fix: SSL error while getting LLM data from GH

When running behind cloud-based security users are struggling to donwload LLM data from Github. Usually the following error is raised

```
SSL certificate verification failed: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /BerriAI/litellm/main/model_prices_and_context_window.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1010)')))
Current CA bundle path: /usr/local/etc///.pem
```

This commit ensures the SSL config is beign provided while requesting data

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,35 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve CrewAI
-title: "[BUG]"
-labels: bug
-assignees: ''
-
----
-
-**Description**
-Provide a clear and concise description of what the bug is.
-
-**Steps to Reproduce**
-Provide a step-by-step process to reproduce the behavior:
-
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots/Code snippets**
-If applicable, add screenshots or code snippets to help explain your problem.
-
-**Environment Details:**
-- **Operating System**: [e.g., Ubuntu 20.04, macOS Catalina, Windows 10]
-- **Python Version**: [e.g., 3.8, 3.9, 3.10]
-- **crewAI Version**: [e.g., 0.30.11]
-- **crewAI Tools Version**: [e.g., 0.2.6]
-
-**Logs**
-Include relevant logs or error messages if applicable.
-
-**Possible Solution**
-Have a solution in mind? Please suggest it here, or write "None".
-
-**Additional context**
-Add any other context about the problem here.

.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/custom.md

@@ -1,24 +0,0 @@
----
-name: Custom issue template
-about: Describe this issue template's purpose here.
-title: "[DOCS]"
-labels: documentation
-assignees: ''
-
----
-
-## Documentation Page
-<!-- Provide a link to the documentation page that needs improvement -->
-
-## Description
-<!-- Describe what needs to be changed or improved in the documentation -->
-
-## Suggested Changes
-<!-- If possible, provide specific suggestions for how to improve the documentation -->
-
-## Additional Context
-<!-- Add any other context about the documentation issue here -->
-
-## Checklist
-- [ ] I have searched the existing issues to make sure this is not a duplicate
-- [ ] I have checked the latest version of the documentation to ensure this hasn't been addressed

.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 +1,27 @@
+## CrewAI Security Vulnerability Reporting Policy
+
+CrewAI prioritizes the security of our software products, services, and GitHub repositories. To promptly address vulnerabilities, follow these steps for reporting security issues:
+
+### Reporting Process
+Do **not** report vulnerabilities via public GitHub issues.
+
+Email all vulnerability reports directly to:  
+**security@crewai.com**
+
+### Required Information
+To help us quickly validate and remediate the issue, your report must include:
+
+- **Vulnerability Type:** Clearly state the vulnerability type (e.g., SQL injection, XSS, privilege escalation).
+- **Affected Source Code:** Provide full file paths and direct URLs (branch, tag, or commit).
+- **Reproduction Steps:** Include detailed, step-by-step instructions. Screenshots are recommended.
+- **Special Configuration:** Document any special settings or configurations required to reproduce.
+- **Proof-of-Concept (PoC):** Provide exploit or PoC code (if available).
+- **Impact Assessment:** Clearly explain the severity and potential exploitation scenarios.
+
+### Our Response
+- We will acknowledge receipt of your report promptly via your provided email.
+- Confirmed vulnerabilities will receive priority remediation based on severity.
+- Patches will be released as swiftly as possible following verification.
+
+### Reward Notice
+Currently, we do not offer a bug bounty program. Rewards, if issued, are discretionary.

.github/workflows/linter.yml

@@ -5,12 +5,32 @@ on: [pull_request]
 jobs:
   lint:
     runs-on: ubuntu-latest
+    env:
+      TARGET_BRANCH: ${{ github.event.pull_request.base.ref }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
-      - name: Install Requirements
+      - name: Fetch Target Branch
+        run: git fetch origin $TARGET_BRANCH --depth=1
+
+      - name: Install Ruff
+        run: pip install ruff
+
+      - name: Get Changed Python Files
+        id: changed-files
         run: |
-          pip install ruff
+          merge_base=$(git merge-base origin/"$TARGET_BRANCH" HEAD)
+          changed_files=$(git diff --name-only --diff-filter=ACMRTUB "$merge_base" | grep '\.py$' || true)
+          echo "files<<EOF" >> $GITHUB_OUTPUT
+          echo "$changed_files" >> $GITHUB_OUTPUT
+          echo "EOF" >> $GITHUB_OUTPUT
 
-      - name: Run Ruff Linter
-        run: ruff check --exclude "templates","__init__.py"
+      - name: Run Ruff on Changed Files
+        if: ${{ steps.changed-files.outputs.files != '' }}
+        run: |
+          echo "${{ steps.changed-files.outputs.files }}" \
+            | tr ' ' '\n' \
+            | grep -v 'src/crewai/cli/templates/' \
+            | xargs -I{} ruff check "{}"

.github/workflows/mkdocs.yml

@@ -1,10 +1,8 @@
 name: Deploy MkDocs
 
 on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
+  release:
+    types: [published]
 
 permissions:
   contents: write
@@ -15,10 +13,10 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: '3.10'
 
@@ -27,7 +25,7 @@ jobs:
         run: echo "::set-output name=hash::$(sha256sum requirements-doc.txt | awk '{print $1}')"
 
       - name: Setup cache
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         with:
           key: mkdocs-material-${{ steps.req-hash.outputs.hash }}
           path: .cache
@@ -44,4 +42,4 @@ jobs:
           GH_TOKEN: ${{ secrets.GH_TOKEN }}
 
       - name: Build and deploy MkDocs
-        run: mkdocs gh-deploy --force
+        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

@@ -1,5 +1,10 @@
 name: Mark stale issues and pull requests
 
+permissions:
+  contents: write
+  issues: write
+  pull-requests: write
+
 on:
   schedule:
     - cron: '10 12 * * *'
@@ -8,9 +13,6 @@ on:
 jobs:
   stale:
     runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
     steps:
     - uses: actions/stale@v9
       with:
@@ -24,3 +26,4 @@ jobs:
         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

@@ -9,23 +9,26 @@ env:
   OPENAI_API_KEY: fake-api-key
 
 jobs:
-  deploy:
+  tests:
     runs-on: ubuntu-latest
-
+    timeout-minutes: 15
+    strategy:
+      matrix:
+        python-version: ['3.10', '3.11', '3.12', '3.13']
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
 
-      - name: Setup Python
-        uses: actions/setup-python@v4
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
         with:
-          python-version: "3.11.9"
+          enable-cache: true
 
-      - name: Install Requirements
-        run: |
-          set -e
-          pip install poetry
-          poetry install
+      - 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: poetry run pytest
+        run: uv run pytest --block-network --timeout=60 -vv

.github/workflows/type-checker.yml

@@ -14,9 +14,9 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11.9"
 
       - name: Install Requirements
         run: |

.gitignore

@@ -2,6 +2,7 @@
 .pytest_cache
 __pycache__
 dist/
+lib/
 .env
 assets/*
 .idea
@@ -15,4 +16,14 @@ rc-tests/*
 *.pkl
 temp/*
 .vscode/*
-crew_tasks_output.json
+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

@@ -1,9 +1,7 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.4
+    rev: v0.8.2
     hooks:
       - id: ruff
         args: ["--fix"]
-        exclude: "templates"
       - id: ruff-format
-        exclude: "templates"

.ruff.toml

@@ -0,0 +1,4 @@
+exclude = [
+    "templates",
+    "__init__.py",
+]

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

@@ -1,164 +1,427 @@
-<div align="center">
+<p align="center">
+  <a href="https://github.com/crewAIInc/crewAI">
+    <img src="docs/images/crewai_logo.png" width="600px" alt="Open source Multi-AI Agent orchestration framework">
+  </a>
+</p>
+<p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;">
+  <a href="https://trendshift.io/repositories/11239" target="_blank">
+    <img src="https://trendshift.io/api/badge/repositories/11239" alt="crewAIInc%2FcrewAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
+  </a>
+</p>
 
-![Logo of crewAI, two people rowing on a boat](./docs/crewai_logo.png)
+<p align="center">
+  <a href="https://crewai.com">Homepage</a>
+  ·
+  <a href="https://docs.crewai.com">Docs</a>
+  ·
+  <a href="https://app.crewai.com">Start Cloud Trial</a>
+  ·
+  <a href="https://blog.crewai.com">Blog</a>
+  ·
+  <a href="https://community.crewai.com">Forum</a>
+</p>
 
-# **crewAI**
+<p align="center">
+  <a href="https://github.com/crewAIInc/crewAI">
+    <img src="https://img.shields.io/github/stars/crewAIInc/crewAI" alt="GitHub Repo stars">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/network/members">
+    <img src="https://img.shields.io/github/forks/crewAIInc/crewAI" alt="GitHub forks">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/issues">
+    <img src="https://img.shields.io/github/issues/crewAIInc/crewAI" alt="GitHub issues">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/pulls">
+    <img src="https://img.shields.io/github/issues-pr/crewAIInc/crewAI" alt="GitHub pull requests">
+  </a>
+  <a href="https://opensource.org/licenses/MIT">
+    <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT">
+  </a>
+</p>
 
-🤖 **crewAI**: Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
+<p align="center">
+  <a href="https://pypi.org/project/crewai/">
+    <img src="https://img.shields.io/pypi/v/crewai" alt="PyPI version">
+  </a>
+  <a href="https://pypi.org/project/crewai/">
+    <img src="https://img.shields.io/pypi/dm/crewai" alt="PyPI downloads">
+  </a>
+  <a href="https://twitter.com/crewAIInc">
+    <img src="https://img.shields.io/twitter/follow/crewAIInc?style=social" alt="Twitter Follow">
+  </a>
+</p>
 
-<h3>
+### Fast and Flexible Multi-Agent Automation Framework
 
-[Homepage](https://www.crewai.io/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Examples](https://github.com/joaomdmoura/crewai-examples) | [Discord](https://discord.com/invite/X4JWnZnxPb)
+> 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.
 
-</h3>
+- **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
 
-[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/joaomdmoura/crewAI)
-[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+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.
 
-</div>
+# 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.
 
 ## 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)
   - [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)
 - [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/images/asset.png" alt="CrewAI Logo" width="100%">
+</div>
+
+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
 
+Setup and run your first CrewAI agents by following this tutorial.
+
+[![CrewAI Getting Started Tutorial](https://img.youtube.com/vi/-kSOTtYzgEw/hqdefault.jpg)](https://www.youtube.com/watch?v=-kSOTtYzgEw "CrewAI Getting Started Tutorial")
+
+###
+ 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
 
+Ensure you have Python >=3.10 <3.14 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: pip install 'crewai[tools]'. This command installs the basic package and also adds extra components which require more dependencies to function."
+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:
 
 ```shell
 pip install 'crewai[tools]'
 ```
 
-### 2. Setting Up Your Crew
+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>
+```
+
+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_API_KEY"
-os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
+@CrewBase
+class LatestAiDevelopmentCrew():
+	"""LatestAiDevelopment crew"""
+	agents: List[BaseAgent]
+	tasks: List[Task]
 
-# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
+	@agent
+	def researcher(self) -> Agent:
+		return Agent(
+			config=self.agents_config['researcher'],
+			verbose=True,
+			tools=[SerperDevTool()]
+		)
 
-# os.environ["OPENAI_API_BASE"] = 'http://localhost:11434/v1'
-# os.environ["OPENAI_MODEL_NAME"] ='openhermes'  # Adjust based on available model
-# os.environ["OPENAI_API_KEY"] ='sk-111111111111111111111111111111111111111111111111'
+	@agent
+	def reporting_analyst(self) -> Agent:
+		return Agent(
+			config=self.agents_config['reporting_analyst'],
+			verbose=True
+		)
 
-# You can pass an optional llm attribute specifying what model you wanna use.
-# It can be a local model through Ollama / LM Studio or a remote
-# model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
-#
-# import os
-# os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
-#
-# OR
-#
-# from langchain_openai import ChatOpenAI
+	@task
+	def research_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['research_task'],
+		)
 
-search_tool = SerperDevTool()
+	@task
+	def reporting_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['reporting_task'],
+			output_file='report.md'
+		)
 
-# Define your agents with roles and goals
-researcher = Agent(
-  role='Senior Research Analyst',
-  goal='Uncover cutting-edge developments in AI and data science',
-  backstory="""You work at a leading tech think tank.
-  Your expertise lies in identifying emerging trends.
-  You have a knack for dissecting complex data and presenting actionable insights.""",
-  verbose=True,
-  allow_delegation=False,
-  # You can pass an optional llm attribute specifying what model you wanna use.
-  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
-  tools=[search_tool]
-)
-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
-)
-
-# 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.""",
-  expected_output="Full analysis report in bullet points",
-  agent=researcher
-)
-
-task2 = Task(
-  description="""Using the insights provided, develop an engaging blog
-  post that highlights the most significant AI advancements.
-  Your post should be informative yet accessible, catering to a tech-savvy audience.
-  Make it sound cool, avoid complex words so it doesn't sound like AI.""",
-  expected_output="Full blog post of at least 4 paragraphs",
-  agent=writer
-)
-
-# Instantiate your crew with a sequential process
-crew = Crew(
-  agents=[researcher, writer],
-  tasks=[task1, task2],
-  verbose=True,
-  process = Process.sequential
-)
-
-# 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,
+		)
 ```
 
+**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 and `hierarchical` processes, but more complex processes like consensual and autonomous are being worked on.
-- **Save output as file**: Save the output of individual tasks as a file, so you can use it later.
-- **Parse output as Pydantic or Json**: Parse the output of individual tasks as a Pydantic model or as a Json if you want to.
-- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models, even ones running locally!
+CrewAI stands apart as a lean, standalone, high-performance multi-AI Agent framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
 
-![CrewAI Mind Map](./docs/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 [crewAI-examples repo](https://github.com/joaomdmoura/crewAI-examples?tab=readme-ov-file):
+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):
 
-- [Landing Page Generator](https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator)
+- [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/joaomdmoura/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
 
 ### Quick Tutorial
 
@@ -166,37 +429,138 @@ You can test different real life examples of AI crews in the [crewAI-examples re
 
 ### Write Job Descriptions
 
-[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner) or watch a video below:
+[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/joaomdmoura/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+[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 crewai.flow.flow import Flow, listen, start, router, or_
+from crewai import Crew, Agent, Task, Process
+from pydantic import BaseModel
+
+# 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.
+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.
+Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models.
 
 ## How CrewAI Compares
 
-- **Autogen**: While Autogen does good in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
+**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:
@@ -210,14 +574,14 @@ CrewAI is open-source and we welcome contributions. If you're looking to contrib
 ### Installing Dependencies
 
 ```bash
-poetry lock
-poetry install
+uv lock
+uv sync
 ```
 
 ### Virtual Env
 
 ```bash
-poetry shell
+uv venv
 ```
 
 ### Pre-commit hooks
@@ -229,19 +593,19 @@ pre-commit install
 ### Running Tests
 
 ```bash
-poetry run pytest
+uv run pytest .
 ```
 
 ### Running static type checks
 
 ```bash
-poetry run mypy
+uvx mypy src
 ```
 
 ### Packaging
 
 ```bash
-poetry build
+uv build
 ```
 
 ### Installing Locally
@@ -254,11 +618,11 @@ pip install dist/*.tar.gz
 
 CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
 
-It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. We don't offer a way to disable it now, but we will in the future.
+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
+- 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
@@ -277,10 +641,137 @@ Data collected includes:
 - Roles of agents in a crew
   - Understand high level use cases so we can build better tools, integrations and examples about it
 - Tools names available
-  - Understand out of the publically available tools, which ones are being used the most so we can improve them
+  - 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.

docs/CNAME

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

docs/api-reference/introduction.mdx

@@ -0,0 +1,119 @@
+---
+title: "Introduction"
+description: "Complete reference for the CrewAI Enterprise REST API"
+icon: "code"
+---
+
+# CrewAI Enterprise API
+
+Welcome to the CrewAI Enterprise API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
+
+## Quick Start
+
+<Steps>
+  <Step title="Get Your API Credentials">
+    Navigate to your crew's detail page in the CrewAI Enterprise dashboard and copy your Bearer Token from the Status tab.
+  </Step>
+  
+  <Step title="Discover Required Inputs">
+    Use the `GET /inputs` endpoint to see what parameters your crew expects.
+  </Step>
+  
+  <Step title="Start a Crew Execution">
+    Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`.
+  </Step>
+  
+  <Step title="Monitor Progress">
+    Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
+  </Step>
+</Steps>
+
+## Authentication
+
+All API requests require authentication using a Bearer token. Include your token in the `Authorization` header:
+
+```bash
+curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
+  https://your-crew-url.crewai.com/inputs
+```
+
+### Token Types
+
+| Token Type | Scope | Use Case |
+|:-----------|:--------|:----------|
+| **Bearer Token** | Organization-level access | Full crew operations, ideal for server-to-server integration |
+| **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
+
+<Tip>
+You can find both token types in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
+</Tip>
+
+## Base URL
+
+Each deployed crew has its own unique API endpoint:
+
+```
+https://your-crew-name.crewai.com
+```
+
+Replace `your-crew-name` with your actual crew's URL from the dashboard.
+
+## Typical Workflow
+
+1. **Discovery**: Call `GET /inputs` to understand what your crew needs
+2. **Execution**: Submit inputs via `POST /kickoff` to start processing  
+3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
+4. **Results**: Extract the final output from the completed response
+
+## Error Handling
+
+The API uses standard HTTP status codes:
+
+| Code | Meaning |
+|------|:--------|
+| `200` | Success |
+| `400` | Bad Request - Invalid input format |
+| `401` | Unauthorized - Invalid bearer token |
+| `404` | Not Found - Resource doesn't exist |
+| `422` | Validation Error - Missing required inputs |
+| `500` | Server Error - Contact support |
+
+## Interactive Testing
+
+<Info>
+**Why no "Send" button?** Since each CrewAI Enterprise user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
+</Info>
+
+Each endpoint page shows you:
+- ✅ **Exact request format** with all parameters
+- ✅ **Response examples** for success and error cases  
+- ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.)
+- ✅ **Authentication examples** with proper Bearer token format
+
+### **To Test Your Actual API:**
+
+<CardGroup cols={2}>
+  <Card title="Copy cURL Examples" icon="terminal">
+    Copy the cURL examples and replace the URL + token with your real values
+  </Card>
+  <Card title="Use Postman/Insomnia" icon="play">
+    Import the examples into your preferred API testing tool
+  </Card>
+</CardGroup>
+
+**Example workflow:**
+1. **Copy this cURL example** from any endpoint page
+2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL  
+3. **Replace the Bearer token** with your real token from the dashboard
+4. **Run the request** in your terminal or API client
+
+## Need Help?
+
+<CardGroup cols={2}>
+  <Card title="Enterprise Support" icon="headset" href="mailto:support@crewai.com">
+    Get help with API integration and troubleshooting
+  </Card>
+  <Card title="Enterprise Dashboard" icon="chart-line" href="https://app.crewai.com">
+    Manage your crews and view execution logs
+  </Card>
+</CardGroup>

docs/changelog.mdx

@@ -0,0 +1,473 @@
+---
+title: Changelog
+description: View the latest updates and changes to CrewAI
+icon: timeline
+---
+
+<Update label="2024-05-22" description="v0.121.0" tags={["Latest"]}>
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01210.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.121.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Fixed encoding error when creating tools
+  - Fixed failing llama test
+  - Updated logging configuration for consistency
+  - Enhanced telemetry initialization and event handling
+
+  **New Features & Enhancements**
+  - Added **markdown attribute** to the Task class
+  - Added **reasoning attribute** to the Agent class
+  - Added **inject_date flag** to Agent for automatic date injection
+  - Implemented **HallucinationGuardrail** (no-op with test coverage)
+
+  **Documentation & Guides**
+  - Added documentation for **StagehandTool** and improved MDX structure
+  - Added documentation for **MCP integration** and updated enterprise docs
+  - Documented knowledge events and updated reasoning docs
+  - Added stop parameter documentation
+  - Fixed import references in doc examples (before_kickoff, after_kickoff)
+  - General docs updates and restructuring for clarity
+</Update>
+
+<Update label="2024-05-15" description="v0.120.1">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01201.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.1">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Fixed **interpolation with hyphens**
+</Update>
+
+<Update label="2024-05-14" description="v0.120.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01200.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Enabled **full Ruff rule set** by default for stricter linting
+  - Addressed race condition in FilteredStream using context managers
+  - Fixed agent knowledge reset issue
+  - Refactored agent fetching logic into utility module
+
+  **New Features & Enhancements**
+  - Added support for **loading an Agent directly from a repository**
+  - Enabled setting an empty context for Task
+  - Enhanced Agent repository feedback and fixed Tool auto-import behavior
+  - Introduced direct initialization of knowledge (bypassing knowledge_sources)
+
+  **Documentation & Guides**
+  - Updated security.md for current security practices
+  - Cleaned up Google setup section for clarity
+  - Added link to AI Studio when entering Gemini key
+  - Updated Arize Phoenix observability guide
+  - Refreshed flow documentation
+</Update>
+
+<Update label="2024-05-08" description="v0.119.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01190.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.119.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Improved test reliability by enhancing pytest handling for flaky tests
+  - Fixed memory reset crash when embedding dimensions mismatch
+  - Enabled parent flow identification for Crew and LiteAgent
+  - Prevented telemetry-related crashes when unavailable
+  - Upgraded **LiteLLM version** for better compatibility
+  - Fixed llama converter tests by removing skip_external_api
+
+  **New Features & Enhancements**
+  - Introduced **knowledge retrieval prompt re-writing** in Agent for improved tracking and debugging
+  - Made LLM setup and quickstart guides model-agnostic
+
+  **Documentation & Guides**
+  - Added advanced configuration docs for the RAG tool
+  - Updated Windows troubleshooting guide
+  - Refined documentation examples for better clarity
+  - Fixed typos across docs and config files
+</Update>
+
+<Update label="2024-04-28" description="v0.118.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01180.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.118.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Fixed issues with missing prompt or system templates
+  - Removed global logging configuration to avoid unintended overrides
+  - Renamed **TaskGuardrail to LLMGuardrail** for improved clarity
+  - Downgraded litellm to version 1.167.1 for compatibility
+  - Added missing init.py files to ensure proper module initialization
+
+  **New Features & Enhancements**
+  - Added support for **no-code Guardrail creation** to simplify AI behavior controls
+
+  **Documentation & Guides**
+  - Removed CrewStructuredTool from public documentation to reflect internal usage
+  - Updated enterprise documentation and YouTube embed for improved onboarding experience
+</Update>
+
+<Update label="2024-04-20" description="v0.117.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01170.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.0">View on GitHub</a>
+  </div>
+
+  **New Features & Enhancements**
+  - Added `result_as_answer` parameter support in `@tool` decorator.
+  - Introduced support for new language models: GPT-4.1, Gemini-2.0, and Gemini-2.5 Pro.
+  - Enhanced knowledge management capabilities.
+  - Added Huggingface provider option in CLI.
+  - Improved compatibility and CI support for Python 3.10+.
+
+  **Core Improvements & Fixes**
+  - Fixed issues with incorrect template parameters and missing inputs.
+  - Improved asynchronous flow handling with coroutine condition checks.
+  - Enhanced memory management with isolated configuration and correct memory object copying.
+  - Fixed initialization of lite agents with correct references.
+  - Addressed Python type hint issues and removed redundant imports.
+  - Updated event placement for improved tool usage tracking.
+  - Raised explicit exceptions when flows fail.
+  - Removed unused code and redundant comments from various modules.
+  - Updated GitHub App token action to v2.
+
+  **Documentation & Guides**
+  - Enhanced documentation structure, including enterprise deployment instructions.
+  - Automatically create output folders for documentation generation.
+  - Fixed broken link in WeaviateVectorSearchTool documentation.
+  - Fixed guardrail documentation usage and import paths for JSON search tools.
+  - Updated documentation for CodeInterpreterTool.
+  - Improved SEO, contextual navigation, and error handling for documentation pages.
+</Update>
+
+<Update label="2024-04-25" description="v0.117.1">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01171.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.1">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Upgraded **crewai-tools** to latest version
+  - Upgraded **liteLLM** to latest version
+  - Fixed **Mem0 OSS**
+</Update>
+
+<Update label="2024-04-07" description="v0.114.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01140.png" />
+  </Frame>
+
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.114.0">View on GitHub</a>
+  </div>
+
+  **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="2024-03-17" description="v0.108.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01080.png" />
+  </Frame>
+
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.108.0">View on GitHub</a>
+  </div>
+
+  **New Features & Enhancements**
+  - 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="2024-03-10" description="v0.105.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01050.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.105.0">View on GitHub</a>
+  </div>
+
+  **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="2024-02-12" description="v0.102.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01020.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.102.0">View on GitHub</a>
+  </div>
+
+  **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="2024-01-28" description="v0.100.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01000.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.100.0">View on GitHub</a>
+  </div>
+
+  **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="2024-01-20" description="v0.98.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0980.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.98.0">View on GitHub</a>
+  </div>
+
+  **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="2024-01-04" description="v0.95.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0950.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.95.0">View on GitHub</a>
+  </div>
+
+  **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">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0860.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.86.0">View on GitHub</a>
+  </div>
+  **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">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0850.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.85.0">View on GitHub</a>
+  </div>
+  **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>

docs/common-room-tracking.js

@@ -0,0 +1,18 @@
+(function() {
+  if (typeof window === 'undefined') return;
+  if (typeof window.signals !== 'undefined') return;
+  var script = document.createElement('script');
+  script.src = 'https://cdn.cr-relay.com/v1/site/883520f4-c431-44be-80e7-e123a1ee7a2b/signals.js';
+  script.async = true;
+  window.signals = Object.assign(
+    [],
+    ['page', 'identify', 'form'].reduce(function (acc, method){
+      acc[method] = function () {
+        signals.push([method, arguments]);
+        return signals;
+      };
+     return acc;
+    }, {})
+  );
+  document.head.appendChild(script);
+})(); 

docs/concepts/agents.mdx

@@ -0,0 +1,593 @@
+---
+title: Agents
+description: Detailed guide on creating and managing agents within the CrewAI framework.
+icon: robot
+---
+
+## Overview of an Agent
+
+In the CrewAI framework, an `Agent` is an autonomous unit that can:
+- Perform specific tasks
+- Make decisions based on its role and goal
+- Use tools to accomplish objectives
+- Communicate and collaborate with other agents
+- Maintain memory of interactions
+- Delegate tasks when allowed
+
+<Tip>
+Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a `Researcher` agent might excel at gathering and analyzing information, while a `Writer` agent might be better at creating content.
+</Tip>
+
+<Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
+CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
+
+![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
+
+The Visual Agent Builder enables:
+- Intuitive agent configuration with form-based interfaces
+- Real-time testing and validation
+- Template library with pre-configured agent types
+- Easy customization of agent attributes and behaviors
+</Note>
+
+## Agent Attributes
+
+| Attribute                               | Parameter                | Type                          | Description                                                                                                          |
+| :-------------------------------------- | :----------------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
+| **Role**                                | `role`                   | `str`                         | Defines the agent's function and expertise within the crew.                                                           |
+| **Goal**                                | `goal`                   | `str`                         | The individual objective that guides the agent's decision-making.                                                     |
+| **Backstory**                           | `backstory`              | `str`                         | Provides context and personality to the agent, enriching interactions.                                                |
+| **LLM** _(optional)_                    | `llm`                    | `Union[str, LLM, Any]`        | Language model that powers the agent. Defaults to the model specified in `OPENAI_MODEL_NAME` or "gpt-4".              |
+| **Tools** _(optional)_                  | `tools`                  | `List[BaseTool]`              | Capabilities or functions available to the agent. Defaults to an empty list.                                          |
+| **Function Calling LLM** _(optional)_   | `function_calling_llm`   | `Optional[Any]`               | Language model for tool calling, overrides crew's LLM if specified.                                                   |
+| **Max Iterations** _(optional)_         | `max_iter`               | `int`                         | Maximum iterations before the agent must provide its best answer. Default is 20.                                      |
+| **Max RPM** _(optional)_                | `max_rpm`                | `Optional[int]`               | Maximum requests per minute to avoid rate limits.                                                                     |
+| **Max Execution Time** _(optional)_     | `max_execution_time`     | `Optional[int]`               | Maximum time (in seconds) for task execution.                                                                         |
+| **Verbose** _(optional)_                | `verbose`                | `bool`                        | Enable detailed execution logs for debugging. Default is False.                                                       |
+| **Allow Delegation** _(optional)_       | `allow_delegation`       | `bool`                        | Allow the agent to delegate tasks to other agents. Default is False.                                                  |
+| **Step Callback** _(optional)_          | `step_callback`          | `Optional[Any]`               | Function called after each agent step, overrides crew callback.                                                       |
+| **Cache** _(optional)_                  | `cache`                  | `bool`                        | Enable caching for tool usage. Default is True.                                                                       |
+| **System Template** _(optional)_        | `system_template`        | `Optional[str]`               | Custom system prompt template for the agent.                                                                          |
+| **Prompt Template** _(optional)_        | `prompt_template`        | `Optional[str]`               | Custom prompt template for the agent.                                                                                 |
+| **Response Template** _(optional)_      | `response_template`      | `Optional[str]`               | Custom response template for the agent.                                                                               |
+| **Allow Code Execution** _(optional)_   | `allow_code_execution`   | `Optional[bool]`              | Enable code execution for the agent. Default is False.                                                                |
+| **Max Retry Limit** _(optional)_        | `max_retry_limit`        | `int`                         | Maximum number of retries when an error occurs. Default is 2.                                                         |
+| **Respect Context Window** _(optional)_ | `respect_context_window` | `bool`                        | Keep messages under context window size by summarizing. Default is True.                                              |
+| **Code Execution Mode** _(optional)_    | `code_execution_mode`    | `Literal["safe", "unsafe"]`   | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'.                               |
+| **Multimodal** _(optional)_             | `multimodal`             | `bool`                        | Whether the agent supports multimodal capabilities. Default is False.                                                  |
+| **Inject Date** _(optional)_            | `inject_date`            | `bool`                        | Whether to automatically inject the current date into tasks. Default is False.                                         |
+| **Date Format** _(optional)_            | `date_format`            | `str`                         | Format string for date when inject_date is enabled. Default is "%Y-%m-%d" (ISO format).                               |
+| **Reasoning** _(optional)_              | `reasoning`              | `bool`                        | Whether the agent should reflect and create a plan before executing a task. Default is False.                         |
+| **Max Reasoning Attempts** _(optional)_ | `max_reasoning_attempts` | `Optional[int]`               | Maximum number of reasoning attempts before executing the task. If None, will try until ready.                         |
+| **Embedder** _(optional)_               | `embedder`               | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
+| **Knowledge Sources** _(optional)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent.                                                                     |
+| **Use System Prompt** _(optional)_      | `use_system_prompt`      | `Optional[bool]`              | Whether to use system prompt (for o1 model support). Default is True.                                                 |
+
+## Creating Agents
+
+There are two ways to create agents in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
+
+### YAML Configuration (Recommended)
+
+Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.
+
+After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
+
+<Note>
+Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
+```python Code
+crew.kickoff(inputs={'topic': 'AI Agents'})
+```
+</Note>
+
+Here's an example of how to configure agents using YAML:
+
+```yaml agents.yaml
+# src/latest_ai_development/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.
+```
+
+To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`:
+
+```python Code
+# src/latest_ai_development/crew.py
+from crewai import Agent, Crew, Process
+from crewai.project import CrewBase, agent, crew
+from crewai_tools import SerperDevTool
+
+@CrewBase
+class LatestAiDevelopmentCrew():
+  """LatestAiDevelopment crew"""
+
+  agents_config = "config/agents.yaml"
+
+  @agent
+  def researcher(self) -> Agent:
+    return Agent(
+      config=self.agents_config['researcher'], # type: ignore[index]
+      verbose=True,
+      tools=[SerperDevTool()]
+    )
+
+  @agent
+  def reporting_analyst(self) -> Agent:
+    return Agent(
+      config=self.agents_config['reporting_analyst'], # type: ignore[index]
+      verbose=True
+    )
+```
+
+<Note>
+The names you use in your YAML files (`agents.yaml`) should match the method names in your Python code.
+</Note>
+
+### Direct Code Definition
+
+You can create agents directly in code by instantiating the `Agent` class. Here's a comprehensive example showing all available parameters:
+
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool
+
+# Create an agent with all available parameters
+agent = Agent(
+    role="Senior Data Scientist",
+    goal="Analyze and interpret complex datasets to provide actionable insights",
+    backstory="With over 10 years of experience in data science and machine learning, "
+              "you excel at finding patterns in complex datasets.",
+    llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
+    function_calling_llm=None,  # Optional: Separate LLM for tool calling
+    verbose=False,  # Default: False
+    allow_delegation=False,  # Default: False
+    max_iter=20,  # Default: 20 iterations
+    max_rpm=None,  # Optional: Rate limit for API calls
+    max_execution_time=None,  # Optional: Maximum execution time in seconds
+    max_retry_limit=2,  # Default: 2 retries on error
+    allow_code_execution=False,  # Default: False
+    code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
+    respect_context_window=True,  # Default: True
+    use_system_prompt=True,  # Default: True
+    multimodal=False,  # Default: False
+    inject_date=False,  # Default: False
+    date_format="%Y-%m-%d",  # Default: ISO format
+    reasoning=False,  # Default: False
+    max_reasoning_attempts=None,  # Default: None
+    tools=[SerperDevTool()],  # Optional: List of tools
+    knowledge_sources=None,  # Optional: List of knowledge sources
+    embedder=None,  # Optional: Custom embedder configuration
+    system_template=None,  # Optional: Custom system prompt template
+    prompt_template=None,  # Optional: Custom prompt template
+    response_template=None,  # Optional: Custom response template
+    step_callback=None,  # Optional: Callback function for monitoring
+)
+```
+
+Let's break down some key parameter combinations for common use cases:
+
+#### Basic Research Agent
+```python Code
+research_agent = Agent(
+    role="Research Analyst",
+    goal="Find and summarize information about specific topics",
+    backstory="You are an experienced researcher with attention to detail",
+    tools=[SerperDevTool()],
+    verbose=True  # Enable logging for debugging
+)
+```
+
+#### Code Development Agent
+```python Code
+dev_agent = Agent(
+    role="Senior Python Developer",
+    goal="Write and debug Python code",
+    backstory="Expert Python developer with 10 years of experience",
+    allow_code_execution=True,
+    code_execution_mode="safe",  # Uses Docker for safety
+    max_execution_time=300,  # 5-minute timeout
+    max_retry_limit=3  # More retries for complex code tasks
+)
+```
+
+#### Long-Running Analysis Agent
+```python Code
+analysis_agent = Agent(
+    role="Data Analyst",
+    goal="Perform deep analysis of large datasets",
+    backstory="Specialized in big data analysis and pattern recognition",
+    memory=True,
+    respect_context_window=True,
+    max_rpm=10,  # Limit API calls
+    function_calling_llm="gpt-4o-mini"  # Cheaper model for tool calls
+)
+```
+
+#### Custom Template Agent
+```python Code
+custom_agent = Agent(
+    role="Customer Service Representative",
+    goal="Assist customers with their inquiries",
+    backstory="Experienced in customer support with a focus on satisfaction",
+    system_template="""<|start_header_id|>system<|end_header_id|>
+                        {{ .System }}<|eot_id|>""",
+    prompt_template="""<|start_header_id|>user<|end_header_id|>
+                        {{ .Prompt }}<|eot_id|>""",
+    response_template="""<|start_header_id|>assistant<|end_header_id|>
+                        {{ .Response }}<|eot_id|>""",
+)
+```
+
+#### Date-Aware Agent with Reasoning
+```python Code
+strategic_agent = Agent(
+    role="Market Analyst",
+    goal="Track market movements with precise date references and strategic planning",
+    backstory="Expert in time-sensitive financial analysis and strategic reporting",
+    inject_date=True,  # Automatically inject current date into tasks
+    date_format="%B %d, %Y",  # Format as "May 21, 2025"
+    reasoning=True,  # Enable strategic planning
+    max_reasoning_attempts=2,  # Limit planning iterations
+    verbose=True
+)
+```
+
+#### Reasoning Agent
+```python Code
+reasoning_agent = Agent(
+    role="Strategic Planner",
+    goal="Analyze complex problems and create detailed execution plans",
+    backstory="Expert strategic planner who methodically breaks down complex challenges",
+    reasoning=True,  # Enable reasoning and planning
+    max_reasoning_attempts=3,  # Limit reasoning attempts
+    max_iter=30,  # Allow more iterations for complex planning
+    verbose=True
+)
+```
+
+#### Multimodal Agent
+```python Code
+multimodal_agent = Agent(
+    role="Visual Content Analyst",
+    goal="Analyze and process both text and visual content",
+    backstory="Specialized in multimodal analysis combining text and image understanding",
+    multimodal=True,  # Enable multimodal capabilities
+    verbose=True
+)
+```
+
+### Parameter Details
+
+#### Critical Parameters
+- `role`, `goal`, and `backstory` are required and shape the agent's behavior
+- `llm` determines the language model used (default: OpenAI's GPT-4)
+
+#### Memory and Context
+- `memory`: Enable to maintain conversation history
+- `respect_context_window`: Prevents token limit issues
+- `knowledge_sources`: Add domain-specific knowledge bases
+
+#### Execution Control
+- `max_iter`: Maximum attempts before giving best answer
+- `max_execution_time`: Timeout in seconds
+- `max_rpm`: Rate limiting for API calls
+- `max_retry_limit`: Retries on error
+
+#### Code Execution
+- `allow_code_execution`: Must be True to run code
+- `code_execution_mode`:
+  - `"safe"`: Uses Docker (recommended for production)
+  - `"unsafe"`: Direct execution (use only in trusted environments)
+
+<Note>
+  This runs a default Docker image. If you want to configure the docker image, the checkout the Code Interpreter Tool in the tools section.
+  Add the code interpreter tool as a tool in the agent as a tool parameter.
+  </Note>
+
+#### Advanced Features
+- `multimodal`: Enable multimodal capabilities for processing text and visual content
+- `reasoning`: Enable agent to reflect and create plans before executing tasks
+- `inject_date`: Automatically inject current date into task descriptions
+
+#### Templates
+- `system_template`: Defines agent's core behavior
+- `prompt_template`: Structures input format
+- `response_template`: Formats agent responses
+
+<Note>
+When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting.
+</Note>  
+
+<Note>
+When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
+</Note>
+
+## Agent Tools
+
+Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from:
+- [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools)
+- [LangChain Tools](https://python.langchain.com/docs/integrations/tools)
+
+Here's how to add tools to an agent:
+
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool, WikipediaTools
+
+# Create tools
+search_tool = SerperDevTool()
+wiki_tool = WikipediaTools()
+
+# Add tools to agent
+researcher = Agent(
+    role="AI Technology Researcher",
+    goal="Research the latest AI developments",
+    tools=[search_tool, wiki_tool],
+    verbose=True
+)
+```
+
+## Agent Memory and Context
+
+Agents can maintain memory of their interactions and use context from previous tasks. This is particularly useful for complex workflows where information needs to be retained across multiple tasks.
+
+```python Code
+from crewai import Agent
+
+analyst = Agent(
+    role="Data Analyst",
+    goal="Analyze and remember complex data patterns",
+    memory=True,  # Enable memory
+    verbose=True
+)
+```
+
+<Note>
+When `memory` is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks.
+</Note>
+
+## Context Window Management
+
+CrewAI includes sophisticated automatic context window management to handle situations where conversations exceed the language model's token limits. This powerful feature is controlled by the `respect_context_window` parameter.
+
+### How Context Window Management Works
+
+When an agent's conversation history grows too large for the LLM's context window, CrewAI automatically detects this situation and can either:
+
+1. **Automatically summarize content** (when `respect_context_window=True`)
+2. **Stop execution with an error** (when `respect_context_window=False`)
+
+### Automatic Context Handling (`respect_context_window=True`)
+
+This is the **default and recommended setting** for most use cases. When enabled, CrewAI will:
+
+```python Code
+# Agent with automatic context management (default)
+smart_agent = Agent(
+    role="Research Analyst",
+    goal="Analyze large documents and datasets",
+    backstory="Expert at processing extensive information",
+    respect_context_window=True,  # 🔑 Default: auto-handle context limits
+    verbose=True
+)
+```
+
+**What happens when context limits are exceeded:**
+- ⚠️ **Warning message**: `"Context length exceeded. Summarizing content to fit the model context window."`
+- 🔄 **Automatic summarization**: CrewAI intelligently summarizes the conversation history
+- ✅ **Continued execution**: Task execution continues seamlessly with the summarized context
+- 📝 **Preserved information**: Key information is retained while reducing token count
+
+### Strict Context Limits (`respect_context_window=False`)
+
+When you need precise control and prefer execution to stop rather than lose any information:
+
+```python Code
+# Agent with strict context limits
+strict_agent = Agent(
+    role="Legal Document Reviewer",
+    goal="Provide precise legal analysis without information loss",
+    backstory="Legal expert requiring complete context for accurate analysis",
+    respect_context_window=False,  # ❌ Stop execution on context limit
+    verbose=True
+)
+```
+
+**What happens when context limits are exceeded:**
+- ❌ **Error message**: `"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools."`
+- 🛑 **Execution stops**: Task execution halts immediately
+- 🔧 **Manual intervention required**: You need to modify your approach
+
+### Choosing the Right Setting
+
+#### Use `respect_context_window=True` (Default) when:
+- **Processing large documents** that might exceed context limits
+- **Long-running conversations** where some summarization is acceptable
+- **Research tasks** where general context is more important than exact details
+- **Prototyping and development** where you want robust execution
+
+```python Code
+# Perfect for document processing
+document_processor = Agent(
+    role="Document Analyst", 
+    goal="Extract insights from large research papers",
+    backstory="Expert at analyzing extensive documentation",
+    respect_context_window=True,  # Handle large documents gracefully
+    max_iter=50,  # Allow more iterations for complex analysis
+    verbose=True
+)
+```
+
+#### Use `respect_context_window=False` when:
+- **Precision is critical** and information loss is unacceptable
+- **Legal or medical tasks** requiring complete context
+- **Code review** where missing details could introduce bugs
+- **Financial analysis** where accuracy is paramount
+
+```python Code
+# Perfect for precision tasks
+precision_agent = Agent(
+    role="Code Security Auditor",
+    goal="Identify security vulnerabilities in code",
+    backstory="Security expert requiring complete code context",
+    respect_context_window=False,  # Prefer failure over incomplete analysis
+    max_retry_limit=1,  # Fail fast on context issues
+    verbose=True
+)
+```
+
+### Alternative Approaches for Large Data
+
+When dealing with very large datasets, consider these strategies:
+
+#### 1. Use RAG Tools
+```python Code
+from crewai_tools import RagTool
+
+# Create RAG tool for large document processing
+rag_tool = RagTool()
+
+rag_agent = Agent(
+    role="Research Assistant",
+    goal="Query large knowledge bases efficiently",
+    backstory="Expert at using RAG tools for information retrieval",
+    tools=[rag_tool],  # Use RAG instead of large context windows
+    respect_context_window=True,
+    verbose=True
+)
+```
+
+#### 2. Use Knowledge Sources
+```python Code
+# Use knowledge sources instead of large prompts
+knowledge_agent = Agent(
+    role="Knowledge Expert",
+    goal="Answer questions using curated knowledge",
+    backstory="Expert at leveraging structured knowledge sources",
+    knowledge_sources=[your_knowledge_sources],  # Pre-processed knowledge
+    respect_context_window=True,
+    verbose=True
+)
+```
+
+### Context Window Best Practices
+
+1. **Monitor Context Usage**: Enable `verbose=True` to see context management in action
+2. **Design for Efficiency**: Structure tasks to minimize context accumulation
+3. **Use Appropriate Models**: Choose LLMs with context windows suitable for your tasks
+4. **Test Both Settings**: Try both `True` and `False` to see which works better for your use case
+5. **Combine with RAG**: Use RAG tools for very large datasets instead of relying solely on context windows
+
+### Troubleshooting Context Issues
+
+**If you're getting context limit errors:**
+```python Code
+# Quick fix: Enable automatic handling
+agent.respect_context_window = True
+
+# Better solution: Use RAG tools for large data
+from crewai_tools import RagTool
+agent.tools = [RagTool()]
+
+# Alternative: Break tasks into smaller pieces
+# Or use knowledge sources instead of large prompts
+```
+
+**If automatic summarization loses important information:**
+```python Code
+# Disable auto-summarization and use RAG instead
+agent = Agent(
+    role="Detailed Analyst",
+    goal="Maintain complete information accuracy",
+    backstory="Expert requiring full context",
+    respect_context_window=False,  # No summarization
+    tools=[RagTool()],  # Use RAG for large data
+    verbose=True
+)
+```
+
+<Note>
+The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
+</Note>
+
+## Important Considerations and Best Practices
+
+### Security and Code Execution
+- When using `allow_code_execution`, be cautious with user input and always validate it
+- Use `code_execution_mode: "safe"` (Docker) in production environments
+- Consider setting appropriate `max_execution_time` limits to prevent infinite loops
+
+### Performance Optimization
+- Use `respect_context_window: true` to prevent token limit issues
+- Set appropriate `max_rpm` to avoid rate limiting
+- Enable `cache: true` to improve performance for repetitive tasks
+- Adjust `max_iter` and `max_retry_limit` based on task complexity
+
+### Memory and Context Management
+- Leverage `knowledge_sources` for domain-specific information
+- Configure `embedder` when using custom embedding models
+- Use custom templates (`system_template`, `prompt_template`, `response_template`) for fine-grained control over agent behavior
+
+### Advanced Features
+- Enable `reasoning: true` for agents that need to plan and reflect before executing complex tasks
+- Set appropriate `max_reasoning_attempts` to control planning iterations (None for unlimited attempts)
+- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
+- Customize the date format with `date_format` using standard Python datetime format codes
+- Enable `multimodal: true` for agents that need to process both text and visual content
+
+### Agent Collaboration
+- Enable `allow_delegation: true` when agents need to work together
+- Use `step_callback` to monitor and log agent interactions
+- Consider using different LLMs for different purposes:
+  - Main `llm` for complex reasoning
+  - `function_calling_llm` for efficient tool usage
+
+### Date Awareness and Reasoning
+- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
+- Customize the date format with `date_format` using standard Python datetime format codes
+- Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
+- Invalid date formats will be logged as warnings and will not modify the task description
+- Enable `reasoning: true` for complex tasks that benefit from upfront planning and reflection
+
+### Model Compatibility
+- Set `use_system_prompt: false` for older models that don't support system messages
+- Ensure your chosen `llm` supports the features you need (like function calling)
+
+## Troubleshooting Common Issues
+
+1. **Rate Limiting**: If you're hitting API rate limits:
+   - Implement appropriate `max_rpm`
+   - Use caching for repetitive operations
+   - Consider batching requests
+
+2. **Context Window Errors**: If you're exceeding context limits:
+   - Enable `respect_context_window`
+   - Use more efficient prompts
+   - Clear agent memory periodically
+
+3. **Code Execution Issues**: If code execution fails:
+   - Verify Docker is installed for safe mode
+   - Check execution permissions
+   - Review code sandbox settings
+
+4. **Memory Issues**: If agent responses seem inconsistent:
+   - Check knowledge source configuration
+   - Review conversation history management
+
+Remember that agents are most effective when configured according to their specific use case. Take time to understand your requirements and adjust these parameters accordingly.

docs/concepts/cli.mdx

@@ -0,0 +1,315 @@
+---
+title: CLI
+description: Learn how to use the CrewAI CLI to interact with CrewAI.
+icon: terminal
+---
+
+## Overview
+
+The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
+
+## Installation
+
+To use the CrewAI CLI, make sure you have CrewAI installed:
+
+```shell Terminal
+pip install crewai
+```
+
+## Basic Usage
+
+The basic structure of a CrewAI CLI command is:
+
+```shell Terminal
+crewai [COMMAND] [OPTIONS] [ARGUMENTS]
+```
+
+## Available Commands
+
+### 1. Create
+
+Create a new crew or flow.
+
+```shell Terminal
+crewai create [OPTIONS] TYPE NAME
+```
+
+- `TYPE`: Choose between "crew" or "flow"
+- `NAME`: Name of the crew or flow
+
+Example:
+```shell Terminal
+crewai create crew my_new_crew
+crewai create flow my_new_flow
+```
+
+### 2. Version
+
+Show the installed version of CrewAI.
+
+```shell Terminal
+crewai version [OPTIONS]
+```
+
+- `--tools`: (Optional) Show the installed version of CrewAI tools
+
+Example:
+```shell Terminal
+crewai version
+crewai version --tools
+```
+
+### 3. Train
+
+Train the crew for a specified number of iterations.
+
+```shell Terminal
+crewai train [OPTIONS]
+```
+
+- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
+- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
+
+Example:
+```shell Terminal
+crewai train -n 10 -f my_training_data.pkl
+```
+
+### 4. Replay
+
+Replay the crew execution from a specific task.
+
+```shell Terminal
+crewai replay [OPTIONS]
+```
+
+- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
+
+Example:
+```shell Terminal    
+crewai replay -t task_123456
+```
+
+### 5. Log-tasks-outputs
+
+Retrieve your latest crew.kickoff() task outputs.
+
+```shell Terminal
+crewai log-tasks-outputs
+```
+
+### 6. Reset-memories
+
+Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
+
+```shell Terminal
+crewai reset-memories [OPTIONS]
+```
+
+- `-l, --long`: Reset LONG TERM memory
+- `-s, --short`: Reset SHORT TERM memory
+- `-e, --entities`: Reset ENTITIES memory
+- `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
+- `-kn, --knowledge`: Reset KNOWLEDGE storage
+- `-akn, --agent-knowledge`: Reset AGENT KNOWLEDGE storage
+- `-a, --all`: Reset ALL memories
+
+Example:
+```shell Terminal
+crewai reset-memories --long --short
+crewai reset-memories --all
+```
+
+### 7. Test
+
+Test the crew and evaluate the results.
+
+```shell Terminal
+crewai test [OPTIONS]
+```
+
+- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
+- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
+
+Example:
+```shell Terminal    
+crewai test -n 5 -m gpt-3.5-turbo
+```
+
+### 8. Run
+
+Run the crew or flow.
+
+```shell Terminal
+crewai run
+```
+
+<Note>
+Starting from version 0.103.0, the `crewai run` command can be used to run both standard crews and flows. For flows, it automatically detects the type from pyproject.toml and runs the appropriate command. This is now the recommended way to run both crews and flows.
+</Note>
+
+<Note>
+Make sure to run these commands from the directory where your CrewAI project is set up. 
+Some commands may require additional configuration or setup within your project structure.
+</Note>
+
+### 9. Chat
+
+Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
+
+After receiving the results, you can continue interacting with the assistant for further instructions or questions.
+
+```shell Terminal
+crewai chat
+```
+<Note>
+Ensure you execute these commands from your CrewAI project's root directory.
+</Note>
+<Note>
+IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
+
+```python
+@crew
+def crew(self) -> Crew:
+    return Crew(
+        agents=self.agents,
+        tasks=self.tasks,
+        process=Process.sequential,
+        verbose=True,
+        chat_llm="gpt-4o",  # LLM for chat orchestration
+    )
+```
+</Note>
+
+### 10. Deploy
+
+Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
+
+- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
+    ```shell Terminal
+    crewai signup
+    ```
+    If you already have an account, you can login with:
+    ```shell Terminal
+    crewai login
+    ```
+
+- **Create a deployment**: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.
+    ```shell Terminal
+    crewai deploy create
+    ```
+    - Reads your local project configuration.
+    - Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
+
+### 11. Organization Management
+
+Manage your CrewAI Enterprise organizations.
+
+```shell Terminal
+crewai org [COMMAND] [OPTIONS]
+```
+
+#### Commands:
+
+- `list`: List all organizations you belong to
+```shell Terminal
+crewai org list
+```
+
+- `current`: Display your currently active organization
+```shell Terminal
+crewai org current
+```
+
+- `switch`: Switch to a specific organization
+```shell Terminal
+crewai org switch <organization_id>
+```
+
+<Note>
+You must be authenticated to CrewAI Enterprise to use these organization management commands.
+</Note>
+
+- **Create a deployment** (continued):
+    - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
+
+- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
+    ```shell Terminal
+    crewai deploy push
+    ``` 
+    - Initiates the deployment process on the CrewAI Enterprise platform.
+    - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
+
+- **Deployment Status**: You can check the status of your deployment with:
+    ```shell Terminal
+    crewai deploy status
+    ```
+    This fetches the latest deployment status of your most recent deployment attempt (e.g., `Building Images for Crew`, `Deploy Enqueued`, `Online`).
+
+- **Deployment Logs**: You can check the logs of your deployment with:
+    ```shell Terminal
+    crewai deploy logs
+    ```
+    This streams the deployment logs to your terminal.
+
+- **List deployments**: You can list all your deployments with:
+    ```shell Terminal
+    crewai deploy list
+    ```
+    This lists all your deployments.
+
+- **Delete a deployment**: You can delete a deployment with:
+    ```shell Terminal
+    crewai deploy remove
+    ```
+    This deletes the deployment from the CrewAI Enterprise platform.
+
+- **Help Command**: You can get help with the CLI with:
+    ```shell Terminal
+    crewai deploy --help
+    ```
+    This shows the help message for the CrewAI Deploy CLI.
+
+Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
+
+<iframe
+  width="100%"
+  height="400"
+  src="https://www.youtube.com/embed/3EqSV-CYDZA"
+  title="CrewAI Deployment Guide"
+  frameborder="0"
+  style={{ borderRadius: '10px' }}
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+  allowfullscreen
+></iframe>
+
+### 11. API Keys
+
+When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
+
+Once you've selected an LLM provider, you will be prompted for API keys.
+
+#### Initial API key providers
+
+The CLI will initially prompt for API keys for the following services:
+
+* OpenAI
+* Groq
+* Anthropic
+* Google Gemini
+* SambaNova
+
+When you select a provider, the CLI will prompt you to enter your API key.
+
+#### Other Options
+
+If you select option 6, you will be able to select from a list of LiteLLM supported providers.
+
+When you select a provider, the CLI will prompt you to enter the Key name and the API key.
+
+See the following link for each provider's key name:
+
+* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
+
+
+

docs/concepts/collaboration.mdx

@@ -0,0 +1,362 @@
+---
+title: Collaboration
+description: How to enable agents to work together, delegate tasks, and communicate effectively within CrewAI teams.
+icon: screen-users
+---
+
+## Overview
+
+Collaboration in CrewAI enables agents to work together as a team by delegating tasks and asking questions to leverage each other's expertise. When `allow_delegation=True`, agents automatically gain access to powerful collaboration tools.
+
+## Quick Start: Enable Collaboration
+
+```python
+from crewai import Agent, Crew, Task
+
+# Enable collaboration for agents
+researcher = Agent(
+    role="Research Specialist",
+    goal="Conduct thorough research on any topic",
+    backstory="Expert researcher with access to various sources",
+    allow_delegation=True,  # 🔑 Key setting for collaboration
+    verbose=True
+)
+
+writer = Agent(
+    role="Content Writer", 
+    goal="Create engaging content based on research",
+    backstory="Skilled writer who transforms research into compelling content",
+    allow_delegation=True,  # 🔑 Enables asking questions to other agents
+    verbose=True
+)
+
+# Agents can now collaborate automatically
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[...],
+    verbose=True
+)
+```
+
+## How Agent Collaboration Works
+
+When `allow_delegation=True`, CrewAI automatically provides agents with two powerful tools:
+
+### 1. **Delegate Work Tool**
+Allows agents to assign tasks to teammates with specific expertise.
+
+```python
+# Agent automatically gets this tool:
+# Delegate work to coworker(task: str, context: str, coworker: str)
+```
+
+### 2. **Ask Question Tool** 
+Enables agents to ask specific questions to gather information from colleagues.
+
+```python
+# Agent automatically gets this tool:
+# Ask question to coworker(question: str, context: str, coworker: str)
+```
+
+## Collaboration in Action
+
+Here's a complete example showing agents collaborating on a content creation task:
+
+```python
+from crewai import Agent, Crew, Task, Process
+
+# Create collaborative agents
+researcher = Agent(
+    role="Research Specialist",
+    goal="Find accurate, up-to-date information on any topic",
+    backstory="""You're a meticulous researcher with expertise in finding 
+    reliable sources and fact-checking information across various domains.""",
+    allow_delegation=True,
+    verbose=True
+)
+
+writer = Agent(
+    role="Content Writer",
+    goal="Create engaging, well-structured content",
+    backstory="""You're a skilled content writer who excels at transforming 
+    research into compelling, readable content for different audiences.""",
+    allow_delegation=True,
+    verbose=True
+)
+
+editor = Agent(
+    role="Content Editor",
+    goal="Ensure content quality and consistency",
+    backstory="""You're an experienced editor with an eye for detail, 
+    ensuring content meets high standards for clarity and accuracy.""",
+    allow_delegation=True,
+    verbose=True
+)
+
+# Create a task that encourages collaboration
+article_task = Task(
+    description="""Write a comprehensive 1000-word article about 'The Future of AI in Healthcare'.
+    
+    The article should include:
+    - Current AI applications in healthcare
+    - Emerging trends and technologies  
+    - Potential challenges and ethical considerations
+    - Expert predictions for the next 5 years
+    
+    Collaborate with your teammates to ensure accuracy and quality.""",
+    expected_output="A well-researched, engaging 1000-word article with proper structure and citations",
+    agent=writer  # Writer leads, but can delegate research to researcher
+)
+
+# Create collaborative crew
+crew = Crew(
+    agents=[researcher, writer, editor],
+    tasks=[article_task],
+    process=Process.sequential,
+    verbose=True
+)
+
+result = crew.kickoff()
+```
+
+## Collaboration Patterns
+
+### Pattern 1: Research → Write → Edit
+```python
+research_task = Task(
+    description="Research the latest developments in quantum computing",
+    expected_output="Comprehensive research summary with key findings and sources",
+    agent=researcher
+)
+
+writing_task = Task(
+    description="Write an article based on the research findings",
+    expected_output="Engaging 800-word article about quantum computing",
+    agent=writer,
+    context=[research_task]  # Gets research output as context
+)
+
+editing_task = Task(
+    description="Edit and polish the article for publication",
+    expected_output="Publication-ready article with improved clarity and flow",
+    agent=editor,
+    context=[writing_task]  # Gets article draft as context
+)
+```
+
+### Pattern 2: Collaborative Single Task
+```python
+collaborative_task = Task(
+    description="""Create a marketing strategy for a new AI product.
+    
+    Writer: Focus on messaging and content strategy
+    Researcher: Provide market analysis and competitor insights
+    
+    Work together to create a comprehensive strategy.""",
+    expected_output="Complete marketing strategy with research backing",
+    agent=writer  # Lead agent, but can delegate to researcher
+)
+```
+
+## Hierarchical Collaboration
+
+For complex projects, use a hierarchical process with a manager agent:
+
+```python
+from crewai import Agent, Crew, Task, Process
+
+# Manager agent coordinates the team
+manager = Agent(
+    role="Project Manager",
+    goal="Coordinate team efforts and ensure project success",
+    backstory="Experienced project manager skilled at delegation and quality control",
+    allow_delegation=True,
+    verbose=True
+)
+
+# Specialist agents
+researcher = Agent(
+    role="Researcher",
+    goal="Provide accurate research and analysis",
+    backstory="Expert researcher with deep analytical skills",
+    allow_delegation=False,  # Specialists focus on their expertise
+    verbose=True
+)
+
+writer = Agent(
+    role="Writer", 
+    goal="Create compelling content",
+    backstory="Skilled writer who creates engaging content",
+    allow_delegation=False,
+    verbose=True
+)
+
+# Manager-led task
+project_task = Task(
+    description="Create a comprehensive market analysis report with recommendations",
+    expected_output="Executive summary, detailed analysis, and strategic recommendations",
+    agent=manager  # Manager will delegate to specialists
+)
+
+# Hierarchical crew
+crew = Crew(
+    agents=[manager, researcher, writer],
+    tasks=[project_task],
+    process=Process.hierarchical,  # Manager coordinates everything
+    manager_llm="gpt-4o",  # Specify LLM for manager
+    verbose=True
+)
+```
+
+## Best Practices for Collaboration
+
+### 1. **Clear Role Definition**
+```python
+# ✅ Good: Specific, complementary roles
+researcher = Agent(role="Market Research Analyst", ...)
+writer = Agent(role="Technical Content Writer", ...)
+
+# ❌ Avoid: Overlapping or vague roles  
+agent1 = Agent(role="General Assistant", ...)
+agent2 = Agent(role="Helper", ...)
+```
+
+### 2. **Strategic Delegation Enabling**
+```python
+# ✅ Enable delegation for coordinators and generalists
+lead_agent = Agent(
+    role="Content Lead",
+    allow_delegation=True,  # Can delegate to specialists
+    ...
+)
+
+# ✅ Disable for focused specialists (optional)
+specialist_agent = Agent(
+    role="Data Analyst", 
+    allow_delegation=False,  # Focuses on core expertise
+    ...
+)
+```
+
+### 3. **Context Sharing**
+```python
+# ✅ Use context parameter for task dependencies
+writing_task = Task(
+    description="Write article based on research",
+    agent=writer,
+    context=[research_task],  # Shares research results
+    ...
+)
+```
+
+### 4. **Clear Task Descriptions**
+```python
+# ✅ Specific, actionable descriptions
+Task(
+    description="""Research competitors in the AI chatbot space.
+    Focus on: pricing models, key features, target markets.
+    Provide data in a structured format.""",
+    ...
+)
+
+# ❌ Vague descriptions that don't guide collaboration
+Task(description="Do some research about chatbots", ...)
+```
+
+## Troubleshooting Collaboration
+
+### Issue: Agents Not Collaborating
+**Symptoms:** Agents work in isolation, no delegation occurs
+```python
+# ✅ Solution: Ensure delegation is enabled
+agent = Agent(
+    role="...",
+    allow_delegation=True,  # This is required!
+    ...
+)
+```
+
+### Issue: Too Much Back-and-Forth
+**Symptoms:** Agents ask excessive questions, slow progress
+```python
+# ✅ Solution: Provide better context and specific roles
+Task(
+    description="""Write a technical blog post about machine learning.
+    
+    Context: Target audience is software developers with basic ML knowledge.
+    Length: 1200 words
+    Include: code examples, practical applications, best practices
+    
+    If you need specific technical details, delegate research to the researcher.""",
+    ...
+)
+```
+
+### Issue: Delegation Loops
+**Symptoms:** Agents delegate back and forth indefinitely
+```python
+# ✅ Solution: Clear hierarchy and responsibilities
+manager = Agent(role="Manager", allow_delegation=True)
+specialist1 = Agent(role="Specialist A", allow_delegation=False)  # No re-delegation
+specialist2 = Agent(role="Specialist B", allow_delegation=False)
+```
+
+## Advanced Collaboration Features
+
+### Custom Collaboration Rules
+```python
+# Set specific collaboration guidelines in agent backstory
+agent = Agent(
+    role="Senior Developer",
+    backstory="""You lead development projects and coordinate with team members.
+    
+    Collaboration guidelines:
+    - Delegate research tasks to the Research Analyst
+    - Ask the Designer for UI/UX guidance  
+    - Consult the QA Engineer for testing strategies
+    - Only escalate blocking issues to the Project Manager""",
+    allow_delegation=True
+)
+```
+
+### Monitoring Collaboration
+```python
+def track_collaboration(output):
+    """Track collaboration patterns"""
+    if "Delegate work to coworker" in output.raw:
+        print("🤝 Delegation occurred")
+    if "Ask question to coworker" in output.raw:
+        print("❓ Question asked")
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    step_callback=track_collaboration,  # Monitor collaboration
+    verbose=True
+)
+```
+
+## Memory and Learning
+
+Enable agents to remember past collaborations:
+
+```python
+agent = Agent(
+    role="Content Lead",
+    memory=True,  # Remembers past interactions
+    allow_delegation=True,
+    verbose=True
+)
+```
+
+With memory enabled, agents learn from previous collaborations and improve their delegation decisions over time.
+
+## Next Steps
+
+- **Try the examples**: Start with the basic collaboration example
+- **Experiment with roles**: Test different agent role combinations  
+- **Monitor interactions**: Use `verbose=True` to see collaboration in action
+- **Optimize task descriptions**: Clear tasks lead to better collaboration
+- **Scale up**: Try hierarchical processes for complex projects
+
+Collaboration transforms individual AI agents into powerful teams that can tackle complex, multi-faceted challenges together.

docs/concepts/crews.mdx

@@ -1,9 +1,10 @@
 ---
-title: crewAI Crews
+title: Crews
 description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
+icon: people-group
 ---
 
-## What is a Crew?
+## Overview
 
 A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
 
@@ -13,95 +14,197 @@ A crew in crewAI represents a collaborative group of agents working together to
 | :------------------------------------ | :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | **Tasks**                             | `tasks`                | A list of tasks assigned to the crew.                                                                                                                                                                                                                     |
 | **Agents**                            | `agents`               | A list of agents that are part of the crew.                                                                                                                                                                                                               |
-| **Process** _(optional)_              | `process`              | The process flow (e.g., sequential, hierarchical) the crew follows.                                                                                                                                                                                       |
-| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution.                                                                                                                                                                                                         |
+| **Process** _(optional)_              | `process`              | The process flow (e.g., sequential, hierarchical) the crew follows. Default is `sequential`.                                                                                                                                                              |
+| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution. Defaults to `False`.                                                                                                                                                                                    |
 | **Manager LLM** _(optional)_          | `manager_llm`          | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.**                                                                                                                                   |
 | **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
 | **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
-| **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution.                                                                                                                                                                                         |
-| **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
-| **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
+| **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
 | **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
-| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution.                                                                                                                                                                             |
-| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                                                                                                                                                                     |
-| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output.                                                                                                                                                           |
+| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
+| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
+| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
+| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defaults to `None`.                                                                      |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
-| **Manager Callbacks** _(optional)_    | `manager_callbacks`    | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used.                                                                                                                            |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
 
-!!! note "Crew Max RPM"
-The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
+<Tip>
+**Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
+</Tip>
 
-## Creating a Crew
+## Creating Crews
 
-When assembling a crew, you combine agents with complementary roles and tools, assign tasks, and select a process that dictates their execution order and interaction.
+There are two ways to create crews in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
 
-### Example: Assembling a Crew
+### YAML Configuration (Recommended)
 
-```python
-from crewai import Crew, Agent, Task, Process
-from langchain_community.tools import DuckDuckGoSearchRun
-from crewai_tools import tool
+Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
 
-@tool('DuckDuckGoSearch')
-def search(search_query: str):
-    """Search the web for information on a given topic"""
-    return DuckDuckGoSearchRun().run(search_query)
+After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
 
-# Define agents with specific roles and tools
-researcher = Agent(
-    role='Senior Research Analyst',
-    goal='Discover innovative AI technologies',
-    backstory="""You're a senior research analyst at a large company.
-        You're responsible for analyzing data and providing insights
-        to the business.
-        You're currently working on a project to analyze the
-        trends and innovations in the space of artificial intelligence.""",
-    tools=[search]
-)
+#### Example Crew Class with Decorators
 
-writer = Agent(
-    role='Content Writer',
-    goal='Write engaging articles on AI discoveries',
-    backstory="""You're a senior writer at a large company.
-        You're responsible for creating content to the business.
-        You're currently working on a project to write about trends
-        and innovations in the space of AI for your next meeting.""",
-    verbose=True
-)
+```python code
+from crewai import Agent, Crew, Task, Process
+from crewai.project import CrewBase, agent, task, crew, before_kickoff, after_kickoff
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
 
-# Create tasks for the agents
-research_task = Task(
-    description='Identify breakthrough AI technologies',
-    agent=researcher,
-    expected_output='A bullet list summary of the top 5 most important AI news'
-)
-write_article_task = Task(
-    description='Draft an article on the latest AI technologies',
-    agent=writer,
-    expected_output='3 paragraph blog post on the latest AI technologies'
-)
+@CrewBase
+class YourCrewName:
+    """Description of your crew"""
 
-# Assemble the crew with a sequential process
-my_crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research_task, write_article_task],
-    process=Process.sequential,
-    full_output=True,
-    verbose=True,
-)
+    agents: List[BaseAgent]
+    tasks: List[Task]
+
+    # Paths to your YAML configuration files
+    # To see an example agent and task defined in YAML, checkout the following:
+    # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
+    # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
+    agents_config = 'config/agents.yaml' 
+    tasks_config = 'config/tasks.yaml' 
+
+    @before_kickoff
+    def prepare_inputs(self, inputs):
+        # Modify inputs before the crew starts
+        inputs['additional_data'] = "Some extra information"
+        return inputs
+
+    @after_kickoff
+    def process_output(self, output):
+        # Modify output after the crew finishes
+        output.raw += "\nProcessed after kickoff."
+        return output
+
+    @agent
+    def agent_one(self) -> Agent:
+        return Agent(
+            config=self.agents_config['agent_one'], # type: ignore[index]
+            verbose=True
+        )
+
+    @agent
+    def agent_two(self) -> Agent:
+        return Agent(
+            config=self.agents_config['agent_two'], # type: ignore[index]
+            verbose=True
+        )
+
+    @task
+    def task_one(self) -> Task:
+        return Task(
+            config=self.tasks_config['task_one'] # type: ignore[index]
+        )
+
+    @task
+    def task_two(self) -> Task:
+        return Task(
+            config=self.tasks_config['task_two'] # type: ignore[index]
+        )
+
+    @crew
+    def crew(self) -> Crew:
+        return Crew(
+            agents=self.agents,  # Automatically collected by the @agent decorator
+            tasks=self.tasks,    # Automatically collected by the @task decorator. 
+            process=Process.sequential,
+            verbose=True,
+        )
 ```
 
