docs: add flow plot visualization example to documentation (#2668)

Co-Authored-By: Joe Moura <joao@crewai.com>

.circleci/config.yml

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

.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 +1,115 @@
+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'
+    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/security.md

@@ -0,0 +1,19 @@
+CrewAI takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organization.
+If you believe you have found a security vulnerability in any CrewAI product or service, please report it to us as described below.
+
+ ## Reporting a Vulnerability
+ Please do not report security vulnerabilities through public GitHub issues.
+ To report a vulnerability, please email us at security@crewai.com.
+ Please include the requested information listed below so that we can triage your report more quickly
+
+ - Type of issue (e.g. SQL injection, cross-site scripting, etc.)
+ - Full paths of source file(s) related to the manifestation of the issue
+ - The location of the affected source code (tag/branch/commit or direct URL)
+ - Any special configuration required to reproduce the issue
+ - Step-by-step instructions to reproduce the issue (please include screenshots if needed)
+ - Proof-of-concept or exploit code (if possible)
+ - Impact of the issue, including how an attacker might exploit the issue
+
+ Once we have received your report, we will respond to you at the email address you provide. If the issue is confirmed, we will release a patch as soon as possible depending on the complexity of the issue.
+
+ At this time, we are not offering a bug bounty program. Any rewards will be at our discretion.

.github/workflows/linter.yml

@@ -0,0 +1,16 @@
+name: Lint
+
+on: [pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install Requirements
+        run: |
+          pip install ruff
+
+      - name: Run Ruff Linter
+        run: ruff check

.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@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
+      - name: Calculate requirements hash
+        id: req-hash
+        run: echo "::set-output name=hash::$(sha256sum requirements-doc.txt | awk '{print $1}')"
+
+      - name: Setup cache
+        uses: actions/cache@v4
+        with:
+          key: mkdocs-material-${{ 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/notify-downstream.yml

@@ -0,0 +1,33 @@
+name: Notify Downstream
+
+on:
+  push:
+    branches:
+      - main
+
+permissions:
+  contents: read
+
+jobs:
+  notify-downstream:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Generate GitHub App token
+        id: app-token
+        uses: tibdex/github-app-token@v2
+        with:
+          app_id: ${{ secrets.OSS_SYNC_APP_ID }}
+          private_key: ${{ secrets.OSS_SYNC_APP_PRIVATE_KEY }}
+
+      - name: Notify Repo B
+        uses: peter-evans/repository-dispatch@v3
+        with:
+          token: ${{ steps.app-token.outputs.token }}
+          repository: ${{ secrets.OSS_SYNC_DOWNSTREAM_REPO }}
+          event-type: upstream-commit
+          client-payload: |
+            {
+              "commit_sha": "${{ github.sha }}"
+            }
+

.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@v5
+        with:
+          python-version: "3.11.9"
+
+      - name: Install dependencies
+        run: pip install bandit
+
+      - name: Run Bandit
+        run: bandit -c pyproject.toml -r src/ -ll
+

.github/workflows/stale.yml

@@ -0,0 +1,29 @@
+name: Mark stale issues and pull requests
+
+permissions:
+  contents: write
+  issues: write
+  pull-requests: write
+
+on:
+  schedule:
+    - cron: '10 12 * * *'
+  workflow_dispatch:
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    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 +1,34 @@
+name: Run Tests
+
+on: [pull_request]
+
+permissions:
+  contents: write
+
+env:
+  OPENAI_API_KEY: fake-api-key
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    strategy:
+      matrix:
+        python-version: ['3.10', '3.11', '3.12']
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          enable-cache: true
+
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
+
+      - name: Install the project
+        run: uv sync --dev --all-extras
+
+      - name: Run tests
+        run: uv run pytest tests -vv

.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@v5
+        with:
+          python-version: "3.11.9"
+
+      - name: Install Requirements
+        run: |
+          pip install mypy
+
+      - name: Run type checks
+        run: mypy src

.gitignore

@@ -2,7 +2,28 @@
 .pytest_cache
 __pycache__
 dist/
+lib/
 .env
 assets/*
 .idea
-test.py
+test/
+docs_crew/
+chroma.sqlite3
+old_en.json
+db/
+test.py
+rc-tests/*
+*.pkl
+temp/*
+.vscode/*
+crew_tasks_output.json
+.codesight
+.mypy_cache
+.ruff_cache
+.venv
+agentops.log
+test_flow.html
+crewairules.mdc
+plan.md
+conceptual_plan.md
+build_image

.pre-commit-config.yaml

@@ -1,21 +1,7 @@
 repos:
-
-  - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.12.1
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.8.2
     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"]
+      - id: ruff-format

.ruff.toml

@@ -0,0 +1,9 @@
+exclude = [
+    "templates",
+    "__init__.py",
+]
+
+[lint]
+select = [
+    "I", # isort rules
+]

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2018 The Python Packaging Authority
+Copyright (c) 2025 crewAI, Inc.
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1,190 +1,523 @@
-# crewAI
+<div align="center">
 
-![Logo of crewAI, tow people rowing on a boat](./crewai_logo.png)
+![Logo of CrewAI](./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.
 
-- [Why CrewAI](#why-crewai)
+</div>
+
+### Fast and Flexible Multi-Agent Automation Framework
+
+CrewAI is a lean, lightning-fast Python framework built entirely from
+scratch—completely **independent of LangChain or other agent frameworks**.
+It empowers developers with both high-level simplicity and precise low-level
+control, ideal for creating autonomous AI agents tailored to any scenario.
+
+- **CrewAI Crews**: Optimize for autonomy and collaborative intelligence.
+- **CrewAI Flows**: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively
+
+With over 100,000 developers certified through our community courses at
+[learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
+standard for enterprise-ready AI automation.
+
+# CrewAI Enterprise Suite
+
+CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations
+that require secure, scalable, and easy-to-manage agent-driven automation.
+
+You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
+
+## Crew Control Plane Key Features:
+- **Tracing & Observability**: Monitor and track your AI agents and workflows in real-time, including metrics, logs, and traces.
+- **Unified Control Plane**: A centralized platform for managing, monitoring, and scaling your AI agents and workflows.
+- **Seamless Integrations**: Easily connect with existing enterprise systems, data sources, and cloud infrastructure.
+- **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
+- **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
+- **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
+- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
+
+CrewAI Enterprise is designed for enterprises seeking a powerful,
+reliable solution to transform complex business processes into efficient,
+intelligent automations.
+
+<h3>
+
+[Homepage](https://www.crewai.com/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [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)
+- [Understanding Flows and Crews](#understanding-flows-and-crews)
+- [CrewAI vs LangGraph](#how-crewai-compares)
 - [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)
+  - [Using Crews and Flows Together](#using-crews-and-flows-together)
+- [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model)
+- [How CrewAI Compares](#how-crewai-compares)
+- [Frequently Asked Questions (FAQ)](#frequently-asked-questions-faq)
 - [Contribution](#contribution)
-- [💬 CrewAI Discord Community](https://discord.gg/4ZqbAStv)
-- [Hire Consulting](#hire-consulting)
+- [Telemetry](#telemetry)
 - [License](#license)
 
