required: true
+  - type: textarea
+    id: expected-behavior
+    attributes:
+      label: Expected behavior
+      description: A clear and concise description of what you expected to happen.
+    validations:
+      required: true
+  - type: textarea
+    id: screenshots-code
+    attributes:
+      label: Screenshots/Code snippets
+      description: If applicable, add screenshots or code snippets to help explain your problem.
+    validations:
+      required: true
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating System
+      description: Select the operating system you're using
+      options:
+        - Ubuntu 20.04
+        - Ubuntu 22.04
+        - Ubuntu 24.04
+        - macOS Catalina
+        - macOS Big Sur
+        - macOS Monterey
+        - macOS Ventura
+        - macOS Sonoma
+        - Windows 10
+        - Windows 11
+        - Other (specify in additional context)
+    validations:
+      required: true
+  - type: dropdown
+    id: python-version
+    attributes:
+      label: Python Version
+      description: Version of Python your Crew is running on
+      options:
+        - '3.10'
+        - '3.11'
+        - '3.12'
+        - '3.13'
+    validations:
+      required: true
+  - type: input
+    id: crewai-version
+    attributes:
+      label: crewAI Version
+      description: What version of CrewAI are you using
+    validations:
+      required: true
+  - type: input
+    id: crewai-tools-version
+    attributes:
+      label: crewAI Tools Version
+      description: What version of CrewAI Tools are you using 
+    validations:
+      required: true
+  - type: dropdown
+    id: virtual-environment
+    attributes:
+      label: Virtual Environment
+      description: What Virtual Environment are you running your crew in.
+      options:
+        - Venv
+        - Conda
+        - Poetry
+    validations:
+      required: true
+  - type: textarea
+    id: evidence
+    attributes:
+      label: Evidence
+      description: Include relevant information, logs or error messages. These can be screenshots.
+    validations:
+      required: true
+  - type: textarea
+    id: possible-solution
+    attributes:
+      label: Possible Solution
+      description: Have a solution in mind? Please suggest it here, or write "None".
+    validations:
+      required: true
+  - type: textarea
+    id: additional-context
+    attributes:
+      label: Additional context
+      description: Add any other context about the problem here.
+    validations:
+      required: true

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1 @@
+blank_issues_enabled: false

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,65 @@
+name: Feature request
+description: Suggest a new feature for CrewAI
+title: "[FEATURE]"
+labels: ["feature-request"]
+assignees: []
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this feature request!
+  - type: dropdown
+    id: feature-area
+    attributes:
+      label: Feature Area
+      description: Which area of CrewAI does this feature primarily relate to?
+      options:
+        - Core functionality
+        - Agent capabilities
+        - Task management
+        - Integration with external tools
+        - Performance optimization
+        - Documentation
+        - Other (please specify in additional context)
+    validations:
+      required: true
+  - type: textarea
+    id: problem
+    attributes:
+      label: Is your feature request related to a an existing bug? Please link it here.
+      description: A link to the bug or NA if not related to an existing bug.
+    validations:
+      required: true
+  - type: textarea
+    id: solution
+    attributes:
+      label: Describe the solution you'd like
+      description: A clear and concise description of what you want to happen.
+    validations:
+      required: true
+  - type: textarea
+    id: alternatives
+    attributes:
+      label: Describe alternatives you've considered
+      description: A clear and concise description of any alternative solutions or features you've considered.
+    validations:
+      required: false
+  - type: textarea
+    id: context
+    attributes:
+      label: Additional context
+      description: Add any other context, screenshots, or examples about the feature request here.
+    validations:
+      required: false
+  - type: dropdown
+    id: willingness-to-contribute
+    attributes:
+      label: Willingness to Contribute
+      description: Would you be willing to contribute to the implementation of this feature?
+      options:
+        - Yes, I'd be happy to submit a pull request
+        - I could provide more detailed specifications
+        - I can test the feature once it's implemented
+        - No, I'm just suggesting the idea
+    validations:
+      required: true

.github/workflows/linter.yml

@@ -0,0 +1,16 @@
+name: Lint
+
+on: [pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Install Requirements
+        run: |
+          pip install ruff
+
+      - name: Run Ruff Linter
+        run: ruff check --exclude "templates","__init__.py"

.github/workflows/mkdocs.yml

@@ -0,0 +1,45 @@
+name: Deploy MkDocs
+
+on:
+  release:
+    types: [published]
+
+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/security-checker.yml