+How to run the above code:
+
+```python code
+YourCrewName().crew().kickoff(inputs={"any": "input here"})
+```
+
+<Note>
+Tasks will be executed in the order they are defined.
+</Note>
+
+The `CrewBase` class, along with these decorators, automates the collection of agents and tasks, reducing the need for manual management.
+
+#### Decorators overview from `annotations.py`
+
+CrewAI provides several decorators in the `annotations.py` file that are used to mark methods within your crew class for special handling:
+
+- `@CrewBase`: Marks the class as a crew base class.
+- `@agent`: Denotes a method that returns an `Agent` object.
+- `@task`: Denotes a method that returns a `Task` object.
+- `@crew`: Denotes the method that returns the `Crew` object.
+- `@before_kickoff`: (Optional) Marks a method to be executed before the crew starts.
+- `@after_kickoff`: (Optional) Marks a method to be executed after the crew finishes.
+
+These decorators help in organizing your crew's structure and automatically collecting agents and tasks without manually listing them.
+
+### Direct Code Definition (Alternative)
+
+Alternatively, you can define the crew directly in code without using YAML configuration files.
+
+```python code
+from crewai import Agent, Crew, Task, Process
+from crewai_tools import YourCustomTool
+
+class YourCrewName:
+    def agent_one(self) -> Agent:
+        return Agent(
+            role="Data Analyst",
+            goal="Analyze data trends in the market",
+            backstory="An experienced data analyst with a background in economics",
+            verbose=True,
+            tools=[YourCustomTool()]
+        )
+
+    def agent_two(self) -> Agent:
+        return Agent(
+            role="Market Researcher",
+            goal="Gather information on market dynamics",
+            backstory="A diligent researcher with a keen eye for detail",
+            verbose=True
+        )
+
+    def task_one(self) -> Task:
+        return Task(
+            description="Collect recent market data and identify trends.",
+            expected_output="A report summarizing key trends in the market.",
+            agent=self.agent_one()
+        )
+
+    def task_two(self) -> Task:
+        return Task(
+            description="Research factors affecting market dynamics.",
+            expected_output="An analysis of factors influencing the market.",
+            agent=self.agent_two()
+        )
+
+    def crew(self) -> Crew:
+        return Crew(
+            agents=[self.agent_one(), self.agent_two()],
+            tasks=[self.task_one(), self.task_two()],
+            process=Process.sequential,
+            verbose=True
+        )
+```
+How to run the above code:
+
+```python code
+YourCrewName().crew().kickoff(inputs={})
+```
+
+In this example:
+
+- Agents and tasks are defined directly within the class without decorators.
+- We manually create and manage the list of agents and tasks.
+- This approach provides more control but can be less maintainable for larger projects.
+
 ## Crew Output
 
-!!! note "Understanding Crew Outputs"
-The output of a crew in the crewAI framework is encapsulated within the `CrewOutput` class.
+The output of a crew in the CrewAI framework is encapsulated within the `CrewOutput` class.
 This class provides a structured way to access results of the crew's execution, including various formats such as raw strings, JSON, and Pydantic models.
 The `CrewOutput` includes the results from the final task output, token usage, and individual task outputs.
 
@@ -129,7 +232,7 @@ Once a crew has been executed, its output can be accessed through the `output` a
 