 ## Why CrewAI?
 
-The power of AI collaboration has too much to offer.
-CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions.
+<div align="center" style="margin-bottom: 30px;">
+  <img src="docs/asset.png" alt="CrewAI Logo" width="100%">
+</div>
 
-- 🤖 [Talk with the Docs](https://chat.openai.com/g/g-qqTuUWsBY-crewai-assistant)
-- 📄 [Documentation Wiki](https://github.com/joaomdmoura/CrewAI/wiki)
+CrewAI unlocks the true potential of multi-agent automation, delivering the best-in-class combination of speed, flexibility, and control with either Crews of AI Agents or Flows of Events:
+
+- **Standalone Framework**: Built from scratch, independent of LangChain or any other agent framework.
+- **High Performance**: Optimized for speed and minimal resource usage, enabling faster execution.
+- **Flexible Low Level Customization**: Complete freedom to customize at both high and low levels - from overall workflows and system architecture to granular agent behaviors, internal prompts, and execution logic.
+- **Ideal for Every Use Case**: Proven effective for both simple tasks and highly complex, real-world, enterprise-grade scenarios.
+- **Robust Community**: Backed by a rapidly growing community of over **100,000 certified** developers offering comprehensive support and resources.
+
+CrewAI empowers developers and enterprises to confidently build intelligent automations, bridging the gap between simplicity, flexibility, and performance.
 
 ## Getting Started
 
+### Learning Resources
+
+Learn CrewAI through our comprehensive courses:
+- [Multi AI Agent Systems with CrewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/) - Master the fundamentals of multi-agent systems
+- [Practical Multi AI Agents and Advanced Use Cases](https://www.deeplearning.ai/short-courses/practical-multi-ai-agents-and-advanced-use-cases-with-crewai/) - Deep dive into advanced implementations
+
+### Understanding Flows and Crews
+
+CrewAI offers two powerful, complementary approaches that work seamlessly together to build sophisticated AI applications:
+
+1. **Crews**: Teams of AI agents with true autonomy and agency, working together to accomplish complex tasks through role-based collaboration. Crews enable:
+   - Natural, autonomous decision-making between agents
+   - Dynamic task delegation and collaboration
+   - Specialized roles with defined goals and expertise
+   - Flexible problem-solving approaches
+
+2. **Flows**: Production-ready, event-driven workflows that deliver precise control over complex automations. Flows provide:
+   - Fine-grained control over execution paths for real-world scenarios
+   - Secure, consistent state management between tasks
+   - Clean integration of AI agents with production Python code
+   - Conditional branching for complex business logic
+
+The true power of CrewAI emerges when combining Crews and Flows. This synergy allows you to:
+- Build complex, production-grade applications
+- Balance autonomy with precise control
+- Handle sophisticated real-world scenarios
+- Maintain clean, maintainable code structure
+
+### Getting Started with Installation
+
 To get started with CrewAI, follow these simple steps:
 
-1. **Installation**:
+### 1. Installation
+
+Ensure you have Python >=3.10 <3.13 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+
+First, install CrewAI:
 
 ```shell
 pip install crewai
 ```
+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:
 
-The example bellow also uses duckduckgo, so also install that
 ```shell
-pip install duckduckgo-search
+pip install 'crewai[tools]'
+```
+The command above installs the basic package and also adds extra components which require more dependencies to function.
+
+### Troubleshooting Dependencies
+
+If you encounter issues during installation or usage, here are some common solutions:
+
+#### Common Issues
+
+1. **ModuleNotFoundError: No module named 'tiktoken'**
+   - Install tiktoken explicitly: `pip install 'crewai[embeddings]'`
+   - If using embedchain or other tools: `pip install 'crewai[tools]'`
+
+2. **Failed building wheel for tiktoken**
+   - Ensure Rust compiler is installed (see installation steps above)
+   - For Windows: Verify Visual C++ Build Tools are installed
+   - Try upgrading pip: `pip install --upgrade pip`
+   - If issues persist, use a pre-built wheel: `pip install tiktoken --prefer-binary`
+
+### 2. Setting Up Your Crew with the YAML Configuration
+
+To create a new CrewAI project, run the following CLI (Command Line Interface) command:
+
+```shell
+crewai create crew <project_name>
 ```
 