@@ -0,0 +1,23 @@
+name: Security Checker
+
+on: [pull_request]
+
+jobs:
+  security-check:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11.9"
+
+      - name: Install dependencies
+        run: pip install bandit
+
+      - name: Run Bandit
+        run: bandit -c pyproject.toml -r src/ -lll
+

.github/workflows/stale.yml

@@ -0,0 +1,27 @@
+name: Mark stale issues and pull requests
+
+on:
+  schedule:
+    - cron: '10 12 * * *'
+  workflow_dispatch:
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+    - uses: actions/stale@v9
+      with:
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        stale-issue-label: 'no-issue-activity'
+        stale-issue-message: 'This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
+        close-issue-message: 'This issue was closed because it has been stalled for 5 days with no activity.'
+        days-before-issue-stale: 30
+        days-before-issue-close: 5
+        stale-pr-label: 'no-pr-activity'
+        stale-pr-message: 'This PR is stale because it has been open for 45 days with no activity.'
+        days-before-pr-stale: 45
+        days-before-pr-close: -1
+        operations-per-run: 1200

.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
+    timeout-minutes: 15
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11.9"
+
+      - name: Install Requirements
+        run: |
+          set -e
+          pip install poetry
+          poetry install
+
+      - name: Run tests
+        run: poetry run pytest

.github/workflows/type-checker.yml

@@ -0,0 +1,26 @@
+name: Run Type Checks
+
+on: [pull_request]
+
+permissions:
+  contents: write
+
+jobs:
+  type-checker:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+
+      - name: Install Requirements
+        run: |
+          pip install mypy
+
+      - name: Run type checks
+        run: mypy src

.gitignore

@@ -2,6 +2,18 @@
 .pytest_cache
 __pycache__
 dist/
+lib/
 .env
 assets/*
-.idea
+.idea
+test/
+docs_crew/
+chroma.sqlite3
+old_en.json
+db/
+test.py
+rc-tests/*
+*.pkl
+temp/*
+.vscode/*
+crew_tasks_output.json

.pre-commit-config.yaml

@@ -1,21 +1,9 @@
 repos:
-
-  - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.12.1
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.4.4
     hooks:
-      - id: black
-        language_version: python3.11
-        files: \.(py)$
-
-  - repo: https://github.com/pycqa/isort
-    rev: 5.13.2
-    hooks:
-      - id: isort
-        name: isort (python)
-        args: ["--profile", "black", "--filter-files"]
-
-  - repo: https://github.com/PyCQA/autoflake
-    rev: v2.2.1
-    hooks:
-      - id: autoflake
-        args: ['--in-place', '--remove-all-unused-imports', '--remove-unused-variables', '--ignore-init-module-imports']
+      - id: ruff
+        args: ["--fix"]
+        exclude: "templates"
+      - id: ruff-format
+        exclude: "templates"

README.md

@@ -1,16 +1,36 @@
-# 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.com/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Examples](https://github.com/crewAIInc/crewAI-examples) | [Discourse](https://community.crewai.com)
+
+</h3>
+
+[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/crewAIInc/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)
+- [Telemetry](#telemetry)
 - [License](#license)
 
 ## Why CrewAI?
@@ -18,111 +38,148 @@
 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 install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command: pip install 'crewai[tools]'. This command installs the basic package and also adds extra components which require more dependencies to function."
+
+```shell
+pip install 'crewai[tools]'
+```
+
+### 2. Setting Up Your Crew
 
 ```python
 import os
 from crewai import Agent, Task, Crew, Process
+from crewai_tools import SerperDevTool
 
-os.environ["OPENAI_API_KEY"] = "Your Key"
+os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
+
+# 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/)
 
 # 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,
+  # You can pass an optional llm attribute specifying what model you wanna use.
+  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
+  tools=[SerperDevTool()]
 )
 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=True,
+  process = Process.sequential
 )
 
 # 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 your 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/crewAIInc/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/crewAIInc/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/crewAIInc/crewAI-examples/tree/main/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/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/crewAIInc/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/crewAIInc/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/crewAIInc/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.
+**CrewAI's Advantage**: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.
+
+- **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.
 
-**CrewAI's Advantage**: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.
-
 ## Contribution
 
 CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:
@@ -134,12 +191,14 @@ 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 +210,96 @@ pre-commit install
 ```
 
 ### Running Tests
+
 ```bash
 poetry run pytest
 ```
 
+### Running static type checks
+
+```bash
+poetry run mypy
+```
+
 ### Packaging
+
 ```bash
 poetry build
 ```
 
 ### Installing Locally
+
 ```bash
 pip install dist/*.tar.gz
 ```
 
+## 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.
+
+It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. We don't offer a way to disable it now, but we will in the future.
+
+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 to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
+
 ## License
-CrewAI is released under the MIT License
 
+CrewAI is released under the MIT License.
 