 #### Example
 
-```python
+```python Code
 # Example crew execution
 crew = Crew(
     agents=[research_agent, writer_agent],
@@ -149,6 +252,23 @@ print(f"Tasks Output: {crew_output.tasks_output}")
 print(f"Token Usage: {crew_output.token_usage}")
 ```
 
+## Accessing Crew Logs
+
+You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
+In case of `True(Boolean)` will save as `logs.txt`.
+
+In case of `output_log_file` is set as `False(Boolean)` or `None`, the logs will not be populated.
+
+```python Code
+# Save crew logs
+crew = Crew(output_log_file = True)  # Logs will be saved as logs.txt
+crew = Crew(output_log_file = file_name)  # Logs will be saved as file_name.txt
+crew = Crew(output_log_file = file_name.txt)  # Logs will be saved as file_name.txt
+crew = Crew(output_log_file = file_name.json)  # Logs will be saved as file_name.json
+```
+
+
+
 ## Memory Utilization
 
 Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
@@ -161,7 +281,7 @@ Caches can be employed to store the results of tools' execution, making the proc
 
 After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.
 
-```python
+```python Code
 # Access the crew's usage metrics
 crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
 crew.kickoff()
@@ -177,7 +297,7 @@ print(crew.usage_metrics)
 
 Once your crew is assembled, initiate the workflow with the `kickoff()` method. This starts the execution process according to the defined process flow.
 
-```python
+```python Code
 # Start the crew's task execution
 result = my_crew.kickoff()
 print(result)
@@ -188,11 +308,11 @@ print(result)
 Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
 
 - `kickoff()`: Starts the execution process according to the defined process flow.
-- `kickoff_for_each()`: Executes tasks for each agent individually.
+- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
 - `kickoff_async()`: Initiates the workflow asynchronously.
-- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
+- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.
 
-```python
+```python Code
 # Start the crew's task execution
 result = my_crew.kickoff()
 print(result)
@@ -205,12 +325,12 @@ for result in results:
 
 # Example of using kickoff_async
 inputs = {'topic': 'AI in healthcare'}
-async_result = my_crew.kickoff_async(inputs=inputs)
+async_result = await my_crew.kickoff_async(inputs=inputs)
 print(async_result)
 
 # Example of using kickoff_for_each_async
 inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
-async_results = my_crew.kickoff_for_each_async(inputs=inputs_array)
+async_results = await my_crew.kickoff_for_each_async(inputs=inputs_array)
 for async_result in async_results:
     print(async_result)
 ```
@@ -245,4 +365,4 @@ Then, to replay from a specific task, use:
 crewai replay -t <task_id>
 ```
 
-These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.
+These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

docs/concepts/event-listener.mdx

@@ -0,0 +1,379 @@
+---
+title: 'Event Listeners'
+description: 'Tap into CrewAI events to build custom integrations and monitoring'
+icon: spinner
+---
+
+## Overview
+
+CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
+
+## How It Works
+
+CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:
+
+1. **CrewAIEventsBus**: A singleton event bus that manages event registration and emission
+2. **BaseEvent**: Base class for all events in the system
+3. **BaseEventListener**: Abstract base class for creating custom event listeners
+
+When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
+
+<Note type="info" title="Enterprise Enhancement: Prompt Tracing">
+CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
+
+![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
+
+With Prompt Tracing you can:
+- View the complete history of all prompts sent to your LLM
+- Track token usage and costs
+- Debug agent reasoning failures
+- Share prompt sequences with your team
+- Compare different prompt strategies
+- Export traces for compliance and auditing
+</Note>
+
+## Creating a Custom Event Listener
+
+To create a custom event listener, you need to:
+
+1. Create a class that inherits from `BaseEventListener`
+2. Implement the `setup_listeners` method
+3. Register handlers for the events you're interested in
+4. Create an instance of your listener in the appropriate file
+
+Here's a simple example of a custom event listener class:
+
+```python
+from crewai.utilities.events import (
+    CrewKickoffStartedEvent,
+    CrewKickoffCompletedEvent,
+    AgentExecutionCompletedEvent,
+)
+from crewai.utilities.events.base_event_listener import BaseEventListener
+
+class MyCustomListener(BaseEventListener):
+    def __init__(self):
+        super().__init__()
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def on_crew_started(source, event):
+            print(f"Crew '{event.crew_name}' has started execution!")
+
+        @crewai_event_bus.on(CrewKickoffCompletedEvent)
+        def on_crew_completed(source, event):
+            print(f"Crew '{event.crew_name}' has completed execution!")
+            print(f"Output: {event.output}")
+
+        @crewai_event_bus.on(AgentExecutionCompletedEvent)
+        def on_agent_execution_completed(source, event):
+            print(f"Agent '{event.agent.role}' completed task")
+            print(f"Output: {event.output}")
+```
+
+## Properly Registering Your Listener
+
+Simply defining your listener class isn't enough. You need to create an instance of it and ensure it's imported in your application. This ensures that:
+
+1. The event handlers are registered with the event bus
+2. The listener instance remains in memory (not garbage collected)
+3. The listener is active when events are emitted
+
+### Option 1: Import and Instantiate in Your Crew or Flow Implementation
+
+The most important thing is to create an instance of your listener in the file where your Crew or Flow is defined and executed:
+
+#### For Crew-based Applications
+
+Create and import your listener at the top of your Crew implementation file:
+
+```python
+# In your crew.py file
+from crewai import Agent, Crew, Task
+from my_listeners import MyCustomListener
+
+# Create an instance of your listener
+my_listener = MyCustomListener()
+
+class MyCustomCrew:
+    # Your crew implementation...
+
+    def crew(self):
+        return Crew(
+            agents=[...],
+            tasks=[...],
+            # ...
+        )
+```
+
+#### For Flow-based Applications
+
+Create and import your listener at the top of your Flow implementation file:
+
+```python
+# In your main.py or flow.py file
+from crewai.flow import Flow, listen, start
+from my_listeners import MyCustomListener
+
+# Create an instance of your listener
+my_listener = MyCustomListener()
+
+class MyCustomFlow(Flow):
+    # Your flow implementation...
+
+    @start()
+    def first_step(self):
+        # ...
+```
+
+This ensures that your listener is loaded and active when your Crew or Flow is executed.
+
+### Option 2: Create a Package for Your Listeners
+
+For a more structured approach, especially if you have multiple listeners:
+
+1. Create a package for your listeners:
+
+```
+my_project/
+  ├── listeners/
+  │   ├── __init__.py
+  │   ├── my_custom_listener.py
+  │   └── another_listener.py
+```
+
+2. In `my_custom_listener.py`, define your listener class and create an instance:
+
+```python
+# my_custom_listener.py
+from crewai.utilities.events.base_event_listener import BaseEventListener
+# ... import events ...
+
+class MyCustomListener(BaseEventListener):
+    # ... implementation ...
+
+# Create an instance of your listener
+my_custom_listener = MyCustomListener()
+```
+
+3. In `__init__.py`, import the listener instances to ensure they're loaded:
+
+```python
+# __init__.py
+from .my_custom_listener import my_custom_listener
+from .another_listener import another_listener
+
+# Optionally export them if you need to access them elsewhere
+__all__ = ['my_custom_listener', 'another_listener']
+```
+
+4. Import your listeners package in your Crew or Flow file:
+
+```python
+# In your crew.py or flow.py file
+import my_project.listeners  # This loads all your listeners
+
+class MyCustomCrew:
+    # Your crew implementation...
+```
+
+This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
+
+```python
+# src/crewai/utilities/events/third_party/__init__.py
+from .agentops_listener import agentops_listener
+```
+
+This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
+
+## Available Event Types
+
+CrewAI provides a wide range of events that you can listen for:
+
+### Crew Events
+
+- **CrewKickoffStartedEvent**: Emitted when a Crew starts execution
+- **CrewKickoffCompletedEvent**: Emitted when a Crew completes execution
+- **CrewKickoffFailedEvent**: Emitted when a Crew fails to complete execution
+- **CrewTestStartedEvent**: Emitted when a Crew starts testing
+- **CrewTestCompletedEvent**: Emitted when a Crew completes testing
+- **CrewTestFailedEvent**: Emitted when a Crew fails to complete testing
+- **CrewTrainStartedEvent**: Emitted when a Crew starts training
+- **CrewTrainCompletedEvent**: Emitted when a Crew completes training
+- **CrewTrainFailedEvent**: Emitted when a Crew fails to complete training
+
+### Agent Events
+
+- **AgentExecutionStartedEvent**: Emitted when an Agent starts executing a task
+- **AgentExecutionCompletedEvent**: Emitted when an Agent completes executing a task
+- **AgentExecutionErrorEvent**: Emitted when an Agent encounters an error during execution
+
+### Task Events
+
+- **TaskStartedEvent**: Emitted when a Task starts execution
+- **TaskCompletedEvent**: Emitted when a Task completes execution
+- **TaskFailedEvent**: Emitted when a Task fails to complete execution
+- **TaskEvaluationEvent**: Emitted when a Task is evaluated
+
+### Tool Usage Events
+
+- **ToolUsageStartedEvent**: Emitted when a tool execution is started
+- **ToolUsageFinishedEvent**: Emitted when a tool execution is completed
+- **ToolUsageErrorEvent**: Emitted when a tool execution encounters an error
+- **ToolValidateInputErrorEvent**: Emitted when a tool input validation encounters an error
+- **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
+- **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
+
+### Knowledge Events
+
+- **KnowledgeRetrievalStartedEvent**: Emitted when a knowledge retrieval is started
+- **KnowledgeRetrievalCompletedEvent**: Emitted when a knowledge retrieval is completed
+- **KnowledgeQueryStartedEvent**: Emitted when a knowledge query is started
+- **KnowledgeQueryCompletedEvent**: Emitted when a knowledge query is completed
+- **KnowledgeQueryFailedEvent**: Emitted when a knowledge query fails
+- **KnowledgeSearchQueryFailedEvent**: Emitted when a knowledge search query fails
+
+### LLM Guardrail Events
+
+- **LLMGuardrailStartedEvent**: Emitted when a guardrail validation starts. Contains details about the guardrail being applied and retry count.
+- **LLMGuardrailCompletedEvent**: Emitted when a guardrail validation completes. Contains details about validation success/failure, results, and error messages if any.
+
+### Flow Events
+
+- **FlowCreatedEvent**: Emitted when a Flow is created
+- **FlowStartedEvent**: Emitted when a Flow starts execution
+- **FlowFinishedEvent**: Emitted when a Flow completes execution
+- **FlowPlotEvent**: Emitted when a Flow is plotted
+- **MethodExecutionStartedEvent**: Emitted when a Flow method starts execution
+- **MethodExecutionFinishedEvent**: Emitted when a Flow method completes execution
+- **MethodExecutionFailedEvent**: Emitted when a Flow method fails to complete execution
+
+### LLM Events
+
+- **LLMCallStartedEvent**: Emitted when an LLM call starts
+- **LLMCallCompletedEvent**: Emitted when an LLM call completes
+- **LLMCallFailedEvent**: Emitted when an LLM call fails
+- **LLMStreamChunkEvent**: Emitted for each chunk received during streaming LLM responses
+
+## Event Handler Structure
+
+Each event handler receives two parameters:
+
+1. **source**: The object that emitted the event
+2. **event**: The event instance, containing event-specific data
+
+The structure of the event object depends on the event type, but all events inherit from `BaseEvent` and include:
+
+- **timestamp**: The time when the event was emitted
+- **type**: A string identifier for the event type
+
+Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
+
+## Real-World Example: Integration with AgentOps
+
+CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
+
+```python
+from typing import Optional
+
+from crewai.utilities.events import (
+    CrewKickoffCompletedEvent,
+    ToolUsageErrorEvent,
+    ToolUsageStartedEvent,
+)
+from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
+from crewai.utilities.events.task_events import TaskEvaluationEvent
+
+try:
+    import agentops
+    AGENTOPS_INSTALLED = True
+except ImportError:
+    AGENTOPS_INSTALLED = False
+
+class AgentOpsListener(BaseEventListener):
+    tool_event: Optional["agentops.ToolEvent"] = None
+    session: Optional["agentops.Session"] = None
+
+    def __init__(self):
+        super().__init__()
+
+    def setup_listeners(self, crewai_event_bus):
+        if not AGENTOPS_INSTALLED:
+            return
+
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
+            self.session = agentops.init()
+            for agent in source.agents:
+                if self.session:
+                    self.session.create_agent(
+                        name=agent.role,
+                        agent_id=str(agent.id),
+                    )
+
+        @crewai_event_bus.on(CrewKickoffCompletedEvent)
+        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
+            if self.session:
+                self.session.end_session(
+                    end_state="Success",
+                    end_state_reason="Finished Execution",
+                )
+
+        @crewai_event_bus.on(ToolUsageStartedEvent)
+        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
+            self.tool_event = agentops.ToolEvent(name=event.tool_name)
+            if self.session:
+                self.session.record(self.tool_event)
+
+        @crewai_event_bus.on(ToolUsageErrorEvent)
+        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
+            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
+```
+
+This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
+
+The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
+
+```python
+from .agentops_listener import agentops_listener
+```
+
+This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
+
+## Advanced Usage: Scoped Handlers
+
+For temporary event handling (useful for testing or specific operations), you can use the `scoped_handlers` context manager:
+
+```python
+from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
+
+with crewai_event_bus.scoped_handlers():
+    @crewai_event_bus.on(CrewKickoffStartedEvent)
+    def temp_handler(source, event):
+        print("This handler only exists within this context")
+
+    # Do something that emits events
+
+# Outside the context, the temporary handler is removed
+```
+
+## Use Cases
+
+Event listeners can be used for a variety of purposes:
+
+1. **Logging and Monitoring**: Track the execution of your Crew and log important events
+2. **Analytics**: Collect data about your Crew's performance and behavior
+3. **Debugging**: Set up temporary listeners to debug specific issues
+4. **Integration**: Connect CrewAI with external systems like monitoring platforms, databases, or notification services
+5. **Custom Behavior**: Trigger custom actions based on specific events
+
+## Best Practices
+
+1. **Keep Handlers Light**: Event handlers should be lightweight and avoid blocking operations
+2. **Error Handling**: Include proper error handling in your event handlers to prevent exceptions from affecting the main execution
+3. **Cleanup**: If your listener allocates resources, ensure they're properly cleaned up
+4. **Selective Listening**: Only listen for events you actually need to handle
+5. **Testing**: Test your event listeners in isolation to ensure they behave as expected
+
+By leveraging CrewAI's event system, you can extend its functionality and integrate it seamlessly with your existing infrastructure.

docs/concepts/flows.mdx

@@ -0,0 +1,910 @@
+---
+title: Flows
+description: Learn how to create and manage AI workflows using CrewAI Flows.
+icon: arrow-progress
+---
+
+## Overview
+
+CrewAI Flows is a powerful feature designed to streamline the creation and management of AI workflows. Flows allow developers to combine and coordinate coding tasks and Crews efficiently, providing a robust framework for building sophisticated AI automations.
+
+Flows allow you to create structured, event-driven workflows. They provide a seamless way to connect multiple tasks, manage state, and control the flow of execution in your AI applications. With Flows, you can easily design and implement multi-step processes that leverage the full potential of CrewAI's capabilities.
+
+1. **Simplified Workflow Creation**: Easily chain together multiple Crews and tasks to create complex AI workflows.
+
+2. **State Management**: Flows make it super easy to manage and share state between different tasks in your workflow.
+
+3. **Event-Driven Architecture**: Built on an event-driven model, allowing for dynamic and responsive workflows.
+
+4. **Flexible Control Flow**: Implement conditional logic, loops, and branching within your workflows.
+
+## Getting Started
+
+Let's create a simple Flow where you will use OpenAI to generate a random city in one task and then use that city to generate a fun fact in another task.
+
+```python Code
+
+from crewai.flow.flow import Flow, listen, start
+from dotenv import load_dotenv
+from litellm import completion
+
+
+class ExampleFlow(Flow):
+    model = "gpt-4o-mini"
+
+    @start()
+    def generate_city(self):
+        print("Starting flow")
+        # Each flow state automatically gets a unique ID
+        print(f"Flow State ID: {self.state['id']}")
+
+        response = completion(
+            model=self.model,
+            messages=[
+                {
+                    "role": "user",
+                    "content": "Return the name of a random city in the world.",
+                },
+            ],
+        )
+
+        random_city = response["choices"][0]["message"]["content"]
+        # Store the city in our state
+        self.state["city"] = random_city
+        print(f"Random City: {random_city}")
+
+        return random_city
+
+    @listen(generate_city)
+    def generate_fun_fact(self, random_city):
+        response = completion(
+            model=self.model,
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"Tell me a fun fact about {random_city}",
+                },
+            ],
+        )
+
+        fun_fact = response["choices"][0]["message"]["content"]
+        # Store the fun fact in our state
+        self.state["fun_fact"] = fun_fact
+        return fun_fact
+
+
+
+flow = ExampleFlow()
+flow.plot()
+result = flow.kickoff()
+
+print(f"Generated fun fact: {result}")
+```
+![Flow Visual image](/images/crewai-flow-1.png)
+In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
+
+Each Flow instance automatically receives a unique identifier (UUID) in its state, which helps track and manage flow executions. The state can also store additional data (like the generated city and fun fact) that persists throughout the flow's execution.
+
+When you run the Flow, it will:
+1. Generate a unique ID for the flow state
+2. Generate a random city and store it in the state
+3. Generate a fun fact about that city and store it in the state
+4. Print the results to the console
+
+The state's unique ID and stored data can be useful for tracking flow executions and maintaining context between tasks.
+
+**Note:** Ensure you have set up your `.env` file to store your `OPENAI_API_KEY`. This key is necessary for authenticating requests to the OpenAI API.
+
+### @start()
+
+The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
+
+### @listen()
+
+The `@listen()` decorator is used to mark a method as a listener for the output of another task in the Flow. The method decorated with `@listen()` will be executed when the specified task emits an output. The method can access the output of the task it is listening to as an argument.
+
+#### Usage
+
+The `@listen()` decorator can be used in several ways:
+
+1. **Listening to a Method by Name**: You can pass the name of the method you want to listen to as a string. When that method completes, the listener method will be triggered.
+
+   ```python Code
+   @listen("generate_city")
+   def generate_fun_fact(self, random_city):
+       # Implementation
+   ```
+
+2. **Listening to a Method Directly**: You can pass the method itself. When that method completes, the listener method will be triggered.
+   ```python Code
+   @listen(generate_city)
+   def generate_fun_fact(self, random_city):
+       # Implementation
+   ```
+
+### Flow Output
+
+Accessing and handling the output of a Flow is essential for integrating your AI workflows into larger applications or systems. CrewAI Flows provide straightforward mechanisms to retrieve the final output, access intermediate results, and manage the overall state of your Flow.
+
+#### Retrieving the Final Output
+
+When you run a Flow, the final output is determined by the last method that completes. The `kickoff()` method returns the output of this final method.
+
+Here's how you can access the final output:
+
+<CodeGroup>
+```python Code
+from crewai.flow.flow import Flow, listen, start
+
+class OutputExampleFlow(Flow):
+    @start()
+    def first_method(self):
+        return "Output from first_method"
+
+    @listen(first_method)
+    def second_method(self, first_output):
+        return f"Second method received: {first_output}"
+
+
+flow = OutputExampleFlow()
+flow.plot("my_flow_plot")
+final_output = flow.kickoff()
+
+print("---- Final Output ----")
+print(final_output)
+```
+
+```text Output
+---- Final Output ----
+Second method received: Output from first_method
+```
+
+</CodeGroup>
+![Flow Visual image](/images/crewai-flow-2.png)
+
+In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow.
+The `kickoff()` method will return the final output, which is then printed to the console. The `plot()` method will generate the HTML file, which will help you understand the flow.
+
+#### Accessing and Updating State
+
+In addition to retrieving the final output, you can also access and update the state within your Flow. The state can be used to store and share data between different methods in the Flow. After the Flow has run, you can access the state to retrieve any information that was added or updated during the execution.
+
+Here's an example of how to update and access the state:
+
+<CodeGroup>
+
+```python Code
+from crewai.flow.flow import Flow, listen, start
+from pydantic import BaseModel
+
+class ExampleState(BaseModel):
+    counter: int = 0
+    message: str = ""
+
+class StateExampleFlow(Flow[ExampleState]):
+
+    @start()
+    def first_method(self):
+        self.state.message = "Hello from first_method"
+        self.state.counter += 1
+
+    @listen(first_method)
+    def second_method(self):
+        self.state.message += " - updated by second_method"
+        self.state.counter += 1
+        return self.state.message
+
+flow = StateExampleFlow()
+flow.plot("my_flow_plot")
+final_output = flow.kickoff()
+print(f"Final Output: {final_output}")
+print("Final State:")
+print(flow.state)
+```
+
+```text Output
+Final Output: Hello from first_method - updated by second_method
+Final State:
+counter=2 message='Hello from first_method - updated by second_method'
+```
+
+</CodeGroup>
+
+![Flow Visual image](/images/crewai-flow-2.png)
+
+In this example, the state is updated by both `first_method` and `second_method`.
+After the Flow has run, you can access the final state to see the updates made by these methods.
+
+By ensuring that the final method's output is returned and providing access to the state, CrewAI Flows make it easy to integrate the results of your AI workflows into larger applications or systems,
+while also maintaining and accessing the state throughout the Flow's execution.
+
+## Flow State Management
+
+Managing state effectively is crucial for building reliable and maintainable AI workflows. CrewAI Flows provides robust mechanisms for both unstructured and structured state management,
+allowing developers to choose the approach that best fits their application's needs.
+
+### Unstructured State Management
+
+In unstructured state management, all state is stored in the `state` attribute of the `Flow` class.
+This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema.
+Even with unstructured states, CrewAI Flows automatically generates and maintains a unique identifier (UUID) for each state instance.
+
+```python Code
+from crewai.flow.flow import Flow, listen, start
+
+class UnstructuredExampleFlow(Flow):
+
+    @start()
+    def first_method(self):
+        # The state automatically includes an 'id' field
+        print(f"State ID: {self.state['id']}")
+        self.state['counter'] = 0
+        self.state['message'] = "Hello from structured flow"
+
+    @listen(first_method)
+    def second_method(self):
+        self.state['counter'] += 1
+        self.state['message'] += " - updated"
+
+    @listen(second_method)
+    def third_method(self):
+        self.state['counter'] += 1
+        self.state['message'] += " - updated again"
+
+        print(f"State after third_method: {self.state}")
+
+
+flow = UnstructuredExampleFlow()
+flow.plot("my_flow_plot")
+flow.kickoff()
+```
+
+![Flow Visual image](/images/crewai-flow-3.png)
+
+**Note:** The `id` field is automatically generated and preserved throughout the flow's execution. You don't need to manage or set it manually, and it will be maintained even when updating the state with new data.
+
+**Key Points:**
+
+- **Flexibility:** You can dynamically add attributes to `self.state` without predefined constraints.
+- **Simplicity:** Ideal for straightforward workflows where state structure is minimal or varies significantly.
+
+### Structured State Management
+
+Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow.
+By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments.
+
+Each state in CrewAI Flows automatically receives a unique identifier (UUID) to help track and manage state instances. This ID is automatically generated and managed by the Flow system.
+
+```python Code
+from crewai.flow.flow import Flow, listen, start
+from pydantic import BaseModel
+
+
+class ExampleState(BaseModel):
+    # Note: 'id' field is automatically added to all states
+    counter: int = 0
+    message: str = ""
+
+
+class StructuredExampleFlow(Flow[ExampleState]):
+
+    @start()
+    def first_method(self):
+        # Access the auto-generated ID if needed
+        print(f"State ID: {self.state.id}")
+        self.state.message = "Hello from structured flow"
+
+    @listen(first_method)
+    def second_method(self):
+        self.state.counter += 1
+        self.state.message += " - updated"
+
+    @listen(second_method)
+    def third_method(self):
+        self.state.counter += 1
+        self.state.message += " - updated again"
+
+        print(f"State after third_method: {self.state}")
+
+
+flow = StructuredExampleFlow()
+flow.kickoff()
+```
+
+![Flow Visual image](/images/crewai-flow-3.png)
+
+**Key Points:**
+
+- **Defined Schema:** `ExampleState` clearly outlines the state structure, enhancing code readability and maintainability.
+- **Type Safety:** Leveraging Pydantic ensures that state attributes adhere to the specified types, reducing runtime errors.
+- **Auto-Completion:** IDEs can provide better auto-completion and error checking based on the defined state model.
+
+### Choosing Between Unstructured and Structured State Management
+
+- **Use Unstructured State Management when:**
+
+  - The workflow's state is simple or highly dynamic.
+  - Flexibility is prioritized over strict state definitions.
+  - Rapid prototyping is required without the overhead of defining schemas.
+
+- **Use Structured State Management when:**
+  - The workflow requires a well-defined and consistent state structure.
+  - Type safety and validation are important for your application's reliability.
+  - You want to leverage IDE features like auto-completion and type checking for better developer experience.
+
+By providing both unstructured and structured state management options, CrewAI Flows empowers developers to build AI workflows that are both flexible and robust, catering to a wide range of application requirements.
+
+## Flow Persistence
+
+The @persist decorator enables automatic state persistence in CrewAI Flows, allowing you to maintain flow state across restarts or different workflow executions. This decorator can be applied at either the class level or method level, providing flexibility in how you manage state persistence.
+
+### Class-Level Persistence
+
+When applied at the class level, the @persist decorator automatically persists all flow method states:
+
+```python
+@persist  # Using SQLiteFlowPersistence by default
+class MyFlow(Flow[MyState]):
+    @start()
+    def initialize_flow(self):
+        # This method will automatically have its state persisted
+        self.state.counter = 1
+        print("Initialized flow. State ID:", self.state.id)
+
+    @listen(initialize_flow)
+    def next_step(self):
+        # The state (including self.state.id) is automatically reloaded
+        self.state.counter += 1
+        print("Flow state is persisted. Counter:", self.state.counter)
+```
+
+### Method-Level Persistence
+
+For more granular control, you can apply @persist to specific methods:
+
+```python
+class AnotherFlow(Flow[dict]):
+    @persist  # Persists only this method's state
+    @start()
+    def begin(self):
+        if "runs" not in self.state:
+            self.state["runs"] = 0
+        self.state["runs"] += 1
+        print("Method-level persisted runs:", self.state["runs"])
+```
+
+### How It Works
+
+1. **Unique State Identification**
+   - Each flow state automatically receives a unique UUID
+   - The ID is preserved across state updates and method calls
+   - Supports both structured (Pydantic BaseModel) and unstructured (dictionary) states
+
+2. **Default SQLite Backend**
+   - SQLiteFlowPersistence is the default storage backend
+   - States are automatically saved to a local SQLite database
+   - Robust error handling ensures clear messages if database operations fail
+
+3. **Error Handling**
+   - Comprehensive error messages for database operations
+   - Automatic state validation during save and load
+   - Clear feedback when persistence operations encounter issues
+
+### Important Considerations
+
+- **State Types**: Both structured (Pydantic BaseModel) and unstructured (dictionary) states are supported
+- **Automatic ID**: The `id` field is automatically added if not present
+- **State Recovery**: Failed or restarted flows can automatically reload their previous state
+- **Custom Implementation**: You can provide your own FlowPersistence implementation for specialized storage needs
+
+### Technical Advantages
+
+1. **Precise Control Through Low-Level Access**
+   - Direct access to persistence operations for advanced use cases
+   - Fine-grained control via method-level persistence decorators
+   - Built-in state inspection and debugging capabilities
+   - Full visibility into state changes and persistence operations
+
+2. **Enhanced Reliability**
+   - Automatic state recovery after system failures or restarts
+   - Transaction-based state updates for data integrity
+   - Comprehensive error handling with clear error messages
+   - Robust validation during state save and load operations
+
+3. **Extensible Architecture**
+   - Customizable persistence backend through FlowPersistence interface
+   - Support for specialized storage solutions beyond SQLite
+   - Compatible with both structured (Pydantic) and unstructured (dict) states
+   - Seamless integration with existing CrewAI flow patterns
+
+The persistence system's architecture emphasizes technical precision and customization options, allowing developers to maintain full control over state management while benefiting from built-in reliability features.
+
+## Flow Control
+
+### Conditional Logic: `or`
+
+The `or_` function in Flows allows you to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
+
+<CodeGroup>
+
+```python Code
+from crewai.flow.flow import Flow, listen, or_, start
+
+class OrExampleFlow(Flow):
+
+    @start()
+    def start_method(self):
+        return "Hello from the start method"
+
+    @listen(start_method)
+    def second_method(self):
+        return "Hello from the second method"
+
+    @listen(or_(start_method, second_method))
+    def logger(self, result):
+        print(f"Logger: {result}")
+
+
+
+flow = OrExampleFlow()
+flow.plot("my_flow_plot")
+flow.kickoff()
+```
+
+```text Output
+Logger: Hello from the start method
+Logger: Hello from the second method
+```
+
+</CodeGroup>
+
+![Flow Visual image](/images/crewai-flow-4.png)
+
+When you run this Flow, the `logger` method will be triggered by the output of either the `start_method` or the `second_method`.
+The `or_` function is used to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
+
+### Conditional Logic: `and`
+
+The `and_` function in Flows allows you to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
+
+<CodeGroup>
+
+```python Code
+from crewai.flow.flow import Flow, and_, listen, start
+
+class AndExampleFlow(Flow):
+
+    @start()
+    def start_method(self):
+        self.state["greeting"] = "Hello from the start method"
+
+    @listen(start_method)
+    def second_method(self):
+        self.state["joke"] = "What do computers eat? Microchips."
+
+    @listen(and_(start_method, second_method))
+    def logger(self):
+        print("---- Logger ----")
+        print(self.state)
+
+flow = AndExampleFlow()
+flow.plot()
+flow.kickoff()
+```
+
+```text Output
+---- Logger ----
+{'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'}
+```
+
+</CodeGroup>
+
+![Flow Visual image](/images/crewai-flow-5.png)
+
+When you run this Flow, the `logger` method will be triggered only when both the `start_method` and the `second_method` emit an output.
+The `and_` function is used to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
+
+### Router
+
+The `@router()` decorator in Flows allows you to define conditional routing logic based on the output of a method.
+You can specify different routes based on the output of the method, allowing you to control the flow of execution dynamically.
+
+<CodeGroup>
+
+```python Code
+import random
+from crewai.flow.flow import Flow, listen, router, start
+from pydantic import BaseModel
+
+class ExampleState(BaseModel):
+    success_flag: bool = False
+
+class RouterFlow(Flow[ExampleState]):
+
+    @start()
+    def start_method(self):
+        print("Starting the structured flow")
+        random_boolean = random.choice([True, False])
+        self.state.success_flag = random_boolean
+
+    @router(start_method)
+    def second_method(self):
+        if self.state.success_flag:
+            return "success"
+        else:
+            return "failed"
+
+    @listen("success")
+    def third_method(self):
+        print("Third method running")
+
+    @listen("failed")
+    def fourth_method(self):
+        print("Fourth method running")
+
+
+flow = RouterFlow()
+flow.plot("my_flow_plot")
+flow.kickoff()
+```
+
+```text Output
+Starting the structured flow
+Third method running
+Fourth method running
+```
+
+</CodeGroup>
+
+![Flow Visual image](/images/crewai-flow-6.png)
+
+In the above example, the `start_method` generates a random boolean value and sets it in the state.
+The `second_method` uses the `@router()` decorator to define conditional routing logic based on the value of the boolean.
+If the boolean is `True`, the method returns `"success"`, and if it is `False`, the method returns `"failed"`.
+The `third_method` and `fourth_method` listen to the output of the `second_method` and execute based on the returned value.
+
+When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
+
+## Adding Agents to Flows
+
+Agents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use an Agent within a flow to perform market research:
+
+```python
+import asyncio
+from typing import Any, Dict, List
+
+from crewai_tools import SerperDevTool
+from pydantic import BaseModel, Field
+
+from crewai.agent import Agent
+from crewai.flow.flow import Flow, listen, start
+
+
+# Define a structured output format
+class MarketAnalysis(BaseModel):
+    key_trends: List[str] = Field(description="List of identified market trends")
+    market_size: str = Field(description="Estimated market size")
+    competitors: List[str] = Field(description="Major competitors in the space")
+
+
+# Define flow state
+class MarketResearchState(BaseModel):
+    product: str = ""
+    analysis: MarketAnalysis | None = None
+
+
+# Create a flow class
+class MarketResearchFlow(Flow[MarketResearchState]):
+    @start()
+    def initialize_research(self) -> Dict[str, Any]:
+        print(f"Starting market research for {self.state.product}")
+        return {"product": self.state.product}
+
+    @listen(initialize_research)
+    async def analyze_market(self) -> Dict[str, Any]:
+        # Create an Agent for market research
+        analyst = Agent(
+            role="Market Research Analyst",
+            goal=f"Analyze the market for {self.state.product}",
+            backstory="You are an experienced market analyst with expertise in "
+            "identifying market trends and opportunities.",
+            tools=[SerperDevTool()],
+            verbose=True,
+        )
+
+        # Define the research query
+        query = f"""
+        Research the market for {self.state.product}. Include:
+        1. Key market trends
+        2. Market size
+        3. Major competitors
+
+        Format your response according to the specified structure.
+        """
+
+        # Execute the analysis with structured output format
+        result = await analyst.kickoff_async(query, response_format=MarketAnalysis)
+        if result.pydantic:
+            print("result", result.pydantic)
+        else:
+            print("result", result)
+
+        # Return the analysis to update the state
+        return {"analysis": result.pydantic}
+
+    @listen(analyze_market)
+    def present_results(self, analysis) -> None:
+        print("\nMarket Analysis Results")
+        print("=====================")
+
+        if isinstance(analysis, dict):
+            # If we got a dict with 'analysis' key, extract the actual analysis object
+            market_analysis = analysis.get("analysis")
+        else:
+            market_analysis = analysis
+
+        if market_analysis and isinstance(market_analysis, MarketAnalysis):
+            print("\nKey Market Trends:")
+            for trend in market_analysis.key_trends:
+                print(f"- {trend}")
+
+            print(f"\nMarket Size: {market_analysis.market_size}")
+
+            print("\nMajor Competitors:")
+            for competitor in market_analysis.competitors:
+                print(f"- {competitor}")
+        else:
+            print("No structured analysis data available.")
+            print("Raw analysis:", analysis)
+
+
+# Usage example
+async def run_flow():
+    flow = MarketResearchFlow()
+    flow.plot("MarketResearchFlowPlot")
+    result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
+    return result
+
+
+# Run the flow
+if __name__ == "__main__":
+    asyncio.run(run_flow())
+```
+
+![Flow Visual image](/images/crewai-flow-7.png)
+
+This example demonstrates several key features of using Agents in flows:
+
+1. **Structured Output**: Using Pydantic models to define the expected output format (`MarketAnalysis`) ensures type safety and structured data throughout the flow.
+
+2. **State Management**: The flow state (`MarketResearchState`) maintains context between steps and stores both inputs and outputs.
+
+3. **Tool Integration**: Agents can use tools (like `WebsiteSearchTool`) to enhance their capabilities.
+
+## Adding Crews to Flows
+
+Creating a flow with multiple crews in CrewAI is straightforward.
+
+You can generate a new CrewAI project that includes all the scaffolding needed to create a flow with multiple crews by running the following command:
+
+```bash
+crewai create flow name_of_flow
+```
+
+This command will generate a new CrewAI project with the necessary folder structure. The generated project includes a prebuilt crew called `poem_crew` that is already working. You can use this crew as a template by copying, pasting, and editing it to create other crews.
+
+### Folder Structure
+
+After running the `crewai create flow name_of_flow` command, you will see a folder structure similar to the following:
+
+| Directory/File         | Description                                                        |
+| :--------------------- | :----------------------------------------------------------------- |
+| `name_of_flow/`        | Root directory for the flow.                                       |
+| ├── `crews/`           | Contains directories for specific crews.                           |
+| │ └── `poem_crew/`     | Directory for the "poem_crew" with its configurations and scripts. |
+| │ ├── `config/`        | Configuration files directory for the "poem_crew".                 |
+| │ │ ├── `agents.yaml`  | YAML file defining the agents for "poem_crew".                     |
+| │ │ └── `tasks.yaml`   | YAML file defining the tasks for "poem_crew".                      |
+| │ ├── `poem_crew.py`   | Script for "poem_crew" functionality.                              |
+| ├── `tools/`           | Directory for additional tools used in the flow.                   |
+| │ └── `custom_tool.py` | Custom tool implementation.                                        |
+| ├── `main.py`          | Main script for running the flow.                                  |
+| ├── `README.md`        | Project description and instructions.                              |
+| ├── `pyproject.toml`   | Configuration file for project dependencies and settings.          |
+| └── `.gitignore`       | Specifies files and directories to ignore in version control.      |
+
+### Building Your Crews
+
+In the `crews` folder, you can define multiple crews. Each crew will have its own folder containing configuration files and the crew definition file. For example, the `poem_crew` folder contains:
+
+- `config/agents.yaml`: Defines the agents for the crew.
+- `config/tasks.yaml`: Defines the tasks for the crew.
+- `poem_crew.py`: Contains the crew definition, including agents, tasks, and the crew itself.
+
+You can copy, paste, and edit the `poem_crew` to create other crews.
+
+### Connecting Crews in `main.py`
+
+The `main.py` file is where you create your flow and connect the crews together. You can define your flow by using the `Flow` class and the decorators `@start` and `@listen` to specify the flow of execution.
+
+Here's an example of how you can connect the `poem_crew` in the `main.py` file:
+
+```python Code
+#!/usr/bin/env python
+from random import randint
+
+from pydantic import BaseModel
+from crewai.flow.flow import Flow, listen, start
+from .crews.poem_crew.poem_crew import PoemCrew
+
+class PoemState(BaseModel):
+    sentence_count: int = 1
+    poem: str = ""
+
+class PoemFlow(Flow[PoemState]):
+
+    @start()
+    def generate_sentence_count(self):
+        print("Generating sentence count")
+        self.state.sentence_count = randint(1, 5)
+
+    @listen(generate_sentence_count)
+    def generate_poem(self):
+        print("Generating poem")
+        result = PoemCrew().crew().kickoff(inputs={"sentence_count": self.state.sentence_count})
+
+        print("Poem generated", result.raw)
+        self.state.poem = result.raw
+
+    @listen(generate_poem)
+    def save_poem(self):
+        print("Saving poem")
+        with open("poem.txt", "w") as f:
+            f.write(self.state.poem)
+
+def kickoff():
+    poem_flow = PoemFlow()
+    poem_flow.kickoff()
+
+
+def plot():
+    poem_flow = PoemFlow()
+    poem_flow.plot("PoemFlowPlot")
+
+if __name__ == "__main__":
+    kickoff()
+    plot()
+```
+
+In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method. The PoemFlowPlot will be generated by `plot()` method.
+
+![Flow Visual image](/images/crewai-flow-8.png)
+
+### Running the Flow
+
+(Optional) Before running the flow, you can install the dependencies by running:
+
+```bash
+crewai install
+```
+
+Once all of the dependencies are installed, you need to activate the virtual environment by running:
+
+```bash
+source .venv/bin/activate
+```
+
+After activating the virtual environment, you can run the flow by executing one of the following commands:
+
+```bash
+crewai flow kickoff
+```
+
+or
+
+```bash
+uv run kickoff
+```
+
+The flow will execute, and you should see the output in the console.
+
+## Plot Flows
+
+Visualizing your AI workflows can provide valuable insights into the structure and execution paths of your flows. CrewAI offers a powerful visualization tool that allows you to generate interactive plots of your flows, making it easier to understand and optimize your AI workflows.
+
+### What are Plots?
+
+Plots in CrewAI are graphical representations of your AI workflows. They display the various tasks, their connections, and the flow of data between them. This visualization helps in understanding the sequence of operations, identifying bottlenecks, and ensuring that the workflow logic aligns with your expectations.
+
+### How to Generate a Plot
+
+CrewAI provides two convenient methods to generate plots of your flows:
+
+#### Option 1: Using the `plot()` Method
+
+If you are working directly with a flow instance, you can generate a plot by calling the `plot()` method on your flow object. This method will create an HTML file containing the interactive plot of your flow.
+
+```python Code
+# Assuming you have a flow instance
+flow.plot("my_flow_plot")
+```
+
+This will generate a file named `my_flow_plot.html` in your current directory. You can open this file in a web browser to view the interactive plot.
+
+#### Option 2: Using the Command Line
+
+If you are working within a structured CrewAI project, you can generate a plot using the command line. This is particularly useful for larger projects where you want to visualize the entire flow setup.
+
+```bash
+crewai flow plot
+```
+
+This command will generate an HTML file with the plot of your flow, similar to the `plot()` method. The file will be saved in your project directory, and you can open it in a web browser to explore the flow.
+
+### Understanding the Plot
+
+The generated plot will display nodes representing the tasks in your flow, with directed edges indicating the flow of execution. The plot is interactive, allowing you to zoom in and out, and hover over nodes to see additional details.
+
+By visualizing your flows, you can gain a clearer understanding of the workflow's structure, making it easier to debug, optimize, and communicate your AI processes to others.
+
+### Conclusion
+
+Plotting your flows is a powerful feature of CrewAI that enhances your ability to design and manage complex AI workflows. Whether you choose to use the `plot()` method or the command line, generating plots will provide you with a visual representation of your workflows, aiding in both development and presentation.
+
+## Next Steps
+
+If you're interested in exploring additional examples of flows, we have a variety of recommendations in our examples repository. Here are four specific flow examples, each showcasing unique use cases to help you match your current problem type to a specific example:
+
+1. **Email Auto Responder Flow**: This example demonstrates an infinite loop where a background job continually runs to automate email responses. It's a great use case for tasks that need to be performed repeatedly without manual intervention. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow)
+
+2. **Lead Score Flow**: This flow showcases adding human-in-the-loop feedback and handling different conditional branches using the router. It's an excellent example of how to incorporate dynamic decision-making and human oversight into your workflows. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/lead-score-flow)
+
+3. **Write a Book Flow**: This example excels at chaining multiple crews together, where the output of one crew is used by another. Specifically, one crew outlines an entire book, and another crew generates chapters based on the outline. Eventually, everything is connected to produce a complete book. This flow is perfect for complex, multi-step processes that require coordination between different tasks. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/write_a_book_with_flows)
+
+4. **Meeting Assistant Flow**: This flow demonstrates how to broadcast one event to trigger multiple follow-up actions. For instance, after a meeting is completed, the flow can update a Trello board, send a Slack message, and save the results. It's a great example of handling multiple outcomes from a single event, making it ideal for comprehensive task management and notification systems. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow)
+
+By exploring these examples, you can gain insights into how to leverage CrewAI Flows for various use cases, from automating repetitive tasks to managing complex, multi-step processes with dynamic decision-making and human feedback.
+
+Also, check out our YouTube video on how to use flows in CrewAI below!
+
+<iframe
+  width="560"
+  height="315"
+  src="https://www.youtube.com/embed/MTb5my6VOT8"
+  title="YouTube video player"
+  frameborder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+  referrerpolicy="strict-origin-when-cross-origin"
+  allowfullscreen
+></iframe>
+
+## Running Flows
+
+There are two ways to run a flow:
+
+### Using the Flow API
+
+You can run a flow programmatically by creating an instance of your flow class and calling the `kickoff()` method:
+
+```python
+flow = ExampleFlow()
+result = flow.kickoff()
+```
+
+### Using the CLI
+
+Starting from version 0.103.0, you can run flows using the `crewai run` command:
+
+```shell
+crewai run
+```
+
+This command automatically detects if your project is a flow (based on the `type = "flow"` setting in your pyproject.toml) and runs it accordingly. This is the recommended way to run flows from the command line.
+
+For backward compatibility, you can also use:
+
+```shell
+crewai flow kickoff
+```
+
+However, the `crewai run` command is now the preferred method as it works for both crews and flows.

docs/concepts/llms.mdx

@@ -0,0 +1,887 @@
+---
+title: 'LLMs'
+description: 'A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects'
+icon: 'microchip-ai'
+---
+
+## Overview
+
+CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
+
+
+## What are LLMs?
+
+Large Language Models (LLMs) are the core intelligence behind CrewAI agents. They enable agents to understand context, make decisions, and generate human-like responses. Here's what you need to know:
+
+<CardGroup cols={2}>
+  <Card title="LLM Basics" icon="brain">
+    Large Language Models are AI systems trained on vast amounts of text data. They power the intelligence of your CrewAI agents, enabling them to understand and generate human-like text.
+  </Card>
+  <Card title="Context Window" icon="window">
+    The context window determines how much text an LLM can process at once. Larger windows (e.g., 128K tokens) allow for more context but may be more expensive and slower.
+  </Card>
+  <Card title="Temperature" icon="temperature-three-quarters">
+    Temperature (0.0 to 1.0) controls response randomness. Lower values (e.g., 0.2) produce more focused, deterministic outputs, while higher values (e.g., 0.8) increase creativity and variability.
+  </Card>
+  <Card title="Provider Selection" icon="server">
+    Each LLM provider (e.g., OpenAI, Anthropic, Google) offers different models with varying capabilities, pricing, and features. Choose based on your needs for accuracy, speed, and cost.
+  </Card>
+</CardGroup>
+
+## Setting up your LLM
+
+There are different places in CrewAI code where you can specify the model to use. Once you specify the model you are using, you will need to provide the configuration (like an API key) for each of the model providers you use. See the [provider configuration examples](#provider-configuration-examples) section for your provider.
+
+<Tabs>
+  <Tab title="1. Environment Variables">
+    The simplest way to get started. Set the model in your environment directly, through an `.env` file or in your app code. If you used `crewai create` to bootstrap your project, it will be set already.
+
+    ```bash .env
+    MODEL=model-id  # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...
+
+    # Be sure to set your API keys here too. See the Provider
+    # section below.
+    ```
+
+    <Warning>
+      Never commit API keys to version control. Use environment files (.env) or your system's secret management.
+    </Warning>
+  </Tab>
+  <Tab title="2. YAML Configuration">
+    Create a YAML file to define your agent configurations. This method is great for version control and team collaboration:
+
+    ```yaml agents.yaml {6}
+    researcher:
+        role: Research Specialist
+        goal: Conduct comprehensive research and analysis
+        backstory: A dedicated research professional with years of experience
+        verbose: true
+        llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+        # (see provider configuration examples below for more)
+    ```
+
+    <Info>
+      The YAML configuration allows you to:
+      - Version control your agent settings
+      - Easily switch between different models
+      - Share configurations across team members
+      - Document model choices and their purposes
+    </Info>
+  </Tab>
+  <Tab title="3. Direct Code">
+    For maximum flexibility, configure LLMs directly in your Python code:
+
+    ```python {4,8}
+    from crewai import LLM
+
+    # Basic configuration
+    llm = LLM(model="model-id-here")  # gpt-4o, gemini-2.0-flash, anthropic/claude...
+
+    # Advanced configuration with detailed parameters
+    llm = LLM(
+        model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
+        temperature=0.7,        # Higher for more creative outputs
+        timeout=120,            # Seconds to wait for response
+        max_tokens=4000,        # Maximum length of response
+        top_p=0.9,              # Nucleus sampling parameter
+        frequency_penalty=0.1 , # Reduce repetition
+        presence_penalty=0.1,   # Encourage topic diversity
+        response_format={"type": "json"},  # For structured outputs
+        seed=42                 # For reproducible results
+    )
+    ```
+
+    <Info>
+      Parameter explanations:
+      - `temperature`: Controls randomness (0.0-1.0)
+      - `timeout`: Maximum wait time for response
+      - `max_tokens`: Limits response length
+      - `top_p`: Alternative to temperature for sampling
+      - `frequency_penalty`: Reduces word repetition
+      - `presence_penalty`: Encourages new topics
+      - `response_format`: Specifies output structure
+      - `seed`: Ensures consistent outputs
+    </Info>
+  </Tab>
+</Tabs>
+
+## Provider Configuration Examples
+
+CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
+In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.
+
+<AccordionGroup>
+  <Accordion title="OpenAI">
+    Set the following environment variables in your `.env` file:
+
+    ```toml Code
+    # Required
+    OPENAI_API_KEY=sk-...
+
+    # Optional
+    OPENAI_API_BASE=<custom-base-url>
+    OPENAI_ORGANIZATION=<your-org-id>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="openai/gpt-4", # call model by provider/model_name
+        temperature=0.8,
+        max_tokens=150,
+        top_p=0.9,
+        frequency_penalty=0.1,
+        presence_penalty=0.1,
+        stop=["END"],
+        seed=42
+    )
+    ```
+
+    OpenAI is one of the leading providers of LLMs with a wide range of models and features.
+
+    | Model               | Context Window   | Best For                                      |
+    |---------------------|------------------|-----------------------------------------------|
+    | GPT-4               | 8,192 tokens     | High-accuracy tasks, complex reasoning        |
+    | GPT-4 Turbo         | 128,000 tokens   | Long-form content, document analysis          |
+    | GPT-4o & GPT-4o-mini  | 128,000 tokens  | Cost-effective large context processing       |
+    | o3-mini             | 200,000 tokens   | Fast reasoning, complex reasoning             |
+    | o1-mini             | 128,000 tokens   | Fast reasoning, complex reasoning             |
+    | o1-preview          | 128,000 tokens   | Fast reasoning, complex reasoning             |
+    | o1                  | 200,000 tokens   | Fast reasoning, complex reasoning             |
+  </Accordion>
+
+  <Accordion title="Meta-Llama">
+    Meta's Llama API provides access to Meta's family of large language models.
+    The API is available through the [Meta Llama API](https://llama.developer.meta.com?utm_source=partner-crewai&utm_medium=website).
+    Set the following environment variables in your `.env` file:
+
+    ```toml Code
+    # Meta Llama API Key Configuration
+    LLAMA_API_KEY=LLM|your_api_key_here
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    from crewai import LLM
+
+    # Initialize Meta Llama LLM
+    llm = LLM(
+        model="meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8",
+        temperature=0.8,
+        stop=["END"],
+        seed=42
+    )
+    ```
+
+    All models listed here https://llama.developer.meta.com/docs/models/ are supported.
+
+    | Model ID | Input context length | Output context length | Input Modalities | Output Modalities |
+    | --- | --- | --- | --- | --- |
+    | `meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8` | 128k | 4028 | Text, Image | Text |
+    | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Text, Image | Text |
+    | `meta_llama/Llama-3.3-70B-Instruct` | 128k | 4028 | Text | Text |
+    | `meta_llama/Llama-3.3-8B-Instruct` | 128k | 4028 | Text | Text |
+  </Accordion>
+
+  <Accordion title="Anthropic">
+    ```toml Code
+    # Required
+    ANTHROPIC_API_KEY=sk-ant-...
+
+    # Optional
+    ANTHROPIC_API_BASE=<custom-base-url>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="anthropic/claude-3-sonnet-20240229-v1:0",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Google (Gemini API)">
+    Set your API key in your `.env` file. If you need a key, or need to find an
+    existing key, check [AI Studio](https://aistudio.google.com/apikey).
+
+    ```toml .env
+    # https://ai.google.dev/gemini-api/docs/api-key
+    GEMINI_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-2.0-flash",
+        temperature=0.7,
+    )
+    ```
+
+    ### Gemini models
+
+    Google offers a range of powerful models optimized for different use cases.
+
+    | Model                          | Context Window | Best For                                                          |
+    |--------------------------------|----------------|-------------------------------------------------------------------|
+    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
+    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
+    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
+    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
+    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
+    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
+    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+
+    The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
+
+    ### Gemma
+
+    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
+
+    | Model          | Context Window |
+    |----------------|----------------|
+    | gemma-3-1b-it  | 32k tokens     |
+    | gemma-3-4b-it  | 32k tokens     |
+    | gemma-3-12b-it | 32k tokens     |
+    | gemma-3-27b-it | 128k tokens    |
+
+  </Accordion>
+  <Accordion title="Google (Vertex AI)">
+    Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
+    ```python Code
+    import json
+
+    file_path = 'path/to/vertex_ai_service_account.json'
+
+    # Load the JSON file
+    with open(file_path, 'r') as file:
+        vertex_credentials = json.load(file)
+
+    # Convert the credentials to a JSON string
+    vertex_credentials_json = json.dumps(vertex_credentials)
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-1.5-pro-latest",
+        temperature=0.7,
+        vertex_credentials=vertex_credentials_json
+    )
+    ```
+
+    Google offers a range of powerful models optimized for different use cases:
+
+    | Model                          | Context Window | Best For                                                          |
+    |--------------------------------|----------------|-------------------------------------------------------------------|
+    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
+    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
+    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
+    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
+    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
+    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
+    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+  </Accordion>
+
+  <Accordion title="Azure">
+    ```toml Code
+    # Required
+    AZURE_API_KEY=<your-api-key>
+    AZURE_API_BASE=<your-resource-url>
+    AZURE_API_VERSION=<api-version>
+
+    # Optional
+    AZURE_AD_TOKEN=<your-azure-ad-token>
+    AZURE_API_TYPE=<your-azure-api-type>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="azure/gpt-4",
+        api_version="2023-05-15"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="AWS Bedrock">
+    ```toml Code
+    AWS_ACCESS_KEY_ID=<your-access-key>
+    AWS_SECRET_ACCESS_KEY=<your-secret-key>
+    AWS_DEFAULT_REGION=<your-region>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
+    )
+    ```
+
+    Before using Amazon Bedrock, make sure you have boto3 installed in your environment
+
+    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API, enabling secure and responsible AI application development.
+
+    | Model                   | Context Window       | Best For                                                          |
+    |-------------------------|----------------------|-------------------------------------------------------------------|
+    | Amazon Nova Pro         | Up to 300k tokens    | High-performance, model balancing accuracy, speed, and cost-effectiveness across diverse tasks. |
+    | Amazon Nova Micro       | Up to 128k tokens    | High-performance, cost-effective text-only model optimized for lowest latency responses. |
+    | Amazon Nova Lite        | Up to 300k tokens    | High-performance, affordable multimodal processing for images, video, and text with real-time capabilities. |
+    | Claude 3.7 Sonnet       | Up to 128k tokens    | High-performance, best for complex reasoning, coding & AI agents |
+    | Claude 3.5 Sonnet v2    | Up to 200k tokens    | State-of-the-art model specialized in software engineering, agentic capabilities, and computer interaction at optimized cost. |
+    | Claude 3.5 Sonnet       | Up to 200k tokens    | High-performance model delivering superior intelligence and reasoning across diverse tasks with optimal speed-cost balance. |
+    | Claude 3.5 Haiku        | Up to 200k tokens    | Fast, compact multimodal model optimized for quick responses and seamless human-like interactions |
+    | Claude 3 Sonnet         | Up to 200k tokens    | Multimodal model balancing intelligence and speed for high-volume deployments. |
+    | Claude 3 Haiku          | Up to 200k tokens    | Compact, high-speed multimodal model optimized for quick responses and natural conversational interactions |
+    | Claude 3 Opus           | Up to 200k tokens    | Most advanced multimodal model exceling at complex tasks with human-like reasoning and superior contextual understanding. |
+    | Claude 2.1              | Up to 200k tokens    | Enhanced version with expanded context window, improved reliability, and reduced hallucinations for long-form and RAG applications |
+    | Claude                  | Up to 100k tokens    | Versatile model excelling in sophisticated dialogue, creative content, and precise instruction following. |
+    | Claude Instant          | Up to 100k tokens    | Fast, cost-effective model for everyday tasks like dialogue, analysis, summarization, and document Q&A |
+    | Llama 3.1 405B Instruct | Up to 128k tokens    | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
+    | Llama 3.1 70B Instruct  | Up to 128k tokens    | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
+    | Llama 3.1 8B Instruct   | Up to 128k tokens    | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
+    | Llama 3 70B Instruct    | Up to 8k tokens      | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
+    | Llama 3 8B Instruct     | Up to 8k tokens      | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
+    | Titan Text G1 - Lite    | Up to 4k tokens      | Lightweight, cost-effective model optimized for English tasks and fine-tuning with focus on summarization and content generation. |
+    | Titan Text G1 - Express | Up to 8k tokens      | Versatile model for general language tasks, chat, and RAG applications with support for English and 100+ languages. |
+    | Cohere Command          | Up to 4k tokens      | Model specialized in following user commands and delivering practical enterprise solutions. |
+    | Jurassic-2 Mid          | Up to 8,191 tokens   | Cost-effective model balancing quality and affordability for diverse language tasks like Q&A, summarization, and content generation. |
+    | Jurassic-2 Ultra        | Up to 8,191 tokens   | Model for advanced text generation and comprehension, excelling in complex tasks like analysis and content creation. |
+    | Jamba-Instruct          | Up to 256k tokens    | Model with extended context window optimized for cost-effective text generation, summarization, and Q&A. |
+    | Mistral 7B Instruct     | Up to 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
+    | Mistral 8x7B Instruct   | Up to 32k tokens     | An MOE LLM that follows instructions, completes requests, and generates creative text. |
+
+  </Accordion>
+
+  <Accordion title="Amazon SageMaker">
+    ```toml Code
+    AWS_ACCESS_KEY_ID=<your-access-key>
+    AWS_SECRET_ACCESS_KEY=<your-secret-key>
+    AWS_DEFAULT_REGION=<your-region>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="sagemaker/<my-endpoint>"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Mistral">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    MISTRAL_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="mistral/mistral-large-latest",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Nvidia NIM">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    NVIDIA_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="nvidia_nim/meta/llama3-70b-instruct",
+        temperature=0.7
+    )
+    ```
+
+    Nvidia NIM provides a comprehensive suite of models for various use cases, from general-purpose tasks to specialized applications.
+
+    | Model                                                                   | Context Window | Best For                                                          |
+    |-------------------------------------------------------------------------|----------------|-------------------------------------------------------------------|
+    | nvidia/mistral-nemo-minitron-8b-8k-instruct                              | 8,192 tokens   | State-of-the-art small language model delivering superior accuracy for chatbot, virtual assistants, and content generation. |
+    | nvidia/nemotron-4-mini-hindi-4b-instruct                                 | 4,096 tokens   | A bilingual Hindi-English SLM for on-device inference, tailored specifically for Hindi Language. |
+    | nvidia/llama-3.1-nemotron-70b-instruct                                  | 128k tokens    | Customized for enhanced helpfulness in responses                  |
+    | nvidia/llama3-chatqa-1.5-8b                                                | 128k tokens    | Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines. |
+    | nvidia/llama3-chatqa-1.5-70b                                               | 128k tokens    | Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines. |
+    | nvidia/vila                                                             | 128k tokens    | Multi-modal vision-language model that understands text/img/video and creates informative responses |
+    | nvidia/neva-22                                                          | 4,096 tokens   | Multi-modal vision-language model that understands text/images and generates informative responses |
+    | nvidia/nemotron-mini-4b-instruct                                         | 8,192 tokens   | General-purpose tasks |
+    | nvidia/usdcode-llama3-70b-instruct                                       | 128k tokens    | State-of-the-art LLM that answers OpenUSD knowledge queries and generates USD-Python code. |
+    | nvidia/nemotron-4-340b-instruct                                          | 4,096 tokens   | Creates diverse synthetic data that mimics the characteristics of real-world data. |
+    | meta/codellama-70b                                                      | 100k tokens    | LLM capable of generating code from natural language and vice versa. |
+    | meta/llama2-70b                                                         | 4,096 tokens   | Cutting-edge large language AI model capable of generating text and code in response to prompts. |
+    | meta/llama3-8b-instruct                                                | 8,192 tokens   | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
+    | meta/llama3-70b-instruct                                               | 8,192 tokens   | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
+    | meta/llama-3.1-8b-instruct                                             | 128k tokens    | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
+    | meta/llama-3.1-70b-instruct                                            | 128k tokens    | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
+    | meta/llama-3.1-405b-instruct                                           | 128k tokens    | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
+    | meta/llama-3.2-1b-instruct                                             | 128k tokens    | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
+    | meta/llama-3.2-3b-instruct                                             | 128k tokens    | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
+    | meta/llama-3.2-11b-vision-instruct                                     | 128k tokens    | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
+    | meta/llama-3.2-90b-vision-instruct                                     | 128k tokens    | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
+    | google/gemma-7b                                                        | 8,192 tokens   | Cutting-edge text generation model text understanding, transformation, and code generation. |
+    | google/gemma-2b                                                        | 8,192 tokens   | Cutting-edge text generation model text understanding, transformation, and code generation. |
+    | google/codegemma-7b                                                    | 8,192 tokens   | Cutting-edge model built on Google's Gemma-7B specialized for code generation and code completion. |
+    | google/codegemma-1.1-7b                                               | 8,192 tokens   | Advanced programming model for code generation, completion, reasoning, and instruction following. |
+    | google/recurrentgemma-2b                                              | 8,192 tokens   | Novel recurrent architecture based language model for faster inference when generating long sequences. |
+    | google/gemma-2-9b-it                                                  | 8,192 tokens   | Cutting-edge text generation model text understanding, transformation, and code generation. |
+    | google/gemma-2-27b-it                                                 | 8,192 tokens   | Cutting-edge text generation model text understanding, transformation, and code generation. |
+    | google/gemma-2-2b-it                                                  | 8,192 tokens   | Cutting-edge text generation model text understanding, transformation, and code generation. |
+    | google/deplot                                                         | 512 tokens     | One-shot visual language understanding model that translates images of plots into tables. |
+    | google/paligemma                                                      | 8,192 tokens   | Vision language model adept at comprehending text and visual inputs to produce informative responses. |
+    | mistralai/mistral-7b-instruct-v0.2                                   | 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
+    | mistralai/mixtral-8x7b-instruct-v0.1                                 | 8,192 tokens   | An MOE LLM that follows instructions, completes requests, and generates creative text. |
+    | mistralai/mistral-large                                              | 4,096 tokens   | Creates diverse synthetic data that mimics the characteristics of real-world data. |
+    | mistralai/mixtral-8x22b-instruct-v0.1                               | 8,192 tokens   | Creates diverse synthetic data that mimics the characteristics of real-world data. |
+    | mistralai/mistral-7b-instruct-v0.3                                  | 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
+    | nv-mistralai/mistral-nemo-12b-instruct                              | 128k tokens    | Most advanced language model for reasoning, code, multilingual tasks; runs on a single GPU. |
+    | mistralai/mamba-codestral-7b-v0.1                                   | 256k tokens    | Model for writing and interacting with code across a wide range of programming languages and tasks. |
+    | microsoft/phi-3-mini-128k-instruct                                  | 128K tokens    | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3-mini-4k-instruct                                    | 4,096 tokens   | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3-small-8k-instruct                                   | 8,192 tokens   | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3-small-128k-instruct                                 | 128K tokens    | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3-medium-4k-instruct                                  | 4,096 tokens   | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3-medium-128k-instruct                                | 128K tokens    | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
+    | microsoft/phi-3.5-mini-instruct                                     | 128K tokens    | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
+    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecture to deliver compute efficient content generation |
+    | microsoft/kosmos-2                                                  | 1,024 tokens   | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
+    | microsoft/phi-3-vision-128k-instruct                               | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
+    | microsoft/phi-3.5-vision-instruct                                  | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
+    | databricks/dbrx-instruct                                           | 12k tokens     | A general-purpose LLM with state-of-the-art performance in language understanding, coding, and RAG. |
+    | snowflake/arctic                                                   | 1,024 tokens   | Delivers high efficiency inference for enterprise applications focused on SQL generation and coding. |
+    | aisingapore/sea-lion-7b-instruct                                  | 4,096 tokens   | LLM to represent and serve the linguistic and cultural diversity of Southeast Asia |
+    | ibm/granite-8b-code-instruct                                      | 4,096 tokens   | Software programming LLM for code generation, completion, explanation, and multi-turn conversion. |
+    | ibm/granite-34b-code-instruct                                     | 8,192 tokens   | Software programming LLM for code generation, completion, explanation, and multi-turn conversion. |
+    | ibm/granite-3.0-8b-instruct                                       | 4,096 tokens   | Advanced Small Language Model supporting RAG, summarization, classification, code, and agentic AI |
+    | ibm/granite-3.0-3b-a800m-instruct                                | 4,096 tokens   | Highly efficient Mixture of Experts model for RAG, summarization, entity extraction, and classification |
+    | mediatek/breeze-7b-instruct                                       | 4,096 tokens   | Creates diverse synthetic data that mimics the characteristics of real-world data. |
+    | upstage/solar-10.7b-instruct                                      | 4,096 tokens   | Excels in NLP tasks, particularly in instruction-following, reasoning, and mathematics. |
+    | writer/palmyra-med-70b-32k                                        | 32k tokens     | Leading LLM for accurate, contextually relevant responses in the medical domain. |
+    | writer/palmyra-med-70b                                            | 32k tokens     | Leading LLM for accurate, contextually relevant responses in the medical domain. |
+    | writer/palmyra-fin-70b-32k                                        | 32k tokens     | Specialized LLM for financial analysis, reporting, and data processing |
+    | 01-ai/yi-large                                                    | 32k tokens     | Powerful model trained on English and Chinese for diverse tasks including chatbot and creative writing. |
+    | deepseek-ai/deepseek-coder-6.7b-instruct                         | 2k tokens      | Powerful coding model offering advanced capabilities in code generation, completion, and infilling |
+    | rakuten/rakutenai-7b-instruct                                     | 1,024 tokens   | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
+    | rakuten/rakutenai-7b-chat                                         | 1,024 tokens   | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
+    | baichuan-inc/baichuan2-13b-chat                                  | 4,096 tokens   | Support Chinese and English chat, coding, math, instruction following, solving quizzes |
+  </Accordion>
+
+  <Accordion title="Local NVIDIA NIM Deployed using WSL2">
+
+    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
+    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services.
+    Perfect for development, testing, or production scenarios where data privacy or offline capabilities are required.
+
+    Here is a step-by-step guide to setting up a local NVIDIA NIM model:
+
+    1. Follow installation instructions from [NVIDIA Website](https://docs.nvidia.com/nim/wsl2/latest/getting-started.html)
+
+    2. Install the local model. For Llama 3.1-8b follow [instructions](https://build.nvidia.com/meta/llama-3_1-8b-instruct/deploy)
+
+    3. Configure your crewai local models:
+
+    ```python Code
+    from crewai.llm import LLM
+
+    local_nvidia_nim_llm = LLM(
+        model="openai/meta/llama-3.1-8b-instruct", # it's an openai-api compatible model
+        base_url="http://localhost:8000/v1",
+        api_key="<your_api_key|any text if you have not configured it>", # api_key is required, but you can use any text
+    )
+
+    # Then you can use it in your crew:
+
+    @CrewBase
+    class MyCrew():
+        # ...
+
+        @agent
+        def researcher(self) -> Agent:
+            return Agent(
+                config=self.agents_config['researcher'], # type: ignore[index]
+                llm=local_nvidia_nim_llm
+            )
+
+        # ...
+    ```
+  </Accordion>
+
+  <Accordion title="Groq">
+    Set the following environment variables in your `.env` file:
+
+    ```toml Code
+    GROQ_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="groq/llama-3.2-90b-text-preview",
+        temperature=0.7
+    )
+    ```
+    | Model             | Context Window   | Best For                                   |
+    |-------------------|------------------|--------------------------------------------|
+    | Llama 3.1 70B/8B  | 131,072 tokens   | High-performance, large context tasks      |
+    | Llama 3.2 Series  | 8,192 tokens     | General-purpose tasks                      |
+    | Mixtral 8x7B      | 32,768 tokens    | Balanced performance and context           |
+  </Accordion>
+
+  <Accordion title="IBM watsonx.ai">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    # Required
+    WATSONX_URL=<your-url>
+    WATSONX_APIKEY=<your-apikey>
+    WATSONX_PROJECT_ID=<your-project-id>
+
+    # Optional
+    WATSONX_TOKEN=<your-token>
+    WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="watsonx/meta-llama/llama-3-1-70b-instruct",
+        base_url="https://api.watsonx.ai/v1"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Ollama (Local LLMs)">
+    1. Install Ollama: [ollama.ai](https://ollama.ai/)
+    2. Run a model: `ollama run llama3`
+    3. Configure:
+
+    ```python Code
+    llm = LLM(
+        model="ollama/llama3:70b",
+        base_url="http://localhost:11434"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Fireworks AI">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    FIREWORKS_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
+        temperature=0.7
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Perplexity AI">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    PERPLEXITY_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="llama-3.1-sonar-large-128k-online",
+        base_url="https://api.perplexity.ai/"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="Hugging Face">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    HF_TOKEN=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct"
+    )
+    ```
+  </Accordion>
+
+  <Accordion title="SambaNova">
+    Set the following environment variables in your `.env` file:
+
+    ```toml Code
+    SAMBANOVA_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="sambanova/Meta-Llama-3.1-8B-Instruct",
+        temperature=0.7
+    )
+    ```
+    | Model              | Context Window         | Best For                                     |
+    |--------------------|------------------------|----------------------------------------------|
+    | Llama 3.1 70B/8B   | Up to 131,072 tokens   | High-performance, large context tasks        |
+    | Llama 3.1 405B     | 8,192 tokens           | High-performance and output quality          |
+    | Llama 3.2 Series   | 8,192 tokens           | General-purpose, multimodal tasks            |
+    | Llama 3.3 70B      | Up to 131,072 tokens   | High-performance and output quality          |
+    | Qwen2 familly      | 8,192 tokens           | High-performance and output quality          |
+  </Accordion>
+
+  <Accordion title="Cerebras">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    # Required
+    CEREBRAS_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="cerebras/llama3.1-70b",
+        temperature=0.7,
+        max_tokens=8192
+    )
+    ```
+
+    <Info>
+      Cerebras features:
+      - Fast inference speeds
+      - Competitive pricing
+      - Good balance of speed and quality
+      - Support for long context windows
+    </Info>
+  </Accordion>
+
+  <Accordion title="Open Router">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    OPENROUTER_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="openrouter/deepseek/deepseek-r1",
+        base_url="https://openrouter.ai/api/v1",
+        api_key=OPENROUTER_API_KEY
+    )
+    ```
+
+    <Info>
+      Open Router models:
+      - openrouter/deepseek/deepseek-r1
+      - openrouter/deepseek/deepseek-chat
+    </Info>
+  </Accordion>
+</AccordionGroup>
+
+## Streaming Responses
+
+CrewAI supports streaming responses from LLMs, allowing your application to receive and process outputs in real-time as they're generated.
+
+<Tabs>
+  <Tab title="Basic Setup">
+    Enable streaming by setting the `stream` parameter to `True` when initializing your LLM:
+
+    ```python
+    from crewai import LLM
+
+    # Create an LLM with streaming enabled
+    llm = LLM(
+        model="openai/gpt-4o",
+        stream=True  # Enable streaming
+    )
+    ```
+
+    When streaming is enabled, responses are delivered in chunks as they're generated, creating a more responsive user experience.
+  </Tab>
+
+  <Tab title="Event Handling">
+    CrewAI emits events for each chunk received during streaming:
+
+    ```python
+    from crewai.utilities.events import (
+      LLMStreamChunkEvent
+    )
+    from crewai.utilities.events.base_event_listener import BaseEventListener
+
+    class MyCustomListener(BaseEventListener):
+        def setup_listeners(self, crewai_event_bus):
+            @crewai_event_bus.on(LLMStreamChunkEvent)
+            def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
+              # Process each chunk as it arrives
+              print(f"Received chunk: {event.chunk}")
+
+    my_listener = MyCustomListener()
+    ```
+
+    <Tip>
+      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details 
+    </Tip>
+  </Tab>
+</Tabs>
+
+## Structured LLM Calls
+
+CrewAI supports structured responses from LLM calls by allowing you to define a `response_format` using a Pydantic model. This enables the framework to automatically parse and validate the output, making it easier to integrate the response into your application without manual post-processing.
+
+For example, you can define a Pydantic model to represent the expected response structure and pass it as the `response_format` when instantiating the LLM. The model will then be used to convert the LLM output into a structured Python object.
+
+```python Code
+from crewai import LLM
+
+class Dog(BaseModel):
+    name: str
+    age: int
+    breed: str
+
+
+llm = LLM(model="gpt-4o", response_format=Dog)
+
+response = llm.call(
+    "Analyze the following messages and return the name, age, and breed. "
+    "Meet Kona! She is 3 years old and is a black german shepherd."
+)
+print(response)
+
+# Output:
+# Dog(name='Kona', age=3, breed='black german shepherd')
+```
+
+## Advanced Features and Optimization
+
+Learn how to get the most out of your LLM configuration:
+
+<AccordionGroup>
+  <Accordion title="Context Window Management">
+    CrewAI includes smart context management features:
+
+    ```python
+    from crewai import LLM
+
+    # CrewAI automatically handles:
+    # 1. Token counting and tracking
+    # 2. Content summarization when needed
+    # 3. Task splitting for large contexts
+
+    llm = LLM(
+        model="gpt-4",
+        max_tokens=4000,  # Limit response length
+    )
+    ```
+
+    <Info>
+      Best practices for context management:
+      1. Choose models with appropriate context windows
+      2. Pre-process long inputs when possible
+      3. Use chunking for large documents
+      4. Monitor token usage to optimize costs
+    </Info>
+  </Accordion>
+
+  <Accordion title="Performance Optimization">
+    <Steps>
+      <Step title="Token Usage Optimization">
+        Choose the right context window for your task:
+        - Small tasks (up to 4K tokens): Standard models
+        - Medium tasks (between 4K-32K): Enhanced models
+        - Large tasks (over 32K): Large context models
+
+        ```python
+        # Configure model with appropriate settings
+        llm = LLM(
+            model="openai/gpt-4-turbo-preview",
+            temperature=0.7,    # Adjust based on task
+            max_tokens=4096,    # Set based on output needs
+            timeout=300        # Longer timeout for complex tasks
+        )
+        ```
+        <Tip>
+          - Lower temperature (0.1 to 0.3) for factual responses
+          - Higher temperature (0.7 to 0.9) for creative tasks
+        </Tip>
+      </Step>
+
+      <Step title="Best Practices">
+        1. Monitor token usage
+        2. Implement rate limiting
+        3. Use caching when possible
+        4. Set appropriate max_tokens limits
+      </Step>
+    </Steps>
+
+    <Info>
+      Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
+    </Info>
+  </Accordion>
+  
+  <Accordion title="Drop Additional Parameters">
+    CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
+    For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:
+
+    ```python
+    from crewai import LLM
+    import os
+
+    os.environ["OPENAI_API_KEY"] = "<api-key>"
+
+    o3_llm = LLM(
+        model="o3",
+        drop_params=True,
+        additional_drop_params=["stop"]
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Issues and Solutions
+
+<Tabs>
+  <Tab title="Authentication">
+    <Warning>
+      Most authentication issues can be resolved by checking API key format and environment variable names.
+    </Warning>
+
+    ```bash
+    # OpenAI
+    OPENAI_API_KEY=sk-...
+
+    # Anthropic
+    ANTHROPIC_API_KEY=sk-ant-...
+    ```
+  </Tab>
+  <Tab title="Model Names">
+    <Check>
+      Always include the provider prefix in model names
+    </Check>
+
+    ```python
+    # Correct
+    llm = LLM(model="openai/gpt-4")
+
+    # Incorrect
+    llm = LLM(model="gpt-4")
+    ```
+  </Tab>
+  <Tab title="Context Length">
+    <Tip>
+      Use larger context models for extensive tasks
+    </Tip>
+
+    ```python
+    # Large context model
+    llm = LLM(model="openai/gpt-4o")  # 128K tokens
+    ```
+  </Tab>
+</Tabs>