-2. **Setting Up Your Crew**:
+This command creates a new project folder with the following structure:
+
+```
+my_project/
+├── .gitignore
+├── pyproject.toml
+├── README.md
+├── .env
+└── src/
+    └── my_project/
+        ├── __init__.py
+        ├── main.py
+        ├── crew.py
+        ├── tools/
+        │   ├── custom_tool.py
+        │   └── __init__.py
+        └── config/
+            ├── agents.yaml
+            └── tasks.yaml
+```
+
+You can now start developing your crew by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of the project, the `crew.py` file is where you define your crew, the `agents.yaml` file is where you define your agents, and the `tasks.yaml` file is where you define your tasks.
+
+#### To customize your project, you can:
+
+- Modify `src/my_project/config/agents.yaml` to define your agents.
+- Modify `src/my_project/config/tasks.yaml` to define your tasks.
+- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
+- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
+- Add your environment variables into the `.env` file.
+
+#### Example of a simple crew with a sequential process:
+
+Instantiate your crew:
+
+```shell
+crewai create crew latest-ai-development
+```
+
+Modify the files as needed to fit your use case:
+
+**agents.yaml**
+
+```yaml
+# src/my_project/config/agents.yaml
+researcher:
+  role: >
+    {topic} Senior Data Researcher
+  goal: >
+    Uncover cutting-edge developments in {topic}
+  backstory: >
+    You're a seasoned researcher with a knack for uncovering the latest
+    developments in {topic}. Known for your ability to find the most relevant
+    information and present it in a clear and concise manner.
+
+reporting_analyst:
+  role: >
+    {topic} Reporting Analyst
+  goal: >
+    Create detailed reports based on {topic} data analysis and research findings
+  backstory: >
+    You're a meticulous analyst with a keen eye for detail. You're known for
+    your ability to turn complex data into clear and concise reports, making
+    it easy for others to understand and act on the information you provide.
+```
+
+**tasks.yaml**
+
+```yaml
+# src/my_project/config/tasks.yaml
+research_task:
+  description: >
+    Conduct a thorough research about {topic}
+    Make sure you find any interesting and relevant information given
+    the current year is 2025.
+  expected_output: >
+    A list with 10 bullet points of the most relevant information about {topic}
+  agent: researcher
+
+reporting_task:
+  description: >
+    Review the context you got and expand each topic into a full section for a report.
+    Make sure the report is detailed and contains any and all relevant information.
+  expected_output: >
+    A fully fledge reports with the mains topics, each with a full section of information.
+    Formatted as markdown without '```'
+  agent: reporting_analyst
+  output_file: report.md
+```
+
+**crew.py**
 
 ```python
-import os
-from crewai import Agent, Task, Crew, Process
+# src/my_project/crew.py
+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+from crewai_tools import SerperDevTool
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
 
-os.environ["OPENAI_API_KEY"] = "YOUR KEY"
+@CrewBase
+class LatestAiDevelopmentCrew():
+	"""LatestAiDevelopment crew"""
+	agents: List[BaseAgent]
+	tasks: List[Task]
 
-# You can choose to use a local model through Ollama for example.
-#
-# from langchain.llms import Ollama
-# ollama_llm = Ollama(model="openhermes")
+	@agent
+	def researcher(self) -> Agent:
+		return Agent(
+			config=self.agents_config['researcher'],
+			verbose=True,
+			tools=[SerperDevTool()]
+		)
 
-# Install duckduckgo-search for this example:
-# !pip install -U duckduckgo-search
+	@agent
+	def reporting_analyst(self) -> Agent:
+		return Agent(
+			config=self.agents_config['reporting_analyst'],
+			verbose=True
+		)
 
-from langchain.tools import DuckDuckGoSearchRun
-search_tool = DuckDuckGoSearchRun()
+	@task
+	def research_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['research_task'],
+		)
 
-# Define your agents with roles and goals
-researcher = Agent(
-  role='Senior Research Analyst',
-  goal='Uncover cutting-edge developments in AI and data science in',
-  backstory="""You work at a leading tech think tank.
-  Your expertise lies in identifying emerging trends.
-  You have a knack for dissecting complex data and presenting
-  actionable insights.""",
-  verbose=True,
-  allow_delegation=False,
-  tools=[search_tool]
-  # You can pass an optional llm attribute specifying what mode you wanna use.
-  # It can be a local model through Ollama / LM Studio or a remote
-  # model like OpenAI, Mistral, Antrophic of others (https://python.langchain.com/docs/integrations/llms/)
-  #
-  # Examples:
-  # llm=ollama_llm # was defined above in the file
-  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
-)
-writer = Agent(
-  role='Tech Content Strategist',
-  goal='Craft compelling content on tech advancements',
-  backstory="""You are a renowned Content Strategist, known for
-  your insightful and engaging articles.
-  You transform complex concepts into compelling narratives.""",
-  verbose=True,
-  allow_delegation=True,
-  # (optional) llm=ollama_llm
-)
+	@task
+	def reporting_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['reporting_task'],
+			output_file='report.md'
+		)
 
-# Create tasks for your agents
-task1 = Task(
-  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-  Identify key trends, breakthrough technologies, and potential industry impacts.
-  Your final answer MUST be a full analysis report""",
-  agent=researcher
-)
-
-task2 = Task(
-  description="""Using the insights provided, develop an engaging blog
-  post that highlights the most significant AI advancements.
-  Your post should be informative yet accessible, catering to a tech-savvy audience.
-  Make it sound cool, avoid complex words so it doesn't sound like AI.
-  Your final answer MUST be the full blog post of at least 4 paragraphs.""",
-  agent=writer
-)
-
-# Instantiate your crew with a sequential process
-crew = Crew(
-  agents=[researcher, writer],
-  tasks=[task1, task2],
-  verbose=2, # You can set it to 1 or 2 to different logging levels
-)
-
-# Get your crew to work!
-result = crew.kickoff()
-
-print("######################")
-print(result)
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the LatestAiDevelopment crew"""
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=True,
+		)
 ```
 
-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.
+**main.py**
+
+```python
+#!/usr/bin/env python
+# src/my_project/main.py
+import sys
+from latest_ai_development.crew import LatestAiDevelopmentCrew
+
+def run():
+    """
+    Run the crew.
+    """
+    inputs = {
+        'topic': 'AI Agents'
+    }
+    LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
+```
+
+### 3. Running Your Crew
+
+Before running your crew, make sure you have the following keys set as environment variables in your `.env` file:
+
+- An [OpenAI API key](https://platform.openai.com/account/api-keys) (or other LLM API key): `OPENAI_API_KEY=sk-...`
+- A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`
+
+Lock the dependencies and install them by using the CLI command but first, navigate to your project directory:
+
+```shell
+cd my_project
+crewai install (Optional)
+```
+
+To run your crew, execute the following command in the root of your project:
+
+```bash
+crewai run
+```
+
+or
+
+```bash
+python src/my_project/main.py
+```
+
+If an error happens due to the usage of poetry, please run the following command to update your crewai package:
+
+```bash
+crewai update
+```
+
+You should see the output in the console and the `report.md` file should be created in the root of your project with the full final report.
+
+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.
+CrewAI stands apart as a lean, standalone, high-performance framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
 
-![CrewAI Mind Map](/crewAI-mindmap.png "CrewAI Mind Map")
+- **Standalone & Lean**: Completely independent from other frameworks like LangChain, offering faster execution and lighter resource demands.
+- **Flexible & Precise**: Easily orchestrate autonomous agents through intuitive [Crews](https://docs.crewai.com/concepts/crews) or precise [Flows](https://docs.crewai.com/concepts/flows), achieving perfect balance for your needs.
+- **Seamless Integration**: Effortlessly combine Crews (autonomy) and Flows (precision) to create complex, real-world automations.
+- **Deep Customization**: Tailor every aspect—from high-level workflows down to low-level internal prompts and agent behaviors.
+- **Reliable Performance**: Consistent results across simple tasks and complex, enterprise-level automations.
+- **Thriving Community**: Backed by robust documentation and over 100,000 certified developers, providing exceptional support and guidance.
+
+Choose CrewAI to easily build powerful, adaptable, and production-ready AI automations.
 