+## Frequently Asked Questions (FAQ)
+
+### Q: What is CrewAI?
+A: CrewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. It enables agents to work together seamlessly, tackling complex tasks through collaborative intelligence.
+
+### Q: How do I install CrewAI?
+A: You can install CrewAI using pip:
+```shell
+pip install crewai
+```
+For additional tools, use:
+```shell
+pip install 'crewai[tools]'
+```
+
+### Q: Can I use CrewAI with local models?
+A: Yes, CrewAI supports various LLMs, including local models. You can configure your agents to use local models via tools like Ollama & LM Studio. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
+
+### Q: What are the key features of CrewAI?
+A: Key features include role-based agent design, autonomous inter-agent delegation, flexible task management, process-driven execution, output saving as files, and compatibility with both open-source and proprietary models.
+
+### Q: How does CrewAI compare to other AI orchestration tools?
+A: CrewAI is designed with production in mind, offering flexibility similar to Autogen's conversational agents and structured processes like ChatDev, but with more adaptability for real-world applications.
+
+### Q: Is CrewAI open-source?
+A: Yes, CrewAI is open-source and welcomes contributions from the community.
+
+### Q: Does CrewAI collect any data?
+A: CrewAI uses anonymous telemetry to collect usage data for improvement purposes. No sensitive data (like prompts, task descriptions, or API calls) is collected. Users can opt-in to share more detailed data by setting `share_crew=True` on their Crews.
+
+### Q: Where can I find examples of CrewAI in action?
+A: You can find various real-life examples in the [crewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), including trip planners, stock analysis tools, and more.
+
+### Q: How can I contribute to CrewAI?
+A: Contributions are welcome! You can fork the repository, create a new branch for your feature, add your improvement, and send a pull request. Check the Contribution section in the README for more details.

crewAI.excalidraw

@@ -1463,11 +1463,11 @@
       "locked": false,
       "fontSize": 20,
       "fontFamily": 3,
-      "text": "Agents have the inert ability of\nreach out to another to delegate\nwork or ask questions.",
+      "text": "Agents have the innate ability of\nreach out to another to delegate\nwork or ask questions.",
       "textAlign": "right",
       "verticalAlign": "top",
       "containerId": null,
-      "originalText": "Agents have the inert ability of\nreach out to another to delegate\nwork or ask questions.",
+      "originalText": "Agents have the innate ability of\nreach out to another to delegate\nwork or ask questions.",
       "lineHeight": 1.2,
       "baseline": 68
     },
@@ -1734,4 +1734,4 @@
     "viewBackgroundColor": "#ffffff"
   },
   "files": {}
-}
+}

crewai/__init__.py

@@ -1,4 +0,0 @@
-from crewai.agent import Agent
-from crewai.crew import Crew
-from crewai.process import Process
-from crewai.task import Task