docs/concepts/memory.mdx

@@ -0,0 +1,992 @@
+---
+title: Memory
+description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
+icon: database
+---
+
+## Overview
+
+The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **three distinct memory approaches** that serve different use cases:
+
+1. **Basic Memory System** - Built-in short-term, long-term, and entity memory
+2. **User Memory** - User-specific memory with Mem0 integration (legacy approach)  
+3. **External Memory** - Standalone external memory providers (new approach)
+
+## Memory System Components
+
+| Component            | Description                                                                                                             |
+| :------------------- | :---------------------------------------------------------------------------------------------------------------------- |
+| **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.|
+| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
+| **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
+| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
+
+## 1. Basic Memory System (Recommended)
+
+The simplest and most commonly used approach. Enable memory for your crew with a single parameter:
+
+### Quick Start
+```python
+from crewai import Crew, Agent, Task, Process
+
+# Enable basic memory system
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,  # Enables short-term, long-term, and entity memory
+    verbose=True
+)
+```
+
+### How It Works
+- **Short-Term Memory**: Uses ChromaDB with RAG for current context
+- **Long-Term Memory**: Uses SQLite3 to store task results across sessions
+- **Entity Memory**: Uses RAG to track entities (people, places, concepts)
+- **Storage Location**: Platform-specific location via `appdirs` package
+- **Custom Storage Directory**: Set `CREWAI_STORAGE_DIR` environment variable
+
+## Storage Location Transparency
+
+<Info>
+**Understanding Storage Locations**: CrewAI uses platform-specific directories to store memory and knowledge files following OS conventions. Understanding these locations helps with production deployments, backups, and debugging.
+</Info>
+
+### Where CrewAI Stores Files
+
+By default, CrewAI uses the `appdirs` library to determine storage locations following platform conventions. Here's exactly where your files are stored:
+
+#### Default Storage Locations by Platform
+
+**macOS:**
+```
+~/Library/Application Support/CrewAI/{project_name}/
+├── knowledge/           # Knowledge base ChromaDB files
+├── short_term_memory/   # Short-term memory ChromaDB files  
+├── long_term_memory/    # Long-term memory ChromaDB files
+├── entities/            # Entity memory ChromaDB files
+└── long_term_memory_storage.db  # SQLite database
+```
+
+**Linux:**
+```
+~/.local/share/CrewAI/{project_name}/
+├── knowledge/
+├── short_term_memory/
+├── long_term_memory/
+├── entities/
+└── long_term_memory_storage.db
+```
+
+**Windows:**
+```
+C:\Users\{username}\AppData\Local\CrewAI\{project_name}\
+├── knowledge\
+├── short_term_memory\
+├── long_term_memory\
+├── entities\
+└── long_term_memory_storage.db
+```
+
+### Finding Your Storage Location
+
+To see exactly where CrewAI is storing files on your system:
+
+```python
+from crewai.utilities.paths import db_storage_path
+import os
+
+# Get the base storage path
+storage_path = db_storage_path()
+print(f"CrewAI storage location: {storage_path}")
+
+# List all CrewAI storage directories
+if os.path.exists(storage_path):
+    print("\nStored files and directories:")
+    for item in os.listdir(storage_path):
+        item_path = os.path.join(storage_path, item)
+        if os.path.isdir(item_path):
+            print(f"📁 {item}/")
+            # Show ChromaDB collections
+            if os.path.exists(item_path):
+                for subitem in os.listdir(item_path):
+                    print(f"   └── {subitem}")
+        else:
+            print(f"📄 {item}")
+else:
+    print("No CrewAI storage directory found yet.")
+```
+
+### Controlling Storage Locations
+
+#### Option 1: Environment Variable (Recommended)
+```python
+import os
+from crewai import Crew
+
+# Set custom storage location
+os.environ["CREWAI_STORAGE_DIR"] = "./my_project_storage"
+
+# All memory and knowledge will now be stored in ./my_project_storage/
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    memory=True
+)
+```
+
+#### Option 2: Custom Storage Paths
+```python
+import os
+from crewai import Crew
+from crewai.memory import LongTermMemory
+from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+
+# Configure custom storage location
+custom_storage_path = "./storage"
+os.makedirs(custom_storage_path, exist_ok=True)
+
+crew = Crew(
+    memory=True,
+    long_term_memory=LongTermMemory(
+        storage=LTMSQLiteStorage(
+            db_path=f"{custom_storage_path}/memory.db"
+        )
+    )
+)
+```
+
+#### Option 3: Project-Specific Storage
+```python
+import os
+from pathlib import Path
+
+# Store in project directory
+project_root = Path(__file__).parent
+storage_dir = project_root / "crewai_storage"
+
+os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
+
+# Now all storage will be in your project directory
+```
+
+### Embedding Provider Defaults
+
+<Info>
+**Default Embedding Provider**: CrewAI defaults to OpenAI embeddings for consistency and reliability. You can easily customize this to match your LLM provider or use local embeddings.
+</Info>
+
+#### Understanding Default Behavior
+```python
+# When using Claude as your LLM...
+from crewai import Agent, LLM
+
+agent = Agent(
+    role="Analyst",
+    goal="Analyze data",
+    backstory="Expert analyst",
+    llm=LLM(provider="anthropic", model="claude-3-sonnet")  # Using Claude
+)
+
+# CrewAI will use OpenAI embeddings by default for consistency
+# You can easily customize this to match your preferred provider
+```
+
+#### Customizing Embedding Providers
+```python
+from crewai import Crew
+
+# Option 1: Match your LLM provider
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    memory=True,
+    embedder={
+        "provider": "anthropic",  # Match your LLM provider
+        "config": {
+            "api_key": "your-anthropic-key",
+            "model": "text-embedding-3-small"
+        }
+    }
+)
+
+# Option 2: Use local embeddings (no external API calls)
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    memory=True,
+    embedder={
+        "provider": "ollama",
+        "config": {"model": "mxbai-embed-large"}
+    }
+)
+```
+
+### Debugging Storage Issues
+
+#### Check Storage Permissions
+```python
+import os
+from crewai.utilities.paths import db_storage_path
+
+storage_path = db_storage_path()
+print(f"Storage path: {storage_path}")
+print(f"Path exists: {os.path.exists(storage_path)}")
+print(f"Is writable: {os.access(storage_path, os.W_OK) if os.path.exists(storage_path) else 'Path does not exist'}")
+
+# Create with proper permissions
+if not os.path.exists(storage_path):
+    os.makedirs(storage_path, mode=0o755, exist_ok=True)
+    print(f"Created storage directory: {storage_path}")
+```
+
+#### Inspect ChromaDB Collections
+```python
+import chromadb
+from crewai.utilities.paths import db_storage_path
+
+# Connect to CrewAI's ChromaDB
+storage_path = db_storage_path()
+chroma_path = os.path.join(storage_path, "knowledge")
+
+if os.path.exists(chroma_path):
+    client = chromadb.PersistentClient(path=chroma_path)
+    collections = client.list_collections()
+    
+    print("ChromaDB Collections:")
+    for collection in collections:
+        print(f"  - {collection.name}: {collection.count()} documents")
+else:
+    print("No ChromaDB storage found")
+```
+
+#### Reset Storage (Debugging)
+```python
+from crewai import Crew
+
+# Reset all memory storage
+crew = Crew(agents=[...], tasks=[...], memory=True)
+
+# Reset specific memory types
+crew.reset_memories(command_type='short')     # Short-term memory
+crew.reset_memories(command_type='long')      # Long-term memory  
+crew.reset_memories(command_type='entity')    # Entity memory
+crew.reset_memories(command_type='knowledge') # Knowledge storage
+```
+
+### Production Best Practices
+
+1. **Set `CREWAI_STORAGE_DIR`** to a known location in production for better control
+2. **Choose explicit embedding providers** to match your LLM setup
+3. **Monitor storage directory size** for large-scale deployments
+4. **Include storage directories** in your backup strategy
+5. **Set appropriate file permissions** (0o755 for directories, 0o644 for files)
+6. **Use project-relative paths** for containerized deployments
+
+### Common Storage Issues
+
+**"ChromaDB permission denied" errors:**
+```bash
+# Fix permissions
+chmod -R 755 ~/.local/share/CrewAI/
+```
+
+**"Database is locked" errors:**
+```python
+# Ensure only one CrewAI instance accesses storage
+import fcntl
+import os
+
+storage_path = db_storage_path()
+lock_file = os.path.join(storage_path, ".crewai.lock")
+
+with open(lock_file, 'w') as f:
+    fcntl.flock(f.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+    # Your CrewAI code here
+```
+
+**Storage not persisting between runs:**
+```python
+# Verify storage location is consistent
+import os
+print("CREWAI_STORAGE_DIR:", os.getenv("CREWAI_STORAGE_DIR"))
+print("Current working directory:", os.getcwd())
+print("Computed storage path:", db_storage_path())
+```
+
+## Custom Embedder Configuration
+
+CrewAI supports multiple embedding providers to give you flexibility in choosing the best option for your use case. Here's a comprehensive guide to configuring different embedding providers for your memory system.
+
+### Why Choose Different Embedding Providers?
+
+- **Cost Optimization**: Local embeddings (Ollama) are free after initial setup
+- **Privacy**: Keep your data local with Ollama or use your preferred cloud provider
+- **Performance**: Some models work better for specific domains or languages
+- **Consistency**: Match your embedding provider with your LLM provider
+- **Compliance**: Meet specific regulatory or organizational requirements
+
+### OpenAI Embeddings (Default)
+
+OpenAI provides reliable, high-quality embeddings that work well for most use cases.
+
+```python
+from crewai import Crew
+
+# Basic OpenAI configuration (uses environment OPENAI_API_KEY)
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    memory=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-small"  # or "text-embedding-3-large"
+        }
+    }
+)
+
+# Advanced OpenAI configuration
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "api_key": "your-openai-api-key",  # Optional: override env var
+            "model": "text-embedding-3-large",
+            "dimensions": 1536,  # Optional: reduce dimensions for smaller storage
+            "organization_id": "your-org-id"  # Optional: for organization accounts
+        }
+    }
+)
+```
+
+### Azure OpenAI Embeddings
+
+For enterprise users with Azure OpenAI deployments.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "openai",  # Use openai provider for Azure
+        "config": {
+            "api_key": "your-azure-api-key",
+            "api_base": "https://your-resource.openai.azure.com/",
+            "api_type": "azure",
+            "api_version": "2023-05-15",
+            "model": "text-embedding-3-small",
+            "deployment_id": "your-deployment-name"  # Azure deployment name
+        }
+    }
+)
+```
+
+### Google AI Embeddings
+
+Use Google's text embedding models for integration with Google Cloud services.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "google",
+        "config": {
+            "api_key": "your-google-api-key",
+            "model": "text-embedding-004"  # or "text-embedding-preview-0409"
+        }
+    }
+)
+```
+
+### Vertex AI Embeddings
+
+For Google Cloud users with Vertex AI access.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "vertexai",
+        "config": {
+            "project_id": "your-gcp-project-id",
+            "region": "us-central1",  # or your preferred region
+            "api_key": "your-service-account-key",
+            "model_name": "textembedding-gecko"
+        }
+    }
+)
+```
+
+### Ollama Embeddings (Local)
+
+Run embeddings locally for privacy and cost savings.
+
+```python
+# First, install and run Ollama locally, then pull an embedding model:
+# ollama pull mxbai-embed-large
+
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "ollama",
+        "config": {
+            "model": "mxbai-embed-large",  # or "nomic-embed-text"
+            "url": "http://localhost:11434/api/embeddings"  # Default Ollama URL
+        }
+    }
+)
+
+# For custom Ollama installations
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "ollama",
+        "config": {
+            "model": "mxbai-embed-large",
+            "url": "http://your-ollama-server:11434/api/embeddings"
+        }
+    }
+)
+```
+
+### Cohere Embeddings
+
+Use Cohere's embedding models for multilingual support.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "cohere",
+        "config": {
+            "api_key": "your-cohere-api-key",
+            "model": "embed-english-v3.0"  # or "embed-multilingual-v3.0"
+        }
+    }
+)
+```
+
+### VoyageAI Embeddings
+
+High-performance embeddings optimized for retrieval tasks.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "voyageai",
+        "config": {
+            "api_key": "your-voyage-api-key",
+            "model": "voyage-large-2",  # or "voyage-code-2" for code
+            "input_type": "document"  # or "query"
+        }
+    }
+)
+```
+
+### AWS Bedrock Embeddings
+
+For AWS users with Bedrock access.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "bedrock",
+        "config": {
+            "aws_access_key_id": "your-access-key",
+            "aws_secret_access_key": "your-secret-key",
+            "region_name": "us-east-1",
+            "model": "amazon.titan-embed-text-v1"
+        }
+    }
+)
+```
+
+### Hugging Face Embeddings
+
+Use open-source models from Hugging Face.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "huggingface",
+        "config": {
+            "api_key": "your-hf-token",  # Optional for public models
+            "model": "sentence-transformers/all-MiniLM-L6-v2",
+            "api_url": "https://api-inference.huggingface.co"  # or your custom endpoint
+        }
+    }
+)
+```
+
+### IBM Watson Embeddings
+
+For IBM Cloud users.
+
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "watson",
+        "config": {
+            "api_key": "your-watson-api-key",
+            "url": "your-watson-instance-url",
+            "model": "ibm/slate-125m-english-rtrvr"
+        }
+    }
+)
+```
+
+### Choosing the Right Embedding Provider
+
+| Provider | Best For | Pros | Cons |
+|:---------|:----------|:------|:------|
+| **OpenAI** | General use, reliability | High quality, well-tested | Cost, requires API key |
+| **Ollama** | Privacy, cost savings | Free, local, private | Requires local setup |
+| **Google AI** | Google ecosystem | Good performance | Requires Google account |
+| **Azure OpenAI** | Enterprise, compliance | Enterprise features | Complex setup |
+| **Cohere** | Multilingual content | Great language support | Specialized use case |
+| **VoyageAI** | Retrieval tasks | Optimized for search | Newer provider |
+
+### Environment Variable Configuration
+
+For security, store API keys in environment variables:
+
+```python
+import os
+
+# Set environment variables
+os.environ["OPENAI_API_KEY"] = "your-openai-key"
+os.environ["GOOGLE_API_KEY"] = "your-google-key"
+os.environ["COHERE_API_KEY"] = "your-cohere-key"
+
+# Use without exposing keys in code
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-small"
+            # API key automatically loaded from environment
+        }
+    }
+)
+```
+
+### Testing Different Embedding Providers
+
+Compare embedding providers for your specific use case:
+
+```python
+from crewai import Crew
+from crewai.utilities.paths import db_storage_path
+
+# Test different providers with the same data
+providers_to_test = [
+    {
+        "name": "OpenAI",
+        "config": {
+            "provider": "openai",
+            "config": {"model": "text-embedding-3-small"}
+        }
+    },
+    {
+        "name": "Ollama",
+        "config": {
+            "provider": "ollama", 
+            "config": {"model": "mxbai-embed-large"}
+        }
+    }
+]
+
+for provider in providers_to_test:
+    print(f"\nTesting {provider['name']} embeddings...")
+    
+    # Create crew with specific embedder
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        memory=True,
+        embedder=provider['config']
+    )
+    
+    # Run your test and measure performance
+    result = crew.kickoff()
+    print(f"{provider['name']} completed successfully")
+```
+
+### Troubleshooting Embedding Issues
+
+**Model not found errors:**
+```python
+# Verify model availability
+from crewai.utilities.embedding_configurator import EmbeddingConfigurator
+
+configurator = EmbeddingConfigurator()
+try:
+    embedder = configurator.configure_embedder({
+        "provider": "ollama",
+        "config": {"model": "mxbai-embed-large"}
+    })
+    print("Embedder configured successfully")
+except Exception as e:
+    print(f"Configuration error: {e}")
+```
+
+**API key issues:**
+```python
+import os
+
+# Check if API keys are set
+required_keys = ["OPENAI_API_KEY", "GOOGLE_API_KEY", "COHERE_API_KEY"]
+for key in required_keys:
+    if os.getenv(key):
+        print(f"✅ {key} is set")
+    else:
+        print(f"❌ {key} is not set")
+```
+
+**Performance comparison:**
+```python
+import time
+
+def test_embedding_performance(embedder_config, test_text="This is a test document"):
+    start_time = time.time()
+    
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        memory=True,
+        embedder=embedder_config
+    )
+    
+    # Simulate memory operation
+    crew.kickoff()
+    
+    end_time = time.time()
+    return end_time - start_time
+
+# Compare performance
+openai_time = test_embedding_performance({
+    "provider": "openai",
+    "config": {"model": "text-embedding-3-small"}
+})
+
+ollama_time = test_embedding_performance({
+    "provider": "ollama", 
+    "config": {"model": "mxbai-embed-large"}
+})
+
+print(f"OpenAI: {openai_time:.2f}s")
+print(f"Ollama: {ollama_time:.2f}s")
+```
+
+## 2. User Memory with Mem0 (Legacy)
+
+<Warning>
+**Legacy Approach**: While fully functional, this approach is considered legacy. For new projects requiring user-specific memory, consider using External Memory instead.
+</Warning>
+
+User Memory integrates with [Mem0](https://mem0.ai/) to provide user-specific memory that persists across sessions and integrates with the crew's contextual memory system.
+
+### Prerequisites
+```bash
+pip install mem0ai
+```
+
+### Mem0 Cloud Configuration
+```python
+import os
+from crewai import Crew, Process
+
+# Set your Mem0 API key
+os.environ["MEM0_API_KEY"] = "m0-your-api-key"
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    memory=True,  # Required for contextual memory integration
+    memory_config={
+        "provider": "mem0",
+        "config": {"user_id": "john"},
+        "user_memory": {}  # Required - triggers user memory initialization
+    },
+    process=Process.sequential,
+    verbose=True
+)
+```
+
+### Advanced Mem0 Configuration
+```python
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    memory=True,
+    memory_config={
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "org_id": "my_org_id",        # Optional
+            "project_id": "my_project_id", # Optional
+            "api_key": "custom-api-key"    # Optional - overrides env var
+        },
+        "user_memory": {}
+    }
+)
+```
+
+### Local Mem0 Configuration
+```python
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    memory=True,
+    memory_config={
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "local_mem0_config": {
+                "vector_store": {
+                    "provider": "qdrant",
+                    "config": {"host": "localhost", "port": 6333}
+                },
+                "llm": {
+                    "provider": "openai",
+                    "config": {"api_key": "your-api-key", "model": "gpt-4"}
+                },
+                "embedder": {
+                    "provider": "openai",
+                    "config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}
+                }
+            }
+        },
+        "user_memory": {}
+    }
+)
+```
+
+## 3. External Memory (New Approach)
+
+External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing.
+
+### Basic External Memory with Mem0
+```python
+import os
+from crewai import Agent, Crew, Process, Task
+from crewai.memory.external.external_memory import ExternalMemory
+
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+# Create external memory instance
+external_memory = ExternalMemory(
+    embedder_config={
+        "provider": "mem0", 
+        "config": {"user_id": "U-123"}
+    }
+)
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    external_memory=external_memory,  # Separate from basic memory
+    process=Process.sequential,
+    verbose=True
+)
+```
+
+### Custom Storage Implementation
+```python
+from crewai.memory.external.external_memory import ExternalMemory
+from crewai.memory.storage.interface import Storage
+
+class CustomStorage(Storage):
+    def __init__(self):
+        self.memories = []
+
+    def save(self, value, metadata=None, agent=None):
+        self.memories.append({
+            "value": value, 
+            "metadata": metadata, 
+            "agent": agent
+        })
+
+    def search(self, query, limit=10, score_threshold=0.5):
+        # Implement your search logic here
+        return [m for m in self.memories if query.lower() in str(m["value"]).lower()]
+
+    def reset(self):
+        self.memories = []
+
+# Use custom storage
+external_memory = ExternalMemory(storage=CustomStorage())
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    external_memory=external_memory
+)
+```
+
+## Memory System Comparison
+
+| Feature | Basic Memory | User Memory (Legacy) | External Memory |
+|---------|-------------|---------------------|----------------|
+| **Setup Complexity** | Simple | Medium | Medium |
+| **Integration** | Built-in contextual | Contextual + User-specific | Standalone |
+| **Storage** | Local files | Mem0 Cloud/Local | Custom/Mem0 |
+| **Cross-session** | ✅ | ✅ | ✅ |
+| **User-specific** | ❌ | ✅ | ✅ |
+| **Custom providers** | Limited | Mem0 only | Any provider |
+| **Recommended for** | Most use cases | Legacy projects | Specialized needs |
+
+## Supported Embedding Providers
+
+### OpenAI (Default)
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "openai",
+        "config": {"model": "text-embedding-3-small"}
+    }
+)
+```
+
+### Ollama
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "ollama",
+        "config": {"model": "mxbai-embed-large"}
+    }
+)
+```
+
+### Google AI
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "google",
+        "config": {
+            "api_key": "your-api-key",
+            "model": "text-embedding-004"
+        }
+    }
+)
+```
+
+### Azure OpenAI
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "api_key": "your-api-key",
+            "api_base": "https://your-resource.openai.azure.com/",
+            "api_version": "2023-05-15",
+            "model_name": "text-embedding-3-small"
+        }
+    }
+)
+```
+
+### Vertex AI
+```python
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "vertexai",
+        "config": {
+            "project_id": "your-project-id",
+            "region": "your-region",
+            "api_key": "your-api-key",
+            "model_name": "textembedding-gecko"
+        }
+    }
+)
+```
+
+## Security Best Practices
+
+### Environment Variables
+```python
+import os
+from crewai import Crew
+
+# Store sensitive data in environment variables
+crew = Crew(
+    memory=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "api_key": os.getenv("OPENAI_API_KEY"),
+            "model": "text-embedding-3-small"
+        }
+    }
+)
+```
+
+### Storage Security
+```python
+import os
+from crewai import Crew
+from crewai.memory import LongTermMemory
+from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+
+# Use secure storage paths
+storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
+os.makedirs(storage_path, mode=0o700, exist_ok=True)  # Restricted permissions
+
+crew = Crew(
+    memory=True,
+    long_term_memory=LongTermMemory(
+        storage=LTMSQLiteStorage(
+            db_path=f"{storage_path}/memory.db"
+        )
+    )
+)
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**Memory not persisting between sessions?**
+- Check `CREWAI_STORAGE_DIR` environment variable
+- Ensure write permissions to storage directory
+- Verify memory is enabled with `memory=True`
+
+**Mem0 authentication errors?**
+- Verify `MEM0_API_KEY` environment variable is set
+- Check API key permissions on Mem0 dashboard
+- Ensure `mem0ai` package is installed
+
+**High memory usage with large datasets?**
+- Consider using External Memory with custom storage
+- Implement pagination in custom storage search methods
+- Use smaller embedding models for reduced memory footprint
+
+### Performance Tips
+
+- Use `memory=True` for most use cases (simplest and fastest)
+- Only use User Memory if you need user-specific persistence
+- Consider External Memory for high-scale or specialized requirements
+- Choose smaller embedding models for faster processing
+- Set appropriate search limits to control memory retrieval size
+
+## Benefits of Using CrewAI's Memory System
+
+- 🦾 **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
+- 🫡 **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
+- 🧠 **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
+
+## Conclusion
+
+Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, 
+you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

docs/concepts/planning.mdx

@@ -1,15 +1,20 @@
 ---
-title: crewAI Planning
-description: Learn how to add planning to your crewAI Crew and improve their performance.
+title: Planning
+description: Learn how to add planning to your CrewAI Crew and improve their performance.
+icon: ruler-combined
 ---
 
-## Introduction
-The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration, all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.
+## Overview
+
+The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration, 
+all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.
 
 ### Using the Planning Feature
+
 Getting started with the planning feature is very easy, the only step required is to add `planning=True` to your Crew:
 
-```python
+<CodeGroup>
+```python Code
 from crewai import Crew, Agent, Task, Process
 
 # Assemble your crew with planning capabilities
@@ -20,16 +25,24 @@ my_crew = Crew(
     planning=True,
 )
 ```
+</CodeGroup>
 
 From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
 
+<Warning>
+When planning is enabled, crewAI will use `gpt-4o-mini` as the default LLM for planning, which requires a valid OpenAI API key. Since your agents might be using different LLMs, this could cause confusion if you don't have an OpenAI API key configured or if you're experiencing unexpected behavior related to LLM API calls.
+</Warning>
+
 #### Planning LLM
 
-Now you can define the LLM that will be used to plan the tasks. You can use any ChatOpenAI LLM model available.
+Now you can define the LLM that will be used to plan the tasks. 
 
-```python
+When running the base case example, you will see something like the output below, which represents the output of the `AgentPlanner` 
+responsible for creating the step-by-step logic to add to the Agents' tasks.
+
+<CodeGroup>
+```python Code
 from crewai import Crew, Agent, Task, Process
-from langchain_openai import ChatOpenAI
 
 # Assemble your crew with planning capabilities and custom LLM
 my_crew = Crew(
@@ -37,15 +50,14 @@ my_crew = Crew(
     tasks=self.tasks,
     process=Process.sequential,
     planning=True,
-    planning_llm=ChatOpenAI(model="gpt-4o")
+    planning_llm="gpt-4o"
 )
+
+# Run the crew
+my_crew.kickoff()
 ```
 
-### Example
-
-When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks.
-
-```
+```markdown Result
 [2024-07-15 16:49:11][INFO]: Planning the crew execution
 **Step-by-Step Plan for Task Execution**
 
@@ -64,28 +76,35 @@ When running the base case example, you will see something like the following ou
 **Step-by-Step Plan:**
 
 1. **Define Research Scope:**
+
    - Determine the specific areas of AI LLMs to focus on, such as advancements in architecture, use cases, ethical considerations, and performance metrics.
 
 2. **Identify Reliable Sources:**
+
    - List reputable sources for AI research, including academic journals, industry reports, conferences (e.g., NeurIPS, ACL), AI research labs (e.g., OpenAI, Google AI), and online databases (e.g., IEEE Xplore, arXiv).
 
 3. **Collect Data:**
-   - Search for the latest papers, articles, and reports published in 2023 and early 2024.
-   - Use keywords like "Large Language Models 2024", "AI LLM advancements", "AI ethics 2024", etc.
+
+   - Search for the latest papers, articles, and reports published in 2024 and early 2025.
+   - Use keywords like "Large Language Models 2025", "AI LLM advancements", "AI ethics 2025", etc.
 
 4. **Analyze Findings:**
+
    - Read and summarize the key points from each source.
    - Highlight new techniques, models, and applications introduced in the past year.
 
 5. **Organize Information:**
+
    - Categorize the information into relevant topics (e.g., new architectures, ethical implications, real-world applications).
    - Ensure each bullet point is concise but informative.
 
 6. **Create the List:**
+
    - Compile the 10 most relevant pieces of information into a bullet point list.
    - Review the list to ensure clarity and relevance.
 
 **Expected Output:**
+
 A list with 10 bullet points of the most relevant information about AI LLMs.
 
 ---
@@ -96,7 +115,7 @@ A list with 10 bullet points of the most relevant information about AI LLMs.
 
 **Agent Goal:** Create detailed reports based on AI LLMs data analysis and research findings
 
-**Task Expected Output:** A fully fledge report with the main topics, each with a full section of information. Formatted as markdown without '```'
+**Task Expected Output:** A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'
 
 **Task Tools:** None specified
 
@@ -130,5 +149,6 @@ A list with 10 bullet points of the most relevant information about AI LLMs.
    - Double-check formatting and make any necessary adjustments.
 
 **Expected Output:**
-A fully-fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
-```
+A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
+```
+</CodeGroup>

docs/concepts/processes.mdx

@@ -1,11 +1,15 @@
 ---
-title: Managing Processes in CrewAI
+title: Processes
 description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
+icon: bars-staggered
 ---
 
-## Understanding Processes
-!!! note "Core Concept"
-    In CrewAI, processes orchestrate the execution of tasks by agents, akin to project management in human teams. These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.
+## Overview
+
+<Tip>
+  Processes orchestrate the execution of tasks by agents, akin to project management in human teams. 
+  These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.
+</Tip>
 