 ## Examples
-You can test different real life examples of AI crews [in the examples repo](https://github.com/joaomdmoura/crewAI-examples?tab=readme-ov-file)
 
-### Code
-- [Trip Planner](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis)
-- [Landing Page Generator](https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator)
-- [Having Human input on the execution](https://github.com/joaomdmoura/crewAI/wiki/Human-Input-on-Execution)
+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):
 
-### Video
-#### Quick Tutorial
-[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/0.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
+- [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)
 
-#### Trip Planner
-[![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/0.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
+### Quick Tutorial
 
-#### Stock Analysis
-[![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/0.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
+[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/maxresdefault.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
 
-## 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.
+### Write Job Descriptions
 
-### 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`.
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
 
-### 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:
+[![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")
+
+### Using Crews and Flows Together
+
+CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines.
+CrewAI flows support logical operators like `or_` and `and_` to combine multiple conditions. This can be used with `@start`, `@listen`, or `@router` decorators to create complex triggering conditions.
+- `or_`: Triggers when any of the specified conditions are met. 
+- `and_`Triggers when all of the specified conditions are met.
+
+Here's how you can orchestrate multiple Crews within a Flow:
 
 ```python
-from langchain.llms import Ollama
-ollama_openhermes = Ollama(model="openhermes")
-# Pass Ollama Model to Agents: When creating your agents within the CrewAI framework, you can pass the Ollama model as an argument to the Agent constructor. For instance:
+from crewai.flow.flow import Flow, listen, start, router, or_
+from crewai import Crew, Agent, Task, Process
+from pydantic import BaseModel
 
-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
-)
+# Define structured state for precise control
+class MarketState(BaseModel):
+    sentiment: str = "neutral"
+    confidence: float = 0.0
+    recommendations: list = []
+
+class AdvancedAnalysisFlow(Flow[MarketState]):
+    @start()
+    def fetch_market_data(self):
+        # Demonstrate low-level control with structured state
+        self.state.sentiment = "analyzing"
+        return {"sector": "tech", "timeframe": "1W"}  # These parameters match the task description template
+
+    @listen(fetch_market_data)
+    def analyze_with_crew(self, market_data):
+        # Show crew agency through specialized roles
+        analyst = Agent(
+            role="Senior Market Analyst",
+            goal="Conduct deep market analysis with expert insight",
+            backstory="You're a veteran analyst known for identifying subtle market patterns"
+        )
+        researcher = Agent(
+            role="Data Researcher",
+            goal="Gather and validate supporting market data",
+            backstory="You excel at finding and correlating multiple data sources"
+        )
+
+        analysis_task = Task(
+            description="Analyze {sector} sector data for the past {timeframe}",
+            expected_output="Detailed market analysis with confidence score",
+            agent=analyst
+        )
+        research_task = Task(
+            description="Find supporting data to validate the analysis",
+            expected_output="Corroborating evidence and potential contradictions",
+            agent=researcher
+        )
+
+        # Demonstrate crew autonomy
+        analysis_crew = Crew(
+            agents=[analyst, researcher],
+            tasks=[analysis_task, research_task],
+            process=Process.sequential,
+            verbose=True
+        )
+        return analysis_crew.kickoff(inputs=market_data)  # Pass market_data as named inputs
+
+    @router(analyze_with_crew)
+    def determine_next_steps(self):
+        # Show flow control with conditional routing
+        if self.state.confidence > 0.8:
+            return "high_confidence"
+        elif self.state.confidence > 0.5:
+            return "medium_confidence"
+        return "low_confidence"
+
+    @listen("high_confidence")
+    def execute_strategy(self):
+        # Demonstrate complex decision making
+        strategy_crew = Crew(
+            agents=[
+                Agent(role="Strategy Expert",
+                      goal="Develop optimal market strategy")
+            ],
+            tasks=[
+                Task(description="Create detailed strategy based on analysis",
+                     expected_output="Step-by-step action plan")
+            ]
+        )
+        return strategy_crew.kickoff()
+
+    @listen(or_("medium_confidence", "low_confidence"))
+    def request_additional_analysis(self):
+        self.state.recommendations.append("Gather more data")
+        return "Additional analysis required"
 ```
 
+This example demonstrates how to:
+1. Use Python code for basic data operations
+2. Create and execute Crews as steps in your workflow
+3. Use Flow decorators to manage the sequence of operations
+4. Implement conditional branching based on Crew results
+
+## 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 combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control.
+
+- **LangGraph**: While LangGraph provides a foundation for building agent workflows, its approach requires significant boilerplate code and complex state management patterns. The framework's tight coupling with LangChain can limit flexibility when implementing custom agent behaviors or integrating with external systems.
+
+*P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example ([see comparison](https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/QA%20Agent)) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example ([detailed analysis](https://github.com/crewAIInc/crewAI-examples/blob/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/Coding%20Assistant/coding_assistant_eval.ipynb)).*
+
+- **Autogen**: While Autogen excels at 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:
@@ -196,14 +529,16 @@ 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
+uv lock
+uv sync
 ```
 
 ### Virtual Env
+
 ```bash
-poetry shell
+uv venv
 ```
 
 ### Pre-commit hooks
@@ -213,25 +548,160 @@ pre-commit install
 ```
 
 ### Running Tests
+
 ```bash
-poetry run pytest
+uv run pytest .
+```
+
+### Running static type checks
+
+```bash
+uvx mypy src
 ```
 
 ### Packaging
+
 ```bash
-poetry build
+uv build
 ```
 
 ### Installing Locally
+
 ```bash
 pip install dist/*.tar.gz
 ```
 
-## Hire Consulting
-I, [@joaomdmoura](https://github.com/joaomdmoura) (creator or crewAI), offer consulting through my LLC ([AI Nest Labs](https://ainestlabs.com)).
-If you are interested on hiring weekly hours with me on a retainer, feel free to email me at [joao@ainestlabs.com](mailto:joao@ainestlabs.com)
+## Telemetry
+
+CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
+
+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. Users can disable telemetry by setting the environment variable OTEL_SDK_DISABLED to true.
+
+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 publicly 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](https://github.com/crewAIInc/crewAI/blob/main/LICENSE).
 