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/CNAME

@@ -0,0 +1 @@
+docs.crewai.com

docs/core-concepts/Agents.md

@@ -0,0 +1,155 @@
+---
+title: crewAI Agents
+description: What are crewAI Agents and how to use them.
+---
+
+## What is an Agent?
+!!! note "What is an Agent?"
+    An agent is an **autonomous unit** programmed to:
+    <ul>
+      <li class='leading-3'>Perform tasks</li>
+      <li class='leading-3'>Make decisions</li>
+      <li class='leading-3'>Communicate with other agents</li>
+    </ul>
+    <br/>
+    Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.
+
+## Agent Attributes
+
+| Attribute                  | Parameter  | Description                                                                                                                                                                                                                                    |
+| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Role**                   | `role`     | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.                                                                                                                                    |
+| **Goal**                   | `goal`     | The individual objective that the agent aims to achieve. It guides the agent's decision-making process.                                                                                                                                        |
+| **Backstory**              | `backstory`| Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.                                                                                                                                           |
+| **LLM** *(optional)*       | `llm`      | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified.                                                         |
+| **Tools** *(optional)*     | `tools`    | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list.             |
+| **Function Calling LLM** *(optional)* | `function_calling_llm`  | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`.                                                                                          |
+| **Max Iter** *(optional)*  | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`.                                                                                                                           |
+| **Max RPM** *(optional)*   | `max_rpm`  | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
+| **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
+| **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
+| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`.
+| **Step Callback** *(optional)* | `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`.                                                               |
+| **Cache** *(optional)*     | `cache`  | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
+| **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
+| **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
+| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
+| **Use System Prompt** *(optional)*     | `use_system_prompt`  | Adds the ability to not use system prompt (to support o1 models). Default is `True`.                                                                                                                                                                  |
+| **Respect Context Window** *(optional)*     | `respect_context_window`  | Summary strategy to avoid overflowing the context window. Default is `True`.                                                                                                                                                                  |
+
+## Creating an Agent
+
+!!! note "Agent Interaction"
+    Agents can interact with each other using crewAI's built-in delegation and communication mechanisms. This allows for dynamic task management and problem-solving within the crew.
+
+To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example including all attributes:
+
+```python
+# Example: Creating an agent with all attributes
+from crewai import Agent
+
+agent = Agent(
+  role='Data Analyst',
+  goal='Extract actionable insights',
+  backstory="""You're a data analyst at a large company.
+  You're responsible for analyzing data and providing insights
+  to the business.
+  You're currently working on a project to analyze the
+  performance of our marketing campaigns.""",
+  tools=[my_tool1, my_tool2],  # Optional, defaults to an empty list
+  llm=my_llm,  # Optional
+  function_calling_llm=my_llm,  # Optional
+  max_iter=15,  # Optional
+  max_rpm=None, # Optional
+  max_execution_time=None, # Optional
+  verbose=True,  # Optional
+  allow_delegation=False,  # Optional
+  step_callback=my_intermediate_step_callback,  # Optional
+  cache=True,  # Optional
+  system_template=my_system_template,  # Optional
+  prompt_template=my_prompt_template,  # Optional
+  response_template=my_response_template,  # Optional
+  config=my_config,  # Optional
+  crew=my_crew,  # Optional
+  tools_handler=my_tools_handler,  # Optional
+  cache_handler=my_cache_handler,  # Optional
+  callbacks=[callback1, callback2],  # Optional
+  allow_code_execution=True,  # Optional
+  max_retry_limit=2,  # Optional
+  use_system_prompt=True,  # Optional
+  respect_context_window=True,  # Optional
+)
+```
+
+## Setting prompt templates
+
+Prompt templates are used to format the prompt for the agent. You can use to update the system, regular and response templates for the agent. Here's an example of how to set prompt templates:
+
+```python
+agent = Agent(
+        role="{topic} specialist",
+        goal="Figure {goal} out",
+        backstory="I am the master of {role}",
+        system_template="""<|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>""",
+        prompt_template="""<|start_header_id|>user<|end_header_id|>
+
+{{ .Prompt }}<|eot_id|>""",
+        response_template="""<|start_header_id|>assistant<|end_header_id|>
+
+{{ .Response }}<|eot_id|>""",
+    )
+```
+
+## Bring your Third Party Agents
+!!! note "Extend your Third Party Agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the crewai's BaseAgent class."
+
+    BaseAgent includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
+
+    CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems.
+
+
+```py
+from crewai import Agent, Task, Crew
+from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.
+
+from langchain.agents import load_tools
+
+langchain_tools = load_tools(["google-serper"], llm=llm)
+
+agent1 = CustomAgent(
+    role="agent role",
+    goal="who is {input}?",
+    backstory="agent backstory",
+    verbose=True,
+)
+
+task1 = Task(
+    expected_output="a short biography of {input}",
+    description="a short biography of {input}",
+    agent=agent1,
+)
+
+agent2 = Agent(
+    role="agent role",
+    goal="summarize the short bio for {input} and if needed do more research",
+    backstory="agent backstory",
+    verbose=True,
+)
+
+task2 = Task(
+    description="a tldr summary of the short biography",
+    expected_output="5 bullet point summary of the biography",
+    agent=agent2,
+    context=[task1],
+)
+
+my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
+crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
+```
+
+## 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/Cli.md