 ## Process Implementations
 
@@ -20,9 +24,7 @@ Processes enable individual agents to operate as a cohesive unit, streamlining t
 To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. For a hierarchical process, ensure to define `manager_llm` or `manager_agent` for the manager agent.
 
 ```python
-from crewai import Crew
-from crewai.process import Process
-from langchain_openai import ChatOpenAI
+from crewai import Crew, Process
 
 # Example: Creating a crew with a sequential process
 crew = Crew(
@@ -37,7 +39,7 @@ crew = Crew(
     agents=my_agents,
     tasks=my_tasks,
     process=Process.hierarchical,
-    manager_llm=ChatOpenAI(model="gpt-4")
+    manager_llm="gpt-4o"
     # or
     # manager_agent=my_manager_agent
 )
@@ -45,15 +47,20 @@ crew = Crew(
 **Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object, and for the hierarchical process, either `manager_llm` or `manager_agent` is also required.
 
 ## Sequential Process
+
 This method mirrors dynamic team workflows, progressing through tasks in a thoughtful and systematic manner. Task execution follows the predefined order in the task list, with the output of one task serving as context for the next.
 
 To customize task context, utilize the `context` parameter in the `Task` class to specify outputs that should be used as context for subsequent tasks.
 
 ## Hierarchical Process
+
 Emulates a corporate hierarchy, CrewAI allows specifying a custom manager agent or automatically creates one, requiring the specification of a manager language model (`manager_llm`). This agent oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities, reviews outputs, and assesses task completion.
 
 ## Process Class: Detailed Overview
+
 The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`). The consensual process is planned for future inclusion, emphasizing our commitment to continuous development and innovation.
 
 ## Conclusion
-The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. This documentation has been updated to reflect the latest features, enhancements, and the planned integration of the Consensual Process, ensuring users have access to the most current and comprehensive information.
+
+The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. 
+This documentation has been updated to reflect the latest features, enhancements, and the planned integration of the Consensual Process, ensuring users have access to the most current and comprehensive information.

docs/concepts/reasoning.mdx

@@ -0,0 +1,147 @@
+---
+title: Reasoning
+description: "Learn how to enable and use agent reasoning to improve task execution."
+icon: brain
+---
+
+## Overview
+
+Agent reasoning is a feature that allows agents to reflect on a task and create a plan before execution. This helps agents approach tasks more methodically and ensures they're ready to perform the assigned work.
+
+## Usage
+
+To enable reasoning for an agent, simply set `reasoning=True` when creating the agent:
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze complex datasets and provide insights",
+    backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
+    reasoning=True,  # Enable reasoning
+    max_reasoning_attempts=3  # Optional: Set a maximum number of reasoning attempts
+)
+```
+
+## How It Works
+
+When reasoning is enabled, before executing a task, the agent will:
+
+1. Reflect on the task and create a detailed plan
+2. Evaluate whether it's ready to execute the task
+3. Refine the plan as necessary until it's ready or max_reasoning_attempts is reached
+4. Inject the reasoning plan into the task description before execution
+
+This process helps the agent break down complex tasks into manageable steps and identify potential challenges before starting.
+
+## Configuration Options
+
+<ParamField body="reasoning" type="bool" default="False"> 
+    Enable or disable reasoning 
+</ParamField>
+
+<ParamField body="max_reasoning_attempts" type="int" default="None"> 
+    Maximum number of attempts to refine the plan before proceeding with execution. If None (default), the agent will continue refining until it's ready. 
+</ParamField>
+
+## Example
+
+Here's a complete example:
+
+```python
+from crewai import Agent, Task, Crew
+
+# Create an agent with reasoning enabled
+analyst = Agent(
+    role="Data Analyst",
+    goal="Analyze data and provide insights",
+    backstory="You are an expert data analyst.",
+    reasoning=True,
+    max_reasoning_attempts=3  # Optional: Set a limit on reasoning attempts
+)
+
+# Create a task
+analysis_task = Task(
+    description="Analyze the provided sales data and identify key trends.",
+    expected_output="A report highlighting the top 3 sales trends.",
+    agent=analyst
+)
+
+# Create a crew and run the task
+crew = Crew(agents=[analyst], tasks=[analysis_task])
+result = crew.kickoff()
+
+print(result)
+```
+
+## Error Handling
+
+The reasoning process is designed to be robust, with error handling built in. If an error occurs during reasoning, the agent will proceed with executing the task without the reasoning plan. This ensures that tasks can still be executed even if the reasoning process fails.
+
+Here's how to handle potential errors in your code:
+
+```python
+from crewai import Agent, Task
+import logging
+
+# Set up logging to capture any reasoning errors
+logging.basicConfig(level=logging.INFO)
+
+# Create an agent with reasoning enabled
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze data and provide insights",
+    reasoning=True,
+    max_reasoning_attempts=3
+)
+
+# Create a task
+task = Task(
+    description="Analyze the provided sales data and identify key trends.",
+    expected_output="A report highlighting the top 3 sales trends.",
+    agent=agent
+)
+
+# Execute the task
+# If an error occurs during reasoning, it will be logged and execution will continue
+result = agent.execute_task(task)
+```
+
+## Example Reasoning Output
+
+Here's an example of what a reasoning plan might look like for a data analysis task:
+
+```
+Task: Analyze the provided sales data and identify key trends.
+
+Reasoning Plan:
+I'll analyze the sales data to identify the top 3 trends.
+
+1. Understanding of the task:
+   I need to analyze sales data to identify key trends that would be valuable for business decision-making.
+
+2. Key steps I'll take:
+   - First, I'll examine the data structure to understand what fields are available
+   - Then I'll perform exploratory data analysis to identify patterns
+   - Next, I'll analyze sales by time periods to identify temporal trends
+   - I'll also analyze sales by product categories and customer segments
+   - Finally, I'll identify the top 3 most significant trends
+
+3. Approach to challenges:
+   - If the data has missing values, I'll decide whether to fill or filter them
+   - If the data has outliers, I'll investigate whether they're valid data points or errors
+   - If trends aren't immediately obvious, I'll apply statistical methods to uncover patterns
+
+4. Use of available tools:
+   - I'll use data analysis tools to explore and visualize the data
+   - I'll use statistical tools to identify significant patterns
+   - I'll use knowledge retrieval to access relevant information about sales analysis
+
+5. Expected outcome:
+   A concise report highlighting the top 3 sales trends with supporting evidence from the data.
+
+READY: I am ready to execute the task.
+```
+
+This reasoning plan helps the agent organize its approach to the task, consider potential challenges, and ensure it delivers the expected output.

docs/concepts/tasks.mdx

@@ -0,0 +1,999 @@
+---
+title: Tasks
+description: Detailed guide on managing and creating tasks within the CrewAI framework.
+icon: list-check
+---
+
+## Overview
+
+In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
+
+Tasks provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
+
+Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
+
+<Note type="info" title="Enterprise Enhancement: Visual Task Builder">
+CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
+
+![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png)
+
+The Visual Task Builder enables:
+- Drag-and-drop task creation
+- Visual task dependencies and flow
+- Real-time testing and validation
+- Easy sharing and collaboration
+</Note>
+
+### Task Execution Flow
+
+Tasks can be executed in two ways:
+- **Sequential**: Tasks are executed in the order they are defined
+- **Hierarchical**: Tasks are assigned to agents based on their roles and expertise
+
+The execution flow is defined when creating the crew:
+```python Code
+crew = Crew(
+    agents=[agent1, agent2],
+    tasks=[task1, task2],
+    process=Process.sequential  # or Process.hierarchical
+)
+```
+
+## Task Attributes
+
+| Attribute                        | Parameters        | Type                          | Description                                                                                                          |
+| :------------------------------- | :---------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
+| **Description**                  | `description`     | `str`                         | A clear, concise statement of what the task entails.                                                                 |
+| **Expected Output**              | `expected_output` | `str`                         | A detailed description of what the task's completion looks like.                                                     |
+| **Name** _(optional)_            | `name`            | `Optional[str]`               | A name identifier for the task.                                                                                      |
+| **Agent** _(optional)_           | `agent`           | `Optional[BaseAgent]`         | The agent responsible for executing the task.                                                                        |
+| **Tools** _(optional)_           | `tools`           | `List[BaseTool]`              | The tools/resources the agent is limited to use for this task.                                                       |
+| **Context** _(optional)_         | `context`         | `Optional[List["Task"]]`      | Other tasks whose outputs will be used as context for this task.                                                     |
+| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]`              | Whether the task should be executed asynchronously. Defaults to False.                                               |
+| **Human Input** _(optional)_     | `human_input`     | `Optional[bool]`              | Whether the task should have a human review the final answer of the agent. Defaults to False.                        |
+| **Markdown** _(optional)_        | `markdown`        | `Optional[bool]`              | Whether the task should instruct the agent to return the final answer formatted in Markdown. Defaults to False.      |
+| **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Task-specific configuration parameters.                                                                              |
+| **Output File** _(optional)_     | `output_file`     | `Optional[str]`               | File path for storing the task output.                                                                               |
+| **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
+| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
+| **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
+
+## Creating Tasks
+
+There are two ways to create tasks in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
+
+### YAML Configuration (Recommended)
+
+Using YAML configuration provides a cleaner, more maintainable way to define tasks. We strongly recommend using this approach to define tasks in your CrewAI projects.
+
+After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
+
+<Note>
+Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
+```python Code
+crew.kickoff(inputs={'topic': 'AI Agents'})
+```
+</Note>
+
+Here's an example of how to configure tasks using YAML:
+
+```yaml 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
+  markdown: true
+  output_file: report.md
+```
+
+To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`:
+
+```python crew.py
+# src/latest_ai_development/crew.py
+
+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+from crewai_tools import SerperDevTool
+
+@CrewBase
+class LatestAiDevelopmentCrew():
+  """LatestAiDevelopment crew"""
+
+  @agent
+  def researcher(self) -> Agent:
+    return Agent(
+      config=self.agents_config['researcher'], # type: ignore[index]
+      verbose=True,
+      tools=[SerperDevTool()]
+    )
+
+  @agent
+  def reporting_analyst(self) -> Agent:
+    return Agent(
+      config=self.agents_config['reporting_analyst'], # type: ignore[index]
+      verbose=True
+    )
+
+  @task
+  def research_task(self) -> Task:
+    return Task(
+      config=self.tasks_config['research_task'] # type: ignore[index]
+    )
+
+  @task
+  def reporting_task(self) -> Task:
+    return Task(
+      config=self.tasks_config['reporting_task'] # type: ignore[index]
+    )
+
+  @crew
+  def crew(self) -> Crew:
+    return Crew(
+      agents=[
+        self.researcher(),
+        self.reporting_analyst()
+      ],
+      tasks=[
+        self.research_task(),
+        self.reporting_task()
+      ],
+      process=Process.sequential
+    )
+```
+
+<Note>
+The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code.
+</Note>
+
+### Direct Code Definition (Alternative)
+
+Alternatively, you can define tasks directly in your code without using YAML configuration:
+
+```python task.py
+from crewai import Task
+
+research_task = Task(
+    description="""
+        Conduct a thorough research about AI Agents.
+        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 AI Agents
+    """,
+    agent=researcher
+)
+
+reporting_task = 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.
+    """,
+    agent=reporting_analyst,
+    markdown=True,  # Enable markdown formatting for the final output
+    output_file="report.md"
+)
+```
+
+<Tip>
+  Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
+</Tip>
+
+## Task Output
+
+Understanding task outputs is crucial for building effective AI workflows. CrewAI provides a structured way to handle task results through the `TaskOutput` class, which supports multiple output formats and can be easily passed between tasks.
+
+The output of a task in CrewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw output, JSON, and Pydantic models.
+
+By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` will only include the `pydantic` or `json_dict` output if the original `Task` object was configured with `output_pydantic` or `output_json`, respectively.
+
+### Task Output Attributes
+
+| Attribute         | Parameters      | Type                       | Description                                                                                        |
+| :---------------- | :-------------- | :------------------------- | :------------------------------------------------------------------------------------------------- |
+| **Description**   | `description`   | `str`                      | Description of the task.                                                                           |
+| **Summary**       | `summary`       | `Optional[str]`            | Summary of the task, auto-generated from the first 10 words of the description.                    |
+| **Raw**           | `raw`           | `str`                      | The raw output of the task. This is the default format for the output.                             |
+| **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the task.                            |
+| **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
+| **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
+| **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
+
+### Task Methods and Properties
+
+| Method/Property | Description                                                                                       |
+| :-------------- | :------------------------------------------------------------------------------------------------ |
+| **json**        | Returns the JSON string representation of the task output if the output format is JSON.           |
+| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
+| **str**         | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
+
+### Accessing Task Outputs
+
+Once a task has been executed, its output can be accessed through the `output` attribute of the `Task` object. The `TaskOutput` class provides various ways to interact with and present this output.
+
+#### Example
+
+```python Code
+# Example task
+task = Task(
+    description='Find and summarize the latest AI news',
+    expected_output='A bullet list summary of the top 5 most important AI news',
+    agent=research_agent,
+    tools=[search_tool]
+)
+
+# Execute the crew
+crew = Crew(
+    agents=[research_agent],
+    tasks=[task],
+    verbose=True
+)
+
+result = crew.kickoff()
+
+# Accessing the task output
+task_output = task.output
+
+print(f"Task Description: {task_output.description}")
+print(f"Task Summary: {task_output.summary}")
+print(f"Raw Output: {task_output.raw}")
+if task_output.json_dict:
+    print(f"JSON Output: {json.dumps(task_output.json_dict, indent=2)}")
+if task_output.pydantic:
+    print(f"Pydantic Output: {task_output.pydantic}")
+```
+
+## Markdown Output Formatting
+
+The `markdown` parameter enables automatic markdown formatting for task outputs. When set to `True`, the task will instruct the agent to format the final answer using proper Markdown syntax.
+
+### Using Markdown Formatting
+
+```python Code
+# Example task with markdown formatting enabled
+formatted_task = Task(
+    description="Create a comprehensive report on AI trends",
+    expected_output="A well-structured report with headers, sections, and bullet points",
+    agent=reporter_agent,
+    markdown=True  # Enable automatic markdown formatting
+)
+```
+
+When `markdown=True`, the agent will receive additional instructions to format the output using:
+- `#` for headers
+- `**text**` for bold text  
+- `*text*` for italic text
+- `-` or `*` for bullet points
+- `` `code` `` for inline code
+- ``` ```language ``` for code blocks
+
+### YAML Configuration with Markdown
+
+```yaml tasks.yaml
+analysis_task:
+  description: >
+    Analyze the market data and create a detailed report
+  expected_output: >
+    A comprehensive analysis with charts and key findings
+  agent: analyst
+  markdown: true  # Enable markdown formatting
+  output_file: analysis.md
+```
+
+### Benefits of Markdown Output
+
+- **Consistent Formatting**: Ensures all outputs follow proper markdown conventions
+- **Better Readability**: Structured content with headers, lists, and emphasis
+- **Documentation Ready**: Output can be directly used in documentation systems
+- **Cross-Platform Compatibility**: Markdown is universally supported
+
+<Note>
+The markdown formatting instructions are automatically added to the task prompt when `markdown=True`, so you don't need to specify formatting requirements in your task description.
+</Note>
+
+## Task Dependencies and Context
+
+Tasks can depend on the output of other tasks using the `context` attribute. For example:
+
+```python Code
+research_task = Task(
+    description="Research the latest developments in AI",
+    expected_output="A list of recent AI developments",
+    agent=researcher
+)
+
+analysis_task = Task(
+    description="Analyze the research findings and identify key trends",
+    expected_output="Analysis report of AI trends",
+    agent=analyst,
+    context=[research_task]  # This task will wait for research_task to complete
+)
+```
+
+## Task Guardrails
+
+Task guardrails provide a way to validate and transform task outputs before they
+are passed to the next task. This feature helps ensure data quality and provides
+feedback to agents when their output doesn't meet specific criteria.
+
+### Using Task Guardrails
+
+To add a guardrail to a task, provide a validation function through the `guardrail` parameter:
+
+```python Code
+from typing import Tuple, Union, Dict, Any
+from crewai import TaskOutput
+
+def validate_blog_content(result: TaskOutput) -> Tuple[bool, Any]:
+    """Validate blog content meets requirements."""
+    try:
+        # Check word count
+        word_count = len(result.split())
+        if word_count > 200:
+            return (False, "Blog content exceeds 200 words")
+
+        # Additional validation logic here
+        return (True, result.strip())
+    except Exception as e:
+        return (False, "Unexpected error during validation")
+
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A blog post under 200 words",
+    agent=blog_agent,
+    guardrail=validate_blog_content  # Add the guardrail function
+)
+```
+
+### Guardrail Function Requirements
+
+1. **Function Signature**:
+   - Must accept exactly one parameter (the task output)
+   - Should return a tuple of `(bool, Any)`
+   - Type hints are recommended but optional
+
+2. **Return Values**:
+   - On success: it returns a tuple of `(bool, Any)`. For example: `(True, validated_result)`
+   - On Failure: it returns a tuple of `(bool, str)`. For example: `(False, "Error message explain the failure")`
+
+### LLMGuardrail
+
+The `LLMGuardrail` class offers a robust mechanism for validating task outputs.
+
+### Error Handling Best Practices
+
+1. **Structured Error Responses**:
+```python Code
+from crewai import TaskOutput, LLMGuardrail
+
+def validate_with_context(result: TaskOutput) -> Tuple[bool, Any]:
+    try:
+        # Main validation logic
+        validated_data = perform_validation(result)
+        return (True, validated_data)
+    except ValidationError as e:
+        return (False, f"VALIDATION_ERROR: {str(e)}")
+    except Exception as e:
+        return (False, str(e))
+```
+
+2. **Error Categories**:
+   - Use specific error codes
+   - Include relevant context
+   - Provide actionable feedback
+
+3. **Validation Chain**:
+```python Code
+from typing import Any, Dict, List, Tuple, Union
+from crewai import TaskOutput
+
+def complex_validation(result: TaskOutput) -> Tuple[bool, Any]:
+    """Chain multiple validation steps."""
+    # Step 1: Basic validation
+    if not result:
+        return (False, "Empty result")
+
+    # Step 2: Content validation
+    try:
+        validated = validate_content(result)
+        if not validated:
+            return (False, "Invalid content")
+
+        # Step 3: Format validation
+        formatted = format_output(validated)
+        return (True, formatted)
+    except Exception as e:
+        return (False, str(e))
+```
+
+### Handling Guardrail Results
+
+When a guardrail returns `(False, error)`:
+1. The error is sent back to the agent
+2. The agent attempts to fix the issue
+3. The process repeats until:
+   - The guardrail returns `(True, result)`
+   - Maximum retries are reached
+
+Example with retry handling:
+```python Code
+from typing import Optional, Tuple, Union
+from crewai import TaskOutput, Task
+
+def validate_json_output(result: TaskOutput) -> Tuple[bool, Any]:
+    """Validate and parse JSON output."""
+    try:
+        # Try to parse as JSON
+        data = json.loads(result)
+        return (True, data)
+    except json.JSONDecodeError as e:
+        return (False, "Invalid JSON format")
+
+task = Task(
+    description="Generate a JSON report",
+    expected_output="A valid JSON object",
+    agent=analyst,
+    guardrail=validate_json_output,
+    max_retries=3  # Limit retry attempts
+)
+```
+
+## Getting Structured Consistent Outputs from Tasks
+
+<Note>
+It's also important to note that the output of the final task of a crew becomes the final output of the actual crew itself.
+</Note>
+
+### Using `output_pydantic`
+The `output_pydantic` property allows you to define a Pydantic model that the task output should conform to. This ensures that the output is not only structured but also validated according to the Pydantic model.
+
+Here's an example demonstrating how to use output_pydantic:
+
+```python Code
+import json
+
+from crewai import Agent, Crew, Process, Task
+from pydantic import BaseModel
+
+
+class Blog(BaseModel):
+    title: str
+    content: str
+
+
+blog_agent = Agent(
+    role="Blog Content Generator Agent",
+    goal="Generate a blog title and content",
+    backstory="""You are an expert content creator, skilled in crafting engaging and informative blog posts.""",
+    verbose=False,
+    allow_delegation=False,
+    llm="gpt-4o",
+)
+
+task1 = Task(
+    description="""Create a blog title and content on a given topic. Make sure the content is under 200 words.""",
+    expected_output="A compelling blog title and well-written content.",
+    agent=blog_agent,
+    output_pydantic=Blog,
+)
+
+# Instantiate your crew with a sequential process
+crew = Crew(
+    agents=[blog_agent],
+    tasks=[task1],
+    verbose=True,
+    process=Process.sequential,
+)
+
+result = crew.kickoff()
+
+# Option 1: Accessing Properties Using Dictionary-Style Indexing
+print("Accessing Properties - Option 1")
+title = result["title"]
+content = result["content"]
+print("Title:", title)
+print("Content:", content)
+
+# Option 2: Accessing Properties Directly from the Pydantic Model
+print("Accessing Properties - Option 2")
+title = result.pydantic.title
+content = result.pydantic.content
+print("Title:", title)
+print("Content:", content)
+
+# Option 3: Accessing Properties Using the to_dict() Method
+print("Accessing Properties - Option 3")
+output_dict = result.to_dict()
+title = output_dict["title"]
+content = output_dict["content"]
+print("Title:", title)
+print("Content:", content)
+
+# Option 4: Printing the Entire Blog Object
+print("Accessing Properties - Option 5")
+print("Blog:", result)
+
+```
+In this example:
+* A Pydantic model Blog is defined with title and content fields.
+* The task task1 uses the output_pydantic property to specify that its output should conform to the Blog model.
+* After executing the crew, you can access the structured output in multiple ways as shown.
+
+#### Explanation of Accessing the Output
+1. Dictionary-Style Indexing: You can directly access the fields using result["field_name"]. This works because the CrewOutput class implements the __getitem__ method.
+2. Directly from Pydantic Model: Access the attributes directly from the result.pydantic object.
+3. Using to_dict() Method: Convert the output to a dictionary and access the fields.
+4. Printing the Entire Object: Simply print the result object to see the structured output.
+
+### Using `output_json`
+The `output_json` property allows you to define the expected output in JSON format. This ensures that the task's output is a valid JSON structure that can be easily parsed and used in your application.
+
+Here's an example demonstrating how to use `output_json`:
+
+```python Code
+import json
+
+from crewai import Agent, Crew, Process, Task
+from pydantic import BaseModel
+
+
+# Define the Pydantic model for the blog
+class Blog(BaseModel):
+    title: str
+    content: str
+
+
+# Define the agent
+blog_agent = Agent(
+    role="Blog Content Generator Agent",
+    goal="Generate a blog title and content",
+    backstory="""You are an expert content creator, skilled in crafting engaging and informative blog posts.""",
+    verbose=False,
+    allow_delegation=False,
+    llm="gpt-4o",
+)
+
+# Define the task with output_json set to the Blog model
+task1 = Task(
+    description="""Create a blog title and content on a given topic. Make sure the content is under 200 words.""",
+    expected_output="A JSON object with 'title' and 'content' fields.",
+    agent=blog_agent,
+    output_json=Blog,
+)
+
+# Instantiate the crew with a sequential process
+crew = Crew(
+    agents=[blog_agent],
+    tasks=[task1],
+    verbose=True,
+    process=Process.sequential,
+)
+
+# Kickoff the crew to execute the task
+result = crew.kickoff()
+
+# Option 1: Accessing Properties Using Dictionary-Style Indexing
+print("Accessing Properties - Option 1")
+title = result["title"]
+content = result["content"]
+print("Title:", title)
+print("Content:", content)
+
+# Option 2: Printing the Entire Blog Object
+print("Accessing Properties - Option 2")
+print("Blog:", result)
+```
+
+In this example:
+* A Pydantic model Blog is defined with title and content fields, which is used to specify the structure of the JSON output.
+* The task task1 uses the output_json property to indicate that it expects a JSON output conforming to the Blog model.
+* After executing the crew, you can access the structured JSON output in two ways as shown.
+
+#### Explanation of Accessing the Output
+
+1. Accessing Properties Using Dictionary-Style Indexing: You can access the fields directly using result["field_name"]. This is possible because the CrewOutput class implements the __getitem__ method, allowing you to treat the output like a dictionary. In this option, we're retrieving the title and content from the result.
+2. Printing the Entire Blog Object: By printing result, you get the string representation of the CrewOutput object. Since the __str__ method is implemented to return the JSON output, this will display the entire output as a formatted string representing the Blog object.
+
+---
+
+By using output_pydantic or output_json, you ensure that your tasks produce outputs in a consistent and structured format, making it easier to process and utilize the data within your application or across multiple tasks.
+
+## Integrating Tools with Tasks
+
+Leverage tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools) for enhanced task performance and agent interaction.
+
+## Creating a Task with Tools
+
+```python Code
+import os
+os.environ["OPENAI_API_KEY"] = "Your Key"
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
+
+from crewai import Agent, Task, Crew
+from crewai_tools import SerperDevTool
+
+research_agent = Agent(
+  role='Researcher',
+  goal='Find and summarize the latest AI news',
+  backstory="""You're a researcher at a large company.
+  You're responsible for analyzing data and providing insights
+  to the business.""",
+  verbose=True
+)
+
+# to perform a semantic search for a specified query from a text's content across the internet
+search_tool = SerperDevTool()
+
+task = Task(
+  description='Find and summarize the latest AI news',
+  expected_output='A bullet list summary of the top 5 most important AI news',
+  agent=research_agent,
+  tools=[search_tool]
+)
+
+crew = Crew(
+    agents=[research_agent],
+    tasks=[task],
+    verbose=True
+)
+
+result = crew.kickoff()
+print(result)
+```
+
+This demonstrates how tasks with specific tools can override an agent's default set for tailored task execution.
+
+## Referring to Other Tasks
+
+In CrewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output, including multiple, should be used as context for another task.
+
+This is useful when you have a task that depends on the output of another task that is not performed immediately after it. This is done through the `context` attribute of the task:
+
+```python Code
+# ...
+
+research_ai_task = Task(
+    description="Research the latest developments in AI",
+    expected_output="A list of recent AI developments",
+    async_execution=True,
+    agent=research_agent,
+    tools=[search_tool]
+)
+
+research_ops_task = Task(
+    description="Research the latest developments in AI Ops",
+    expected_output="A list of recent AI Ops developments",
+    async_execution=True,
+    agent=research_agent,
+    tools=[search_tool]
+)
+
+write_blog_task = Task(
+    description="Write a full blog post about the importance of AI and its latest news",
+    expected_output="Full blog post that is 4 paragraphs long",
+    agent=writer_agent,
+    context=[research_ai_task, research_ops_task]
+)
+
+#...
+```
+
+## Asynchronous Execution
+
+You can define a task to be executed asynchronously. This means that the crew will not wait for it to be completed to continue with the next task. This is useful for tasks that take a long time to be completed, or that are not crucial for the next tasks to be performed.
+
+You can then use the `context` attribute to define in a future task that it should wait for the output of the asynchronous task to be completed.
+
+```python Code
+#...
+
+list_ideas = Task(
+    description="List of 5 interesting ideas to explore for an article about AI.",
+    expected_output="Bullet point list of 5 ideas for an article.",
+    agent=researcher,
+    async_execution=True # Will be executed asynchronously
+)
+
+list_important_history = Task(
+    description="Research the history of AI and give me the 5 most important events.",
+    expected_output="Bullet point list of 5 important events.",
+    agent=researcher,
+    async_execution=True # Will be executed asynchronously
+)
+
+write_article = Task(
+    description="Write an article about AI, its history, and interesting ideas.",
+    expected_output="A 4 paragraph article about AI.",
+    agent=writer,
+    context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
+)
+
+#...
+```
+
+## Callback Mechanism
+
+The callback function is executed after the task is completed, allowing for actions or notifications to be triggered based on the task's outcome.
+
+```python Code
+# ...
+
+def callback_function(output: TaskOutput):
+    # Do something after the task is completed
+    # Example: Send an email to the manager
+    print(f"""
+        Task completed!
+        Task: {output.description}
+        Output: {output.raw}
+    """)
+
+research_task = Task(
+    description='Find and summarize the latest AI news',
+    expected_output='A bullet list summary of the top 5 most important AI news',
+    agent=research_agent,
+    tools=[search_tool],
+    callback=callback_function
+)
+
+#...
+```
+
+## Accessing a Specific Task Output
+
+Once a crew finishes running, you can access the output of a specific task by using the `output` attribute of the task object:
+
+```python Code
+# ...
+task1 = Task(
+    description='Find and summarize the latest AI news',
+    expected_output='A bullet list summary of the top 5 most important AI news',
+    agent=research_agent,
+    tools=[search_tool]
+)
+
+#...
+
+crew = Crew(
+    agents=[research_agent],
+    tasks=[task1, task2, task3],
+    verbose=True
+)
+
+result = crew.kickoff()
+
+# Returns a TaskOutput object with the description and results of the task
+print(f"""
+    Task completed!
+    Task: {task1.output.description}
+    Output: {task1.output.raw}
+""")
+```
+
+## Tool Override Mechanism
+
+Specifying tools in a task allows for dynamic adaptation of agent capabilities, emphasizing CrewAI's flexibility.
+
+## Error Handling and Validation Mechanisms
+
+While creating and executing tasks, certain validation mechanisms are in place to ensure the robustness and reliability of task attributes. These include but are not limited to:
+
+- Ensuring only one output type is set per task to maintain clear output expectations.
+- Preventing the manual assignment of the `id` attribute to uphold the integrity of the unique identifier system.
+
+These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
+
+## Task Guardrails
+
+Task guardrails provide a powerful way to validate, transform, or filter task outputs before they are passed to the next task. Guardrails are optional functions that execute before the next task starts, allowing you to ensure that task outputs meet specific requirements or formats.
+
+### Basic Usage
+
+#### Define your own logic to validate
+
+```python Code
+from typing import Tuple, Union
+from crewai import Task
+
+def validate_json_output(result: str) -> Tuple[bool, Union[dict, str]]:
+    """Validate that the output is valid JSON."""
+    try:
+        json_data = json.loads(result)
+        return (True, json_data)
+    except json.JSONDecodeError:
+        return (False, "Output must be valid JSON")
+
+task = Task(
+    description="Generate JSON data",
+    expected_output="Valid JSON object",
+    guardrail=validate_json_output
+)
+```
+
+#### Leverage a no-code approach for validation
+
+```python Code
+from crewai import Task
+
+task = Task(
+    description="Generate JSON data",
+    expected_output="Valid JSON object",
+    guardrail="Ensure the response is a valid JSON object"
+)
+```
+
+#### Using YAML
+
+```yaml
+research_task:
+  ...
+  guardrail: make sure each bullet contains a minimum of 100 words
+  ...
+```
+
+```python Code
+@CrewBase
+class InternalCrew:
+    agents_config = "config/agents.yaml"
+    tasks_config = "config/tasks.yaml"
+
+    ...
+    @task
+    def research_task(self):
+        return Task(config=self.tasks_config["research_task"])  # type: ignore[index]
+    ...
+```
+
+
+#### Use custom models for code generation
+
+```python Code
+from crewai import Task
+from crewai.llm import LLM
+
+task = Task(
+    description="Generate JSON data",
+    expected_output="Valid JSON object",
+    guardrail=LLMGuardrail(
+        description="Ensure the response is a valid JSON object",
+        llm=LLM(model="gpt-4o-mini"),
+    )
+)
+```
+
+### How Guardrails Work
+
+1. **Optional Attribute**: Guardrails are an optional attribute at the task level, allowing you to add validation only where needed.
+2. **Execution Timing**: The guardrail function is executed before the next task starts, ensuring valid data flow between tasks.
+3. **Return Format**: Guardrails must return a tuple of `(success, data)`:
+   - If `success` is `True`, `data` is the validated/transformed result
+   - If `success` is `False`, `data` is the error message
+4. **Result Routing**:
+   - On success (`True`), the result is automatically passed to the next task
+   - On failure (`False`), the error is sent back to the agent to generate a new answer
+
+### Common Use Cases
+
+#### Data Format Validation
+```python Code
+def validate_email_format(result: str) -> Tuple[bool, Union[str, str]]:
+    """Ensure the output contains a valid email address."""
+    import re
+    email_pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
+    if re.match(email_pattern, result.strip()):
+        return (True, result.strip())
+    return (False, "Output must be a valid email address")
+```
+
+#### Content Filtering
+```python Code
+def filter_sensitive_info(result: str) -> Tuple[bool, Union[str, str]]:
+    """Remove or validate sensitive information."""
+    sensitive_patterns = ['SSN:', 'password:', 'secret:']
+    for pattern in sensitive_patterns:
+        if pattern.lower() in result.lower():
+            return (False, f"Output contains sensitive information ({pattern})")
+    return (True, result)
+```
+
+#### Data Transformation
+```python Code
+def normalize_phone_number(result: str) -> Tuple[bool, Union[str, str]]:
+    """Ensure phone numbers are in a consistent format."""
+    import re
+    digits = re.sub(r'\D', '', result)
+    if len(digits) == 10:
+        formatted = f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
+        return (True, formatted)
+    return (False, "Output must be a 10-digit phone number")
+```
+
+### Advanced Features
+
+#### Chaining Multiple Validations
+```python Code
+def chain_validations(*validators):
+    """Chain multiple validators together."""
+    def combined_validator(result):
+        for validator in validators:
+            success, data = validator(result)
+            if not success:
+                return (False, data)
+            result = data
+        return (True, result)
+    return combined_validator
+
+# Usage
+task = Task(
+    description="Get user contact info",
+    expected_output="Email and phone",
+    guardrail=chain_validations(
+        validate_email_format,
+        filter_sensitive_info
+    )
+)
+```
+
+#### Custom Retry Logic
+```python Code
+task = Task(
+    description="Generate data",
+    expected_output="Valid data",
+    guardrail=validate_data,
+    max_retries=5  # Override default retry limit
+)
+```
+
+## Creating Directories when Saving Files
+
+You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
+
+```python Code
+# ...
+
+save_output_task = Task(
+    description='Save the summarized AI news to a file',
+    expected_output='File saved successfully',
+    agent=research_agent,
+    tools=[file_save_tool],
+    output_file='outputs/ai_news_summary.txt',
+    create_directory=True
+)
+
+#...
+```
+
+Check out the video below to see how to use structured outputs in CrewAI:
+
+<iframe
+  width="560"
+  height="315"
+  src="https://www.youtube.com/embed/dNpKQk5uxHw"
+  title="YouTube video player"
+  frameborder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+  referrerpolicy="strict-origin-when-cross-origin"
+  allowfullscreen
+></iframe>
+
+## Conclusion
+
+Tasks are the driving force behind the actions of agents in CrewAI.
+By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit.
+Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential,
+ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/concepts/testing.mdx

@@ -0,0 +1,48 @@
+---
+title: Testing
+description: Learn how to test your CrewAI Crew and evaluate their performance.
+icon: vial
+---
+
+## Overview
+
+Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. With crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
+
+### Using the Testing Feature
+
+We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model`, which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
+
+```bash
+crewai test
+```
+
+If you want to run more iterations or use a different model, you can specify the parameters like this:
+
+```bash
+crewai test --n_iterations 5 --model gpt-4o
+```
+
+or using the short forms:
+
+```bash
+crewai test -n 5 -m gpt-4o
+```
+
+When you run the `crewai test` command, the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
+
+A table of scores at the end will show the performance of the crew in terms of the following metrics:
+
+<center>**Tasks Scores (1-10 Higher is better)**</center>
+
+| Tasks/Crew/Agents  | Run 1 | Run 2 | Avg. Total | Agents                         | Additional Info                  |
+|:------------------|:-----:|:-----:|:----------:|:------------------------------:|:---------------------------------|
+| Task 1        |  9.0  |  9.5  |    **9.2**     | Professional Insights          |                                  |
+|                    |       |       |            | Researcher                     |                                  |
+| Task 2        |  9.0  | 10.0  |    **9.5**     | Company Profile Investigator   |                                  |
+| Task 3        |  9.0  |  9.0  |    **9.0**     | Automation Insights            |                                  |
+|                    |       |       |            | Specialist                     |                                  |
+| Task 4        |  9.0  |  9.0  |    **9.0**     | Final Report Compiler          | Automation Insights Specialist   |
+| Crew         | 9.00  | 9.38  |    **9.2**     |                                |                                  |
+| Execution Time (s) |  126  |  145  |    **135**     |                                |                                  |
+
+The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole.

docs/concepts/tools.mdx

@@ -0,0 +1,285 @@
+---
+title: Tools
+description: Understanding and leveraging tools within the CrewAI framework for agent collaboration and task execution.
+icon: screwdriver-wrench
+---
+
+## Overview
+
+CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
+This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
+
+## What is a Tool?
+
+A tool in CrewAI is a skill or function that agents can utilize to perform various actions.
+This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools),
+enabling everything from simple searches to complex interactions and effective teamwork among agents.
+
+<Note type="info" title="Enterprise Enhancement: Tools Repository">
+CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
+
+The Enterprise Tools Repository includes:
+- Pre-built connectors for popular enterprise systems
+- Custom tool creation interface
+- Version control and sharing capabilities
+- Security and compliance features
+</Note>
+
+## Key Characteristics of Tools
+
+- **Utility**: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
+- **Integration**: Boosts agent capabilities by seamlessly integrating tools into their workflow.
+- **Customizability**: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
+- **Error Handling**: Incorporates robust error handling mechanisms to ensure smooth operation.
+- **Caching Mechanism**: Features intelligent caching to optimize performance and reduce redundant operations.
+- **Asynchronous Support**: Handles both synchronous and asynchronous tools, enabling non-blocking operations.
+
+## Using CrewAI Tools
+
+To enhance your agents' capabilities with crewAI tools, begin by installing our extra tools package:
+
+```bash
+pip install 'crewai[tools]'
+```
+
+Here's an example demonstrating their use:
+
+```python Code
+import os
+from crewai import Agent, Task, Crew
+# Importing crewAI tools
+from crewai_tools import (
+    DirectoryReadTool,
+    FileReadTool,
+    SerperDevTool,
+    WebsiteSearchTool
+)
+
+# Set up API keys
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
+os.environ["OPENAI_API_KEY"] = "Your Key"
+
+# Instantiate tools
+docs_tool = DirectoryReadTool(directory='./blog-posts')
+file_tool = FileReadTool()
+search_tool = SerperDevTool()
+web_rag_tool = WebsiteSearchTool()
+
+# Create agents
+researcher = Agent(
+    role='Market Research Analyst',
+    goal='Provide up-to-date market analysis of the AI industry',
+    backstory='An expert analyst with a keen eye for market trends.',
+    tools=[search_tool, web_rag_tool],
+    verbose=True
+)
+
+writer = Agent(
+    role='Content Writer',
+    goal='Craft engaging blog posts about the AI industry',
+    backstory='A skilled writer with a passion for technology.',
+    tools=[docs_tool, file_tool],
+    verbose=True
+)
+
+# Define tasks
+research = Task(
+    description='Research the latest trends in the AI industry and provide a summary.',
+    expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
+    agent=researcher
+)
+
+write = Task(
+    description='Write an engaging blog post about the AI industry, based on the research analyst's summary. Draw inspiration from the latest blog posts in the directory.',
+    expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
+    agent=writer,
+    output_file='blog-posts/new_post.md'  # The final blog post will be saved here
+)
+
+# Assemble a crew with planning enabled
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research, write],
+    verbose=True,
+    planning=True,  # Enable planning feature
+)
+
+# Execute tasks
+crew.kickoff()
+```
+
+## Available CrewAI Tools
+
+- **Error Handling**: All tools are built with error handling capabilities, allowing agents to gracefully manage exceptions and continue their tasks.
+- **Caching Mechanism**: All tools support caching, enabling agents to efficiently reuse previously obtained results, reducing the load on external resources and speeding up the execution time. You can also define finer control over the caching mechanism using the `cache_function` attribute on the tool.
+
+Here is a list of the available tools and their descriptions:
+
+| Tool                             | Description                                                                                    |
+| :------------------------------- | :--------------------------------------------------------------------------------------------- |
+| **ApifyActorsTool**              | A tool that integrates Apify Actors with your workflows for web scraping and automation tasks. |
+| **BrowserbaseLoadTool**          | A tool for interacting with and extracting data from web browsers.                             |
+| **CodeDocsSearchTool**           | A RAG tool optimized for searching through code documentation and related technical documents. |
+| **CodeInterpreterTool**          | A tool for interpreting python code.                                                           |
+| **ComposioTool**                 | Enables use of Composio tools.                                                                 |
+| **CSVSearchTool**                | A RAG tool designed for searching within CSV files, tailored to handle structured data.        |
+| **DALL-E Tool**                  | A tool for generating images using the DALL-E API.                                             |
+| **DirectorySearchTool**          | A RAG tool for searching within directories, useful for navigating through file systems.       |
+| **DOCXSearchTool**               | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.          |
+| **DirectoryReadTool**            | Facilitates reading and processing of directory structures and their contents.                 |
+| **EXASearchTool**                | A tool designed for performing exhaustive searches across various data sources.                |
+| **FileReadTool**                 | Enables reading and extracting data from files, supporting various file formats.               |
+| **FirecrawlSearchTool**          | A tool to search webpages using Firecrawl and return the results.                              |
+| **FirecrawlCrawlWebsiteTool**    | A tool for crawling webpages using Firecrawl.                                                  |
+| **FirecrawlScrapeWebsiteTool**   | A tool for scraping webpages URL using Firecrawl and returning its contents.                   |
+| **GithubSearchTool**             | A RAG tool for searching within GitHub repositories, useful for code and documentation search. |
+| **SerperDevTool**                | A specialized tool for development purposes, with specific functionalities under development.  |
+| **TXTSearchTool**                | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.      |
+| **JSONSearchTool**               | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
+| **LlamaIndexTool**               | Enables the use of LlamaIndex tools.                                                           |
+| **MDXSearchTool**                | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.       |
+| **PDFSearchTool**                | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
+| **PGSearchTool**                 | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
+| **Vision Tool**                  | A tool for generating images using the DALL-E API.                                             |
+| **RagTool**                      | A general-purpose RAG tool capable of handling various data sources and types.                 |
+| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.         |
+| **ScrapeWebsiteTool**            | Facilitates scraping entire websites, ideal for comprehensive data collection.                 |
+| **WebsiteSearchTool**            | A RAG tool for searching website content, optimized for web data extraction.                   |
+| **XMLSearchTool**                | A RAG tool designed for searching within XML files, suitable for structured data formats.      |
+| **YoutubeChannelSearchTool**     | A RAG tool for searching within YouTube channels, useful for video content analysis.           |
+| **YoutubeVideoSearchTool**       | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.          |
+
+## Creating your own Tools
+
+<Tip>
+  Developers can craft `custom tools` tailored for their agent's needs or
+  utilize pre-built options.
+</Tip>
+
+There are two main ways for one to create a CrewAI tool:
+
+### Subclassing `BaseTool`
+
+```python Code
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+class MyToolInput(BaseModel):
+    """Input schema for MyCustomTool."""
+    argument: str = Field(..., description="Description of the argument.")
+
+class MyCustomTool(BaseTool):
+    name: str = "Name of my tool"
+    description: str = "What this tool does. It's vital for effective utilization."
+    args_schema: Type[BaseModel] = MyToolInput
+
+    def _run(self, argument: str) -> str:
+        # Your tool's logic here
+        return "Tool's result"
+```
+
+## Asynchronous Tool Support
+
+CrewAI supports asynchronous tools, allowing you to implement tools that perform non-blocking operations like network requests, file I/O, or other async operations without blocking the main execution thread.
+
+### Creating Async Tools
+
+You can create async tools in two ways:
+
+#### 1. Using the `tool` Decorator with Async Functions
+
+```python Code
+from crewai.tools import tool
+
+@tool("fetch_data_async")
+async def fetch_data_async(query: str) -> str:
+    """Asynchronously fetch data based on the query."""
+    # Simulate async operation
+    await asyncio.sleep(1)
+    return f"Data retrieved for {query}"
+```
+
+#### 2. Implementing Async Methods in Custom Tool Classes
+
+```python Code
+from crewai.tools import BaseTool
+
+class AsyncCustomTool(BaseTool):
+    name: str = "async_custom_tool"
+    description: str = "An asynchronous custom tool"
+    
+    async def _run(self, query: str = "") -> str:
+        """Asynchronously run the tool"""
+        # Your async implementation here
+        await asyncio.sleep(1)
+        return f"Processed {query} asynchronously"
+```
+
+### Using Async Tools
+
+Async tools work seamlessly in both standard Crew workflows and Flow-based workflows:
+
+```python Code
+# In standard Crew
+agent = Agent(role="researcher", tools=[async_custom_tool])
+
+# In Flow
+class MyFlow(Flow):
+    @start()
+    async def begin(self):
+        crew = Crew(agents=[agent])
+        result = await crew.kickoff_async()
+        return result
+```
+
+The CrewAI framework automatically handles the execution of both synchronous and asynchronous tools, so you don't need to worry about how to call them differently.
+
+### Utilizing the `tool` Decorator
+
+```python Code
+from crewai.tools import tool
+@tool("Name of my tool")
+def my_tool(question: str) -> str:
+    """Clear description for what this tool is useful for, your agent will need this information to use it."""
+    # Function logic here
+    return "Result from your custom tool"
+```
+
+### Custom Caching Mechanism
+
+<Tip>
+  Tools can optionally implement a `cache_function` to fine-tune caching
+  behavior. This function determines when to cache results based on specific
+  conditions, offering granular control over caching logic.
+</Tip>
+
+```python Code
+from crewai.tools import tool
+
+@tool
+def multiplication_tool(first_number: int, second_number: int) -> str:
+    """Useful for when you need to multiply two numbers together."""
+    return first_number * second_number
+
+def cache_func(args, result):
+    # In this case, we only cache the result if it's a multiple of 2
+    cache = result % 2 == 0
+    return cache
+
+multiplication_tool.cache_function = cache_func
+
+writer1 = Agent(
+        role="Writer",
+        goal="You write lessons of math for kids.",
+        backstory="You're an expert in writing and you love to teach kids but you know nothing of math.",
+        tools=[multiplication_tool],
+        allow_delegation=False,
+    )
+    #...
+```
+
+## Conclusion
+
+Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively.
+When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling,
+caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.

docs/concepts/training.mdx

@@ -1,14 +1,19 @@
 ---
-title: crewAI Train
-description: Learn how to train your crewAI agents by giving them feedback early on and get consistent results.
+title: Training
+description: Learn how to train your CrewAI agents by giving them feedback early on and get consistent results.
+icon: dumbbell
 ---
 
-## Introduction
-The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
+## Overview
 
-During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. This helps the agents improve their understanding, decision-making, and problem-solving abilities.
+The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
+By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
+
+During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. 
+This helps the agents improve their understanding, decision-making, and problem-solving abilities.
 
 ### Training Your Crew Using the CLI
+
 To use the training feature, follow these steps:
 
 1. Open your terminal or command prompt.
@@ -16,31 +21,38 @@ To use the training feature, follow these steps:
 3. Run the following command:
 
 ```shell
-crewai train -n <n_iterations> <filename>
+crewai train -n <n_iterations> <filename> (optional)
 ```
-
-!!! note "Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`."
+<Tip>
+  Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
+</Tip>
 
 ### Training Your Crew Programmatically
+
 To train your crew programmatically, use the following steps:
 
 1. Define the number of iterations for training.
 2. Specify the input parameters for the training process.
 3. Execute the training command within a try-except block to handle potential errors.
 
-```python
+```python Code
 n_iterations = 2
 inputs = {"topic": "CrewAI Training"}
 filename = "your_model.pkl"
 
 try:
-    YourCrewName_Crew().crew().train(n_iterations=n_iterations, inputs=inputs, filename=filename)
+    YourCrewName_Crew().crew().train(
+      n_iterations=n_iterations, 
+      inputs=inputs, 
+      filename=filename
+    )
 
 except Exception as e:
     raise Exception(f"An error occurred while training the crew: {e}")
 ```
 
-### Key Points to Note:
+### Key Points to Note
+
 - **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
 - **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
 - **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
@@ -51,4 +63,5 @@ Once the training is complete, your agents will be equipped with enhanced capabi
 
 Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
 
-Happy training with CrewAI!
+Happy training with CrewAI! 🚀
+  

docs/core-concepts/Agents.md

@@ -1,151 +0,0 @@
----
-title: crewAI Agents
-description: What are crewAI Agents and how to use them.
----
-
-## What is an Agent?
-!!! note "What is an Agent?"
-    An agent is an **autonomous unit** programmed to:
-    <ul>
-      <li class='leading-3'>Perform tasks</li>
-      <li class='leading-3'>Make decisions</li>
-      <li class='leading-3'>Communicate with other agents</li>
-    </ul>
-      <br/>
-    Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.
-
-## Agent Attributes
-
-| Attribute                  | Parameter  | Description                                                                                                                                                                                                                                    |
-| :------------------------- | :---- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Role**                   | `role`  | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.                                                                                                                                    |
-| **Goal**                   | `goal`  | The individual objective that the agent aims to achieve. It guides the agent's decision-making process.                                                                                                                                        |
-| **Backstory**              | `backstory`  | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.                                                                                                                                           |
-| **LLM** *(optional)*       | `llm`  | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified.                                                         |
-| **Tools** *(optional)*     | `tools`  | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list.             |
-| **Function Calling LLM** *(optional)* | `function_calling_llm`  | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`.                                                                                          |
-| **Max Iter** *(optional)*  | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`.                                                                                                                           |
-| **Max RPM** *(optional)*   | `max_rpm`  | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
-| **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
-| **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
-| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`.                                                                                                       |
-| **Step Callback** *(optional)* | `step_callback`  | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
-| **Cache** *(optional)*     | `cache`  | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
-| **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
-| **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
-| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.                                                                                                                                                                  |
-
-## Creating an Agent
-
-!!! note "Agent Interaction"
-    Agents can interact with each other using crewAI's built-in delegation and communication mechanisms. This allows for dynamic task management and problem-solving within the crew.
-
-To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example including all attributes:
-
-```python
-# Example: Creating an agent with all attributes
-from crewai import Agent
-
-agent = Agent(
-  role='Data Analyst',
-  goal='Extract actionable insights',
-  backstory="""You're a data analyst at a large company.
-  You're responsible for analyzing data and providing insights
-  to the business.
-  You're currently working on a project to analyze the
-  performance of our marketing campaigns.""",
-  tools=[my_tool1, my_tool2],  # Optional, defaults to an empty list
-  llm=my_llm,  # Optional
-  function_calling_llm=my_llm,  # Optional
-  max_iter=15,  # Optional
-  max_rpm=None, # Optional
-  max_execution_time=None, # Optional
-  verbose=True,  # Optional
-  allow_delegation=True,  # Optional
-  step_callback=my_intermediate_step_callback,  # Optional
-  cache=True,  # Optional
-  system_template=my_system_template,  # Optional
-  prompt_template=my_prompt_template,  # Optional
-  response_template=my_response_template,  # Optional
-  config=my_config,  # Optional
-  crew=my_crew,  # Optional
-  tools_handler=my_tools_handler,  # Optional
-  cache_handler=my_cache_handler,  # Optional
-  callbacks=[callback1, callback2],  # Optional
-  allow_code_execution=True,  # Optiona
-  max_retry_limit=2,  # Optional
-)
-```
-
-## Setting prompt templates
-
-Prompt templates are used to format the prompt for the agent. You can use to update the system, regular and response templates for the agent. Here's an example of how to set prompt templates:
-
-```python
-agent = Agent(
-        role="{topic} specialist",
-        goal="Figure {goal} out",
-        backstory="I am the master of {role}",
-        system_template="""<|start_header_id|>system<|end_header_id|>
-
-{{ .System }}<|eot_id|>""",
-        prompt_template="""<|start_header_id|>user<|end_header_id|>
-
-{{ .Prompt }}<|eot_id|>""",
-        response_template="""<|start_header_id|>assistant<|end_header_id|>
-
-{{ .Response }}<|eot_id|>""",
-    )
-```
-
-## Bring your Third Party Agents
-!!! note "Extend your Third Party Agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the crewai's BaseAgent class."
-
-    BaseAgent includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
-
-    CrewAI is a universal multi agent framework that allows for all agents to work together to automate tasks and solve problems.
-
-
-```py
-from crewai import Agent, Task, Crew
-from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.
-
-from langchain.agents import load_tools
-
-langchain_tools = load_tools(["google-serper"], llm=llm)
-
-agent1 = CustomAgent(
-    role="agent role",
-    goal="who is {input}?",
-    backstory="agent backstory",
-    verbose=True,
-)
-
-task1 = Task(
-    expected_output="a short biography of {input}",
-    description="a short biography of {input}",
-    agent=agent1,
-)
-
-agent2 = Agent(
-    role="agent role",
-    goal="summarize the short bio for {input} and if needed do more research",
-    backstory="agent backstory",
-    verbose=True,
-)
-
-task2 = Task(
-    description="a tldr summary of the short biography",
-    expected_output="5 bullet point summary of the biography",
-    agent=agent2,
-    context=[task1],
-)
-
-my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
-crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
-```
-
-## Conclusion
-Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

docs/core-concepts/Collaboration.md

@@ -1,44 +0,0 @@
----
-title: How Agents Collaborate in CrewAI
-description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.
----
-
-## Collaboration Fundamentals
-!!! note "Core of Agent Interaction"
-    Collaboration in CrewAI is fundamental, enabling agents to combine their skills, share information, and assist each other in task execution, embodying a truly cooperative ecosystem.
-
-- **Information Sharing**: Ensures all agents are well-informed and can contribute effectively by sharing data and findings.
-- **Task Assistance**: Allows agents to seek help from peers with the required expertise for specific tasks.
-- **Resource Allocation**: Optimizes task execution through the efficient distribution and sharing of resources among agents.
-
-## Enhanced Attributes for Improved Collaboration
-The `Crew` class has been enriched with several attributes to support advanced functionalities:
-
-- **Language Model Management (`manager_llm`, `function_calling_llm`)**: Manages language models for executing tasks and tools, facilitating sophisticated agent-tool interactions. Note that while `manager_llm` is mandatory for hierarchical processes to ensure proper execution flow, `function_calling_llm` is optional, with a default value provided for streamlined tool interaction.
-- **Custom Manager Agent (`manager_agent`)**: Allows specifying a custom agent as the manager instead of using the default manager provided by CrewAI.
-- **Process Flow (`process`)**: Defines the execution logic (e.g., sequential, hierarchical) to streamline task distribution and execution.
-- **Verbose Logging (`verbose`)**: Offers detailed logging capabilities for monitoring and debugging purposes. It supports both integer and boolean types to indicate the verbosity level. For example, setting `verbose` to 1 might enable basic logging, whereas setting it to True enables more detailed logs.
-- **Rate Limiting (`max_rpm`)**: Ensures efficient utilization of resources by limiting requests per minute. Guidelines for setting `max_rpm` should consider the complexity of tasks and the expected load on resources.
-- **Internationalization / Customization Support (`language`, `prompt_file`)**: Facilitates full customization of the inner prompts, enhancing global usability. Supported languages and the process for utilizing the `prompt_file` attribute for customization should be clearly documented. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json)
-- **Execution and Output Handling (`full_output`)**: Distinguishes between full and final outputs for nuanced control over task results. Examples showcasing the difference in outputs can aid in understanding the practical implications of this attribute.
-- **Callback and Telemetry (`step_callback`, `task_callback`)**: Integrates callbacks for step-wise and task-level execution monitoring, alongside telemetry for performance analytics. The purpose and usage of `task_callback` alongside `step_callback` for granular monitoring should be clearly explained.
-- **Crew Sharing (`share_crew`)**: Enables sharing of crew information with CrewAI for continuous improvement and training models. The privacy implications and benefits of this feature, including how it contributes to model improvement, should be outlined.
-- **Usage Metrics (`usage_metrics`)**: Stores all metrics for the language model (LLM) usage during all tasks' execution, providing insights into operational efficiency and areas for improvement. Detailed information on accessing and interpreting these metrics for performance analysis should be provided.
-- **Memory Usage (`memory`)**: Indicates whether the crew should use memory to store memories of its execution, enhancing task execution and agent learning.
-- **Embedder Configuration (`embedder`)**: Specifies the configuration for the embedder to be used by the crew for understanding and generating language. This attribute supports customization of the language model provider.
-- **Cache Management (`cache`)**: Determines whether the crew should use a cache to store the results of tool executions, optimizing performance.
-- **Output Logging (`output_log_file`)**: Specifies the file path for logging the output of the crew execution.
-- **Planning Mode (`planning`)**: Allows crews to plan their actions before executing tasks by setting `planning=True` when creating the `Crew` instance. This feature enhances coordination and efficiency.
-- **Replay Feature**: Introduces a new CLI for listing tasks from the last run and replaying from a specific task, enhancing task management and troubleshooting.
-
-## Delegation: Dividing to Conquer
-Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.
-
-## Implementing Collaboration and Delegation
-Setting up a crew involves defining the roles and capabilities of each agent. CrewAI seamlessly manages their interactions, ensuring efficient collaboration and delegation, with enhanced customization and monitoring features to adapt to various operational needs.
-
-## Example Scenario
-Consider a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The integration of advanced language model management and process flow attributes allows for more sophisticated interactions, such as the writer delegating complex research tasks to the researcher or querying specific information, thereby facilitating a seamless workflow.
-
-## Conclusion
-The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.

docs/core-concepts/Memory.md

@@ -1,206 +0,0 @@
----
-title: crewAI Memory Systems
-description: Leveraging memory systems in the crewAI framework to enhance agent capabilities.
----
-
-## Introduction to Memory Systems in crewAI
-!!! note "Enhancing Agent Intelligence"
-    The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. This system comprises short-term memory, long-term memory, entity memory, and contextual memory, each serving a unique purpose in aiding agents to remember, reason, and learn from past interactions.
-
-## Memory System Components
-
-| Component            | Description                                                  |
-| :------------------- | :----------------------------------------------------------- |
-| **Short-Term Memory**| Temporarily stores recent interactions and outcomes, enabling agents to recall and utilize information relevant to their current context during the current executions. |
-| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. So Agents can remember what they did right and wrong across multiple executions |
-| **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. |
-| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
-
-## How Memory Systems Empower Agents
-
-1. **Contextual Awareness**: With short-term and contextual memory, agents gain the ability to maintain context over a conversation or task sequence, leading to more coherent and relevant responses.
-
-2. **Experience Accumulation**: Long-term memory allows agents to accumulate experiences, learning from past actions to improve future decision-making and problem-solving.
-
-3. **Entity Understanding**: By maintaining entity memory, agents can recognize and remember key entities, enhancing their ability to process and interact with complex information.
-
-## Implementing Memory in Your Crew
-
-When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
-By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI Embeddings by default, but you can change it by setting `embedder` to a different model.
-
-The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using EmbedChain package.
-The **Long-Term Memory** uses SQLLite3 to store task results.  Currently, there is no way to override these storage implementations.
-The data storage files are saved into a platform specific location found using the appdirs package
-and the name of the project which can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
-
-### Example: Configuring Memory for a Crew
-
-```python
-from crewai import Crew, Agent, Task, Process
-
-# Assemble your crew with memory capabilities
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True
-)
-```
-
-## Additional Embedding Providers
-
-### Using OpenAI embeddings (already default)
-```python
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-				"provider": "openai",
-				"config":{
-						"model": 'text-embedding-3-small'
-				}
-		}
-)
-```
-
-### Using Google AI embeddings
-```python
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "google",
-			"config":{
-				"model": 'models/embedding-001',
-				"task_type": "retrieval_document",
-				"title": "Embeddings for Embedchain"
-			}
-		}
-)
-```
-
-### Using Azure OpenAI embeddings
-```python
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "azure_openai",
-			"config":{
-				"model": 'text-embedding-ada-002',
-				"deployment_name": "your_embedding_model_deployment_name"
-			}
-		}
-)
-```
-
-### Using GPT4ALL embeddings
-```python
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "gpt4all"
-		}
-)
-```
-
-### Using Vertex AI embeddings
-```python
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "vertexai",
-			"config":{
-				"model": 'textembedding-gecko'
-			}
-		}
-)
-```
-
-### Using Cohere embeddings
-```python
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-		agents=[...],
-		tasks=[...],
-		process=Process.sequential,
-		memory=True,
-		verbose=True,
-		embedder={
-			"provider": "cohere",
-			"config":{
-				"model": "embed-english-v3.0",
-        "vector_dimension": 1024
-			}
-		}
-)
-```
-
-### Resetting Memory
-```sh
-crewai reset_memories [OPTIONS]
-```
-
-#### Resetting Memory Options
-- **`-l, --long`**
-  - **Description:** Reset LONG TERM memory.
-  - **Type:** Flag (boolean)
-  - **Default:** False
-
-- **`-s, --short`**
-  - **Description:** Reset SHORT TERM memory.
-  - **Type:** Flag (boolean)
-  - **Default:** False
-
-- **`-e, --entities`**
-  - **Description:** Reset ENTITIES memory.
-  - **Type:** Flag (boolean)
-  - **Default:** False
-
-- **`-k, --kickoff-outputs`**
-  - **Description:** Reset LATEST KICKOFF TASK OUTPUTS.
-  - **Type:** Flag (boolean)
-  - **Default:** False
-
-- **`-a, --all`**
-  - **Description:** Reset ALL memories.
-  - **Type:** Flag (boolean)
-  - **Default:** False
-
-## Benefits of Using crewAI's Memory System
-- **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
-- **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
-- **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
-
-## Getting Started
-Integrating crewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

docs/core-concepts/Pipeline.md

@@ -1,267 +0,0 @@
----
-title: crewAI Pipelines
-description: Understanding and utilizing pipelines in the crewAI framework for efficient multi-stage task processing.
----
-
-## What is a Pipeline?
-
-A pipeline in crewAI represents a structured workflow that allows for the sequential or parallel execution of multiple crews. It provides a way to organize complex processes involving multiple stages, where the output of one stage can serve as input for subsequent stages.
-
-## Key Terminology
-
-Understanding the following terms is crucial for working effectively with pipelines:
-
-- **Stage**: A distinct part of the pipeline, which can be either sequential (a single crew) or parallel (multiple crews executing concurrently).
-- **Run**: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
-- **Branch**: Parallel executions within a stage (e.g., concurrent crew operations).
-- **Trace**: The journey of an individual input through the entire pipeline, capturing the path and transformations it undergoes.
-
-Example pipeline structure:
-
-```
-crew1 >> [crew2, crew3] >> crew4
-```
-
-This represents a pipeline with three stages:
-
-1. A sequential stage (crew1)
-2. A parallel stage with two branches (crew2 and crew3 executing concurrently)
-3. Another sequential stage (crew4)
-
-Each input creates its own run, flowing through all stages of the pipeline. Multiple runs can be processed concurrently, each following the defined pipeline structure.
-
-## Pipeline Attributes
-
-| Attribute  | Parameters | Description                                                                           |
-| :--------- | :--------- | :------------------------------------------------------------------------------------ |
-| **Stages** | `stages`   | A list of crews, lists of crews, or routers representing the stages to be executed in sequence. |
-
-## Creating a Pipeline
-
-When creating a pipeline, you define a series of stages, each consisting of either a single crew or a list of crews for parallel execution. The pipeline ensures that each stage is executed in order, with the output of one stage feeding into the next.
-
-### Example: Assembling a Pipeline
-
-```python
-from crewai import Crew, Agent, Task, Pipeline
-
-# Define your crews
-research_crew = Crew(
-    agents=[researcher],
-    tasks=[research_task],
-    process=Process.sequential
-)
-
-analysis_crew = Crew(
-    agents=[analyst],
-    tasks=[analysis_task],
-    process=Process.sequential
-)
-
-writing_crew = Crew(
-    agents=[writer],
-    tasks=[writing_task],
-    process=Process.sequential
-)
-
-# Assemble the pipeline
-my_pipeline = Pipeline(
-    stages=[research_crew, analysis_crew, writing_crew]
-)
-```
-
-## Pipeline Methods
-
-| Method           | Description                                                                                                                                                                    |
-| :--------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **process_runs** | Executes the pipeline, processing all stages and returning the results. This method initiates one or more runs through the pipeline, handling the flow of data between stages. |
-
-## Pipeline Output
-
-!!! note "Understanding Pipeline Outputs"
-The output of a pipeline in the crewAI framework is encapsulated within the `PipelineKickoffResult` class. This class provides a structured way to access the results of the pipeline's execution, including various formats such as raw strings, JSON, and Pydantic models.
-
-### Pipeline Output Attributes
-
-| Attribute       | Parameters    | Type                      | Description                                                                                               |
-| :-------------- | :------------ | :------------------------ | :-------------------------------------------------------------------------------------------------------- |
-| **ID**          | `id`          | `UUID4`                   | A unique identifier for the pipeline output.                                                              |
-| **Run Results** | `run_results` | `List[PipelineRunResult]` | A list of `PipelineRunResult` objects, each representing the output of a single run through the pipeline. |
-
-### Pipeline Output Methods
-
-| Method/Property    | Description                                            |
-| :----------------- | :----------------------------------------------------- |
-| **add_run_result** | Adds a `PipelineRunResult` to the list of run results. |
-
-### Pipeline Run Result Attributes
-
-| Attribute         | Parameters      | Type                       | Description                                                                                   |
-| :---------------- | :-------------- | :------------------------- | :-------------------------------------------------------------------------------------------- |
-| **ID**            | `id`            | `UUID4`                    | A unique identifier for the run result.                                                       |
-| **Raw**           | `raw`           | `str`                      | The raw output of the final stage in the pipeline run.                                        |
-| **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the final stage, if applicable. |
-| **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the final stage, if applicable.                  |
-| **Token Usage**   | `token_usage`   | `Dict[str, Any]`           | A summary of token usage across all stages of the pipeline run.                               |
-| **Trace**         | `trace`         | `List[Any]`                | A trace of the journey of inputs through the pipeline run.                                    |
-| **Crews Outputs** | `crews_outputs` | `List[CrewOutput]`         | A list of `CrewOutput` objects, representing the outputs from each crew in the pipeline run.  |
-
-### Pipeline Run Result Methods and Properties
-
-| Method/Property | Description                                                                                              |
-| :-------------- | :------------------------------------------------------------------------------------------------------- |
-| **json**        | Returns the JSON string representation of the run result if the output format of the final task is JSON. |
-| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                                  |
-| \***\*str\*\*** | Returns the string representation of the run result, prioritizing Pydantic, then JSON, then raw.         |
-
-### Accessing Pipeline Outputs
-
-Once a pipeline has been executed, its output can be accessed through the `PipelineOutput` object returned by the `process_runs` method. The `PipelineOutput` class provides access to individual `PipelineRunResult` objects, each representing a single run through the pipeline.
-
-#### Example
-
-```python
-# Define input data for the pipeline
-input_data = [{"initial_query": "Latest advancements in AI"}, {"initial_query": "Future of robotics"}]
-
-# Execute the pipeline
-pipeline_output = await my_pipeline.process_runs(input_data)
-
-# Access the results
-for run_result in pipeline_output.run_results:
-    print(f"Run ID: {run_result.id}")
-    print(f"Final Raw Output: {run_result.raw}")
-    if run_result.json_dict:
-        print(f"JSON Output: {json.dumps(run_result.json_dict, indent=2)}")
-    if run_result.pydantic:
-        print(f"Pydantic Output: {run_result.pydantic}")
-    print(f"Token Usage: {run_result.token_usage}")
-    print(f"Trace: {run_result.trace}")
-    print("Crew Outputs:")
-    for crew_output in run_result.crews_outputs:
-        print(f"  Crew: {crew_output.raw}")
-    print("\n")
-```
-
-This example demonstrates how to access and work with the pipeline output, including individual run results and their associated data.
-
-## Using Pipelines
-
-Pipelines are particularly useful for complex workflows that involve multiple stages of processing, analysis, or content generation. They allow you to:
-
-1. **Sequence Operations**: Execute crews in a specific order, ensuring that the output of one crew is available as input to the next.
-2. **Parallel Processing**: Run multiple crews concurrently within a stage for increased efficiency.
-3. **Manage Complex Workflows**: Break down large tasks into smaller, manageable steps executed by specialized crews.
-
-### Example: Running a Pipeline
-
-```python
-# Define input data for the pipeline
-input_data = [{"initial_query": "Latest advancements in AI"}]
-
-# Execute the pipeline, initiating a run for each input
-results = await my_pipeline.process_runs(input_data)
-
-# Access the results
-for result in results:
-    print(f"Final Output: {result.raw}")
-    print(f"Token Usage: {result.token_usage}")
-    print(f"Trace: {result.trace}")  # Shows the path of the input through all stages
-```
-
-## Advanced Features
-
-### Parallel Execution within Stages
-
-You can define parallel execution within a stage by providing a list of crews, creating multiple branches:
-
-```python
-parallel_analysis_crew = Crew(agents=[financial_analyst], tasks=[financial_analysis_task])
-market_analysis_crew = Crew(agents=[market_analyst], tasks=[market_analysis_task])
-
-my_pipeline = Pipeline(
-    stages=[
-        research_crew,
-        [parallel_analysis_crew, market_analysis_crew],  # Parallel execution (branching)
-        writing_crew
-    ]
-)
-```
-
-### Routers in Pipelines
-
-Routers are a powerful feature in crewAI pipelines that allow for dynamic decision-making and branching within your workflow. They enable you to direct the flow of execution based on specific conditions or criteria, making your pipelines more flexible and adaptive.
-
-#### What is a Router?
-
-A router in crewAI is a special component that can be included as a stage in your pipeline. It evaluates the input data and determines which path the execution should take next. This allows for conditional branching in your pipeline, where different crews or sub-pipelines can be executed based on the router's decision.
-
-#### Key Components of a Router
-
-1. **Routes**: A dictionary of named routes, each associated with a condition and a pipeline to execute if the condition is met.
-2. **Default Route**: A fallback pipeline that is executed if none of the defined route conditions are met.
-
-#### Creating a Router
-
-Here's an example of how to create a router:
-
-```python
-from crewai import Router, Route, Pipeline, Crew, Agent, Task
-
-# Define your agents
-classifier = Agent(name="Classifier", role="Email Classifier")
-urgent_handler = Agent(name="Urgent Handler", role="Urgent Email Processor")
-normal_handler = Agent(name="Normal Handler", role="Normal Email Processor")
-
-# Define your tasks
-classify_task = Task(description="Classify the email based on its content and metadata.")
-urgent_task = Task(description="Process and respond to urgent email quickly.")
-normal_task = Task(description="Process and respond to normal email thoroughly.")
-
-# Define your crews
-classification_crew = Crew(agents=[classifier], tasks=[classify_task]) # classify email between high and low urgency 1-10
-urgent_crew = Crew(agents=[urgent_handler], tasks=[urgent_task])
-normal_crew = Crew(agents=[normal_handler], tasks=[normal_task])
-
-# Create pipelines for different urgency levels
-urgent_pipeline = Pipeline(stages=[urgent_crew])
-normal_pipeline = Pipeline(stages=[normal_crew])
-
-# Create a router
-email_router = Router(
-    routes={
-        "high_urgency": Route(
-            condition=lambda x: x.get("urgency_score", 0) > 7,
-            pipeline=urgent_pipeline
-        ),
-        "low_urgency": Route(
-            condition=lambda x: x.get("urgency_score", 0) <= 7,
-            pipeline=normal_pipeline
-        )
-    },
-    default=Pipeline(stages=[normal_pipeline])  # Default to just classification if no urgency score
-)
-
-# Use the router in a main pipeline
-main_pipeline = Pipeline(stages=[classification_crew, email_router])
-
-inputs = [{"email": "..."}, {"email": "..."}]  # List of email data
-
-main_pipeline.kickoff(inputs=inputs)
-```
-
-In this example, the router decides between an urgent pipeline and a normal pipeline based on the urgency score of the email. If the urgency score is greater than 7, it routes to the urgent pipeline; otherwise, it uses the normal pipeline. If the input doesn't include an urgency score, it defaults to just the classification crew.
-
-#### Benefits of Using Routers
-
-1. **Dynamic Workflow**: Adapt your pipeline's behavior based on input characteristics or intermediate results.
-2. **Efficiency**: Route urgent tasks to quicker processes, reserving more thorough pipelines for less time-sensitive inputs.
-3. **Flexibility**: Easily modify or extend your pipeline's logic without changing the core structure.
-4. **Scalability**: Handle a wide range of email types and urgency levels with a single pipeline structure.
-
-### Error Handling and Validation
-
-The Pipeline class includes validation mechanisms to ensure the robustness of the pipeline structure:
-
-- Validates that stages contain only Crew instances or lists of Crew instances.
-- Prevents double nesting of stages to maintain a clear structure.