 
+## Frequently Asked Questions (FAQ)
+
+### General
+- [What exactly is CrewAI?](#q-what-exactly-is-crewai)
+- [How do I install CrewAI?](#q-how-do-i-install-crewai)
+- [Does CrewAI depend on LangChain?](#q-does-crewai-depend-on-langchain)
+- [Is CrewAI open-source?](#q-is-crewai-open-source)
+- [Does CrewAI collect data from users?](#q-does-crewai-collect-data-from-users)
+
+### Features and Capabilities
+- [Can CrewAI handle complex use cases?](#q-can-crewai-handle-complex-use-cases)
+- [Can I use CrewAI with local AI models?](#q-can-i-use-crewai-with-local-ai-models)
+- [What makes Crews different from Flows?](#q-what-makes-crews-different-from-flows)
+- [How is CrewAI better than LangChain?](#q-how-is-crewai-better-than-langchain)
+- [Does CrewAI support fine-tuning or training custom models?](#q-does-crewai-support-fine-tuning-or-training-custom-models)
+
+### Resources and Community
+- [Where can I find real-world CrewAI examples?](#q-where-can-i-find-real-world-crewai-examples)
+- [How can I contribute to CrewAI?](#q-how-can-i-contribute-to-crewai)
+
+### Enterprise Features
+- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
+- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
+- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
+
+
+
+### Q: What exactly is CrewAI?
+A: CrewAI is a standalone, lean, and fast Python framework built specifically for orchestrating autonomous AI agents. Unlike frameworks like LangChain, CrewAI does not rely on external dependencies, making it leaner, faster, and simpler.
+
+### Q: How do I install CrewAI?
+A: Install CrewAI using pip:
+```shell
+pip install crewai
+```
+For additional tools, use:
+```shell
+pip install 'crewai[tools]'
+```
+### Q: Does CrewAI depend on LangChain?
+A: No. CrewAI is built entirely from the ground up, with no dependencies on LangChain or other agent frameworks. This ensures a lean, fast, and flexible experience.
+
+### Q: Can CrewAI handle complex use cases?
+A: Yes. CrewAI excels at both simple and highly complex real-world scenarios, offering deep customization options at both high and low levels, from internal prompts to sophisticated workflow orchestration.
+
+### Q: Can I use CrewAI with local AI models?
+A: Absolutely! CrewAI supports various language models, including local ones. Tools like Ollama and LM Studio allow seamless integration. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
+
+### Q: What makes Crews different from Flows?
+A: Crews provide autonomous agent collaboration, ideal for tasks requiring flexible decision-making and dynamic interaction. Flows offer precise, event-driven control, ideal for managing detailed execution paths and secure state management. You can seamlessly combine both for maximum effectiveness.
+
+### Q: How is CrewAI better than LangChain?
+A: CrewAI provides simpler, more intuitive APIs, faster execution speeds, more reliable and consistent results, robust documentation, and an active community—addressing common criticisms and limitations associated with LangChain.
+
+### Q: Is CrewAI open-source?
+A: Yes, CrewAI is open-source and actively encourages community contributions and collaboration.
+
+### Q: Does CrewAI collect data from users?
+A: CrewAI collects anonymous telemetry data strictly for improvement purposes. Sensitive data such as prompts, tasks, or API responses are never collected unless explicitly enabled by the user.
+
+### Q: Where can I find real-world CrewAI examples?
+A: Check out practical examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), covering use cases like trip planners, stock analysis, and job postings.
+
+### Q: How can I contribute to CrewAI?
+A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
+
+### Q: What additional features does CrewAI Enterprise offer?
+A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
+
+### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
+A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
+
+### Q: Can I try CrewAI Enterprise for free?
+A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
+
+### Q: Does CrewAI support fine-tuning or training custom models?
+A: Yes, CrewAI can integrate with custom-trained or fine-tuned models, allowing you to enhance your agents with domain-specific knowledge and accuracy.
+
+### Q: Can CrewAI agents interact with external tools and APIs?
+A: Absolutely! CrewAI agents can easily integrate with external tools, APIs, and databases, empowering them to leverage real-world data and resources.
+
+### Q: Is CrewAI suitable for production environments?
+A: Yes, CrewAI is explicitly designed with production-grade standards, ensuring reliability, stability, and scalability for enterprise deployments.
+
+### Q: How scalable is CrewAI?
+A: CrewAI is highly scalable, supporting simple automations and large-scale enterprise workflows involving numerous agents and complex tasks simultaneously.
+
+### Q: Does CrewAI offer debugging and monitoring tools?
+A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
+
+### Q: What programming languages does CrewAI support?
+A: CrewAI is primarily Python-based but easily integrates with services and APIs written in any programming language through its flexible API integration capabilities.
+
+### Q: Does CrewAI offer educational resources for beginners?
+A: Yes, CrewAI provides extensive beginner-friendly tutorials, courses, and documentation through learn.crewai.com, supporting developers at all skill levels.
+
+### Q: Can CrewAI automate human-in-the-loop workflows?
+A: Yes, CrewAI fully supports human-in-the-loop workflows, allowing seamless collaboration between human experts and AI agents for enhanced decision-making.

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

@@ -1,181 +0,0 @@
-import uuid
-from typing import Any, List, Optional
-
-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 (
-    UUID4,
-    BaseModel,
-    ConfigDict,
-    Field,
-    InstanceOf,
-    field_validator,
-    model_validator,
-)
-from pydantic_core import PydanticCustomError
-
-from crewai.agents import (
-    CacheHandler,
-    CrewAgentExecutor,
-    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 CrewAgentExecutor 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.
-    """
-
-    __hash__ = object.__hash__
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-    id: UUID4 = Field(
-        default_factory=uuid.uuid4,
-        frozen=True,
-        description="Unique identifier for the object, not set by user.",
-    )
-    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[CrewAgentExecutor]] = Field(
-        default=None, description="An instance of the CrewAgentExecutor 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."
-    )
-
-    @field_validator("id", mode="before")
-    @classmethod
-    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
-        if v:
-            raise PydanticCustomError(
-                "may_not_set_field", "This field is not to be set by the user.", {}
-            )
-
-    @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) -> CrewAgentExecutor:
-        """Create an agent executor for the agent.
-
-        Returns:
-            An instance of the CrewAgentExecutor 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()
-        else:
-            prompt = Prompts().task_execution()
-
-        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 = CrewAgentExecutor(agent=inner_agent, **executor_args)
-
-    @staticmethod
-    def __tools_names(tools) -> str:
-        return ", ".join([t.name for t in tools])

crewai/agents/__init__.py

@@ -1,4 +0,0 @@
-from .cache.cache_handler import CacheHandler
-from .executor import CrewAgentExecutor
-from .output_parser import CrewAgentOutputParser
-from .tools_handler import ToolsHandler

crewai/agents/cache/cache_hit.py

@@ -1,14 +0,0 @@
-from langchain_core.agents import AgentAction
-from pydantic.v1 import BaseModel, Field
-
-from .cache_handler import CacheHandler
-
-
-class CacheHit(BaseModel):
-    """Cache Hit Object."""
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    action: AgentAction = Field(description="Action taken")
-    cache: CacheHandler = Field(description="Cache Handler for the tool")

crewai/agents/exceptions.py

@@ -1,24 +0,0 @@
-from langchain_core.exceptions import OutputParserException
-
-
-class TaskRepeatedUsageException(OutputParserException):
-    """Exception raised when a task is used twice in a roll."""
-
-    error: str = "TaskRepeatedUsageException"
-    message: str = "I just used the {tool} tool with input {tool_input}. So I already know the result of that and don't need to use it now.\n"
-
-    def __init__(self, tool: str, tool_input: str, text: str):
-        self.text = text
-        self.tool = tool
-        self.tool_input = tool_input
-        self.message = self.message.format(tool=tool, tool_input=tool_input)
-
-        super().__init__(
-            error=self.error,
-            observation=self.message,
-            send_to_llm=True,
-            llm_output=self.text,
-        )
-
-    def __str__(self):
-        return self.message