@@ -0,0 +1,142 @@
+# CrewAI CLI Documentation
+
+The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews and pipelines.
+
+## Installation
+
+To use the CrewAI CLI, make sure you have CrewAI & Poetry installed:
+
+```
+pip install crewai poetry
+```
+
+## Basic Usage
+
+The basic structure of a CrewAI CLI command is:
+
+```
+crewai [COMMAND] [OPTIONS] [ARGUMENTS]
+```
+
+## Available Commands
+
+### 1. create
+
+Create a new crew or pipeline.
+
+```
+crewai create [OPTIONS] TYPE NAME
+```
+
+- `TYPE`: Choose between "crew" or "pipeline"
+- `NAME`: Name of the crew or pipeline
+- `--router`: (Optional) Create a pipeline with router functionality
+
+Example:
+```
+crewai create crew my_new_crew
+crewai create pipeline my_new_pipeline --router
+```
+
+### 2. version
+
+Show the installed version of CrewAI.
+
+```
+crewai version [OPTIONS]
+```
+
+- `--tools`: (Optional) Show the installed version of CrewAI tools
+
+Example:
+```
+crewai version
+crewai version --tools
+```
+
+### 3. train
+
+Train the crew for a specified number of iterations.
+
+```
+crewai train [OPTIONS]
+```
+
+- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
+- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
+
+Example:
+```
+crewai train -n 10 -f my_training_data.pkl
+```
+
+### 4. replay
+
+Replay the crew execution from a specific task.
+
+```
+crewai replay [OPTIONS]
+```
+
+- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
+
+Example:
+```
+crewai replay -t task_123456
+```
+
+### 5. log_tasks_outputs
+
+Retrieve your latest crew.kickoff() task outputs.
+
+```
+crewai log_tasks_outputs
+```
+
+### 6. reset_memories
+
+Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
+
+```
+crewai reset_memories [OPTIONS]
+```
+
+- `-l, --long`: Reset LONG TERM memory
+- `-s, --short`: Reset SHORT TERM memory
+- `-e, --entities`: Reset ENTITIES memory
+- `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
+- `-a, --all`: Reset ALL memories
+
+Example:
+```
+crewai reset_memories --long --short
+crewai reset_memories --all
+```
+
+### 7. test
+
+Test the crew and evaluate the results.
+
+```
+crewai test [OPTIONS]
+```
+
+- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
+- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
+
+Example:
+```
+crewai test -n 5 -m gpt-3.5-turbo
+```
+
+### 8. run
+
+Run the crew.
+
+```
+crewai run
+```
+
+## Note
+
+Make sure to run these commands from the directory where your CrewAI project is set up. Some commands may require additional configuration or setup within your project structure.