docs/core-concepts/Tasks.md

@@ -1,315 +0,0 @@
----
-title: crewAI Tasks
-description: Detailed guide on managing and creating tasks within the crewAI framework, reflecting the latest codebase updates.
----
-
-## Overview of a Task
-
-!!! note "What is a Task?"
-In the crewAI framework, tasks are specific assignments completed by agents. They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
-
-Tasks within crewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
-
-## Task Attributes
-
-| Attribute                        | Parameters        | Description                                                                                                          |
-| :------------------------------- | :---------------- | :------------------------------------------------------------------------------------------------------------------- |
-| **Description**                  | `description`     | A clear, concise statement of what the task entails.                                                                 |
-| **Agent**                        | `agent`           | The agent responsible for the task, assigned either directly or by the crew's process.                               |
-| **Expected Output**              | `expected_output` | A detailed description of what the task's completion looks like.                                                     |
-| **Tools** _(optional)_           | `tools`           | The functions or capabilities the agent can utilize to perform the task. Defaults to an empty list.                  |
-| **Async Execution** _(optional)_ | `async_execution` | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False.    |
-| **Context** _(optional)_         | `context`         | Specifies tasks whose outputs are used as context for this task.                                                     |
-| **Config** _(optional)_          | `config`          | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. |
-| **Output JSON** _(optional)_     | `output_json`     | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.                                |
-| **Output Pydantic** _(optional)_ | `output_pydantic` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.                      |
-| **Output File** _(optional)_     | `output_file`     | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
-| **Output** _(optional)_          | `output`          | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details.                  |
-| **Callback** _(optional)_        | `callback`        | A callable that is executed with the task's output upon completion.                                                  |
-| **Human Input** _(optional)_     | `human_input`     | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. Defaults to False.|
-| **Converter Class** _(optional)_ | `converter_cls`   | A converter class used to export structured output. Defaults to None.                                                |
-
-## Creating a Task
-
-Creating a task involves defining its scope, responsible agent, and any additional attributes for flexibility:
-
-```python
-from crewai import Task
-
-task = Task(
-    description='Find and summarize the latest and most relevant news on AI',
-    agent=sales_agent,
-    expected_output='A bullet list summary of the top 5 most important AI news',
-)
-```
-
-!!! note "Task Assignment"
-Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
-
-## Task Output
-
-!!! note "Understanding Task Outputs"
-The output of a task in the crewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw strings, JSON, and Pydantic models.
-By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` will only include the `pydantic` or `json_dict` output if the original `Task` object was configured with `output_pydantic` or `output_json`, respectively.
-
-### Task Output Attributes
-
-| Attribute         | Parameters      | Type                       | Description                                                                                        |
-| :---------------- | :-------------- | :------------------------- | :------------------------------------------------------------------------------------------------- |
-| **Description**   | `description`   | `str`                      | A brief description of the task.                                                                   |
-| **Summary**       | `summary`       | `Optional[str]`            | A short summary of the task, auto-generated from the first 10 words of the description.            |
-| **Raw**           | `raw`           | `str`                      | The raw output of the task. This is the default format for the output.                             |
-| **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the task.                            |
-| **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
-| **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
-| **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
-
-### Task Output Methods and Properties
-
-| Method/Property | Description                                                                                       |
-| :-------------- | :------------------------------------------------------------------------------------------------ |
-| **json**        | Returns the JSON string representation of the task output if the output format is JSON.           |
-| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
-| \***\*str\*\*** | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
-
-### Accessing Task Outputs
-
-Once a task has been executed, its output can be accessed through the `output` attribute of the `Task` object. The `TaskOutput` class provides various ways to interact with and present this output.
-
-#### Example
-
-```python
-# Example task
-task = Task(
-    description='Find and summarize the latest AI news',
-    expected_output='A bullet list summary of the top 5 most important AI news',
-    agent=research_agent,
-    tools=[search_tool]
-)
-
-# Execute the crew
-crew = Crew(
-    agents=[research_agent],
-    tasks=[task],
-    verbose=True
-)
-
-result = crew.kickoff()
-
-# Accessing the task output
-task_output = task.output
-
-print(f"Task Description: {task_output.description}")
-print(f"Task Summary: {task_output.summary}")
-print(f"Raw Output: {task_output.raw}")
-if task_output.json_dict:
-    print(f"JSON Output: {json.dumps(task_output.json_dict, indent=2)}")
-if task_output.pydantic:
-    print(f"Pydantic Output: {task_output.pydantic}")
-```
-
-## Integrating Tools with Tasks
-
-Leverage tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools) for enhanced task performance and agent interaction.
-
-## Creating a Task with Tools
-
-```python
-import os
-os.environ["OPENAI_API_KEY"] = "Your Key"
-os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
-
-from crewai import Agent, Task, Crew
-from crewai_tools import SerperDevTool
-
-research_agent = Agent(
-  role='Researcher',
-  goal='Find and summarize the latest AI news',
-  backstory="""You're a researcher at a large company.
-  You're responsible for analyzing data and providing insights
-  to the business.""",
-  verbose=True
-)
-
-search_tool = SerperDevTool()
-
-task = Task(
-  description='Find and summarize the latest AI news',
-  expected_output='A bullet list summary of the top 5 most important AI news',
-  agent=research_agent,
-  tools=[search_tool]
-)
-
-crew = Crew(
-    agents=[research_agent],
-    tasks=[task],
-    verbose=True
-)
-
-result = crew.kickoff()
-print(result)
-```
-
-This demonstrates how tasks with specific tools can override an agent's default set for tailored task execution.
-
-## Referring to Other Tasks
-
-In crewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output, including multiple, should be used as context for another task.
-
-This is useful when you have a task that depends on the output of another task that is not performed immediately after it. This is done through the `context` attribute of the task:
-
-```python
-# ...
-
-research_ai_task = Task(
-    description='Find and summarize the latest AI news',
-    expected_output='A bullet list summary of the top 5 most important AI news',
-    async_execution=True,
-    agent=research_agent,
-    tools=[search_tool]
-)
-
-research_ops_task = Task(
-    description='Find and summarize the latest AI Ops news',
-    expected_output='A bullet list summary of the top 5 most important AI Ops news',
-    async_execution=True,
-    agent=research_agent,
-    tools=[search_tool]
-)
-
-write_blog_task = Task(
-    description="Write a full blog post about the importance of AI and its latest news",
-    expected_output='Full blog post that is 4 paragraphs long',
-    agent=writer_agent,
-    context=[research_ai_task, research_ops_task]
-)
-
-#...
-```
-
-## Asynchronous Execution
-
-You can define a task to be executed asynchronously. This means that the crew will not wait for it to be completed to continue with the next task. This is useful for tasks that take a long time to be completed, or that are not crucial for the next tasks to be performed.
-
-You can then use the `context` attribute to define in a future task that it should wait for the output of the asynchronous task to be completed.
-
-```python
-#...
-
-list_ideas = Task(
-    description="List of 5 interesting ideas to explore for an article about AI.",
-    expected_output="Bullet point list of 5 ideas for an article.",
-    agent=researcher,
-    async_execution=True # Will be executed asynchronously
-)
-
-list_important_history = Task(
-    description="Research the history of AI and give me the 5 most important events.",
-    expected_output="Bullet point list of 5 important events.",
-    agent=researcher,
-    async_execution=True # Will be executed asynchronously
-)
-
-write_article = Task(
-    description="Write an article about AI, its history, and interesting ideas.",
-    expected_output="A 4 paragraph article about AI.",
-    agent=writer,
-    context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
-)
-
-#...
-```
-
-## Callback Mechanism
-
-The callback function is executed after the task is completed, allowing for actions or notifications to be triggered based on the task's outcome.
-
-```python
-# ...
-
-def callback_function(output: TaskOutput):
-    # Do something after the task is completed
-    # Example: Send an email to the manager
-    print(f"""
-        Task completed!
-        Task: {output.description}
-        Output: {output.raw_output}
-    """)
-
-research_task = Task(
-    description='Find and summarize the latest AI news',
-    expected_output='A bullet list summary of the top 5 most important AI news',
-    agent=research_agent,
-    tools=[search_tool],
-    callback=callback_function
-)
-
-#...
-```
-
-## Accessing a Specific Task Output
-
-Once a crew finishes running, you can access the output of a specific task by using the `output` attribute of the task object:
-
-```python
-# ...
-task1 = Task(
-    description='Find and summarize the latest AI news',
-    expected_output='A bullet list summary of the top 5 most important AI news',
-    agent=research_agent,
-    tools=[search_tool]
-)
-
-#...
-
-crew = Crew(
-    agents=[research_agent],
-    tasks=[task1, task2, task3],
-    verbose=True
-)
-
-result = crew.kickoff()
-
-# Returns a TaskOutput object with the description and results of the task
-print(f"""
-    Task completed!
-    Task: {task1.output.description}
-    Output: {task1.output.raw_output}
-""")
-```
-
-## Tool Override Mechanism
-
-Specifying tools in a task allows for dynamic adaptation of agent capabilities, emphasizing CrewAI's flexibility.
-
-## Error Handling and Validation Mechanisms
-
-While creating and executing tasks, certain validation mechanisms are in place to ensure the robustness and reliability of task attributes. These include but are not limited to:
-
-- Ensuring only one output type is set per task to maintain clear output expectations.
-- Preventing the manual assignment of the `id` attribute to uphold the integrity of the unique identifier system.
-
-These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
-
-## Creating Directories when Saving Files
-
-You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
-
-```python
-# ...
-
-save_output_task = Task(
-    description='Save the summarized AI news to a file',
-    expected_output='File saved successfully',
-    agent=research_agent,
-    tools=[file_save_tool],
-    output_file='outputs/ai_news_summary.txt',
-    create_directory=True
-)
-
-#...
-```
-
-## Conclusion
-
-Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/core-concepts/Testing.md

@@ -1,40 +0,0 @@
----
-title: crewAI Testing
-description: Learn how to test your crewAI Crew and evaluate their performance.
----
-
-## Introduction
-
-Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. With crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
-
-### Using the Testing Feature
-
-We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model` which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
-
-```bash
-crewai test
-```
-
-If you want to run more iterations or use a different model, you can specify the parameters like this:
-
-```bash
-crewai test --n_iterations 5 --model gpt-4o
-```
-
-When you run the `crewai test` command, the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
-
-A table of scores at the end will show the performance of the crew in terms of the following metrics:
-
-```
-                Task Scores
-          (1-10 Higher is better)
-┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
-┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃
-┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
-│ Task 1     │ 10.0  │ 9.0   │ 9.5        │
-│ Task 2     │ 9.0   │ 9.0   │ 9.0        │
-│ Crew       │ 9.5   │ 9.0   │ 9.2        │
-└────────────┴───────┴───────┴────────────┘
-```
-
-The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole.

docs/core-concepts/Tools.md

@@ -1,202 +0,0 @@
----
-title: crewAI Tools
-description: Understanding and leveraging tools within the crewAI framework for agent collaboration and task execution.
----
-
-## Introduction
-CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers. This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
-
-## What is a Tool?
-!!! note "Definition"
-    A tool in CrewAI is a skill or function that agents can utilize to perform various actions. This includes tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools), enabling everything from simple searches to complex interactions and effective teamwork among agents.
-
-## Key Characteristics of Tools
-
-- **Utility**: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
-- **Integration**: Boosts agent capabilities by seamlessly integrating tools into their workflow.
-- **Customizability**: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
-- **Error Handling**: Incorporates robust error handling mechanisms to ensure smooth operation.
-- **Caching Mechanism**: Features intelligent caching to optimize performance and reduce redundant operations.
-
-## Using crewAI Tools
-
-To enhance your agents' capabilities with crewAI tools, begin by installing our extra tools package:
-
-```bash
-pip install 'crewai[tools]'
-```
-
-Here's an example demonstrating their use:
-
-```python
-import os
-from crewai import Agent, Task, Crew
-# Importing crewAI tools
-from crewai_tools import (
-    DirectoryReadTool,
-    FileReadTool,
-    SerperDevTool,
-    WebsiteSearchTool
-)
-
-# Set up API keys
-os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
-os.environ["OPENAI_API_KEY"] = "Your Key"
-
-# Instantiate tools
-docs_tool = DirectoryReadTool(directory='./blog-posts')
-file_tool = FileReadTool()
-search_tool = SerperDevTool()
-web_rag_tool = WebsiteSearchTool()
-
-# Create agents
-researcher = Agent(
-    role='Market Research Analyst',
-    goal='Provide up-to-date market analysis of the AI industry',
-    backstory='An expert analyst with a keen eye for market trends.',
-    tools=[search_tool, web_rag_tool],
-    verbose=True
-)
-
-writer = Agent(
-    role='Content Writer',
-    goal='Craft engaging blog posts about the AI industry',
-    backstory='A skilled writer with a passion for technology.',
-    tools=[docs_tool, file_tool],
-    verbose=True
-)
-
-# Define tasks
-research = Task(
-    description='Research the latest trends in the AI industry and provide a summary.',
-    expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
-    agent=researcher
-)
-
-write = Task(
-    description='Write an engaging blog post about the AI industry, based on the research analyst’s summary. Draw inspiration from the latest blog posts in the directory.',
-    expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
-    agent=writer,
-    output_file='blog-posts/new_post.md'  # The final blog post will be saved here
-)
-
-# Assemble a crew with planning enabled
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research, write],
-    verbose=True,
-    planning=True,  # Enable planning feature
-)
-
-# Execute tasks
-crew.kickoff()
-```
-
-## Available crewAI Tools
-
-- **Error Handling**: All tools are built with error handling capabilities, allowing agents to gracefully manage exceptions and continue their tasks.
-- **Caching Mechanism**: All tools support caching, enabling agents to efficiently reuse previously obtained results, reducing the load on external resources and speeding up the execution time. You can also define finer control over the caching mechanism using the `cache_function` attribute on the tool.
-
-Here is a list of the available tools and their descriptions:
-
-| Tool                        | Description                                                                                   |
-| :-------------------------- | :-------------------------------------------------------------------------------------------- |
-| **BrowserbaseLoadTool**     | A tool for interacting with and extracting data from web browsers.                            |
-| **CodeDocsSearchTool**      | A RAG tool optimized for searching through code documentation and related technical documents. |
-| **CodeInterpreterTool**     | A tool for interpreting python code.                                                          |
-| **ComposioTool**            | Enables use of Composio tools.                                                                |
-| **CSVSearchTool**           | A RAG tool designed for searching within CSV files, tailored to handle structured data.       |
-| **DALL-E Tool**            | A tool for generating images using the DALL-E API.                                        |
-| **DirectorySearchTool**     | A RAG tool for searching within directories, useful for navigating through file systems.      |
-| **DOCXSearchTool**          | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.         |
-| **DirectoryReadTool**       | Facilitates reading and processing of directory structures and their contents.                |
-| **EXASearchTool**           | A tool designed for performing exhaustive searches across various data sources.               |
-| **FileReadTool**            | Enables reading and extracting data from files, supporting various file formats.              |
-| **FirecrawlSearchTool**     | A tool to search webpages using Firecrawl and return the results.                             |
-| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl.                                               |
-| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages url using Firecrawl and returning its contents.               |
-| **GithubSearchTool**        | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
-| **SerperDevTool**           | A specialized tool for development purposes, with specific functionalities under development. |
-| **TXTSearchTool**           | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.     |
-| **JSONSearchTool**          | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
-| **LlamaIndexTool**          | Enables the use of LlamaIndex tools.                                                          |
-| **MDXSearchTool**           | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.      |
-| **PDFSearchTool**           | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
-| **PGSearchTool**            | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
-| **Vision Tool**            | A tool for generating images using the DALL-E API.                                        |
-| **RagTool**                 | A general-purpose RAG tool capable of handling various data sources and types.                 |
-| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.     |
-| **ScrapeWebsiteTool**       | Facilitates scraping entire websites, ideal for comprehensive data collection.                 |
-| **WebsiteSearchTool**       | A RAG tool for searching website content, optimized for web data extraction.                   |
-| **XMLSearchTool**           | A RAG tool designed for searching within XML files, suitable for structured data formats.      |
-| **YoutubeChannelSearchTool**| A RAG tool for searching within YouTube channels, useful for video content analysis.           |
-| **YoutubeVideoSearchTool**  | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.          |
-
-## Creating your own Tools
-
-!!! example "Custom Tool Creation"
-    Developers can craft custom tools tailored for their agent’s needs or utilize pre-built options:
-
-To create your own crewAI tools you will need to install our extra tools package:
-
-```bash
-pip install 'crewai[tools]'
-```
-
-Once you do that there are two main ways for one to create a crewAI tool:
-### Subclassing `BaseTool`
-
-```python
-from crewai_tools import BaseTool
-
-class MyCustomTool(BaseTool):
-    name: str = "Name of my tool"
-    description: str = "Clear description for what this tool is useful for, your agent will need this information to use it."
-
-    def _run(self, argument: str) -> str:
-        # Implementation goes here
-        return "Result from custom tool"
-```
-
-### Utilizing the `tool` Decorator
-
-```python
-from crewai_tools import tool
-@tool("Name of my tool")
-def my_tool(question: str) -> str:
-    """Clear description for what this tool is useful for, your agent will need this information to use it."""
-    # Function logic here
-    return "Result from your custom tool"
-```
-
-### Custom Caching Mechanism
-!!! note "Caching"
-    Tools can optionally implement a `cache_function` to fine-tune caching behavior. This function determines when to cache results based on specific conditions, offering granular control over caching logic.
-
-```python
-from crewai_tools import tool
-
-@tool
-def multiplication_tool(first_number: int, second_number: int) -> str:
-    """Useful for when you need to multiply two numbers together."""
-    return first_number * second_number
-
-def cache_func(args, result):
-    # In this case, we only cache the result if it's a multiple of 2
-    cache = result % 2 == 0
-    return cache
-
-multiplication_tool.cache_function = cache_func
-
-writer1 = Agent(
-        role="Writer",
-        goal="You write lessons of math for kids.",
-        backstory="You're an expert in writing and you love to teach kids but you know nothing of math.",
-        tools=[multiplication_tool],
-        allow_delegation=False,
-    )
-    #...
-```
-
-## Conclusion
-Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.

docs/core-concepts/Using-LangChain-Tools.md

@@ -1,38 +0,0 @@
----
-title: Using LangChain Tools
-description: Learn how to integrate LangChain tools with CrewAI agents to enhance search-based queries and more.
----
-
-## Using LangChain Tools
-!!! info "LangChain Integration"
-    CrewAI seamlessly integrates with LangChain’s comprehensive toolkit for search-based queries and more, here are the available built-in tools that are offered by Langchain [LangChain Toolkit](https://python.langchain.com/docs/integrations/tools/)
-
-```python
-from crewai import Agent
-from langchain.agents import Tool
-from langchain.utilities import GoogleSerperAPIWrapper
-
-# Setup API keys
-os.environ["SERPER_API_KEY"] = "Your Key"
-
-search = GoogleSerperAPIWrapper()
-
-# Create and assign the search tool to an agent
-serper_tool = Tool(
-  name="Intermediate Answer",
-  func=search.run,
-  description="Useful for search-based queries",
-)
-
-agent = Agent(
-  role='Research Analyst',
-  goal='Provide up-to-date market analysis',
-  backstory='An expert analyst with a keen eye for market trends.',
-  tools=[serper_tool]
-)
-
-# rest of the code ...
-```
-
-## Conclusion
-Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.

docs/core-concepts/Using-LlamaIndex-Tools.md

@@ -1,57 +0,0 @@
----
-title: Using LlamaIndex Tools
-description: Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more.
----
-
-## Using LlamaIndex Tools
-
-!!! info "LlamaIndex Integration"
-    CrewAI seamlessly integrates with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more. Here are the available built-in tools offered by LlamaIndex.
-
-```python
-from crewai import Agent
-from crewai_tools import LlamaIndexTool
-
-# Example 1: Initialize from FunctionTool
-from llama_index.core.tools import FunctionTool
-
-your_python_function = lambda ...: ...
-og_tool = FunctionTool.from_defaults(your_python_function, name="<name>", description='<description>')
-tool = LlamaIndexTool.from_tool(og_tool)
-
-# Example 2: Initialize from LlamaHub Tools
-from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
-wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
-wolfram_tools = wolfram_spec.to_tool_list()
-tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
-
-# Example 3: Initialize Tool from a LlamaIndex Query Engine
-query_engine = index.as_query_engine()
-query_tool = LlamaIndexTool.from_query_engine(
-    query_engine,
-    name="Uber 2019 10K Query Tool",
-    description="Use this tool to lookup the 2019 Uber 10K Annual Report"
-)
-
-# Create and assign the tools to an agent
-agent = Agent(
-  role='Research Analyst',
-  goal='Provide up-to-date market analysis',
-  backstory='An expert analyst with a keen eye for market trends.',
-  tools=[tool, *tools, query_tool]
-)
-
-# rest of the code ...
-```
-
-## Steps to Get Started
-
-To effectively use the LlamaIndexTool, follow these steps:
-
-1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
-
-    ```shell
-    pip install 'crewai[tools]'
-    ```
-
-2. **Install and Use LlamaIndex**: Follow LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.

docs/docs.json

@@ -0,0 +1,410 @@
+{
+  "$schema": "https://mintlify.com/docs.json",
+  "theme": "mint",
+  "name": "CrewAI",
+  "colors": {
+    "primary": "#EB6658",
+    "light": "#F3A78B",
+    "dark": "#C94C3C"
+  },
+  "favicon": "images/favicon.svg",
+  "contextual": {
+    "options": [
+      "copy",
+      "view",
+      "chatgpt",
+      "claude"
+    ]
+  },
+  "navigation": {
+    "tabs": [
+      {
+        "tab": "Documentation",
+        "groups": [
+          {
+            "group": "Get Started",
+            "pages": [
+              "introduction",
+              "installation",
+              "quickstart"
+            ]
+          },
+          {
+            "group": "Guides",
+            "pages": [
+              {
+                "group": "Strategy",
+                "pages": [
+                  "guides/concepts/evaluating-use-cases"
+                ]
+              },
+              {
+                "group": "Agents",
+                "pages": [
+                  "guides/agents/crafting-effective-agents"
+                ]
+              },
+              {
+                "group": "Crews",
+                "pages": [
+                  "guides/crews/first-crew"
+                ]
+              },
+              {
+                "group": "Flows",
+                "pages": [
+                  "guides/flows/first-flow",
+                  "guides/flows/mastering-flow-state"
+                ]
+              },
+              {
+                "group": "Advanced",
+                "pages": [
+                  "guides/advanced/customizing-prompts",
+                  "guides/advanced/fingerprinting"
+                ]
+              }
+            ]
+          },
+          {
+            "group": "Core Concepts",
+            "pages": [
+              "concepts/agents",
+              "concepts/tasks",
+              "concepts/crews",
+              "concepts/flows",
+              "concepts/knowledge",
+              "concepts/llms",
+              "concepts/processes",
+              "concepts/collaboration",
+              "concepts/training",
+              "concepts/memory",
+              "concepts/reasoning",
+              "concepts/planning",
+              "concepts/testing",
+              "concepts/cli",
+              "concepts/tools",
+              "concepts/event-listener"
+            ]
+          },
+          {
+            "group": "MCP Integration",
+            "pages": [
+              "mcp/overview",
+              "mcp/stdio",
+              "mcp/sse",
+              "mcp/streamable-http",
+              "mcp/multiple-servers",
+              "mcp/security"
+            ]
+          },
+          {
+            "group": "Tools",
+            "pages": [
+              "tools/overview",
+              {
+                "group": "File & Document",
+                "pages": [
+                  "tools/file-document/overview",
+                  "tools/file-document/filereadtool",
+                  "tools/file-document/filewritetool",
+                  "tools/file-document/pdfsearchtool",
+                  "tools/file-document/docxsearchtool",
+                  "tools/file-document/mdxsearchtool",
+                  "tools/file-document/xmlsearchtool",
+                  "tools/file-document/txtsearchtool",
+                  "tools/file-document/jsonsearchtool",
+                  "tools/file-document/csvsearchtool",
+                  "tools/file-document/directorysearchtool",
+                  "tools/file-document/directoryreadtool"
+                ]
+              },
+              {
+                "group": "Web Scraping & Browsing",
+                "pages": [
+                  "tools/web-scraping/overview",
+                  "tools/web-scraping/scrapewebsitetool",
+                  "tools/web-scraping/scrapeelementfromwebsitetool",
+                  "tools/web-scraping/scrapflyscrapetool",
+                  "tools/web-scraping/seleniumscrapingtool",
+                  "tools/web-scraping/scrapegraphscrapetool",
+                  "tools/web-scraping/spidertool",
+                  "tools/web-scraping/browserbaseloadtool",
+                  "tools/web-scraping/hyperbrowserloadtool",
+                  "tools/web-scraping/stagehandtool",
+                  "tools/web-scraping/firecrawlcrawlwebsitetool",
+                  "tools/web-scraping/firecrawlscrapewebsitetool",
+                  "tools/web-scraping/firecrawlsearchtool"
+                ]
+              },
+              {
+                "group": "Search & Research",
+                "pages": [
+                  "tools/search-research/overview",
+                  "tools/search-research/serperdevtool",
+                  "tools/search-research/bravesearchtool",
+                  "tools/search-research/exasearchtool",
+                  "tools/search-research/linkupsearchtool",
+                  "tools/search-research/githubsearchtool",
+                  "tools/search-research/websitesearchtool",
+                  "tools/search-research/codedocssearchtool",
+                  "tools/search-research/youtubechannelsearchtool",
+                  "tools/search-research/youtubevideosearchtool"
+                ]
+              },
+              {
+                "group": "Database & Data",
+                "pages": [
+                  "tools/database-data/overview",
+                  "tools/database-data/mysqltool",
+                  "tools/database-data/pgsearchtool",
+                  "tools/database-data/snowflakesearchtool",
+                  "tools/database-data/nl2sqltool",
+                  "tools/database-data/qdrantvectorsearchtool",
+                  "tools/database-data/weaviatevectorsearchtool"
+                ]
+              },
+              {
+                "group": "AI & Machine Learning",
+                "pages": [
+                  "tools/ai-ml/overview",
+                  "tools/ai-ml/dalletool",
+                  "tools/ai-ml/visiontool",
+                  "tools/ai-ml/aimindtool",
+                  "tools/ai-ml/llamaindextool",
+                  "tools/ai-ml/langchaintool",
+                  "tools/ai-ml/ragtool",
+                  "tools/ai-ml/codeinterpretertool"
+                ]
+              },
+              {
+                "group": "Cloud & Storage",
+                "pages": [
+                  "tools/cloud-storage/overview",
+                  "tools/cloud-storage/s3readertool",
+                  "tools/cloud-storage/s3writertool",
+                  "tools/cloud-storage/bedrockinvokeagenttool",
+                  "tools/cloud-storage/bedrockkbretriever"
+                ]
+              },
+              {
+                "group": "Automation & Integration",
+                "pages": [
+                  "tools/automation/overview",
+                  "tools/automation/apifyactorstool",
+                  "tools/automation/composiotool",
+                  "tools/automation/multiontool"
+                ]
+              }
+            ]
+          },
+          {
+            "group": "Observability",
+            "pages": [
+              "observability/overview",
+              "observability/agentops",
+              "observability/arize-phoenix",
+              "observability/langfuse",
+              "observability/langtrace",
+              "observability/maxim",
+              "observability/mlflow",
+              "observability/openlit",
+              "observability/opik",
+              "observability/patronus-evaluation",
+              "observability/portkey",
+              "observability/weave"
+            ]
+          },
+          {
+            "group": "Learn",
+            "pages": [
+              "learn/overview",
+              "learn/llm-selection-guide",
+              "learn/conditional-tasks",
+              "learn/coding-agents",
+              "learn/create-custom-tools",
+              "learn/custom-llm",
+              "learn/custom-manager-agent",
+              "learn/customizing-agents",
+              "learn/dalle-image-generation",
+              "learn/force-tool-output-as-result",
+              "learn/hierarchical-process",
+              "learn/human-input-on-execution",
+              "learn/kickoff-async",
+              "learn/kickoff-for-each",
+              "learn/llm-connections",
+              "learn/multimodal-agents",
+              "learn/replay-tasks-from-latest-crew-kickoff",
+              "learn/sequential-process",
+              "learn/using-annotations"
+            ]
+          },
+          {
+            "group": "Telemetry",
+            "pages": [
+              "telemetry"
+            ]
+          }
+        ]
+      },
+      {
+        "tab": "Enterprise",
+        "groups": [
+          {
+            "group": "Getting Started",
+            "pages": [
+              "enterprise/introduction"
+            ]
+          },
+          {
+            "group": "Features",
+            "pages": [
+              "enterprise/features/tool-repository",
+              "enterprise/features/webhook-streaming",
+              "enterprise/features/traces",
+              "enterprise/features/hallucination-guardrail",
+              "enterprise/features/integrations"
+            ]
+          },
+          {
+            "group": "How-To Guides",
+            "pages": [
+              "enterprise/guides/build-crew",
+              "enterprise/guides/deploy-crew",
+              "enterprise/guides/kickoff-crew",
+              "enterprise/guides/update-crew",
+              "enterprise/guides/enable-crew-studio",
+              "enterprise/guides/azure-openai-setup",
+              "enterprise/guides/hubspot-trigger",
+              "enterprise/guides/react-component-export",
+              "enterprise/guides/salesforce-trigger",
+              "enterprise/guides/slack-trigger",
+              "enterprise/guides/team-management",
+              "enterprise/guides/webhook-automation",
+              "enterprise/guides/human-in-the-loop",
+              "enterprise/guides/zapier-trigger"
+            ]
+          },
+          {
+            "group": "Resources",
+            "pages": [
+              "enterprise/resources/frequently-asked-questions"
+            ]
+          }
+        ]
+      },
+      {
+        "tab": "API Reference",
+        "groups": [
+          {
+            "group": "Getting Started",
+            "pages": [
+              "api-reference/introduction"
+            ]
+          },
+          {
+            "group": "Endpoints",
+            "openapi": "enterprise-api.yaml"
+          }
+        ]
+      },
+      {
+        "tab": "Examples",
+        "groups": [
+          {
+            "group": "Examples",
+            "pages": [
+              "examples/example"
+            ]
+          }
+        ]
+      },
+      {
+        "tab": "Releases",
+        "groups": [
+          {
+            "group": "Releases",
+            "pages": [
+              "changelog"
+            ]
+          }
+        ]
+      }
+    ],
+    "global": {
+      "anchors": [
+        {
+          "anchor": "Website",
+          "href": "https://crewai.com",
+          "icon": "globe"
+        },
+        {
+          "anchor": "Forum",
+          "href": "https://community.crewai.com",
+          "icon": "discourse"
+        },
+        {
+          "anchor": "Crew GPT",
+          "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
+          "icon": "robot"
+        },
+        {
+          "anchor": "Get Help",
+          "href": "mailto:support@crewai.com",
+          "icon": "headset"
+        }
+      ]
+    }
+  },
+  "logo": {
+    "light": "images/crew_only_logo.png",
+    "dark": "images/crew_only_logo.png"
+  },
+  "appearance": {
+    "default": "dark",
+    "strict": false
+  },
+  "navbar": {
+    "links": [
+      {
+        "label": "Start Cloud Trial",
+        "href": "https://app.crewai.com"
+      }
+    ],
+    "primary": {
+      "type": "github",
+      "href": "https://github.com/crewAIInc/crewAI"
+    }
+  },
+  "search": {
+    "prompt": "Search CrewAI docs"
+  },
+  "api": {
+    "baseUrl": "https://your-actual-crew-name.crewai.com",
+    "auth": {
+      "method": "bearer",
+      "name": "Authorization"
+    },
+    "playground": {
+      "mode": "simple"
+    }
+  },
+  "seo": {
+    "indexing": "all"
+  },
+  "errors": {
+    "404": {
+      "redirect": true
+    }
+  },
+  "footer": {
+    "socials": {
+      "website": "https://crewai.com",
+      "x": "https://x.com/crewAIInc",
+      "github": "https://github.com/crewAIInc/crewAI",
+      "linkedin": "https://www.linkedin.com/company/crewai-inc",
+      "youtube": "https://youtube.com/@crewAIInc",
+      "reddit": "https://www.reddit.com/r/crewAIInc/"
+    }
+  }
+}

docs/enterprise-api.yaml

@@ -0,0 +1,434 @@
+openapi: 3.0.3
+info:
+  title: CrewAI Enterprise API
+  description: |
+    REST API for interacting with your deployed CrewAI crews on CrewAI Enterprise.
+    
+    ## Getting Started
+    
+    1. **Find your crew URL**: Get your unique crew URL from the CrewAI Enterprise dashboard
+    2. **Copy examples**: Use the code examples from each endpoint page as templates
+    3. **Replace placeholders**: Update URLs and tokens with your actual values
+    4. **Test with your tools**: Use cURL, Postman, or your preferred API client
+    
+    ## Authentication
+    
+    All API requests require a bearer token for authentication. There are two types of tokens:
+    
+    - **Bearer Token**: Organization-level token for full crew operations
+    - **User Bearer Token**: User-scoped token for individual access with limited permissions
+    
+    You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
+    
+    ## Reference Documentation
+    
+    This documentation provides comprehensive examples for each endpoint:
+    
+    - **Request formats** with all required and optional parameters
+    - **Response examples** for success and error scenarios
+    - **Code samples** in multiple programming languages
+    - **Authentication patterns** with proper Bearer token usage
+    
+    Copy the examples and customize them with your actual crew URL and authentication tokens.
+    
+    ## Workflow
+    
+    1. **Discover inputs** using `GET /inputs`
+    2. **Start execution** using `POST /kickoff`  
+    3. **Monitor progress** using `GET /status/{kickoff_id}`
+  version: 1.0.0
+  contact:
+    name: CrewAI Support
+    email: support@crewai.com
+    url: https://crewai.com
+servers:
+  - url: https://your-actual-crew-name.crewai.com
+    description: Replace with your actual deployed crew URL from the CrewAI Enterprise dashboard
+  - url: https://my-travel-crew.crewai.com
+    description: Example travel planning crew (replace with your URL)
+  - url: https://content-creation-crew.crewai.com
+    description: Example content creation crew (replace with your URL)
+  - url: https://research-assistant-crew.crewai.com
+    description: Example research assistant crew (replace with your URL)
+security:
+  - BearerAuth: []
+paths:
+  /inputs:
+    get:
+      summary: Get Required Inputs
+      description: |
+        **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
+        
+        Retrieves the list of all required input parameters that your crew expects for execution.
+        Use this endpoint to discover what inputs you need to provide when starting a crew execution.
+      operationId: getRequiredInputs
+      responses:
+        '200':
+          description: Successfully retrieved required inputs
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  inputs:
+                    type: array
+                    items:
+                      type: string
+                    description: Array of required input parameter names
+                    example: ["budget", "interests", "duration", "age"]
+              examples:
+                travel_crew:
+                  summary: Travel planning crew inputs
+                  value:
+                    inputs: ["budget", "interests", "duration", "age"]
+                outreach_crew:
+                  summary: Outreach crew inputs  
+                  value:
+                    inputs: ["name", "title", "company", "industry", "our_product", "linkedin_url"]
+        '401':
+          $ref: '#/components/responses/UnauthorizedError'
+        '404':
+          $ref: '#/components/responses/NotFoundError'
+        '500':
+          $ref: '#/components/responses/ServerError'
+
+  /kickoff:
+    post:
+      summary: Start Crew Execution
+      description: |
+        **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
+        
+        Initiates a new crew execution with the provided inputs. Returns a kickoff ID that can be used 
+        to track the execution progress and retrieve results.
+        
+        Crew executions can take anywhere from seconds to minutes depending on their complexity.
+        Consider using webhooks for real-time notifications or implement polling with the status endpoint.
+      operationId: startCrewExecution
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              required:
+                - inputs
+              properties:
+                inputs:
+                  type: object
+                  description: Key-value pairs of all required inputs for your crew
+                  additionalProperties:
+                    type: string
+                  example:
+                    budget: "1000 USD"
+                    interests: "games, tech, ai, relaxing hikes, amazing food"
+                    duration: "7 days"
+                    age: "35"
+                meta:
+                  type: object
+                  description: Additional metadata to pass to the crew
+                  additionalProperties: true
+                  example:
+                    requestId: "user-request-12345"
+                    source: "mobile-app"
+                taskWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each task completion
+                  example: "https://your-server.com/webhooks/task"
+                stepWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each agent thought/action
+                  example: "https://your-server.com/webhooks/step"
+                crewWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed when the crew execution completes
+                  example: "https://your-server.com/webhooks/crew"
+            examples:
+              travel_planning:
+                summary: Travel planning crew
+                value:
+                  inputs:
+                    budget: "1000 USD"
+                    interests: "games, tech, ai, relaxing hikes, amazing food"
+                    duration: "7 days" 
+                    age: "35"
+                  meta:
+                    requestId: "travel-req-123"
+                    source: "web-app"
+              outreach_campaign:
+                summary: Outreach crew with webhooks
+                value:
+                  inputs:
+                    name: "John Smith"
+                    title: "CTO"
+                    company: "TechCorp"
+                    industry: "Software"
+                    our_product: "AI Development Platform"
+                    linkedin_url: "https://linkedin.com/in/johnsmith"
+                  taskWebhookUrl: "https://api.example.com/webhooks/task"
+                  crewWebhookUrl: "https://api.example.com/webhooks/crew"
+      responses:
+        '200':
+          description: Crew execution started successfully
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  kickoff_id:
+                    type: string
+                    format: uuid
+                    description: Unique identifier for tracking this execution
+                    example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+        '400':
+          description: Invalid request body or missing required inputs
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+        '401':
+          $ref: '#/components/responses/UnauthorizedError'
+        '422':
+          description: Validation error - ensure all required inputs are provided
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/ValidationError'
+        '500':
+          $ref: '#/components/responses/ServerError'
+
+  /status/{kickoff_id}:
+    get:
+      summary: Get Execution Status
+      description: |
+        **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
+        
+        Retrieves the current status and results of a crew execution using its kickoff ID.
+        
+        The response structure varies depending on the execution state:
+        - **running**: Execution in progress with current task info
+        - **completed**: Execution finished with full results
+        - **error**: Execution failed with error details
+      operationId: getExecutionStatus
+      parameters:
+        - name: kickoff_id
+          in: path
+          required: true
+          description: The kickoff ID returned from the /kickoff endpoint
+          schema:
+            type: string
+            format: uuid
+            example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+      responses:
+        '200':
+          description: Successfully retrieved execution status
+          content:
+            application/json:
+              schema:
+                oneOf:
+                  - $ref: '#/components/schemas/ExecutionRunning'
+                  - $ref: '#/components/schemas/ExecutionCompleted'
+                  - $ref: '#/components/schemas/ExecutionError'
+              examples:
+                running:
+                  summary: Execution in progress
+                  value:
+                    status: "running"
+                    current_task: "research_task"
+                    progress:
+                      completed_tasks: 1
+                      total_tasks: 3
+                completed:
+                  summary: Execution completed successfully
+                  value:
+                    status: "completed"
+                    result:
+                      output: "Comprehensive travel itinerary for 7 days in Japan focusing on tech culture..."
+                      tasks:
+                        - task_id: "research_task"
+                          output: "Research findings on tech destinations in Japan..."
+                          agent: "Travel Researcher"
+                          execution_time: 45.2
+                        - task_id: "planning_task"
+                          output: "7-day detailed itinerary with activities and recommendations..."
+                          agent: "Trip Planner"
+                          execution_time: 62.8
+                    execution_time: 108.5
+                error:
+                  summary: Execution failed
+                  value:
+                    status: "error"
+                    error: "Task execution failed: Invalid API key for external service"
+                    execution_time: 23.1
+        '401':
+          $ref: '#/components/responses/UnauthorizedError'
+        '404':
+          description: Kickoff ID not found
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+              example:
+                error: "Execution not found"
+                message: "No execution found with ID: abcd1234-5678-90ef-ghij-klmnopqrstuv"
+        '500':
+          $ref: '#/components/responses/ServerError'
+
+components:
+  securitySchemes:
+    BearerAuth:
+      type: http
+      scheme: bearer
+      description: |
+        **📋 Reference Documentation** - *The tokens shown in examples are placeholders for reference only.*
+        
+        Use your actual Bearer Token or User Bearer Token from the CrewAI Enterprise dashboard for real API calls.
+        
+        **Bearer Token**: Organization-level access for full crew operations
+        **User Bearer Token**: User-scoped access with limited permissions
+
+  schemas:
+    ExecutionRunning:
+      type: object
+      properties:
+        status:
+          type: string
+          enum: ["running"]
+          example: "running"
+        current_task:
+          type: string
+          description: Name of the currently executing task
+          example: "research_task"
+        progress:
+          type: object
+          properties:
+            completed_tasks:
+              type: integer
+              description: Number of completed tasks
+              example: 1
+            total_tasks:
+              type: integer  
+              description: Total number of tasks in the crew
+              example: 3
+
+    ExecutionCompleted:
+      type: object
+      properties:
+        status:
+          type: string
+          enum: ["completed"]
+          example: "completed"
+        result:
+          type: object
+          properties:
+            output:
+              type: string
+              description: Final output from the crew execution
+              example: "Comprehensive travel itinerary..."
+            tasks:
+              type: array
+              items:
+                $ref: '#/components/schemas/TaskResult'
+        execution_time:
+          type: number
+          description: Total execution time in seconds
+          example: 108.5
+
+    ExecutionError:
+      type: object
+      properties:
+        status:
+          type: string
+          enum: ["error"]
+          example: "error"
+        error:
+          type: string
+          description: Error message describing what went wrong
+          example: "Task execution failed: Invalid API key"
+        execution_time:
+          type: number
+          description: Time until error occurred in seconds
+          example: 23.1
+
+    TaskResult:
+      type: object
+      properties:
+        task_id:
+          type: string
+          description: Unique identifier for the task
+          example: "research_task"
+        output:
+          type: string
+          description: Output generated by this task
+          example: "Research findings..."
+        agent:
+          type: string
+          description: Name of the agent that executed this task
+          example: "Travel Researcher"
+        execution_time:
+          type: number
+          description: Time taken to execute this task in seconds
+          example: 45.2
+
+    Error:
+      type: object
+      properties:
+        error:
+          type: string
+          description: Error type or title
+          example: "Authentication Error"
+        message:
+          type: string
+          description: Detailed error message
+          example: "Invalid bearer token provided"
+
+    ValidationError:
+      type: object
+      properties:
+        error:
+          type: string
+          example: "Validation Error"
+        message:
+          type: string
+          example: "Missing required inputs"
+        details:
+          type: object
+          properties:
+            missing_inputs:
+              type: array
+              items:
+                type: string
+              example: ["budget", "interests"]
+
+  responses:
+    UnauthorizedError:
+      description: Authentication failed - check your bearer token
+      content:
+        application/json:
+          schema:
+            $ref: '#/components/schemas/Error'
+          example:
+            error: "Unauthorized"
+            message: "Invalid or missing bearer token"
+
+    NotFoundError:
+      description: Resource not found
+      content:
+        application/json:
+          schema:
+            $ref: '#/components/schemas/Error'
+          example:
+            error: "Not Found"
+            message: "The requested resource was not found"
+
+    ServerError:
+      description: Internal server error
+      content:
+        application/json:
+          schema:
+            $ref: '#/components/schemas/Error'
+          example:
+            error: "Internal Server Error" 
+            message: "An unexpected error occurred" 