crewai/agents/executor.py

@@ -1,130 +0,0 @@
-from typing import Dict, Iterator, List, Optional, Tuple, Union
-
-from langchain.agents import AgentExecutor
-from langchain.agents.agent import ExceptionTool
-from langchain.agents.tools import InvalidTool
-from langchain.callbacks.manager import CallbackManagerForChainRun
-from langchain_core.agents import AgentAction, AgentFinish, AgentStep
-from langchain_core.exceptions import OutputParserException
-from langchain_core.tools import BaseTool
-
-from ..tools.cache_tools import CacheTools
-from .cache.cache_hit import CacheHit
-
-
-class CrewAgentExecutor(AgentExecutor):
-    def _iter_next_step(
-        self,
-        name_to_tool_map: Dict[str, BaseTool],
-        color_mapping: Dict[str, str],
-        inputs: Dict[str, str],
-        intermediate_steps: List[Tuple[AgentAction, str]],
-        run_manager: Optional[CallbackManagerForChainRun] = None,
-    ) -> Iterator[Union[AgentFinish, AgentAction, AgentStep]]:
-        """Take a single step in the thought-action-observation loop.
-
-        Override this to take control of how the agent makes and acts on choices.
-        """
-        try:
-            intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
-
-            # Call the LLM to see what to do.
-            output = self.agent.plan(
-                intermediate_steps,
-                callbacks=run_manager.get_child() if run_manager else None,
-                **inputs,
-            )
-        except OutputParserException as e:
-            if isinstance(self.handle_parsing_errors, bool):
-                raise_error = not self.handle_parsing_errors
-            else:
-                raise_error = False
-            if raise_error:
-                raise ValueError(
-                    "An output parsing error occurred. "
-                    "In order to pass this error back to the agent and have it try "
-                    "again, pass `handle_parsing_errors=True` to the AgentExecutor. "
-                    f"This is the error: {str(e)}"
-                )
-            text = str(e)
-            if isinstance(self.handle_parsing_errors, bool):
-                if e.send_to_llm:
-                    observation = str(e.observation)
-                    text = str(e.llm_output)
-                else:
-                    observation = "Invalid or incomplete response"
-            elif isinstance(self.handle_parsing_errors, str):
-                observation = self.handle_parsing_errors
-            elif callable(self.handle_parsing_errors):
-                observation = self.handle_parsing_errors(e)
-            else:
-                raise ValueError("Got unexpected type of `handle_parsing_errors`")
-            output = AgentAction("_Exception", observation, text)
-            if run_manager:
-                run_manager.on_agent_action(output, color="green")
-            tool_run_kwargs = self.agent.tool_run_logging_kwargs()
-            observation = ExceptionTool().run(
-                output.tool_input,
-                verbose=self.verbose,
-                color=None,
-                callbacks=run_manager.get_child() if run_manager else None,
-                **tool_run_kwargs,
-            )
-            yield AgentStep(action=output, observation=observation)
-            return
-
-        # If the tool chosen is the finishing tool, then we end and return.
-        if isinstance(output, AgentFinish):
-            yield output
-            return
-
-        # Override tool usage to use CacheTools
-        if isinstance(output, CacheHit):
-            cache = output.cache
-            action = output.action
-            tool = CacheTools(cache_handler=cache).tool()
-            output = action.copy()
-            output.tool_input = f"tool:{action.tool}|input:{action.tool_input}"
-            output.tool = tool.name
-            name_to_tool_map[tool.name] = tool
-            color_mapping[tool.name] = color_mapping[action.tool]
-
-        actions: List[AgentAction]
-        if isinstance(output, AgentAction):
-            actions = [output]
-        else:
-            actions = output
-        for agent_action in actions:
-            yield agent_action
-        for agent_action in actions:
-            if run_manager:
-                run_manager.on_agent_action(agent_action, color="green")
-            # Otherwise we lookup the tool
-            if agent_action.tool in name_to_tool_map:
-                tool = name_to_tool_map[agent_action.tool]
-                return_direct = tool.return_direct
-                color = color_mapping[agent_action.tool]
-                tool_run_kwargs = self.agent.tool_run_logging_kwargs()
-                if return_direct:
-                    tool_run_kwargs["llm_prefix"] = ""
-                # We then call the tool on the tool input to get an observation
-                observation = tool.run(
-                    agent_action.tool_input,
-                    verbose=self.verbose,
-                    color=color,
-                    callbacks=run_manager.get_child() if run_manager else None,
-                    **tool_run_kwargs,
-                )
-            else:
-                tool_run_kwargs = self.agent.tool_run_logging_kwargs()
-                observation = InvalidTool().run(
-                    {
-                        "requested_tool_name": agent_action.tool,
-                        "available_tool_names": list(name_to_tool_map.keys()),
-                    },
-                    verbose=self.verbose,
-                    color=None,
-                    callbacks=run_manager.get_child() if run_manager else None,
-                    **tool_run_kwargs,
-                )
-            yield AgentStep(action=agent_action, observation=observation)

crewai/agents/output_parser.py

@@ -1,78 +0,0 @@
-import re
-from typing import Union
-
-from langchain.agents.output_parsers import ReActSingleInputOutputParser
-from langchain_core.agents import AgentAction, AgentFinish
-
-from .cache import CacheHandler, CacheHit
-from .exceptions import TaskRepeatedUsageException
-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, CacheHit]:
-        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:
-            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 TaskRepeatedUsageException(
-                        tool=action, tool_input=tool_input, text=text
-                    )
-
-            result = self.cache.read(action, tool_input)
-            if result:
-                action = AgentAction(action, tool_input, text)
-                return CacheHit(action=action, cache=self.cache)
-
-        return super().parse(text)

crewai/agents/tools_handler.py

@@ -1,44 +0,0 @@
-from typing import Any, Dict
-
-from langchain.callbacks.base import BaseCallbackHandler
-
-from ..tools.cache_tools import CacheTools
-from .cache.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
-        ):
-            if self.last_used_tool["tool"] != CacheTools().name:
-                self.cache.add(
-                    tool=self.last_used_tool["tool"],
-                    input=self.last_used_tool["input"],
-                    output=output,
-                )

crewai/crew.py

@@ -1,137 +0,0 @@
-import json
-import uuid
-from typing import Any, Dict, List, Optional, Union
-
-from pydantic import (
-    UUID4,
-    BaseModel,
-    ConfigDict,
-    Field,
-    InstanceOf,
-    Json,
-    field_validator,
-    model_validator,
-)
-from pydantic_core import PydanticCustomError
-
-from crewai.agent import Agent
-from crewai.agents.cache 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."""
-
-    __hash__ = object.__hash__
-    model_config = ConfigDict(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."
-    )
-    id: UUID4 = Field(
-        default_factory=uuid.uuid4,
-        frozen=True,
-        description="Unique identifier for the object, not set by user.",
-    )
-
-    @field_validator("id", mode="before")
-    @classmethod
-    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
-        if v:
-            raise PydanticCustomError(
-                "may_not_set_field", "This field is not to be set by the user.", {}
-            )
-
-    @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_output = None
-        for task in self.tasks:
-            # Add delegation tools to the task if the agent allows it
-            if task.agent.allow_delegation:
-                agent_tools = AgentTools(agents=self.agents).tools()
-                task.tools += agent_tools
-
-            self.__log("debug", f"Working Agent: {task.agent.role}")
-            self.__log("info", f"Starting Task: {task.description}")
-
-            task_output = task.execute(task_output)
-            self.__log(
-                "debug", f"\n\n[{task.agent.role}] Task output: {task_output}\n\n"
-            )
-        return task_output
-
-    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