docs/core-concepts/Collaboration.md

@@ -0,0 +1,44 @@
+---
+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. Note that while `manager_llm` is mandatory for hierarchical processes to ensure proper execution flow, `function_calling_llm` is optional, with a default value provided for streamlined tool interaction.
+- **Custom Manager Agent (`manager_agent`)**: Allows specifying a custom agent as the manager instead of using the default manager provided by CrewAI.
+- **Process Flow (`process`)**: Defines the execution logic (e.g., sequential, hierarchical) to streamline task distribution and execution.
+- **Verbose Logging (`verbose`)**: Offers detailed logging capabilities for monitoring and debugging purposes. It supports both integer and boolean types to indicate the verbosity level. For example, setting `verbose` to 1 might enable basic logging, whereas setting it to True enables more detailed logs.
+- **Rate Limiting (`max_rpm`)**: Ensures efficient utilization of resources by limiting requests per minute. Guidelines for setting `max_rpm` should consider the complexity of tasks and the expected load on resources.
+- **Internationalization / Customization Support (`language`, `prompt_file`)**: Facilitates full customization of the inner prompts, enhancing global usability. Supported languages and the process for utilizing the `prompt_file` attribute for customization should be clearly documented. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json)
+- **Execution and Output Handling (`full_output`)**: Distinguishes between full and final outputs for nuanced control over task results. Examples showcasing the difference in outputs can aid in understanding the practical implications of this attribute.
+- **Callback and Telemetry (`step_callback`, `task_callback`)**: Integrates callbacks for step-wise and task-level execution monitoring, alongside telemetry for performance analytics. The purpose and usage of `task_callback` alongside `step_callback` for granular monitoring should be clearly explained.
+- **Crew Sharing (`share_crew`)**: Enables sharing of crew information with CrewAI for continuous improvement and training models. The privacy implications and benefits of this feature, including how it contributes to model improvement, should be outlined.
+- **Usage Metrics (`usage_metrics`)**: Stores all metrics for the language model (LLM) usage during all tasks' execution, providing insights into operational efficiency and areas for improvement. Detailed information on accessing and interpreting these metrics for performance analysis should be provided.
+- **Memory Usage (`memory`)**: Indicates whether the crew should use memory to store memories of its execution, enhancing task execution and agent learning.
+- **Embedder Configuration (`embedder`)**: Specifies the configuration for the embedder to be used by the crew for understanding and generating language. This attribute supports customization of the language model provider.
+- **Cache Management (`cache`)**: Determines whether the crew should use a cache to store the results of tool executions, optimizing performance.
+- **Output Logging (`output_log_file`)**: Specifies the file path for logging the output of the crew's execution.
+- **Planning Mode (`planning`)**: Allows crews to plan their actions before executing tasks by setting `planning=True` when creating the `Crew` instance. This feature enhances coordination and efficiency.
+- **Replay Feature**: Introduces a new CLI for listing tasks from the last run and replaying from a specific task, enhancing task management and troubleshooting.
+
+## 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 +1,189 @@
+---
+title: crewAI Crews
+description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
+---
+
+## What is 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                             | Parameters             | Description                                                                                                                                                                                                                                               |
+| :------------------------------------ | :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Tasks**                             | `tasks`                | A list of tasks assigned to the crew.                                                                                                                                                                                                                     |
+| **Agents**                            | `agents`               | A list of agents that are part of the crew.                                                                                                                                                                                                               |
+| **Process** _(optional)_              | `process`              | The process flow (e.g., sequential, hierarchical) the crew follows. Default is `sequential`.                                                                                                                                                              |
+| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution. Defaults to `False`.                                                                                                                                                                                    |
+| **Manager LLM** _(optional)_          | `manager_llm`          | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.**                                                                                                                                   |
+| **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
+| **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
+| **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
+| **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
+| **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory). Defaults to `False`.                                                                                                                                                       |
+| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                          |
+| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                                                     |
+| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                       |
+| **Step Callback** _(optional)_        | `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`.                                                               |
+| **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