docs/enterprise/features/hallucination-guardrail.mdx

@@ -0,0 +1,250 @@
+---
+title: Hallucination Guardrail
+description: "Prevent and detect AI hallucinations in your CrewAI tasks"
+icon: "shield-check"
+---
+
+## Overview
+
+The Hallucination Guardrail is an enterprise feature that validates AI-generated content to ensure it's grounded in facts and doesn't contain hallucinations. It analyzes task outputs against reference context and provides detailed feedback when potentially hallucinated content is detected.
+
+## What are Hallucinations?
+
+AI hallucinations occur when language models generate content that appears plausible but is factually incorrect or not supported by the provided context. The Hallucination Guardrail helps prevent these issues by:
+
+- Comparing outputs against reference context
+- Evaluating faithfulness to source material
+- Providing detailed feedback on problematic content
+- Supporting custom thresholds for validation strictness
+
+## Basic Usage
+
+### Setting Up the Guardrail
+
+```python
+from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
+from crewai import LLM
+
+# Basic usage - will use task's expected_output as context
+guardrail = HallucinationGuardrail(
+    llm=LLM(model="gpt-4o-mini")
+)
+
+# With explicit reference context
+context_guardrail = HallucinationGuardrail(
+    context="AI helps with various tasks including analysis and generation.",
+    llm=LLM(model="gpt-4o-mini")
+)
+```
+
+### Adding to Tasks
+
+```python
+from crewai import Task
+
+# Create your task with the guardrail
+task = Task(
+    description="Write a summary about AI capabilities",
+    expected_output="A factual summary based on the provided context",
+    agent=my_agent,
+    guardrail=guardrail  # Add the guardrail to validate output
+)
+```
+
+## Advanced Configuration
+
+### Custom Threshold Validation
+
+For stricter validation, you can set a custom faithfulness threshold (0-10 scale):
+
+```python
+# Strict guardrail requiring high faithfulness score
+strict_guardrail = HallucinationGuardrail(
+    context="Quantum computing uses qubits that exist in superposition states.",
+    llm=LLM(model="gpt-4o-mini"),
+    threshold=8.0  # Requires score >= 8 to pass validation
+)
+```
+
+### Including Tool Response Context
+
+When your task uses tools, you can include tool responses for more accurate validation:
+
+```python
+# Guardrail with tool response context
+weather_guardrail = HallucinationGuardrail(
+    context="Current weather information for the requested location",
+    llm=LLM(model="gpt-4o-mini"),
+    tool_response="Weather API returned: Temperature 22°C, Humidity 65%, Clear skies"
+)
+```
+
+## How It Works
+
+### Validation Process
+
+1. **Context Analysis**: The guardrail compares task output against the provided reference context
+2. **Faithfulness Scoring**: Uses an internal evaluator to assign a faithfulness score (0-10)
+3. **Verdict Determination**: Determines if content is faithful or contains hallucinations
+4. **Threshold Checking**: If a custom threshold is set, validates against that score
+5. **Feedback Generation**: Provides detailed reasons when validation fails
+
+### Validation Logic
+
+- **Default Mode**: Uses verdict-based validation (FAITHFUL vs HALLUCINATED)
+- **Threshold Mode**: Requires faithfulness score to meet or exceed the specified threshold
+- **Error Handling**: Gracefully handles evaluation errors and provides informative feedback
+
+## Guardrail Results
+
+The guardrail returns structured results indicating validation status:
+
+```python
+# Example of guardrail result structure
+{
+    "valid": False,
+    "feedback": "Content appears to be hallucinated (score: 4.2/10, verdict: HALLUCINATED). The output contains information not supported by the provided context."
+}
+```
+
+### Result Properties
+
+- **valid**: Boolean indicating whether the output passed validation
+- **feedback**: Detailed explanation when validation fails, including:
+  - Faithfulness score
+  - Verdict classification
+  - Specific reasons for failure
+
+## Integration with Task System
+
+### Automatic Validation
+
+When a guardrail is added to a task, it automatically validates the output before the task is marked as complete:
+
+```python
+# Task output validation flow
+task_output = agent.execute_task(task)
+validation_result = guardrail(task_output)
+
+if validation_result.valid:
+    # Task completes successfully
+    return task_output
+else:
+    # Task fails with validation feedback
+    raise ValidationError(validation_result.feedback)
+```
+
+### Event Tracking
+
+The guardrail integrates with CrewAI's event system to provide observability:
+
+- **Validation Started**: When guardrail evaluation begins
+- **Validation Completed**: When evaluation finishes with results
+- **Validation Failed**: When technical errors occur during evaluation
+
+## Best Practices
+
+### Context Guidelines
+
+<Steps>
+  <Step title="Provide Comprehensive Context">
+    Include all relevant factual information that the AI should base its output on:
+
+    ```python
+    context = """
+    Company XYZ was founded in 2020 and specializes in renewable energy solutions.
+    They have 150 employees and generated $50M revenue in 2023.
+    Their main products include solar panels and wind turbines.
+    """
+    ```
+  </Step>
+
+  <Step title="Keep Context Relevant">
+    Only include information directly related to the task to avoid confusion:
+
+    ```python
+    # Good: Focused context
+    context = "The current weather in New York is 18°C with light rain."
+
+    # Avoid: Unrelated information
+    context = "The weather is 18°C. The city has 8 million people. Traffic is heavy."
+    ```
+  </Step>
+
+  <Step title="Update Context Regularly">
+    Ensure your reference context reflects current, accurate information.
+  </Step>
+</Steps>
+
+### Threshold Selection
+
+<Steps>
+  <Step title="Start with Default Validation">
+    Begin without custom thresholds to understand baseline performance.
+  </Step>
+
+  <Step title="Adjust Based on Requirements">
+    - **High-stakes content**: Use threshold 8-10 for maximum accuracy
+    - **General content**: Use threshold 6-7 for balanced validation
+    - **Creative content**: Use threshold 4-5 or default verdict-based validation
+  </Step>
+
+  <Step title="Monitor and Iterate">
+    Track validation results and adjust thresholds based on false positives/negatives.
+  </Step>
+</Steps>
+
+## Performance Considerations
+
+### Impact on Execution Time
+
+- **Validation Overhead**: Each guardrail adds ~1-3 seconds per task
+- **LLM Efficiency**: Choose efficient models for evaluation (e.g., gpt-4o-mini)
+
+### Cost Optimization
+
+- **Model Selection**: Use smaller, efficient models for guardrail evaluation
+- **Context Size**: Keep reference context concise but comprehensive
+- **Caching**: Consider caching validation results for repeated content
+
+## Troubleshooting
+
+<Accordion title="Validation Always Fails">
+  **Possible Causes:**
+  - Context is too restrictive or unrelated to task output
+  - Threshold is set too high for the content type
+  - Reference context contains outdated information
+
+  **Solutions:**
+  - Review and update context to match task requirements
+  - Lower threshold or use default verdict-based validation
+  - Ensure context is current and accurate
+</Accordion>
+
+<Accordion title="False Positives (Valid Content Marked Invalid)">
+  **Possible Causes:**
+  - Threshold too high for creative or interpretive tasks
+  - Context doesn't cover all valid aspects of the output
+  - Evaluation model being overly conservative
+
+  **Solutions:**
+  - Lower threshold or use default validation
+  - Expand context to include broader acceptable content
+  - Test with different evaluation models
+</Accordion>
+
+<Accordion title="Evaluation Errors">
+  **Possible Causes:**
+  - Network connectivity issues
+  - LLM model unavailable or rate limited
+  - Malformed task output or context
+
+  **Solutions:**
+  - Check network connectivity and LLM service status
+  - Implement retry logic for transient failures
+  - Validate task output format before guardrail evaluation
+</Accordion>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with hallucination guardrail configuration or troubleshooting.
+</Card>

docs/enterprise/features/integrations.mdx

@@ -0,0 +1,185 @@
+---
+title: Integrations
+description: "Connected applications for your agents to take actions."
+icon: "plug"
+---
+
+## Overview
+
+Enable your agents to authenticate with any OAuth enabled provider and take actions. From Salesforce and HubSpot to Google and GitHub, we've got you covered with 16+ integrated services.
+
+<Frame>
+  ![Integrations](/images/enterprise/crew_connectors.png)
+</Frame>
+
+## Supported Integrations
+
+### **Communication & Collaboration**
+- **Gmail** - Manage emails and drafts
+- **Slack** - Workspace notifications and alerts
+- **Microsoft** - Office 365 and Teams integration
+
+### **Project Management**
+- **Jira** - Issue tracking and project management
+- **ClickUp** - Task and productivity management
+- **Asana** - Team task and project coordination
+- **Notion** - Page and database management
+- **Linear** - Software project and bug tracking
+- **GitHub** - Repository and issue management
+
+### **Customer Relationship Management**
+- **Salesforce** - CRM account and opportunity management
+- **HubSpot** - Sales pipeline and contact management
+- **Zendesk** - Customer support ticket management
+
+### **Business & Finance**
+- **Stripe** - Payment processing and customer management
+- **Shopify** - E-commerce store and product management
+
+### **Productivity & Storage**
+- **Google Sheets** - Spreadsheet data synchronization
+- **Google Calendar** - Event and schedule management
+- **Box** - File storage and document management
+
+and more to come!
+
+## Prerequisites
+
+Before using Authentication Integrations, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account. You can get started with a free trial.
+
+
+## Setting Up Integrations
+
+### 1. Connect Your Account
+
+1. Navigate to [CrewAI Enterprise](https://app.crewai.com)
+2. Go to **Integrations** tab - https://app.crewai.com/crewai_plus/connectors
+3. Click **Connect** on your desired service from the Authentication Integrations section
+4. Complete the OAuth authentication flow
+5. Grant necessary permissions for your use case
+6. Get your Enterprise Token from your [CrewAI Enterprise](https://app.crewai.com) account page - https://app.crewai.com/crewai_plus/settings/account
+
+<Frame>
+  ![Integrations](/images/enterprise/enterprise_action_auth_token.png)
+</Frame>
+
+### 2. Install Integration Tools
+
+All you need is the latest version of `crewai-tools` package.
+
+```bash
+uv add crewai-tools
+```
+
+## Usage Examples
+
+### Basic Usage
+<Tip>
+  All the services you are authenticated into will be available as tools. So all you need to do is add the `CrewaiEnterpriseTools` to your agent and you are good to go.
+</Tip>
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Gmail tool will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+# print the tools
+print(enterprise_tools)
+
+# Create an agent with Gmail capabilities
+email_agent = Agent(
+    role="Email Manager",
+    goal="Manage and organize email communications",
+    backstory="An AI assistant specialized in email management and communication.",
+    tools=enterprise_tools
+)
+
+# Task to send an email
+email_task = Task(
+    description="Draft and send a follow-up email to john@example.com about the project update",
+    agent=email_agent,
+    expected_output="Confirmation that email was sent successfully"
+)
+
+# Run the task
+crew = Crew(
+    agents=[email_agent],
+    tasks=[email_task]
+)
+
+# Run the crew
+crew.kickoff()
+```
+
+### Filtering Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    actions_list=["gmail_find_email"] # only gmail_find_email tool will be available
+)
+gmail_tool = enterprise_tools[0]
+
+gmail_agent = Agent(
+    role="Gmail Manager",
+    goal="Manage gmail communications and notifications",
+    backstory="An AI assistant that helps coordinate gmail communications.",
+    tools=[gmail_tool]
+)
+
+notification_task = Task(
+    description="Find the email from john@example.com",
+    agent=gmail_agent,
+    expected_output="Email found from john@example.com"
+)
+
+# Run the task
+crew = Crew(
+    agents=[slack_agent],
+    tasks=[notification_task]
+)
+```
+
+## Best Practices
+
+### Security
+- **Principle of Least Privilege**: Only grant the minimum permissions required for your agents' tasks
+- **Regular Audits**: Periodically review connected integrations and their permissions
+- **Secure Credentials**: Never hardcode credentials; use CrewAI's secure authentication flow
+
+
+### Filtering Tools
+On a deployed crew, you can specify which actions are avialbel for each integration from the settings page of the service you connected to.
+
+<Frame>
+  ![Integrations](/images/enterprise/filtering_enterprise_action_tools.png)
+</Frame>
+
+
+### Scoped Deployments for multi user organizations
+You can deploy your crew and scope each integration to a specific user. For example, a crew that connects to google can use a specific user's gmail account.
+
+<Tip>
+  This is useful for multi user organizations where you want to scope the integration to a specific user.
+</Tip>
+
+
+Use the `user_bearer_token` to scope the integration to a specific user so that when the crew is kicked off, it will use the user's bearer token to authenticate with the integration. If user is not logged in, then the crew will not use any connected integrations. Use the default bearer token to authenticate with the integrations thats deployed with the crew.
+
+<Frame>
+  ![Integrations](/images/enterprise/user_bearer_token.png)
+</Frame>
+
+
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with integration setup or troubleshooting.
+</Card>

docs/enterprise/features/tool-repository.mdx

@@ -0,0 +1,107 @@
+---
+title: Tool Repository
+description: "Using the Tool Repository to manage your tools"
+icon: "toolbox"
+---
+
+## Overview
+
+The Tool Repository is a package manager for CrewAI tools. It allows users to publish, install, and manage tools that integrate with CrewAI crews and flows.
+
+Tools can be:
+
+- **Private**: accessible only within your organization (default)
+- **Public**: accessible to all CrewAI users if published with the `--public` flag
+
+The repository is not a version control system. Use Git to track code changes and enable collaboration.
+
+## Prerequisites
+
+Before using the Tool Repository, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account
+- [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) installed
+- uv>=0.5.0 installed. Check out [how to upgrade](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv)
+- [Git](https://git-scm.com) installed and configured
+- Access permissions to publish or install tools in your CrewAI Enterprise organization
+
+## Installing Tools
+
+To install a tool:
+
+```bash
+crewai tool install <tool-name>
+```
+
+This installs the tool and adds it to `pyproject.toml`.
+
+## Creating and Publishing Tools
+
+To create a new tool project:
+
+```bash
+crewai tool create <tool-name>
+```
+
+This generates a scaffolded tool project locally.
+
+After making changes, initialize a Git repository and commit the code:
+
+```bash
+git init
+git add .
+git commit -m "Initial version"
+```
+
+To publish the tool:
+
+```bash
+crewai tool publish
+```
+
+By default, tools are published as private. To make a tool public:
+
+```bash
+crewai tool publish --public
+```
+
+For more details on how to build tools, see [Creating your own tools](https://docs.crewai.com/concepts/tools#creating-your-own-tools).
+
+## Updating Tools
+
+To update a published tool:
+
+1. Modify the tool locally
+2. Update the version in `pyproject.toml` (e.g., from `0.1.0` to `0.1.1`)
+3. Commit the changes and publish
+
+```bash
+git commit -m "Update version to 0.1.1"
+crewai tool publish
+```
+
+## Deleting Tools
+
+To delete a tool:
+
+1. Go to [CrewAI Enterprise](https://app.crewai.com)
+2. Navigate to **Tools**
+3. Select the tool
+4. Click **Delete**
+
+<Warning>
+Deletion is permanent. Deleted tools cannot be restored or re-installed.
+</Warning>
+
+## Security Checks
+
+Every published version undergoes automated security checks, and are only available to install after they pass. 
+
+You can check the security check status of a tool at:
+
+`CrewAI Enterprise > Tools > Your Tool > Versions`
+
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with API integration or troubleshooting.
+</Card>

docs/enterprise/features/traces.mdx

@@ -0,0 +1,146 @@
+---
+title: Traces
+description: "Using Traces to monitor your Crews"
+icon: "timeline"
+---
+
+## Overview
+
+Traces provide comprehensive visibility into your crew executions, helping you monitor performance, debug issues, and optimize your AI agent workflows.
+
+## What are Traces?
+
+Traces in CrewAI Enterprise are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record:
+
+- Agent thoughts and reasoning
+- Task execution details
+- Tool usage and outputs
+- Token consumption metrics
+- Execution times
+- Cost estimates
+
+<Frame>
+  ![Traces Overview](/images/enterprise/traces-overview.png)
+</Frame>
+
+## Accessing Traces
+
+<Steps>
+  <Step title="Navigate to the Traces Tab">
+    Once in your CrewAI Enterprise dashboard, click on the **Traces** to view all execution records.
+  </Step>
+  
+  <Step title="Select an Execution">
+    You'll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace.
+  </Step>
+</Steps>
+
+## Understanding the Trace Interface
+
+The trace interface is divided into several sections, each providing different insights into your crew's execution:
+
+### 1. Execution Summary
+
+The top section displays high-level metrics about the execution:
+
+- **Total Tokens**: Number of tokens consumed across all tasks
+- **Prompt Tokens**: Tokens used in prompts to the LLM
+- **Completion Tokens**: Tokens generated in LLM responses
+- **Requests**: Number of API calls made
+- **Execution Time**: Total duration of the crew run
+- **Estimated Cost**: Approximate cost based on token usage
+
+<Frame>
+  ![Execution Summary](/images/enterprise/trace-summary.png)
+</Frame>
+
+### 2. Tasks & Agents
+
+This section shows all tasks and agents that were part of the crew execution:
+
+- Task name and agent assignment
+- Agents and LLMs used for each task
+- Status (completed/failed)
+- Individual execution time of the task
+
+<Frame>
+  ![Task List](/images/enterprise/trace-tasks.png)
+</Frame>
+
+### 3. Final Output
+
+Displays the final result produced by the crew after all tasks are completed.
+
+<Frame>
+  ![Final Output](/images/enterprise/final-output.png)
+</Frame>
+
+### 4. Execution Timeline
+
+A visual representation of when each task started and ended, helping you identify bottlenecks or parallel execution patterns.
+
+<Frame>
+  ![Execution Timeline](/images/enterprise/trace-timeline.png)
+</Frame>
+
+### 5. Detailed Task View
+
+When you click on a specific task in the timeline or task list, you'll see:
+
+<Frame>
+  ![Detailed Task View](/images/enterprise/trace-detailed-task.png)
+</Frame>
+
+- **Task Key**: Unique identifier for the task
+- **Task ID**: Technical identifier in the system
+- **Status**: Current state (completed/running/failed)
+- **Agent**: Which agent performed the task
+- **LLM**: Language model used for this task
+- **Start/End Time**: When the task began and completed
+- **Execution Time**: Duration of this specific task
+- **Task Description**: What the agent was instructed to do
+- **Expected Output**: What output format was requested
+- **Input**: Any input provided to this task from previous tasks
+- **Output**: The actual result produced by the agent
+
+
+## Using Traces for Debugging
+
+Traces are invaluable for troubleshooting issues with your crews:
+
+<Steps>
+  <Step title="Identify Failure Points">
+    When a crew execution doesn't produce the expected results, examine the trace to find where things went wrong. Look for:
+    
+    - Failed tasks
+    - Unexpected agent decisions
+    - Tool usage errors
+    - Misinterpreted instructions
+
+    <Frame>
+      ![Failure Points](/images/enterprise/failure.png)
+    </Frame>
+  </Step>
+  
+  <Step title="Optimize Performance">
+    Use execution metrics to identify performance bottlenecks:
+    
+    - Tasks that took longer than expected
+    - Excessive token usage
+    - Redundant tool operations
+    - Unnecessary API calls
+  </Step>
+  
+  <Step title="Improve Cost Efficiency">
+    Analyze token usage and cost estimates to optimize your crew's efficiency:
+    
+    - Consider using smaller models for simpler tasks
+    - Refine prompts to be more concise
+    - Cache frequently accessed information
+    - Structure tasks to minimize redundant operations
+  </Step>
+</Steps>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with trace analysis or any other CrewAI Enterprise features.
+</Card>

docs/enterprise/features/webhook-streaming.mdx

@@ -0,0 +1,82 @@
+---
+title: Webhook Streaming
+description: "Using Webhook Streaming to stream events to your webhook"
+icon: "webhook"
+---
+
+## Overview
+
+Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to 
+CrewAI Enterprise, such as model calls, tool usage, and flow steps.
+
+## Usage
+
+When using the Kickoff API, include a `webhooks` object to your request, for example:
+
+```json
+{
+  "inputs": {"foo": "bar"},
+  "webhooks": {
+    "events": ["crew_kickoff_started", "llm_call_started"],
+    "url": "https://your.endpoint/webhook",
+    "realtime": false,
+    "authentication": {
+      "strategy": "bearer",
+      "token": "my-secret-token"
+    }
+  }
+}
+```
+
+If `realtime` is set to `true`, each event is delivered individually and immediately, at the cost of crew/flow performance.
+
+## Webhook Format
+
+Each webhook sends a list of events:
+
+```json
+{
+  "events": [
+    {
+      "id": "event-id",
+      "execution_id": "crew-run-id",
+      "timestamp": "2025-02-16T10:58:44.965Z",
+      "type": "llm_call_started",
+      "data": {
+        "model": "gpt-4",
+        "messages": [
+          {"role": "system", "content": "You are an assistant."},
+          {"role": "user", "content": "Summarize this article."}
+        ]
+      }
+    }
+  ]
+}
+```
+
+The `data` object structure varies by event type. Refer to the [event list](https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events) on GitHub.
+
+As requests are sent over HTTP, the order of events can't be guaranteed. If you need ordering, use the `timestamp` field.
+
+## Supported Events
+
+CrewAI supports both system events and custom events in Enterprise Event Streaming. These events are sent to your configured webhook endpoint during crew and flow execution.
+
+- `crew_kickoff_started`
+- `crew_step_started`
+- `crew_step_completed`
+- `crew_execution_completed`
+- `llm_call_started`
+- `llm_call_completed`
+- `tool_usage_started`
+- `tool_usage_completed`
+- `crew_test_failed`
+- *...and others*
+
+Event names match the internal event bus. See [GitHub source](https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events) for the full list.
+
+You can emit your own custom events, and they will be delivered through the webhook stream alongside system events.
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with webhook integration or troubleshooting.
+</Card>

docs/enterprise/guides/azure-openai-setup.mdx

@@ -0,0 +1,51 @@
+---
+title: "Azure OpenAI Setup"
+description: "Configure Azure OpenAI with Crew Studio for enterprise LLM connections"
+icon: "microsoft"
+---
+
+This guide walks you through connecting Azure OpenAI with Crew Studio for seamless enterprise AI operations.
+
+## Setup Process
+
+<Steps>
+    <Step title="Access Azure OpenAI Studio">
+        1. In Azure, go to `Azure AI Services > select your deployment > open Azure OpenAI Studio`.
+        2. On the left menu, click `Deployments`. If you don't have one, create a deployment with your desired model.
+        3. Once created, select your deployment and locate the `Target URI` and `Key` on the right side of the page. Keep this page open, as you'll need this information.
+            <Frame>
+                <img src="/images/enterprise/azure-openai-studio.png" alt="Azure OpenAI Studio" />
+            </Frame>
+    </Step>
+
+    <Step title="Configure CrewAI Enterprise Connection">
+        4. In another tab, open `CrewAI Enterprise > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure.
+        5. On the same page, add environment variables from step 3:
+            - One named `AZURE_DEPLOYMENT_TARGET_URL` (using the Target URI). The URL should look like this: https://your-deployment.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview
+            - Another named `AZURE_API_KEY` (using the Key).
+        6. Click `Add Connection` to save your LLM Connection.
+    </Step>
+
+    <Step title="Set Default Configuration">
+        7. In `CrewAI Enterprise > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults.
+    </Step>
+
+    <Step title="Configure Network Access">
+        8. Ensure network access settings:
+            - In Azure, go to `Azure OpenAI > select your deployment`.
+            - Navigate to `Resource Management > Networking`.
+            - Ensure that `Allow access from all networks` is enabled. If this setting is restricted, CrewAI may be blocked from accessing your Azure OpenAI endpoint.
+    </Step>
+</Steps>
+
+## Verification
+
+You're all set! Crew Studio will now use your Azure OpenAI connection. Test the connection by creating a simple crew or task to ensure everything is working properly.
+
+## Troubleshooting
+
+If you encounter issues:
+- Verify the Target URI format matches the expected pattern
+- Check that the API key is correct and has proper permissions
+- Ensure network access is configured to allow CrewAI connections
+- Confirm the deployment model matches what you've configured in CrewAI 

docs/enterprise/guides/build-crew.mdx

@@ -0,0 +1,43 @@
+---
+title: "Build Crew"
+description: "A Crew is a group of agents that work together to complete a task."
+icon: "people-arrows"
+---
+
+## Overview
+
+[CrewAI Enterprise](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments.
+
+## Getting Started
+
+<iframe 
+    width="100%"
+    height="400"
+    src="https://www.youtube.com/embed/-kSOTtYzgEw"
+    title="Building Crews with CrewAI CLI" 
+    frameborder="0"
+    style={{ borderRadius: '10px' }}
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowfullscreen
+></iframe>
+
+### Installation and Setup
+
+<Card title="Follow Standard Installation" icon="wrench" href="/installation">
+  Follow our standard installation guide to set up CrewAI CLI and create your first project.
+</Card>
+
+### Building Your Crew
+
+<Card title="Quickstart Tutorial" icon="rocket" href="/quickstart">
+  Follow our quickstart guide to create your first agent crew using YAML configuration.
+</Card>
+
+## Support and Resources
+
+For Enterprise-specific support or questions, contact our dedicated support team at [support@crewai.com](mailto:support@crewai.com).
+
+
+<Card title="Schedule a Demo" icon="calendar" href="mailto:support@crewai.com">
+  Book time with our team to learn more about Enterprise features and how they can benefit your organization.
+</Card>

docs/enterprise/guides/deploy-crew.mdx

@@ -0,0 +1,293 @@
+---
+title: "Deploy Crew"
+description: "Deploying a Crew on CrewAI Enterprise"
+icon: "rocket"
+---
+
+<Note>
+After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI Enterprise platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow.
+</Note>
+
+## Prerequisites
+
+<CardGroup cols={2}>
+  <Card title="Crew Ready for Deployment" icon="users">
+    You should have a working crew either built locally or created through Crew Studio
+  </Card>
+  <Card title="GitHub Repository" icon="github">
+    Your crew code should be in a GitHub repository (for GitHub integration method)
+  </Card>
+</CardGroup>
+
+## Option 1: Deploy Using CrewAI CLI
+
+The CLI provides the fastest way to deploy locally developed crews to the Enterprise platform.
+
+<Steps>
+  <Step title="Install CrewAI CLI">
+    If you haven't already, install the CrewAI CLI:
+
+    ```bash
+    pip install crewai[tools]
+    ```
+
+    <Tip>
+    The CLI comes with the main CrewAI package, but the `[tools]` extra ensures you have all deployment dependencies.
+    </Tip>
+
+  </Step>
+
+  <Step title="Authenticate with the Enterprise Platform">
+    First, you need to authenticate your CLI with the CrewAI Enterprise platform:
+
+    ```bash
+    # If you already have a CrewAI Enterprise account
+    crewai login
+
+    # If you're creating a new account
+    crewai signup
+    ```
+
+    When you run either command, the CLI will:
+    1. Display a URL and a unique device code
+    2. Open your browser to the authentication page
+    3. Prompt you to confirm the device
+    4. Complete the authentication process
+
+    Upon successful authentication, you'll see a confirmation message in your terminal!
+
+  </Step>
+
+  <Step title="Create a Deployment">
+
+    From your project directory, run:
+
+    ```bash
+    crewai deploy create
+    ```
+
+    This command will:
+    1. Detect your GitHub repository information
+    2. Identify environment variables in your local `.env` file
+    3. Securely transfer these variables to the Enterprise platform
+    4. Create a new deployment with a unique identifier
+
+    On successful creation, you'll see a message like:
+    ```shell
+    Deployment created successfully!
+    Name: your_project_name
+    Deployment ID: 01234567-89ab-cdef-0123-456789abcdef
+    Current Status: Deploy Enqueued
+    ```
+
+  </Step>
+
+  <Step title="Monitor Deployment Progress">
+
+    Track the deployment status with:
+
+    ```bash
+    crewai deploy status
+    ```
+
+    For detailed logs of the build process:
+
+    ```bash
+    crewai deploy logs
+    ```
+
+    <Tip>
+    The first deployment typically takes 10-15 minutes as it builds the container images. Subsequent deployments are much faster.
+    </Tip>
+
+  </Step>
+</Steps>
+
+## Additional CLI Commands
+
+The CrewAI CLI offers several commands to manage your deployments:
+
+  ```bash
+  # List all your deployments
+  crewai deploy list
+
+  # Get the status of your deployment
+  crewai deploy status
+
+  # View the logs of your deployment
+  crewai deploy logs
+
+  # Push updates after code changes
+  crewai deploy push
+
+  # Remove a deployment
+  crewai deploy remove <deployment_id>
+  ``` 
+
+## Option 2: Deploy Directly via Web Interface
+
+You can also deploy your crews directly through the CrewAI Enterprise web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine.
+
+<Steps>
+
+  <Step title="Pushing to GitHub">
+
+  You need to push your crew to a GitHub repository. If you haven't created a crew yet, you can [follow this tutorial](/quickstart).
+
+  </Step>
+
+  <Step title="Connecting GitHub to CrewAI Enterprise">
+
+    1. Log in to [CrewAI Enterprise](https://app.crewai.com)
+    2. Click on the button "Connect GitHub" 
+
+    <Frame>
+      ![Connect GitHub Button](/images/enterprise/connect-github.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="Select the Repository">
+
+    After connecting your GitHub account, you'll be able to select which repository to deploy:
+
+    <Frame>
+      ![Select Repository](/images/enterprise/select-repo.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="Set Environment Variables">
+
+    Before deploying, you'll need to set up your environment variables to connect to your LLM provider or other services:
+
+    1. You can add variables individually or in bulk
+    2. Enter your environment variables in `KEY=VALUE` format (one per line)
+
+    <Frame>
+      ![Set Environment Variables](/images/enterprise/set-env-variables.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="Deploy Your Crew">
+
+    1. Click the "Deploy" button to start the deployment process
+    2. You can monitor the progress through the progress bar
+    3. The first deployment typically takes around 10-15 minutes; subsequent deployments will be faster
+
+    <Frame>
+      ![Deploy Progress](/images/enterprise/deploy-progress.png)
+    </Frame>
+
+    Once deployment is complete, you'll see:
+    - Your crew's unique URL
+    - A Bearer token to protect your crew API
+    - A "Delete" button if you need to remove the deployment
+
+  </Step>
+
+</Steps>
+
+## ⚠️ Environment Variable Security Requirements
+
+<Warning>
+**Important**: CrewAI Enterprise has security restrictions on environment variable names that can cause deployment failures if not followed.
+</Warning>
+
+### Blocked Environment Variable Patterns
+
+For security reasons, the following environment variable naming patterns are **automatically filtered** and will cause deployment issues:
+
+**Blocked Patterns:**
+- Variables ending with `_TOKEN` (e.g., `MY_API_TOKEN`)
+- Variables ending with `_PASSWORD` (e.g., `DB_PASSWORD`) 
+- Variables ending with `_SECRET` (e.g., `API_SECRET`)
+- Variables ending with `_KEY` in certain contexts
+
+**Specific Blocked Variables:**
+- `GITHUB_USER`, `GITHUB_TOKEN`
+- `AWS_REGION`, `AWS_DEFAULT_REGION` 
+- Various internal CrewAI system variables
+
+### Allowed Exceptions
+
+Some variables are explicitly allowed despite matching blocked patterns:
+- `AZURE_AD_TOKEN`
+- `AZURE_OPENAI_AD_TOKEN` 
+- `ENTERPRISE_ACTION_TOKEN`
+- `CREWAI_ENTEPRISE_TOOLS_TOKEN`
+
+### How to Fix Naming Issues
+
+If your deployment fails due to environment variable restrictions:
+
+```bash
+# ❌ These will cause deployment failures
+OPENAI_TOKEN=sk-...
+DATABASE_PASSWORD=mypassword
+API_SECRET=secret123
+
+# ✅ Use these naming patterns instead  
+OPENAI_API_KEY=sk-...
+DATABASE_CREDENTIALS=mypassword
+API_CONFIG=secret123
+```
+
+### Best Practices
+
+1. **Use standard naming conventions**: `PROVIDER_API_KEY` instead of `PROVIDER_TOKEN`
+2. **Test locally first**: Ensure your crew works with the renamed variables
+3. **Update your code**: Change any references to the old variable names
+4. **Document changes**: Keep track of renamed variables for your team
+
+<Tip>
+If you encounter deployment failures with cryptic environment variable errors, check your variable names against these patterns first.
+</Tip>
+
+### Interact with Your Deployed Crew
+
+Once deployment is complete, you can access your crew through:
+
+1. **REST API**: The platform generates a unique HTTPS endpoint with these key routes:
+   - `/inputs`: Lists the required input parameters
+   - `/kickoff`: Initiates an execution with provided inputs
+   - `/status/{kickoff_id}`: Checks the execution status
+
+2. **Web Interface**: Visit [app.crewai.com](https://app.crewai.com) to access:
+   - **Status tab**: View deployment information, API endpoint details, and authentication token
+   - **Run tab**: Visual representation of your crew's structure
+   - **Executions tab**: History of all executions
+   - **Metrics tab**: Performance analytics
+   - **Traces tab**: Detailed execution insights
+
+### Trigger an Execution
+
+From the Enterprise dashboard, you can:
+
+1. Click on your crew's name to open its details
+2. Select "Trigger Crew" from the management interface
+3. Enter the required inputs in the modal that appears
+4. Monitor progress as the execution moves through the pipeline
+
+### Monitoring and Analytics
+
+The Enterprise platform provides comprehensive observability features:
+
+- **Execution Management**: Track active and completed runs
+- **Traces**: Detailed breakdowns of each execution
+- **Metrics**: Token usage, execution times, and costs
+- **Timeline View**: Visual representation of task sequences
+
+### Advanced Features
+
+The Enterprise platform also offers:
+
+- **Environment Variables Management**: Securely store and manage API keys
+- **LLM Connections**: Configure integrations with various LLM providers
+- **Custom Tools Repository**: Create, share, and install tools
+- **Crew Studio**: Build crews through a chat interface without writing code
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with deployment issues or questions about the Enterprise platform.
+</Card>

docs/enterprise/guides/enable-crew-studio.mdx

@@ -0,0 +1,166 @@
+---
+title: "Enable Crew Studio"
+description: "Enabling Crew Studio on CrewAI Enterprise"
+icon: "comments"
+---
+
+<Tip>
+Crew Studio is a powerful **no-code/low-code** tool that allows you to quickly scaffold or build Crews through a conversational interface. 
+</Tip>
+
+## What is Crew Studio?
+
+Crew Studio is an innovative way to create AI agent crews without writing code. 
+
+<Frame>
+  ![Crew Studio Interface](/images/enterprise/crew-studio-interface.png)
+</Frame>
+
+With Crew Studio, you can:
+
+- Chat with the Crew Assistant to describe your problem
+- Automatically generate agents and tasks
+- Select appropriate tools
+- Configure necessary inputs
+- Generate downloadable code for customization
+- Deploy directly to the CrewAI Enterprise platform
+
+## Configuration Steps
+
+Before you can start using Crew Studio, you need to configure your LLM connections:
+
+<Steps>
+  <Step title="Set Up LLM Connection">
+    Go to the **LLM Connections** tab in your CrewAI Enterprise dashboard and create a new LLM connection.
+
+    <Note>
+      Feel free to use any LLM provider you want that is supported by CrewAI.
+    </Note>
+    
+    Configure your LLM connection:
+    
+    - Enter a `Connection Name` (e.g., `OpenAI`)
+    - Select your model provider: `openai` or `azure`
+    - Select models you'd like to use in your Studio-generated Crews
+      - We recommend at least `gpt-4o`, `o1-mini`, and `gpt-4o-mini`
+    - Add your API key as an environment variable:
+      - For OpenAI: Add `OPENAI_API_KEY` with your API key
+      - For Azure OpenAI: Refer to [this article](https://blog.crewai.com/configuring-azure-openai-with-crewai-a-comprehensive-guide/) for configuration details
+    - Click `Add Connection` to save your configuration
+    
+    <Frame>
+      ![LLM Connection Configuration](/images/enterprise/llm-connection-config.png)
+    </Frame>
+  </Step>
+  
+  <Step title="Verify Connection Added">
+    Once you complete the setup, you'll see your new connection added to the list of available connections.
+    
+    <Frame>
+      ![Connection Added](/images/enterprise/connection-added.png)
+    </Frame>
+  </Step>
+  
+  <Step title="Configure LLM Defaults">
+    In the main menu, go to **Settings → Defaults** and configure the LLM Defaults settings:
+    
+    - Select default models for agents and other components
+    - Set default configurations for Crew Studio
+    
+    Click `Save Settings` to apply your changes.
+    
+    <Frame>
+      ![LLM Defaults Configuration](/images/enterprise/llm-defaults.png)
+    </Frame>
+  </Step>
+</Steps>
+
+## Using Crew Studio
+
+Now that you've configured your LLM connection and default settings, you're ready to start using Crew Studio!
+
+<Steps>
+  <Step title="Access Studio">
+    Navigate to the **Studio** section in your CrewAI Enterprise dashboard.
+  </Step>
+  
+  <Step title="Start a Conversation">
+    Start a conversation with the Crew Assistant by describing the problem you want to solve:
+    
+    ```md
+    I need a crew that can research the latest AI developments and create a summary report.
+    ```
+    
+    The Crew Assistant will ask clarifying questions to better understand your requirements.
+  </Step>
+  
+  <Step title="Review Generated Crew">
+    Review the generated crew configuration, including:
+    
+    - Agents and their roles
+    - Tasks to be performed
+    - Required inputs
+    - Tools to be used
+    
+    This is your opportunity to refine the configuration before proceeding.
+  </Step>
+  
+  <Step title="Deploy or Download">
+    Once you're satisfied with the configuration, you can:
+    
+    - Download the generated code for local customization
+    - Deploy the crew directly to the CrewAI Enterprise platform
+    - Modify the configuration and regenerate the crew
+  </Step>
+  
+  <Step title="Test Your Crew">
+    After deployment, test your crew with sample inputs to ensure it performs as expected.
+  </Step>
+</Steps>
+
+<Tip>
+For best results, provide clear, detailed descriptions of what you want your crew to accomplish. Include specific inputs and expected outputs in your description.
+</Tip>
+
+## Example Workflow
+
+Here's a typical workflow for creating a crew with Crew Studio:
+
+<Steps>
+  <Step title="Describe Your Problem">
+    Start by describing your problem:
+    
+    ```md
+    I need a crew that can analyze financial news and provide investment recommendations
+    ```
+  </Step>
+  
+  <Step title="Answer Questions">
+    Respond to clarifying questions from the Crew Assistant to refine your requirements.
+  </Step>
+  
+  <Step title="Review the Plan">
+    Review the generated crew plan, which might include:
+    
+    - A Research Agent to gather financial news
+    - An Analysis Agent to interpret the data
+    - A Recommendations Agent to provide investment advice
+  </Step>
+  
+  <Step title="Approve or Modify">
+    Approve the plan or request changes if necessary.
+  </Step>
+  
+  <Step title="Download or Deploy">
+    Download the code for customization or deploy directly to the platform.
+  </Step>
+  
+  <Step title="Test and Refine">
+    Test your crew with sample inputs and refine as needed.
+  </Step>
+</Steps>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Crew Studio or any other CrewAI Enterprise features.
+</Card>
+

docs/enterprise/guides/hubspot-trigger.mdx

@@ -0,0 +1,53 @@
+---
+title: "HubSpot Trigger"
+description: "Trigger CrewAI crews directly from HubSpot Workflows"
+icon: "hubspot"
+---
+
+This guide provides a step-by-step process to set up HubSpot triggers for CrewAI Enterprise, enabling you to initiate crews directly from HubSpot Workflows.
+
+## Prerequisites
+
+- A CrewAI Enterprise account
+- A HubSpot account with the [HubSpot Workflows](https://knowledge.hubspot.com/workflows/create-workflows) feature
+
+## Setup Steps
+
+<Steps>
+    <Step title="Connect your HubSpot account with CrewAI Enterprise">
+        - Log in to your `CrewAI Enterprise account > Triggers`
+        - Select `HubSpot` from the list of available triggers
+        - Choose the HubSpot account you want to connect with CrewAI Enterprise
+        - Follow the on-screen prompts to authorize CrewAI Enterprise access to your HubSpot account
+        - A confirmation message will appear once HubSpot is successfully connected with CrewAI Enterprise
+    </Step>
+    <Step title="Create a HubSpot Workflow">
+        - Log in to your `HubSpot account > Automations > Workflows > New workflow`
+        - Select the workflow type that fits your needs (e.g., Start from scratch)
+        - In the workflow builder, click the Plus (+) icon to add a new action.
+        - Choose `Integrated apps > CrewAI > Kickoff a Crew`.
+        - Select the Crew you want to initiate.
+        - Click `Save` to add the action to your workflow
+        <Frame>
+            <img src="/images/enterprise/hubspot-workflow-1.png" alt="HubSpot Workflow 1" />
+        </Frame>
+    </Step>
+    <Step title="Use Crew results with other actions">
+        - After the Kickoff a Crew step, click the Plus (+) icon to add a new action.
+        - For example, to send an internal email notification, choose `Communications > Send internal email notification`
+        - In the Body field, click `Insert data`, select `View properties or action outputs from > Action outputs > Crew Result` to include Crew data in the email
+            <Frame>
+                <img src="/images/enterprise/hubspot-workflow-2.png" alt="HubSpot Workflow 2" />
+            </Frame>
+        - Configure any additional actions as needed
+        - Review your workflow steps to ensure everything is set up correctly
+        - Activate the workflow
+            <Frame>
+                <img src="/images/enterprise/hubspot-workflow-3.png" alt="HubSpot Workflow 3" />
+            </Frame>
+    </Step>
+</Steps>
+
+## Additional Resources
+
+For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows). 

docs/enterprise/guides/human-in-the-loop.mdx

@@ -0,0 +1,78 @@
+---
+title: "HITL Workflows"
+description: "Learn how to implement Human-In-The-Loop workflows in CrewAI for enhanced decision-making"
+icon: "user-check"
+---
+
+Human-In-The-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. This guide shows you how to implement HITL within CrewAI.
+
+## Setting Up HITL Workflows
+
+<Steps>
+    <Step title="Configure Your Task">
+        Set up your task with human input enabled:
+        <Frame>
+            <img src="/images/enterprise/crew-human-input.png" alt="Crew Human Input" />
+        </Frame>
+    </Step>
+
+    <Step title="Provide Webhook URL">
+        When kicking off your crew, include a webhook URL for human input:
+        <Frame>
+            <img src="/images/enterprise/crew-webhook-url.png" alt="Crew Webhook URL" />
+        </Frame>
+    </Step>
+
+    <Step title="Receive Webhook Notification">
+        Once the crew completes the task requiring human input, you'll receive a webhook notification containing:
+            - **Execution ID**
+            - **Task ID**
+            - **Task output**
+    </Step>
+
+    <Step title="Review Task Output">
+        The system will pause in the `Pending Human Input` state. Review the task output carefully.
+    </Step>
+
+    <Step title="Submit Human Feedback">
+        Call the resume endpoint of your crew with the following information:
+        <Frame>
+            <img src="/images/enterprise/crew-resume-endpoint.png" alt="Crew Resume Endpoint" />
+        </Frame>
+        <Warning>
+            **Feedback Impact on Task Execution**:
+            It's crucial to exercise care when providing feedback, as the entire feedback content will be incorporated as additional context for further task executions.
+        </Warning>
+        This means:
+        - All information in your feedback becomes part of the task's context.
+        - Irrelevant details may negatively influence it.
+        - Concise, relevant feedback helps maintain task focus and efficiency.
+        - Always review your feedback carefully before submission to ensure it contains only pertinent information that will positively guide the task's execution.
+    </Step>
+    <Step title="Handle Negative Feedback">
+        If you provide negative feedback:
+        - The crew will retry the task with added context from your feedback.
+        - You'll receive another webhook notification for further review.
+        - Repeat steps 4-6 until satisfied.
+    </Step>
+
+    <Step title="Execution Continuation">
+        When you submit positive feedback, the execution will proceed to the next steps.
+    </Step>
+</Steps>
+
+## Best Practices
+
+- **Be Specific**: Provide clear, actionable feedback that directly addresses the task at hand
+- **Stay Relevant**: Only include information that will help improve the task execution
+- **Be Timely**: Respond to HITL prompts promptly to avoid workflow delays
+- **Review Carefully**: Double-check your feedback before submitting to ensure accuracy
+
+## Common Use Cases
+
+HITL workflows are particularly valuable for:
+- Quality assurance and validation
+- Complex decision-making scenarios
+- Sensitive or high-stakes operations
+- Creative tasks requiring human judgment
+- Compliance and regulatory reviews 

docs/enterprise/guides/kickoff-crew.mdx

@@ -0,0 +1,186 @@
+---
+title: "Kickoff Crew"
+description: "Kickoff a Crew on CrewAI Enterprise"
+icon: "flag-checkered"
+---
+
+## Overview
+
+Once you've deployed your crew to the CrewAI Enterprise platform, you can kickoff executions through the web interface or the API. This guide covers both approaches.
+
+## Method 1: Using the Web Interface
+
+### Step 1: Navigate to Your Deployed Crew
+
+1. Log in to [CrewAI Enterprise](https://app.crewai.com)
+2. Click on the crew name from your projects list
+3. You'll be taken to the crew's detail page
+
+<Frame>
+  ![Crew Dashboard](/images/enterprise/crew-dashboard.png)
+</Frame>
+
+### Step 2: Initiate Execution
+
+From your crew's detail page, you have two options to kickoff an execution:
+
+#### Option A: Quick Kickoff
+
+1. Click the `Kickoff` link in the Test Endpoints section
+2. Enter the required input parameters for your crew in the JSON editor
+3. Click the `Send Request` button
+
+<Frame>
+  ![Kickoff Endpoint](/images/enterprise/kickoff-endpoint.png)
+</Frame>
+
+#### Option B: Using the Visual Interface
+
+1. Click the `Run` tab in the crew detail page
+2. Enter the required inputs in the form fields
+3. Click the `Run Crew` button
+
+<Frame>
+  ![Run Crew](/images/enterprise/run-crew.png)
+</Frame>
+
+### Step 3: Monitor Execution Progress
+
+After initiating the execution:
+
+1. You'll receive a response containing a `kickoff_id` - **copy this ID**
+2. This ID is essential for tracking your execution
+
+<Frame>
+  ![Copy Task ID](/images/enterprise/copy-task-id.png)
+</Frame>
+
+### Step 4: Check Execution Status
+
+To monitor the progress of your execution:
+
+1. Click the "Status" endpoint in the Test Endpoints section
+2. Paste the `kickoff_id` into the designated field
+3. Click the "Get Status" button
+
+<Frame>
+  ![Get Status](/images/enterprise/get-status.png)
+</Frame>
+
+The status response will show:
+- Current execution state (`running`, `completed`, etc.)
+- Details about which tasks are in progress
+- Any outputs produced so far
+
+### Step 5: View Final Results
+
+Once execution is complete:
+
+1. The status will change to `completed`
+2. You can view the full execution results and outputs
+3. For a more detailed view, check the `Executions` tab in the crew detail page
+
+## Method 2: Using the API
+
+You can also kickoff crews programmatically using the CrewAI Enterprise REST API.
+
+### Authentication
+
+All API requests require a bearer token for authentication:
+
+```bash
+curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com
+```
+
+Your bearer token is available on the Status tab of your crew's detail page.
+
+### Checking Crew Health
+
+Before executing operations, you can verify that your crew is running properly:
+
+```bash
+curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com
+```
+
+A successful response will return a message indicating the crew is operational:
+
+```
+Healthy%
+```
+
+### Step 1: Retrieve Required Inputs
+
+First, determine what inputs your crew requires:
+
+```bash
+curl -X GET \
+  -H "Authorization: Bearer YOUR_CREW_TOKEN" \
+  https://your-crew-url.crewai.com/inputs
+```
+
+The response will be a JSON object containing an array of required input parameters, for example:
+
+```json
+{"inputs":["topic","current_year"]}
+```
+
+This example shows that this particular crew requires two inputs: `topic` and `current_year`.
+
+### Step 2: Kickoff Execution
+
+Initiate execution by providing the required inputs:
+
+```bash
+curl -X POST \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer YOUR_CREW_TOKEN" \
+  -d '{"inputs": {"topic": "AI Agent Frameworks", "current_year": "2025"}}' \
+  https://your-crew-url.crewai.com/kickoff
+```
+
+The response will include a `kickoff_id` that you'll need for tracking:
+
+```json
+{"kickoff_id":"abcd1234-5678-90ef-ghij-klmnopqrstuv"}
+```
+
+### Step 3: Check Execution Status
+
+Monitor the execution progress using the kickoff_id:
+
+```bash
+curl -X GET \
+  -H "Authorization: Bearer YOUR_CREW_TOKEN" \
+  https://your-crew-url.crewai.com/status/abcd1234-5678-90ef-ghij-klmnopqrstuv
+```
+
+## Handling Executions
+
+### Long-Running Executions
+
+For executions that may take a long time:
+
+1. Consider implementing a polling mechanism to check status periodically
+2. Use webhooks (if available) for notification when execution completes
+3. Implement error handling for potential timeouts
+
+### Execution Context
+
+The execution context includes:
+
+- Inputs provided at kickoff
+- Environment variables configured during deployment
+- Any state maintained between tasks
+
+### Debugging Failed Executions
+
+If an execution fails:
+
+1. Check the "Executions" tab for detailed logs
+2. Review the "Traces" tab for step-by-step execution details
+3. Look for LLM responses and tool usage in the trace details
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with execution issues or questions about the Enterprise platform.
+</Card>
+

docs/enterprise/guides/react-component-export.mdx

@@ -0,0 +1,103 @@
+---
+title: "React Component Export"
+description: "Learn how to export and integrate CrewAI Enterprise React components into your applications"
+icon: "react"
+---
+
+This guide explains how to export CrewAI Enterprise crews as React components and integrate them into your own applications.
+
+## Exporting a React Component
+
+<Steps>
+    <Step title="Export the Component">
+        Click on the ellipsis (three dots on the right of your deployed crew) and select the export option and save the file locally. We will be using `CrewLead.jsx` for our example.
+
+        <Frame>
+            <img src="/images/enterprise/export-react-component.png" alt="Export React Component" />
+        </Frame>
+    </Step>
+</Steps>
+
+## Setting Up Your React Environment
+
+To run this React component locally, you'll need to set up a React development environment and integrate this component into a React project.
+
+<Steps>
+    <Step title="Install Node.js">
+        - Download and install Node.js from the official website: https://nodejs.org/
+        - Choose the LTS (Long Term Support) version for stability.
+    </Step>
+
+    <Step title="Create a new React project">
+        - Open Command Prompt or PowerShell
+        - Navigate to the directory where you want to create your project
+        - Run the following command to create a new React project:
+
+            ```bash
+            npx create-react-app my-crew-app
+            ```
+        - Change into the project directory:
+        
+            ```bash
+            cd my-crew-app
+            ```
+    </Step>
+
+    <Step title="Install necessary dependencies">
+        ```bash
+        npm install react-dom
+        ```
+    </Step>
+
+    <Step title="Create the CrewLead component">
+        - Move the downloaded file `CrewLead.jsx` into the `src` folder of your project,
+    </Step>
+
+    <Step title="Modify your App.js to use the CrewLead component">
+        - Open `src/App.js`
+        - Replace its contents with something like this:
+
+        ```jsx
+        import React from 'react';
+        import CrewLead from './CrewLead';
+
+        function App() {
+            return (
+                <div className="App">
+                    <CrewLead baseUrl="YOUR_API_BASE_URL" bearerToken="YOUR_BEARER_TOKEN" />
+                </div>
+            );
+        }
+
+        export default App;
+        ```
+        - Replace `YOUR_API_BASE_URL` and `YOUR_BEARER_TOKEN` with the actual values for your API.
+    </Step>
+
+    <Step title="Start the development server">
+        - In your project directory, run:
+        
+            ```bash
+            npm start
+            ```
+        - This will start the development server, and your default web browser should open automatically to http://localhost:3000, where you'll see your React app running.
+    </Step>
+</Steps>
+
+## Customization
+
+You can then customise the `CrewLead.jsx` to add color, title etc
+
+<Frame>
+    <img src="/images/enterprise/customise-react-component.png" alt="Customise React Component" />
+</Frame>
+<Frame>
+    <img src="/images/enterprise/customise-react-component-2.png" alt="Customise React Component" />
+</Frame>
+
+## Next Steps
+
+- Customize the component styling to match your application's design
+- Add additional props for configuration
+- Integrate with your application's state management
+- Add error handling and loading states 

docs/enterprise/guides/salesforce-trigger.mdx

@@ -0,0 +1,44 @@
+---
+title: "Salesforce Trigger"
+description: "Trigger CrewAI crews from Salesforce workflows for CRM automation"
+icon: "salesforce"
+---
+
+CrewAI Enterprise can be triggered from Salesforce to automate customer relationship management workflows and enhance your sales operations.
+
+## Overview
+
+Salesforce is a leading customer relationship management (CRM) platform that helps businesses streamline their sales, service, and marketing operations. By setting up CrewAI triggers from Salesforce, you can:
+
+- Automate lead scoring and qualification
+- Generate personalized sales materials
+- Enhance customer service with AI-powered responses
+- Streamline data analysis and reporting
+
+## Demo
+
+<Frame>
+    <iframe width="100%" height="400" src="https://www.youtube.com/embed/oJunVqjjfu4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+</Frame>
+
+## Getting Started
+
+To set up Salesforce triggers:
+
+1. **Contact Support**: Reach out to CrewAI Enterprise support for assistance with Salesforce trigger setup
+2. **Review Requirements**: Ensure you have the necessary Salesforce permissions and API access
+3. **Configure Connection**: Work with the support team to establish the connection between CrewAI and your Salesforce instance
+4. **Test Triggers**: Verify the triggers work correctly with your specific use cases
+
+## Use Cases
+
+Common Salesforce + CrewAI trigger scenarios include:
+
+- **Lead Processing**: Automatically analyze and score incoming leads
+- **Proposal Generation**: Create customized proposals based on opportunity data
+- **Customer Insights**: Generate analysis reports from customer interaction history
+- **Follow-up Automation**: Create personalized follow-up messages and recommendations
+
+## Next Steps
+
+For detailed setup instructions and advanced configuration options, please contact CrewAI Enterprise support who can provide tailored guidance for your specific Salesforce environment and business needs. 