@@ -1,53 +0,0 @@
-"""Prompts for generic agent."""
-import json
-import os
-from typing import ClassVar, Dict, Optional
-
-from langchain.prompts import PromptTemplate
-from pydantic import BaseModel, Field, PrivateAttr, model_validator
-
-
-class Prompts(BaseModel):
-    """Prompts for generic agent."""
-
-    _prompts: Optional[Dict[str, str]] = PrivateAttr()
-    language: Optional[str] = Field(
-        default="en",
-        description="Language of crewai prompts.",
-    )
-
-    @model_validator(mode="after")
-    def load_prompts(self) -> "Prompts":
-        """Load prompts from file."""
-        dir_path = os.path.dirname(os.path.realpath(__file__))
-        prompts_path = os.path.join(dir_path, f"prompts/{self.language}.json")
-
-        with open(prompts_path, "r") as f:
-            self._prompts = json.load(f)["slices"]
-        return self
-
-    SCRATCHPAD_SLICE: ClassVar[str] = "\n{agent_scratchpad}"
-
-    def task_execution_with_memory(self) -> str:
-        return PromptTemplate.from_template(
-            self._prompts["role_playing"]
-            + self._prompts["tools"]
-            + self._prompts["memory"]
-            + self._prompts["task"]
-            + self.SCRATCHPAD_SLICE
-        )
-
-    def task_execution_without_tools(self) -> str:
-        return PromptTemplate.from_template(
-            self._prompts["role_playing"]
-            + self._prompts["task"]
-            + self.SCRATCHPAD_SLICE
-        )
-
-    def task_execution(self) -> str:
-        return PromptTemplate.from_template(
-            self._prompts["role_playing"]
-            + self._prompts["tools"]
-            + self._prompts["task"]
-            + self.SCRATCHPAD_SLICE
-        )

crewai/prompts/en.json

@@ -1,8 +0,0 @@
-{
-  "slices": {
-    "task": "Begin! This is VERY important to you, your job depends on it!\n\nCurrent Task: {input}",
-    "memory": "This is the summary of your work so far:\n{chat_history}",
-    "role_playing": "You are {role}.\n{backstory}\n\nYour personal goal is: {goal}",
-    "tools": "TOOLS:\n------\nYou have access to the following tools:\n\n{tools}\n\nTo use a tool, please use the exact following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of [{tool_names}], just the name.\nAction Input: the input to the action\nObservation: the result of the action\n```\n\nWhen you have a response for your task, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]"
-  }
-}

crewai/task.py

@@ -1,62 +0,0 @@
-import uuid
-from typing import Any, List, Optional
-
-from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
-from pydantic_core import PydanticCustomError
-
-from crewai.agent import Agent
-from crewai.tasks.task_output import TaskOutput
-
-
-class Task(BaseModel):
-    """Class that represent a task to be executed."""
-
-    __hash__ = object.__hash__
-    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.",
-    )
-    output: Optional[TaskOutput] = Field(
-        description="Task output, it's final result.", default=None
-    )
-    id: UUID4 = Field(
-        default_factory=uuid.uuid4,
-        frozen=True,
-        description="Unique identifier for the object, not set by user.",
-    )
-
-    @field_validator("id", mode="before")
-    @classmethod
-    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
-        if v:
-            raise PydanticCustomError(
-                "may_not_set_field", "This field is not to be set by the user.", {}
-            )
-
-    @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:
-            result = self.agent.execute_task(
-                task=self.description, context=context, tools=self.tools
-            )
-
-            self.output = TaskOutput(description=self.description, result=result)
-            return result
-        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/tasks/task_output.py

@@ -1,17 +0,0 @@
-from typing import Optional
-
-from pydantic import BaseModel, Field, model_validator
-
-
-class TaskOutput(BaseModel):
-    """Class that represents the result of a task."""
-
-    description: str = Field(description="Description of the task")
-    summary: Optional[str] = Field(description="Summary of the task", default=None)
-    result: str = Field(description="Result of the task")
-
-    @model_validator(mode="after")
-    def set_summary(self):
-        excerpt = " ".join(self.description.split(" ")[0:10])
-        self.summary = f"{excerpt}..."
-        return self

crewai/tools/agent_tools.py

@@ -1,76 +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):
-    """Default tools around agent delegation"""
-
-    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 co-worker you want to ask it to (one of the options),
-                the task and all actual context you have for the task.
-                For example, `coworker|task|context`.
-                """
-                ),
-            ),
-            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 co-worker you want to ask it to (one of the options),
-                the question and all actual context you have for the question.
-                For example, `coworker|question|context`.
-                """
-                ),
-            ),
-        ]
-
-    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, context = command.split("|")
-        except ValueError:
-            return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context\n"
-
-        if not agent or not task or not context:
-            return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context.\n"
-
-        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])}.\n"
-
-        agent = agent[0]
-        result = agent.execute_task(task, context)
-        return result