+| **Share Crew** _(optional)_           | `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.                                                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
+| **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
+| **Manager Callbacks** _(optional)_    | `manager_callbacks`    | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used.                                                                                                                            |
+| **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
+| **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
+| **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
+
+!!! 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.
+
+
+## Crew Output
+
+!!! note "Understanding Crew Outputs"
+The output of a crew in the crewAI framework is encapsulated within the `CrewOutput` class.
+This class provides a structured way to access results of the crew's execution, including various formats such as raw strings, JSON, and Pydantic models.
+The `CrewOutput` includes the results from the final task output, token usage, and individual task outputs.
+
+### Crew Output Attributes
+
+| Attribute        | Parameters     | Type                       | Description                                                                                          |
+| :--------------- | :------------- | :------------------------- | :--------------------------------------------------------------------------------------------------- |
+| **Raw**          | `raw`          | `str`                      | The raw output of the crew. This is the default format for the output.                               |
+| **Pydantic**     | `pydantic`     | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the crew.                              |
+| **JSON Dict**    | `json_dict`    | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the crew.                                               |
+| **Tasks Output** | `tasks_output` | `List[TaskOutput]`         | A list of `TaskOutput` objects, each representing the output of a task in the crew.                  |
+| **Token Usage**  | `token_usage`  | `Dict[str, Any]`           | A summary of token usage, providing insights into the language model's performance during execution. |
+
+### Crew Output Methods and Properties
+
+| Method/Property | Description                                                                                       |
+| :-------------- | :------------------------------------------------------------------------------------------------ |
+| **json**        | Returns the JSON string representation of the crew output if the output format is JSON.           |
+| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
+| \***\*str\*\*** | Returns the string representation of the crew output, prioritizing Pydantic, then JSON, then raw. |
+
+### Accessing Crew Outputs
+
+Once a crew has been executed, its output can be accessed through the `output` attribute of the `Crew` object. The `CrewOutput` class provides various ways to interact with and present this output.
+
+#### Example
+
+```python
+# Example crew execution
+crew = Crew(
+    agents=[research_agent, writer_agent],
+    tasks=[research_task, write_article_task],
+    verbose=True
+)
+
+crew_output = crew.kickoff()
+
+# Accessing the crew output
+print(f"Raw Output: {crew_output.raw}")
+if crew_output.json_dict:
+    print(f"JSON Output: {json.dumps(crew_output.json_dict, indent=2)}")
+if crew_output.pydantic:
+    print(f"Pydantic Output: {crew_output.pydantic}")
+print(f"Tasks Output: {crew_output.tasks_output}")
+print(f"Token Usage: {crew_output.token_usage}")
+```
+
+## Memory Utilization
+
+Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
+
+## Cache Utilization
+
+Caches can be employed to store the results of tools' execution, making the process more efficient by reducing the need to re-execute identical tasks.
+
+## Crew Usage Metrics
+
+After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.
+
+```python
+# Access the crew's usage metrics
+crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
+crew.kickoff()
+print(crew.usage_metrics)
+```
+
+## Crew Execution Process
+
+- **Sequential Process**: Tasks are executed one after another, allowing for a linear flow of work.
+- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. **Note**: A `manager_llm` or `manager_agent` is required for this process and it's essential for validating the process flow.
+
+### Kicking Off a Crew
+
+Once your crew is assembled, initiate the workflow with the `kickoff()` method. This starts the execution process according to the defined process flow.
+
+```python
+# Start the crew's task execution
+result = my_crew.kickoff()
+print(result)
+```
+
+### Different Ways to Kick Off a Crew
+
+Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
+
+- `kickoff()`: Starts the execution process according to the defined process flow.
+- `kickoff_for_each()`: Executes tasks for each agent individually.
+- `kickoff_async()`: Initiates the workflow asynchronously.
+- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
+
+```python
+# Start the crew's task execution
+result = my_crew.kickoff()
+print(result)
+
+# Example of using kickoff_for_each
+inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
+results = my_crew.kickoff_for_each(inputs=inputs_array)
+for result in results:
+    print(result)
+
+# Example of using kickoff_async
+inputs = {'topic': 'AI in healthcare'}
+async_result = my_crew.kickoff_async(inputs=inputs)
+print(async_result)
+
+# Example of using kickoff_for_each_async
+inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
+async_results = my_crew.kickoff_for_each_async(inputs=inputs_array)
+for async_result in async_results:
+    print(async_result)
+```
+
+These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs.
+
+### Replaying from a Specific Task
+
+You can now replay from a specific task using our CLI command `replay`.
+
+The replay feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the `task_id` for the replay process.
+
+Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
+
+### Replaying from a Specific Task Using the CLI
+
+To use the replay feature, follow these steps:
+
+1. Open your terminal or command prompt.
+2. Navigate to the directory where your CrewAI project is located.
+3. Run the following command:
+
+To view the latest kickoff task IDs, use:
+
+```shell
+crewai log-tasks-outputs
+```
+
+Then, to replay from a specific task, use:
+
+```shell
+crewai replay -t <task_id>
+```
+
+These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

docs/core-concepts/LLMs.md

@@ -0,0 +1,155 @@
+# Large Language Models (LLMs) in crewAI
+
+## Introduction
+Large Language Models (LLMs) are the backbone of intelligent agents in the crewAI framework. This guide will help you understand, configure, and optimize LLM usage for your crewAI projects.
+
+## Table of Contents
+- [Key Concepts](#key-concepts)
+- [Configuring LLMs for Agents](#configuring-llms-for-agents)
+  - [1. Default Configuration](#1-default-configuration)
+  - [2. String Identifier](#2-string-identifier)
+  - [3. LLM Instance](#3-llm-instance)
+  - [4. Custom LLM Objects](#4-custom-llm-objects)
+- [Connecting to OpenAI-Compatible LLMs](#connecting-to-openai-compatible-llms)
+- [LLM Configuration Options](#llm-configuration-options)
+- [Using Ollama (Local LLMs)](#using-ollama-local-llms)
+- [Changing the Base API URL](#changing-the-base-api-url)
+- [Best Practices](#best-practices)
+- [Troubleshooting](#troubleshooting)
+
+## Key Concepts
+- **LLM**: Large Language Model, the AI powering agent intelligence
+- **Agent**: A crewAI entity that uses an LLM to perform tasks
+- **Provider**: A service that offers LLM capabilities (e.g., OpenAI, Anthropic, Ollama, [more providers](https://docs.litellm.ai/docs/providers))
+
+## Configuring LLMs for Agents
+
+crewAI offers flexible options for setting up LLMs:
+
+### 1. Default Configuration
+By default, crewAI uses the `gpt-4o-mini` model. It uses environment variables if no LLM is specified:
+- `OPENAI_MODEL_NAME` (defaults to "gpt-4o-mini" if not set)
+- `OPENAI_API_BASE`
+- `OPENAI_API_KEY`
+
+### 2. String Identifier
+```python
+agent = Agent(llm="gpt-4o", ...)
+```
+
+### 3. LLM Instance
+List of [more providers](https://docs.litellm.ai/docs/providers).
+```python
+from crewai import LLM
+
+llm = LLM(model="gpt-4", temperature=0.7)
+agent = Agent(llm=llm, ...)
+```
+
+### 4. Custom LLM Objects
+Pass a custom LLM implementation or object from another library.
+
+## Connecting to OpenAI-Compatible LLMs
+
+You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
+
+1. Using environment variables:
+```python
+import os
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
+```
+
+2. Using LLM class attributes:
+```python
+llm = LLM(
+    model="custom-model-name",
+    api_key="your-api-key",
+    base_url="https://api.your-provider.com/v1"
+)
+agent = Agent(llm=llm, ...)
+```
+
+## LLM Configuration Options
+
+When configuring an LLM for your agent, you have access to a wide range of parameters:
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `model` | str | The name of the model to use (e.g., "gpt-4", "gpt-3.5-turbo", "ollama/llama3.1", [more providers](https://docs.litellm.ai/docs/providers)) |
+| `timeout` | float, int | Maximum time (in seconds) to wait for a response |
+| `temperature` | float | Controls randomness in output (0.0 to 1.0) |
+| `top_p` | float | Controls diversity of output (0.0 to 1.0) |
+| `n` | int | Number of completions to generate |
+| `stop` | str, List[str] | Sequence(s) to stop generation |
+| `max_tokens` | int | Maximum number of tokens to generate |
+| `presence_penalty` | float | Penalizes new tokens based on their presence in the text so far |
+| `frequency_penalty` | float | Penalizes new tokens based on their frequency in the text so far |
+| `logit_bias` | Dict[int, float] | Modifies likelihood of specified tokens appearing in the completion |
+| `response_format` | Dict[str, Any] | Specifies the format of the response (e.g., {"type": "json_object"}) |
+| `seed` | int | Sets a random seed for deterministic results |
+| `logprobs` | bool | Whether to return log probabilities of the output tokens |
+| `top_logprobs` | int | Number of most likely tokens to return the log probabilities for |
+| `base_url` | str | The base URL for the API endpoint |
+| `api_version` | str | The version of the API to use |
+| `api_key` | str | Your API key for authentication |
+
+Example:
+```python
+llm = LLM(
+    model="gpt-4",
+    temperature=0.8,
+    max_tokens=150,
+    top_p=0.9,
+    frequency_penalty=0.1,
+    presence_penalty=0.1,
+    stop=["END"],
+    seed=42,
+    base_url="https://api.openai.com/v1",
+    api_key="your-api-key-here"
+)
+agent = Agent(llm=llm, ...)
+```
+
+## Using Ollama (Local LLMs)
+
+crewAI supports using Ollama for running open-source models locally:
+
+1. Install Ollama: [ollama.ai](https://ollama.ai/)
+2. Run a model: `ollama run llama2`
+3. Configure agent:
+```python
+agent = Agent(
+    llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
+    ...
+)
+```
+
+## Changing the Base API URL
+
+You can change the base API URL for any LLM provider by setting the `base_url` parameter:
+
+```python
+llm = LLM(
+    model="custom-model-name",
+    base_url="https://api.your-provider.com/v1",
+    api_key="your-api-key"
+)
+agent = Agent(llm=llm, ...)
+```
+
+This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.
+
+## Best Practices
+1. **Choose the right model**: Balance capability and cost.
+2. **Optimize prompts**: Clear, concise instructions improve output.
+3. **Manage tokens**: Monitor and limit token usage for efficiency.
+4. **Use appropriate temperature**: Lower for factual tasks, higher for creative ones.
+5. **Implement error handling**: Gracefully manage API errors and rate limits.
+
+## Troubleshooting
+- **API Errors**: Check your API key, network connection, and rate limits.
+- **Unexpected Outputs**: Refine your prompts and adjust temperature or top_p.
+- **Performance Issues**: Consider using a more powerful model or optimizing your queries.
+- **Timeout Errors**: Increase the `timeout` parameter or optimize your input.