docs/changelog.mdx

@@ -0,0 +1,217 @@
+---
+title: Changelog
+description: View the latest updates and changes to CrewAI
+icon: timeline
+---
+
+<Update label="2025-04-07" description="v0.114.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/v01140.png" />
+  </Frame>
+
+  **New Features & Enhancements**
+  - Agents as an atomic unit. (`Agent(...).kickoff()`)
+  - Support for [Custom LLM implementations](https://docs.crewai.com/guides/advanced/custom-llm).
+  - Integrated External Memory and [Opik observability](https://docs.crewai.com/how-to/opik-observability).
+  - Enhanced YAML extraction.
+  - Multimodal agent validation.
+  - Added Secure fingerprints for agents and crews.
+
+  **Core Improvements & Fixes**
+  - Improved serialization, agent copying, and Python compatibility.
+  - Added wildcard support to `emit()`
+  - Added support for additional router calls and context window adjustments.
+  - Fixed typing issues, validation, and import statements.
+  - Improved method performance.
+  - Enhanced agent task handling, event emissions, and memory management.
+  - Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs.
+
+  **Documentation & Guides**
+  - Improved documentation structure, theme, and organization.
+  - Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix.
+  - Updated tool configuration examples, prompts, and observability docs.
+  - Guide on using singular agents within Flows.
+</Update>
+
+<Update label="2025-03-17" description="v0.108.0">
+  **Features**
+  - Converted tabs to spaces in `crew.py` template
+  - Enhanced LLM Streaming Response Handling and Event System
+  - Included `model_name`
+  - Enhanced Event Listener with rich visualization and improved logging
+  - Added fingerprints
+
+  **Bug Fixes**
+  - Fixed Mistral issues
+  - Fixed a bug in documentation
+  - Fixed type check error in fingerprint property
+
+  **Documentation Updates**
+  - Improved tool documentation
+  - Updated installation guide for the `uv` tool package
+  - Added instructions for upgrading crewAI with the `uv` tool
+  - Added documentation for `ApifyActorsTool`
+</Update>
+
+<Update label="2025-03-10" description="v0.105.0">
+  **Core Improvements & Fixes**
+  - Fixed issues with missing template variables and user memory configuration
+  - Improved async flow support and addressed agent response formatting
+  - Enhanced memory reset functionality and fixed CLI memory commands
+  - Fixed type issues, tool calling properties, and telemetry decoupling
+
+  **New Features & Enhancements**
+  - Added Flow state export and improved state utilities
+  - Enhanced agent knowledge setup with optional crew embedder
+  - Introduced event emitter for better observability and LLM call tracking
+  - Added support for Python 3.10 and ChatOllama from langchain_ollama
+  - Integrated context window size support for the o3-mini model
+  - Added support for multiple router calls
+
+  **Documentation & Guides**
+  - Improved documentation layout and hierarchical structure
+  - Added QdrantVectorSearchTool guide and clarified event listener usage
+  - Fixed typos in prompts and updated Amazon Bedrock model listings
+</Update>
+
+<Update label="2025-02-12" description="v0.102.0">
+  **Core Improvements & Fixes**
+  - Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
+  - Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
+  - Memory & Storage Fixes: Fixed short-term memory handling with Bedrock, ensured correct embedder initialization, and added a reset memories function in the crew class
+  - Training & Execution Reliability: Fixed broken training and interpolation issues with dict and list input types
+
+  **New Features & Enhancements**
+  - Advanced Knowledge Management: Improved naming conventions and enhanced embedding configuration with custom embedder support
+  - Expanded Logging & Observability: Added JSON format support for logging and integrated MLflow tracing documentation
+  - Data Handling Improvements: Updated excel_knowledge_source.py to process multi-tab files
+  - General Performance & Codebase Clean-Up: Streamlined enterprise code alignment and resolved linting issues
+  - Adding new tool: `QdrantVectorSearchTool`
+
+  **Documentation & Guides**
+  - Updated AI & Memory Docs: Improved Bedrock, Google AI, and long-term memory documentation
+  - Task & Workflow Clarity: Added "Human Input" row to Task Attributes, Langfuse guide, and FileWriterTool documentation
+  - Fixed Various Typos & Formatting Issues
+</Update>
+
+<Update label="2025-01-28" description="v0.100.0">
+  **Features**
+  - Add Composio docs
+  - Add SageMaker as a LLM provider
+  
+  **Fixes**
+  - Overall LLM connection issues
+  - Using safe accessors on training
+  - Add version check to crew_chat.py
+  
+  **Documentation**
+  - New docs for crewai chat
+  - Improve formatting and clarity in CLI and Composio Tool docs
+</Update>
+
+<Update label="2025-01-20" description="v0.98.0">
+  **Features**
+  - Conversation crew v1
+  - Add unique ID to flow states
+  - Add @persist decorator with FlowPersistence interface
+  
+  **Integrations**
+  - Add SambaNova integration
+  - Add NVIDIA NIM provider in cli
+  - Introducing VoyageAI
+  
+  **Fixes**
+  - Fix API Key Behavior and Entity Handling in Mem0 Integration
+  - Fixed core invoke loop logic and relevant tests
+  - Make tool inputs actual objects and not strings
+  - Add important missing parts to creating tools
+  - Drop litellm version to prevent windows issue
+  - Before kickoff if inputs are none
+  - Fixed typos, nested pydantic model issue, and docling issues
+</Update>
+
+<Update label="2025-01-04" description="v0.95.0">
+  **New Features**
+  - Adding Multimodal Abilities to Crew
+  - Programatic Guardrails
+  - HITL multiple rounds
+  - Gemini 2.0 Support
+  - CrewAI Flows Improvements
+  - Add Workflow Permissions
+  - Add support for langfuse with litellm
+  - Portkey Integration with CrewAI
+  - Add interpolate_only method and improve error handling
+  - Docling Support
+  - Weviate Support
+  
+  **Fixes**
+  - output_file not respecting system path
+  - disk I/O error when resetting short-term memory
+  - CrewJSONEncoder now accepts enums
+  - Python max version
+  - Interpolation for output_file in Task
+  - Handle coworker role name case/whitespace properly
+  - Add tiktoken as explicit dependency and document Rust requirement
+  - Include agent knowledge in planning process
+  - Change storage initialization to None for KnowledgeStorage
+  - Fix optional storage checks
+  - include event emitter in flows
+  - Docstring, Error Handling, and Type Hints Improvements
+  - Suppressed userWarnings from litellm pydantic issues
+</Update>
+
+<Update label="2024-12-05" description="v0.86.0">
+  **Changes**
+  - Remove all references to pipeline and pipeline router
+  - Add Nvidia NIM as provider in Custom LLM
+  - Add knowledge demo + improve knowledge docs
+  - Add HITL multiple rounds of followup
+  - New docs about yaml crew with decorators
+  - Simplify template crew
+</Update>
+
+<Update label="2024-12-04" description="v0.85.0">
+  **Features**
+  - Added knowledge to agent level
+  - Feat/remove langchain
+  - Improve typed task outputs
+  - Log in to Tool Repository on crewai login
+  
+  **Fixes**
+  - Fixes issues with result as answer not properly exiting LLM loop
+  - Fix missing key name when running with ollama provider
+  - Fix spelling issue found
+  
+  **Documentation**
+  - Update readme for running mypy
+  - Add knowledge to mint.json
+  - Update Github actions
+  - Update Agents docs to include two approaches for creating an agent
+  - Improvements to LLM Configuration and Usage
+</Update>
+
+<Update label="2024-11-25" description="v0.83.0">
+  **New Features**
+  - New before_kickoff and after_kickoff crew callbacks
+  - Support to pre-seed agents with Knowledge
+  - Add support for retrieving user preferences and memories using Mem0
+  
+  **Fixes**
+  - Fix Async Execution
+  - Upgrade chroma and adjust embedder function generator
+  - Update CLI Watson supported models + docs
+  - Reduce level for Bandit
+  - Fixing all tests
+  
+  **Documentation**
+  - Update Docs
+</Update>
+
+<Update label="2024-11-13" description="v0.80.0">
+  **Fixes**
+  - Fixing Tokens callback replacement bug
+  - Fixing Step callback issue
+  - Add cached prompt tokens info on usage metrics
+  - Fix crew_train_success test
+</Update>