docs/enterprise/guides/slack-trigger.mdx

@@ -0,0 +1,61 @@
+---
+title: "Slack Trigger"
+description: "Trigger CrewAI crews directly from Slack using slash commands"
+icon: "slack"
+---
+
+This guide explains how to start a crew directly from Slack using CrewAI triggers.
+
+## Prerequisites
+
+- CrewAI Slack trigger installed and connected to your Slack workspace
+- At least one crew configured in CrewAI
+
+## Setup Steps
+
+<Steps>
+    <Step title="Ensure the CrewAI Slack trigger is set up">
+        In the CrewAI dashboard, navigate to the **Triggers** section.
+
+        <Frame>
+            <img src="/images/enterprise/slack-integration.png" alt="CrewAI Slack Integration" />
+        </Frame>
+
+        Verify that Slack is listed and is connected.
+    </Step>
+    <Step title="Open your Slack channel">
+        - Navigate to the channel where you want to kickoff the crew.
+        - Type the slash command "**/kickoff**" to initiate the crew kickoff process.
+        - You should see a  "**Kickoff crew**" appear as you type:
+            <Frame>
+                <img src="/images/enterprise/kickoff-slack-crew.png" alt="Kickoff crew" />
+            </Frame>
+        - Press Enter or select the "**Kickoff crew**" option. A dialog box titled "**Kickoff an AI Crew**" will appear.
+    </Step>
+    <Step title="Select the crew you want to start">
+        - In the dropdown menu labeled "**Select of the crews online:**", choose the crew you want to start.
+        - In the example below, "**prep-for-meeting**" is selected:
+            <Frame>
+                <img src="/images/enterprise/kickoff-slack-crew-dropdown.png" alt="Kickoff crew dropdown" />
+            </Frame>
+        - If your crew requires any inputs, click the "**Add Inputs**" button to provide them.
+            <Note>
+                The "**Add Inputs**" button is shown in the example above but is not yet clicked.
+            </Note>
+    </Step>
+    <Step title="Click Kickoff and wait for the crew to complete">
+        - Once you've selected the crew and added any necessary inputs, click "**Kickoff**" to start the crew.
+            <Frame>
+                <img src="/images/enterprise/kickoff-slack-crew-kickoff.png" alt="Kickoff crew" />
+            </Frame>
+        - The crew will start executing and you will see the results in the Slack channel.
+            <Frame>
+                <img src="/images/enterprise/kickoff-slack-crew-results.png" alt="Kickoff crew results" />
+            </Frame>
+    </Step>
+</Steps>
+
+## Tips
+
+- Make sure you have the necessary permissions to use the `/kickoff` command in your Slack workspace.
+- If you don't see your desired crew in the dropdown, ensure it's properly configured and online in CrewAI. 

docs/enterprise/guides/team-management.mdx

@@ -0,0 +1,87 @@
+---
+title: "Team Management"
+description: "Learn how to invite and manage team members in your CrewAI Enterprise organization"
+icon: "users"
+---
+
+As an administrator of a CrewAI Enterprise account, you can easily invite new team members to join your organization. This guide will walk you through the process step-by-step.
+
+## Inviting Team Members
+
+<Steps>
+    <Step title="Access the Settings Page">
+        - Log in to your CrewAI Enterprise account
+        - Look for the gear icon (⚙️) in the top right corner of the dashboard
+        - Click on the gear icon to access the **Settings** page:
+            <Frame>
+                <img src="/images/enterprise/settings-page.png" alt="Settings Page" />
+            </Frame>
+    </Step>
+    <Step title="Navigate to the Members Section">
+        - On the Settings page, you'll see a `Members` tab
+        - Click on the `Members` tab to access the **Members** page:
+            <Frame>
+                <img src="/images/enterprise/members-tab.png" alt="Members Tab" />
+            </Frame>
+    </Step>
+    <Step title="Invite New Members">
+        - In the Members section, you'll see a list of current members (including yourself)
+        - Locate the `Email` input field
+        - Enter the email address of the person you want to invite
+        - Click the `Invite` button to send the invitation
+    </Step>
+    <Step title="Repeat as Needed">
+        - You can repeat this process to invite multiple team members
+        - Each invited member will receive an email invitation to join your organization
+    </Step>
+</Steps>
+
+## Adding Roles
+
+You can add roles to your team members to control their access to different parts of the platform.
+
+<Steps>
+    <Step title="Access the Settings Page">
+        - Log in to your CrewAI Enterprise account
+        - Look for the gear icon (⚙️) in the top right corner of the dashboard
+        - Click on the gear icon to access the **Settings** page:
+            <Frame>
+                <img src="/images/enterprise/settings-page.png" alt="Settings Page" />
+            </Frame>
+    </Step>
+    <Step title="Navigate to the Members Section">
+        - On the Settings page, you'll see a `Roles` tab
+        - Click on the `Roles` tab to access the **Roles** page.
+            <Frame>
+                <img src="/images/enterprise/roles-tab.png" alt="Roles Tab" />
+            </Frame>
+        - Click on the `Add Role` button to add a new role.
+        - Enter the details and permissions of the role and click the `Create Role` button to create the role.
+            <Frame>
+                <img src="/images/enterprise/add-role-modal.png" alt="Add Role Modal" />
+            </Frame>
+    </Step>
+    <Step title="Add Roles to Members">
+        - In the Members section, you'll see a list of current members (including yourself)
+           <Frame>
+                <img src="/images/enterprise/member-accepted-invitation.png" alt="Member Accepted Invitation" />
+           </Frame>
+        - Once the member has accepted the invitation, you can add a role to them.
+        - Navigate back to `Roles` tab
+        - Go to the member you want to add a role to and under the `Role` column, click on the dropdown
+        - Select the role you want to add to the member
+        - Click the `Update` button to save the role
+            <Frame>
+                <img src="/images/enterprise/assign-role.png" alt="Add Role to Member" />
+            </Frame>
+    </Step>
+</Steps>
+
+## Important Notes
+
+- **Admin Privileges**: Only users with administrative privileges can invite new members
+- **Email Accuracy**: Ensure you have the correct email addresses for your team members
+- **Invitation Acceptance**: Invited members will need to accept the invitation to join your organization
+- **Email Notifications**: You may want to inform your team members to check their email (including spam folders) for the invitation
+
+By following these steps, you can easily expand your team and collaborate more effectively within your CrewAI Enterprise organization. 

docs/enterprise/guides/update-crew.mdx

@@ -0,0 +1,89 @@
+---
+title: "Update Crew"
+description: "Updating a Crew on CrewAI Enterprise"
+icon: "pencil"
+---
+
+<Note>
+After deploying your crew to CrewAI Enterprise, you may need to make updates to the code, security settings, or configuration. 
+This guide explains how to perform these common update operations.
+</Note>
+
+## Why Update Your Crew?
+
+CrewAI won't automatically pick up GitHub updates by default, so you'll need to manually trigger updates, unless you checked the `Auto-update` option when deploying your crew.
+
+There are several reasons you might want to update your crew deployment:
+- You want to update the code with a latest commit you pushed to GitHub
+- You want to reset the bearer token for security reasons
+- You want to update environment variables
+
+## 1. Updating Your Crew Code for a Latest Commit
+
+When you've pushed new commits to your GitHub repository and want to update your deployment:
+
+1. Navigate to your crew in the CrewAI Enterprise platform
+2. Click on the `Re-deploy` button on your crew details page
+
+<Frame>
+  ![Re-deploy Button](/images/enterprise/redeploy-button.png)
+</Frame>
+
+This will trigger an update that you can track using the progress bar. The system will pull the latest code from your repository and rebuild your deployment.
+
+## 2. Resetting Bearer Token
+
+If you need to generate a new bearer token (for example, if you suspect the current token might have been compromised):
+
+1. Navigate to your crew in the CrewAI Enterprise platform
+2. Find the `Bearer Token` section
+3. Click the `Reset` button next to your current token
+
+<Frame>
+  ![Reset Token](/images/enterprise/reset-token.png)
+</Frame>
+
+<Warning>
+Resetting your bearer token will invalidate the previous token immediately. Make sure to update any applications or scripts that are using the old token.
+</Warning>
+
+## 3. Updating Environment Variables
+
+To update the environment variables for your crew:
+
+1. First access the deployment page by clicking on your crew's name
+
+<Frame>
+  ![Environment Variables Button](/images/enterprise/env-vars-button.png)
+</Frame>
+
+2. Locate the `Environment Variables` section (you will need to click the `Settings` icon to access it)
+3. Edit the existing variables or add new ones in the fields provided
+4. Click the `Update` button next to each variable you modify
+
+<Frame>
+  ![Update Environment Variables](/images/enterprise/update-env-vars.png)
+</Frame>
+
+5. Finally, click the `Update Deployment` button at the bottom of the page to apply the changes
+
+<Note>
+Updating environment variables will trigger a new deployment, but this will only update the environment configuration and not the code itself.
+</Note>
+
+## After Updating
+
+After performing any update:
+
+1. The system will rebuild and redeploy your crew
+2. You can monitor the deployment progress in real-time
+3. Once complete, test your crew to ensure the changes are working as expected
+
+<Tip>
+If you encounter any issues after updating, you can view deployment logs in the platform or contact support for assistance.
+</Tip>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with updating your crew or troubleshooting deployment issues.
+</Card>
+

docs/enterprise/guides/webhook-automation.mdx

@@ -0,0 +1,121 @@
+---
+title: "Webhook Automation"
+description: "Automate CrewAI Enterprise workflows using webhooks with platforms like ActivePieces, Zapier, and Make.com"
+icon: "webhook"
+---
+
+CrewAI Enterprise allows you to automate your workflow using webhooks. This article will guide you through the process of setting up and using webhooks to kickoff your crew execution, with a focus on integration with ActivePieces, a workflow automation platform similar to Zapier and Make.com.
+
+## Setting Up Webhooks
+
+<Steps>
+    <Step title="Accessing the Kickoff Interface">
+        - Navigate to the CrewAI Enterprise dashboard
+        - Look for the `/kickoff` section, which is used to start the crew execution
+            <Frame>
+                <img src="/images/enterprise/kickoff-interface.png" alt="Kickoff Interface" />
+            </Frame>
+    </Step>
+
+    <Step title="Configuring the JSON Content">
+        In the JSON Content section, you'll need to provide the following information:
+
+        - **inputs**: A JSON object containing:
+            - `company`: The name of the company (e.g., "tesla")
+            - `product_name`: The name of the product (e.g., "crewai")
+            - `form_response`: The type of response (e.g., "financial")
+            - `icp_description`: A brief description of the Ideal Customer Profile
+            - `product_description`: A short description of the product
+            - `taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`: URLs for various webhook endpoints (ActivePieces, Zapier, Make.com or another compatible platform)
+    </Step>
+
+    <Step title="Integrating with ActivePieces">
+        In this example we will be using ActivePieces. You can use other platforms such as Zapier and Make.com
+
+        To integrate with ActivePieces:
+
+        1. Set up a new flow in ActivePieces
+        2. Add a trigger (e.g., `Every Day` schedule)
+            <Frame>
+                <img src="/images/enterprise/activepieces-trigger.png" alt="ActivePieces Trigger" />
+            </Frame>
+
+        3. Add an HTTP action step
+            - Set the action to `Send HTTP request`
+            - Use `POST` as the method
+            - Set the URL to your CrewAI Enterprise kickoff endpoint
+            - Add necessary headers (e.g., `Bearer Token`)
+                <Frame>
+                    <img src="/images/enterprise/activepieces-headers.png" alt="ActivePieces Headers" />
+                </Frame>
+
+            - In the body, include the JSON content as configured in step 2
+                <Frame>
+                    <img src="/images/enterprise/activepieces-body.png" alt="ActivePieces Body" />
+                </Frame>
+
+            - The crew will then kickoff at the pre-defined time.
+    </Step>
+
+    <Step title="Setting Up the Webhook">
+        1. Create a new flow in ActivePieces and name it 
+            <Frame>
+                <img src="/images/enterprise/activepieces-flow.png" alt="ActivePieces Flow" />
+            </Frame>
+
+        2. Add a webhook step as the trigger:
+            - Select `Catch Webhook` as the trigger type
+            - This will generate a unique URL that will receive HTTP requests and trigger your flow
+                <Frame>
+                    <img src="/images/enterprise/activepieces-webhook.png" alt="ActivePieces Webhook" />
+                </Frame>
+
+            - Configure the email to use crew webhook body text
+                <Frame>
+                    <img src="/images/enterprise/activepieces-email.png" alt="ActivePieces Email" />
+                </Frame>
+    </Step>
+</Steps>
+
+## Webhook Output Examples
+
+<Tabs>
+    <Tab title="Step Webhook">
+        `stepWebhookUrl` - Callback that will be executed upon each agent inner thought
+
+        ```json
+        {
+            "action": "**Preliminary Research Report on the Financial Industry for crewai Enterprise Solution**\n1. Industry Overview and Trends\nThe financial industry in ....\nConclusion:\nThe financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance. Further engagement with the lead is recommended to better tailor the crewai solution to their specific needs and scale.",
+            "task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0"
+        }
+        ```
+    </Tab>
+    <Tab title="Task Webhook">
+        `taskWebhookUrl` - Callback that will be executed upon the end of each task
+
+        ```json
+        {
+            "description": "Using the information gathered from the lead's data, conduct preliminary research on the lead's industry, company background, and potential use cases for crewai. Focus on finding relevant data that can aid in scoring the lead and planning a strategy to pitch them crewai.The financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance. Further engagement with the lead is recommended to better tailor the crewai solution to their specific needs and scale.",
+            "task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0"
+        }
+        ```
+    </Tab>
+    <Tab title="Crew Webhook">
+        `crewWebhookUrl` - Callback that will be executed upon the end of the crew execution
+
+        ```json
+        {
+            "task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0",
+            "result": {
+                "lead_score": "Customer service enhancement, and compliance are particularly relevant.",
+                "talking_points": [
+                    "Highlight how crewai's AI solutions can transform customer service with automated, personalized experiences and 24/7 support, improving both customer satisfaction and operational efficiency.",
+                    "Discuss crewai's potential to help the institution achieve its sustainability goals through better data analysis and decision-making, contributing to responsible investing and green initiatives.",
+                    "Emphasize crewai's ability to enhance compliance with evolving regulations through efficient data processing and reporting, reducing the risk of non-compliance penalties.",
+                    "Stress the adaptability of crewai to support both extensive multinational operations and smaller, targeted projects, ensuring the solution grows with the institution's needs."
+                ]
+            }
+        }
+        ```
+    </Tab>
+</Tabs> 

docs/enterprise/guides/zapier-trigger.mdx

@@ -0,0 +1,103 @@
+---
+title: "Zapier Trigger"
+description: "Trigger CrewAI crews from Zapier workflows to automate cross-app workflows"
+icon: "bolt"
+---
+
+This guide will walk you through the process of setting up Zapier triggers for CrewAI Enterprise, allowing you to automate workflows between CrewAI Enterprise and other applications.
+
+## Prerequisites
+
+- A CrewAI Enterprise account
+- A Zapier account
+- A Slack account (for this specific example)
+
+## Step-by-Step Setup
+
+<Steps>
+    <Step title="Set Up the Slack Trigger">
+        - In Zapier, create a new Zap.
+
+        <Frame>
+            <img src="/images/enterprise/zapier-1.png" alt="Zapier 1" />
+        </Frame>
+    </Step>
+
+    <Step title="Choose Slack as your trigger app">
+        <Frame>
+            <img src="/images/enterprise/zapier-2.png" alt="Zapier 2" />
+        </Frame>
+        - Select `New Pushed Message` as the Trigger Event.
+        - Connect your Slack account if you haven't already.
+    </Step>
+
+    <Step title="Configure the CrewAI Enterprise Action">
+        - Add a new action step to your Zap.
+        - Choose CrewAI+ as your action app and Kickoff as the Action Event
+
+        <Frame>
+            <img src="/images/enterprise/zapier-3.png" alt="Zapier 5" />
+        </Frame>
+    </Step>
+
+    <Step title="Connect your CrewAI Enterprise account">
+        - Connect your CrewAI Enterprise account.
+        - Select the appropriate Crew for your workflow.
+
+        <Frame>
+            <img src="/images/enterprise/zapier-4.png" alt="Zapier 6" />
+        </Frame>
+        - Configure the inputs for the Crew using the data from the Slack message.
+    </Step>
+
+    <Step title="Format the CrewAI Enterprise Output">
+        - Add another action step to format the text output from CrewAI Enterprise.
+        - Use Zapier's formatting tools to convert the Markdown output to HTML.
+
+        <Frame>
+            <img src="/images/enterprise/zapier-5.png" alt="Zapier 8" />
+        </Frame>
+        <Frame>
+            <img src="/images/enterprise/zapier-6.png" alt="Zapier 9" />
+        </Frame>
+    </Step>
+
+    <Step title="Send the Output via Email">
+        - Add a final action step to send the formatted output via email.
+        - Choose your preferred email service (e.g., Gmail, Outlook).
+        - Configure the email details, including recipient, subject, and body.
+        - Insert the formatted CrewAI Enterprise output into the email body.
+
+        <Frame>
+            <img src="/images/enterprise/zapier-7.png" alt="Zapier 7" />
+        </Frame>
+    </Step>
+
+    <Step title="Kick Off the crew from Slack">
+        - Enter the text in your Slack channel
+
+        <Frame>
+            <img src="/images/enterprise/zapier-7b.png" alt="Zapier 10" />
+        </Frame>
+
+        - Select the 3 ellipsis button and then chose Push to Zapier
+
+        <Frame>
+            <img src="/images/enterprise/zapier-8.png" alt="Zapier 11" />
+        </Frame>
+    </Step>
+
+    <Step title="Select the crew and then Push to Kick Off">
+        <Frame>
+            <img src="/images/enterprise/zapier-9.png" alt="Zapier 12" />
+        </Frame>
+    </Step>
+</Steps>
+
+## Tips for Success
+
+- Ensure that your CrewAI Enterprise inputs are correctly mapped from the Slack message.
+- Test your Zap thoroughly before turning it on to catch any potential issues.
+- Consider adding error handling steps to manage potential failures in the workflow.
+
+By following these steps, you'll have successfully set up Zapier triggers for CrewAI Enterprise, allowing for automated workflows triggered by Slack messages and resulting in email notifications with CrewAI Enterprise output. 

docs/enterprise/introduction.mdx

@@ -0,0 +1,99 @@
+---
+title: "CrewAI Enterprise"
+description: "Deploy, monitor, and scale your AI agent workflows"
+icon: "globe"
+---
+
+## Introduction
+
+CrewAI Enterprise provides a platform for deploying, monitoring, and scaling your crews and agents in a production environment.
+
+<Frame>
+  <img src="/images/enterprise/crewai-enterprise-dashboard.png" alt="CrewAI Enterprise Dashboard" />
+</Frame>
+
+CrewAI Enterprise extends the power of the open-source framework with features designed for production deployments, collaboration, and scalability. Deploy your crews to a managed infrastructure and monitor their execution in real-time.
+
+## Key Features
+
+<CardGroup cols={2}>
+  <Card title="Crew Deployments" icon="rocket">
+    Deploy your crews to a managed infrastructure with a few clicks
+  </Card>
+  <Card title="API Access" icon="code">
+    Access your deployed crews via REST API for integration with existing systems
+  </Card>
+  <Card title="Observability" icon="chart-line">
+    Monitor your crews with detailed execution traces and logs
+  </Card>
+  <Card title="Tool Repository" icon="toolbox">
+    Publish and install tools to enhance your crews' capabilities
+  </Card>
+  <Card title="Webhook Streaming" icon="webhook">
+    Stream real-time events and updates to your systems
+  </Card>
+  <Card title="Crew Studio" icon="paintbrush">
+    Create and customize crews using a no-code/low-code interface
+  </Card>
+</CardGroup>
+
+## Deployment Options
+
+<CardGroup cols={3}>
+  <Card title="GitHub Integration" icon="github">
+    Connect directly to your GitHub repositories to deploy code
+  </Card>
+  <Card title="Crew Studio" icon="palette">
+    Deploy crews created through the no-code Crew Studio interface
+  </Card>
+  <Card title="CLI Deployment" icon="terminal">
+    Use the CrewAI CLI for more advanced deployment workflows
+  </Card>
+</CardGroup>
+
+## Getting Started
+
+<Steps>
+  <Step title="Sign up for an account">
+    Create your account at [app.crewai.com](https://app.crewai.com)
+    <Card 
+      title="Sign Up" 
+      icon="user"
+      href="https://app.crewai.com/signup"
+    >
+      Sign Up
+    </Card>
+  </Step>
+  <Step title="Build your first crew">
+    Use code or Crew Studio to build your crew
+    <Card 
+      title="Build Crew" 
+      icon="paintbrush"
+      href="/enterprise/guides/build-crew"
+    >
+      Build Crew
+    </Card>
+  </Step>
+  <Step title="Deploy your crew">
+    Deploy your crew to the Enterprise platform
+    <Card 
+      title="Deploy Crew" 
+      icon="rocket"
+      href="/enterprise/guides/deploy-crew"
+    >
+      Deploy Crew
+    </Card>
+  </Step>
+  <Step title="Access your crew">
+    Integrate with your crew via the generated API endpoints
+    <Card 
+      title="API Access" 
+      icon="code"
+      href="/enterprise/guides/use-crew-api"
+    >
+      Use the Crew API
+    </Card>
+  </Step>
+</Steps>
+
+For detailed instructions, check out our [deployment guide](/enterprise/guides/deploy-crew) or click the button below to get started.

docs/enterprise/resources/frequently-asked-questions.mdx

@@ -0,0 +1,151 @@
+---
+title: FAQs
+description: "Frequently asked questions about CrewAI Enterprise"
+icon: "circle-question"
+---
+
+<AccordionGroup>
+    <Accordion title="How is task execution handled in the hierarchical process?">
+        In the hierarchical process, a manager agent is automatically created and coordinates the workflow, delegating tasks and validating outcomes for streamlined and effective execution. The manager agent utilizes tools to facilitate task delegation and execution by agents under the manager's guidance. The manager LLM is crucial for the hierarchical process and must be set up correctly for proper function.
+    </Accordion>
+
+    <Accordion title="Where can I get the latest CrewAI documentation?">
+        The most up-to-date documentation for CrewAI is available on our official documentation website: https://docs.crewai.com/
+        <Card href="https://docs.crewai.com/" icon="books">CrewAI Docs</Card>
+    </Accordion>
+
+    <Accordion title="What are the key differences between Hierarchical and Sequential Processes in CrewAI?">
+        #### Hierarchical Process:
+        - Tasks are delegated and executed based on a structured chain of command
+        - A manager language model (`manager_llm`) must be specified for the manager agent
+        - Manager agent oversees task execution, planning, delegation, and validation
+        - Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities
+
+        #### Sequential Process:
+        - Tasks are executed one after another, ensuring tasks are completed in an orderly progression
+        - Output of one task serves as context for the next
+        - Task execution follows the predefined order in the task list
+
+        #### Which Process is Better for Complex Projects?
+        The hierarchical process is better suited for complex projects because it allows for:
+        - **Dynamic task allocation and delegation**: Manager agent can assign tasks based on agent capabilities
+        - **Structured validation and oversight**: Manager agent reviews task outputs and ensures completion
+        - **Complex task management**: Precise control over tool availability at the agent level
+    </Accordion>
+
+    <Accordion title="What are the benefits of using memory in the CrewAI framework?">
+        - **Adaptive Learning**: Crews become more efficient over time, adapting to new information and refining their approach to tasks
+        - **Enhanced Personalization**: Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences
+        - **Improved Problem Solving**: Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights
+    </Accordion>
+
+    <Accordion title="What is the purpose of setting a maximum RPM limit for an agent?">
+        Setting a maximum RPM limit for an agent prevents the agent from making too many requests to external services, which can help to avoid rate limits and improve performance.
+    </Accordion>
+
+    <Accordion title="What role does human input play in the execution of tasks within a CrewAI crew?">
+        Human input allows agents to request additional information or clarification when necessary. This feature is crucial in complex decision-making processes or when agents require more details to complete a task effectively.
+
+        To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. This input can provide extra context, clarify ambiguities, or validate the agent's output.
+
+        For detailed implementation guidance, see our [Human-in-the-Loop guide](/how-to/human-in-the-loop).
+    </Accordion>
+
+    <Accordion title="What advanced customization options are available for tailoring and enhancing agent behavior and capabilities in CrewAI?">
+        CrewAI provides a range of advanced customization options:
+        
+        - **Language Model Customization**: Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`)
+        - **Performance and Debugging Settings**: Adjust an agent's performance and monitor its operations
+        - **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization
+        - **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`)
+        - **Maximum Iterations**: The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task
+        - **Delegation and Autonomy**: Control an agent's ability to delegate or ask questions with the `allow_delegation` attribute (default: True)
+        - **Human Input Integration**: Agents can request additional information or clarification when necessary
+    </Accordion>
+
+    <Accordion title="In what scenarios is human input particularly useful in agent execution?">
+        Human input is particularly useful when:
+        - **Agents require additional information or clarification**: When agents encounter ambiguity or incomplete data
+        - **Agents need to make complex or sensitive decisions**: Human input can assist in ethical or nuanced decision-making
+        - **Oversight and validation of agent output**: Human input can help validate results and prevent errors
+        - **Customizing agent behavior**: Human input can provide feedback to refine agent responses over time
+        - **Identifying and resolving errors or limitations**: Human input helps address agent capability gaps
+    </Accordion>
+
+    <Accordion title="What are the different types of memory that are available in crewAI?">
+        The different types of memory available in CrewAI are:
+        - **Short-term memory**: Temporary storage for immediate context
+        - **Long-term memory**: Persistent storage for learned patterns and information
+        - **Entity memory**: Focused storage for specific entities and their attributes
+        - **Contextual memory**: Memory that maintains context across interactions
+
+        Learn more about the different types of memory:
+        <Card href="https://docs.crewai.com/concepts/memory" icon="brain">CrewAI Memory</Card>
+    </Accordion>
+
+    <Accordion title="How do I use Output Pydantic in a Task?">
+        To use Output Pydantic in a task, you need to define the expected output of the task as a Pydantic model. Here's a quick example:
+
+        <Steps>
+            <Step title="Define a Pydantic model">
+                ```python
+                from pydantic import BaseModel
+
+                class User(BaseModel):
+                    name: str
+                    age: int
+                ```
+            </Step>
+
+            <Step title="Create a task with Output Pydantic">
+                ```python
+                from crewai import Task, Crew, Agent
+                from my_models import User
+
+                task = Task(
+                    description="Create a user with the provided name and age",
+                    expected_output=User,  # This is the Pydantic model
+                    agent=agent,
+                    tools=[tool1, tool2]
+                )
+                ```
+            </Step>
+
+            <Step title="Set the output_pydantic attribute in your agent">
+                ```python
+                from crewai import Agent
+                from my_models import User
+
+                agent = Agent(
+                    role='User Creator',
+                    goal='Create users',
+                    backstory='I am skilled in creating user accounts',
+                    tools=[tool1, tool2],
+                    output_pydantic=User
+                )
+                ```
+            </Step>
+        </Steps>
+
+        Here's a tutorial on how to consistently get structured outputs from your agents:
+        <Frame>
+            <iframe 
+            height="400"
+            width="100%"
+            src="https://www.youtube.com/embed/dNpKQk5uxHw" 
+            title="YouTube video player" frameborder="0" 
+            allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" 
+            allowfullscreen></iframe>
+        </Frame>
+    </Accordion>
+
+    <Accordion title="How can I create custom tools for my CrewAI agents?">
+        You can create custom tools by subclassing the `BaseTool` class provided by CrewAI or by using the tool decorator. Subclassing involves defining a new class that inherits from `BaseTool`, specifying the name, description, and the `_run` method for operational logic. The tool decorator allows you to create a `Tool` object directly with the required attributes and a functional logic.
+
+        <Card href="https://docs.crewai.com/how-to/create-custom-tools" icon="code">CrewAI Tools Guide</Card>
+    </Accordion>
+
+    <Accordion title="How can you control the maximum number of requests per minute that the entire crew can perform?">
+        The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
+    </Accordion>
+</AccordionGroup>

docs/examples/example.mdx

@@ -0,0 +1,62 @@
+---
+title: CrewAI Examples
+description: A collection of examples that show how to use CrewAI framework to automate workflows.
+icon: rocket-launch
+---
+
+<CardGroup cols={3}>
+  <Card
+  title="Marketing Strategy"
+  color="#F3A78B"
+  href="https://github.com/crewAIInc/crewAI-examples/tree/main/marketing_strategy"
+  icon="bullhorn"
+    iconType="solid"
+  >
+    Automate marketing strategy creation with CrewAI. 
+  </Card>
+  <Card
+    title="Surprise Trip"
+    color="#F3A78B"
+    href="https://github.com/crewAIInc/crewAI-examples/tree/main/surprise_trip"
+    icon="plane"
+      iconType="duotone"
+    >
+      Create a surprise trip itinerary with CrewAI.
+    </Card>
+    <Card
+    title="Match Profile to Positions"
+    color="#F3A78B"
+    href="https://github.com/crewAIInc/crewAI-examples/tree/main/match_profile_to_positions"
+    icon="linkedin"
+      iconType="duotone"
+    >
+      Match a profile to jobpositions with CrewAI.
+    </Card>
+    <Card
+    title="Create Job Posting"
+    color="#F3A78B"
+    href="https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting"
+    icon="newspaper"
+      iconType="duotone"
+    >
+      Create a job posting with CrewAI.
+    </Card>
+    <Card
+    title="Game Generator"
+    color="#F3A78B"
+    href="https://github.com/crewAIInc/crewAI-examples/tree/main/game-builder-crew"
+    icon="gamepad"
+      iconType="duotone"
+    >
+      Create a game with CrewAI.
+    </Card>
+    <Card
+    title="Find Job Candidates"
+    color="#F3A78B"
+    href="https://github.com/crewAIInc/crewAI-examples/tree/main/recruitment"
+    icon="user-group"
+      iconType="duotone"
+    >
+      Find job candidates with CrewAI.
+    </Card>
+</CardGroup>

docs/getting-started/Installing-CrewAI.md

@@ -1,24 +0,0 @@
----
-title: Installing crewAI
-description: A comprehensive guide to installing crewAI and its dependencies, including the latest updates and installation methods.
----
-
-# Installing crewAI
-
-Welcome to crewAI! This guide will walk you through the installation process for crewAI and its dependencies. crewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. Let's get started!
-
-## Installation
-
-To install crewAI, you need to have Python >=3.10 and <=3.13 installed on your system:
-
-```shell
-# Install the main crewAI package
-pip install crewai
-
-# Install the main crewAI package and the tools package
-# that includes a series of helpful tools for your agents
-pip install 'crewai[tools]'
-
-# Alternatively, you can also use:
-pip install crewai crewai-tools
-```

docs/getting-started/Start-a-New-CrewAI-Project-Template-Method.md

@@ -1,265 +0,0 @@
----
-title: Starting a New CrewAI Project - Using Template
-description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods.
----
-
-# Starting Your CrewAI Project
-
-Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
-
-Before we start, there are a couple of things to note:
-
-1. CrewAI is a Python package and requires Python >=3.10 and <=3.13 to run.
-2. The preferred way of setting up CrewAI is using the `crewai create crew` command. This will create a new project folder and install a skeleton template for you to work on.
-
-## Prerequisites
-
-Before getting started with CrewAI, make sure that you have installed it via pip:
-
-```shell
-$ pip install crewai crewai-tools
-```
-
-### Virtual Environments
-It is highly recommended that you use virtual environments to ensure that your CrewAI project is isolated from other projects and dependencies. Virtual environments provide a clean, separate workspace for each project, preventing conflicts between different versions of packages and libraries. This isolation is crucial for maintaining consistency and reproducibility in your development process. You have multiple options for setting up virtual environments depending on your operating system and Python version:
-
-1. Use venv (Python's built-in virtual environment tool):
-   venv is included with Python 3.3 and later, making it a convenient choice for many developers. It's lightweight and easy to use, perfect for simple project setups.
-
-   To set up virtual environments with venv, refer to the official [Python documentation](https://docs.python.org/3/tutorial/venv.html).
-
-2. Use Conda (A Python virtual environment manager):
-   Conda is an open-source package manager and environment management system for Python. It's widely used by data scientists, developers, and researchers to manage dependencies and environments in a reproducible way.
-
-   To set up virtual environments with Conda, refer to the official [Conda documentation](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html).
-
-3. Use Poetry (A Python package manager and dependency management tool):
-   Poetry is an open-source Python package manager that simplifies the installation of packages and their dependencies. Poetry offers a convenient way to manage virtual environments and dependencies.
-   Poetry is CrewAI's preferred tool for package / dependency management in CrewAI.
-
-### Code IDEs
-
-Most users of CrewAI use a Code Editor / Integrated Development Environment (IDE) for building their Crews. You can use any code IDE of your choice. See below for some popular options for Code Editors / Integrated Development Environments (IDE):
-
-- [Visual Studio Code](https://code.visualstudio.com/) - Most popular
-- [PyCharm](https://www.jetbrains.com/pycharm/)
-- [Cursor AI](https://cursor.com)
-
-Pick one that suits your style and needs.
-
-## Creating a New Project
-In this example, we will be using Venv as our virtual environment manager.
-
-To set up a virtual environment, run the following CLI command:
-To create a new CrewAI project, run the following CLI command:
-
-```shell
-$ crewai create crew <project_name>
-```
-
-This command will create a new project folder with the following structure:
-
-```shell
-my_project/
-├── .gitignore
-├── pyproject.toml
-├── README.md
-└── 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 project by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of your project, and the `crew.py` file is where you define your agents and tasks.
-
-## Customizing Your Project
-
-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: Defining Agents and Tasks
-
-#### agents.yaml
-
-```yaml
-researcher:
-  role: >
-    Job Candidate Researcher
-  goal: >
-    Find potential candidates for the job
-  backstory: >
-    You are adept at finding the right candidates by exploring various online
-    resources. Your skill in identifying suitable candidates ensures the best
-    match for job positions.
-```
-
-#### tasks.yaml
-
-```yaml
-research_candidates_task:
-  description: >
-    Conduct thorough research to find potential candidates for the specified job.
-    Utilize various online resources and databases to gather a comprehensive list of potential candidates.
-    Ensure that the candidates meet the job requirements provided.
-
-    Job Requirements:
-    {job_requirements}
-  expected_output: >
-    A list of 10 potential candidates with their contact information and brief profiles highlighting their suitability.
-  agent: researcher # THIS NEEDS TO MATCH THE AGENT NAME IN THE AGENTS.YAML FILE AND THE AGENT DEFINED IN THE crew.py FILE
-  context: # THESE NEED TO MATCH THE TASK NAMES DEFINED ABOVE AND THE TASKS.YAML FILE AND THE TASK DEFINED IN THE crew.py FILE
-    - researcher
-```
-
-### Referencing Variables:
-Your defined functions with the same name will be used. For example, you can reference the agent for specific tasks from task.yaml file. Ensure your annotated agent and function name is the same otherwise your task won't recognize the reference properly.
-
-#### Example References
-agent.yaml
-```yaml
-email_summarizer:
-    role: >
-      Email Summarizer
-    goal: >
-      Summarize emails into a concise and clear summary
-    backstory: >
-      You will create a 5 bullet point summary of the report
-    llm: mixtal_llm
-```
-
-task.yaml
-```yaml
-email_summarizer_task:
-    description: >
-      Summarize the email into a 5 bullet point summary
-    expected_output: >
-      A 5 bullet point summary of the email
-    agent: email_summarizer
-    context:
-      - reporting_task
-      - research_task
-```
-
-Use the annotations to properly reference the agent and task in the crew.py file.
-
-### Annotations include:
-* @agent
-* @task
-* @crew
-* @llm
-* @tool
-* @callback
-* @output_json
-* @output_pydantic
-* @cache_handler
-
-crew.py
-```py
-# ...
-    @llm
-    def mixtal_llm(self):
-        return ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
-
-    @agent
-    def email_summarizer(self) -> Agent:
-        return Agent(
-            config=self.agents_config["email_summarizer"],
-        )
-    ## ...other tasks defined
-    @task
-    def email_summarizer_task(self) -> Task:
-        return Task(
-            config=self.tasks_config["email_summarizer_task"],
-        )
-# ...
-```
-
-## Installing Dependencies
-
-To install the dependencies for your project, you can use Poetry. First, navigate to your project directory:
-
-```shell
-$ cd my_project
-$ poetry lock
-$ poetry install
-```
-
-This will install the dependencies specified in the `pyproject.toml` file.
-
-## Interpolating Variables
-
-Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{variable}` will be replaced by the value of the variable in the `main.py` file.
-
-#### agents.yaml
-
-```yaml
-research_task:
-  description: >
-    Conduct a thorough research about the customer and competitors in the context
-    of {customer_domain}.
-    Make sure you find any interesting and relevant information given the
-    current year is 2024.
-  expected_output: >
-    A complete report on the customer and their customers and competitors,
-    including their demographics, preferences, market positioning and audience engagement.
-```
-
-#### main.py
-
-```python
-# main.py
-def run():
-    inputs = {
-        "customer_domain": "crewai.com"
-    }
-    MyProjectCrew(inputs).crew().kickoff(inputs=inputs)
-```
-
-## Running Your Project
-
-To run your project, use the following command:
-
-```shell
-$ crewai run
-```
-or
-```shell
-$ poetry run my_project
-```
-
-This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
-
-### Replay Tasks from Latest Crew Kickoff
-
-CrewAI now includes a replay feature that allows you to list the tasks from the last run and replay from a specific one. To use this feature, run:
-
-```shell
-$ crewai replay <task_id>
-```
-
-Replace `<task_id>` with the ID of the task you want to replay.
-
-### Reset Crew Memory
-
-If you need to reset the memory of your crew before running it again, you can do so by calling the reset memory feature:
-
-```shell
-$ crewai reset-memory
-```
-
-This will clear the crew's memory, allowing for a fresh start.
-
-## Deploying Your Project
-
-The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.

docs/guides/advanced/customizing-prompts.mdx

@@ -0,0 +1,316 @@
+---
+title: Customizing Prompts
+description: Dive deeper into low-level prompt customization for CrewAI, enabling super custom and complex use cases for different models and languages.
+icon: message-pen
+---
+
+## Why Customize Prompts?
+
+Although CrewAI's default prompts work well for many scenarios, low-level customization opens the door to significantly more flexible and powerful agent behavior. Here's why you might want to take advantage of this deeper control:
+
+1. **Optimize for specific LLMs** – Different models (such as GPT-4, Claude, or Llama) thrive with prompt formats tailored to their unique architectures.
+2. **Change the language** – Build agents that operate exclusively in languages beyond English, handling nuances with precision.
+3. **Specialize for complex domains** – Adapt prompts for highly specialized industries like healthcare, finance, or legal.
+4. **Adjust tone and style** – Make agents more formal, casual, creative, or analytical.
+5. **Support super custom use cases** – Utilize advanced prompt structures and formatting to meet intricate, project-specific requirements.
+
+This guide explores how to tap into CrewAI's prompts at a lower level, giving you fine-grained control over how agents think and interact.
+
+## Understanding CrewAI's Prompt System
+
+Under the hood, CrewAI employs a modular prompt system that you can customize extensively:
+
+- **Agent templates** – Govern each agent's approach to their assigned role.
+- **Prompt slices** – Control specialized behaviors such as tasks, tool usage, and output structure.
+- **Error handling** – Direct how agents respond to failures, exceptions, or timeouts.
+- **Tool-specific prompts** – Define detailed instructions for how tools are invoked or utilized.
+
+Check out the [original prompt templates in CrewAI's repository](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json) to see how these elements are organized. From there, you can override or adapt them as needed to unlock advanced behaviors.
+
+## Understanding Default System Instructions
+
+<Warning>
+**Production Transparency Issue**: CrewAI automatically injects default instructions into your prompts that you might not be aware of. This section explains what's happening under the hood and how to gain full control.
+</Warning>
+
+When you define an agent with `role`, `goal`, and `backstory`, CrewAI automatically adds additional system instructions that control formatting and behavior. Understanding these default injections is crucial for production systems where you need full prompt transparency.
+
+### What CrewAI Automatically Injects
+
+Based on your agent configuration, CrewAI adds different default instructions:
+
+#### For Agents Without Tools
+```text
+"I MUST use these formats, my job depends on it!"
+```
+
+#### For Agents With Tools  
+```text
+"IMPORTANT: Use the following format in your response:
+
+Thought: you should always think about what to do
+Action: the action to take, only one name of [tool_names]
+Action Input: the input to the action, just a simple JSON object...
+```
+
+#### For Structured Outputs (JSON/Pydantic)
+```text
+"Ensure your final answer contains only the content in the following format: {output_format}
+Ensure the final output does not include any code block markers like ```json or ```python."
+```
+
+### Viewing the Complete System Prompt
+
+To see exactly what prompt is being sent to your LLM, you can inspect the generated prompt:
+
+```python
+from crewai import Agent, Crew, Task
+from crewai.utilities.prompts import Prompts
+
+# Create your agent
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze data and provide insights",
+    backstory="You are an expert data analyst with 10 years of experience.",
+    verbose=True
+)
+
+# Create a sample task
+task = Task(
+    description="Analyze the sales data and identify trends",
+    expected_output="A detailed analysis with key insights and trends",
+    agent=agent
+)
+
+# Create the prompt generator
+prompt_generator = Prompts(
+    agent=agent,
+    has_tools=len(agent.tools) > 0,
+    use_system_prompt=agent.use_system_prompt
+)
+
+# Generate and inspect the actual prompt
+generated_prompt = prompt_generator.task_execution()
+
+# Print the complete system prompt that will be sent to the LLM
+if "system" in generated_prompt:
+    print("=== SYSTEM PROMPT ===")
+    print(generated_prompt["system"])
+    print("\n=== USER PROMPT ===")
+    print(generated_prompt["user"])
+else:
+    print("=== COMPLETE PROMPT ===")
+    print(generated_prompt["prompt"])
+
+# You can also see how the task description gets formatted
+print("\n=== TASK CONTEXT ===")
+print(f"Task Description: {task.description}")
+print(f"Expected Output: {task.expected_output}")
+```
+
+### Overriding Default Instructions
+
+You have several options to gain full control over the prompts:
+
+#### Option 1: Custom Templates (Recommended)
+```python
+from crewai import Agent
+
+# Define your own system template without default instructions
+custom_system_template = """You are {role}. {backstory}
+Your goal is: {goal}
+
+Respond naturally and conversationally. Focus on providing helpful, accurate information."""
+
+custom_prompt_template = """Task: {input}
+
+Please complete this task thoughtfully."""
+
+agent = Agent(
+    role="Research Assistant", 
+    goal="Help users find accurate information",
+    backstory="You are a helpful research assistant.",
+    system_template=custom_system_template,
+    prompt_template=custom_prompt_template,
+    use_system_prompt=True  # Use separate system/user messages
+)
+```
+
+#### Option 2: Custom Prompt File
+Create a `custom_prompts.json` file to override specific prompt slices:
+
+```json
+{
+  "slices": {
+    "no_tools": "\nProvide your best answer in a natural, conversational way.",
+    "tools": "\nYou have access to these tools: {tools}\n\nUse them when helpful, but respond naturally.",
+    "formatted_task_instructions": "Format your response as: {output_format}"
+  }
+}
+```
+
+Then use it in your crew:
+
+```python
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    prompt_file="custom_prompts.json",
+    verbose=True
+)
+```
+
+#### Option 3: Disable System Prompts for o1 Models
+```python
+agent = Agent(
+    role="Analyst",
+    goal="Analyze data", 
+    backstory="Expert analyst",
+    use_system_prompt=False  # Disables system prompt separation
+)
+```
+
+### Debugging with Observability Tools
+
+For production transparency, integrate with observability platforms to monitor all prompts and LLM interactions. This allows you to see exactly what prompts (including default instructions) are being sent to your LLMs.
+
+See our [Observability documentation](/how-to/observability) for detailed integration guides with various platforms including Langfuse, MLflow, Weights & Biases, and custom logging solutions.
+
+### Best Practices for Production
+
+1. **Always inspect generated prompts** before deploying to production
+2. **Use custom templates** when you need full control over prompt content
+3. **Integrate observability tools** for ongoing prompt monitoring (see [Observability docs](/how-to/observability))
+4. **Test with different LLMs** as default instructions may work differently across models
+5. **Document your prompt customizations** for team transparency
+
+<Tip>
+The default instructions exist to ensure consistent agent behavior, but they can interfere with domain-specific requirements. Use the customization options above to maintain full control over your agent's behavior in production systems.
+</Tip>
+
+## Best Practices for Managing Prompt Files
+
+When engaging in low-level prompt customization, follow these guidelines to keep things organized and maintainable:
+
+1. **Keep files separate** – Store your customized prompts in dedicated JSON files outside your main codebase.
+2. **Version control** – Track changes within your repository, ensuring clear documentation of prompt adjustments over time.
+3. **Organize by model or language** – Use naming schemes like `prompts_llama.json` or `prompts_es.json` to quickly identify specialized configurations.
+4. **Document changes** – Provide comments or maintain a README detailing the purpose and scope of your customizations.
+5. **Minimize alterations** – Only override the specific slices you genuinely need to adjust, keeping default functionality intact for everything else.
+
+## The Simplest Way to Customize Prompts
+
+One straightforward approach is to create a JSON file for the prompts you want to override and then point your Crew at that file:
+
+1. Craft a JSON file with your updated prompt slices.
+2. Reference that file via the `prompt_file` parameter in your Crew.
+
+CrewAI then merges your customizations with the defaults, so you don't have to redefine every prompt. Here's how:
+
+### Example: Basic Prompt Customization
+
+Create a `custom_prompts.json` file with the prompts you want to modify. Ensure you list all top-level prompts it should contain, not just your changes:
+
+```json
+{
+  "slices": {
+    "format": "When responding, follow this structure:\n\nTHOUGHTS: Your step-by-step thinking\nACTION: Any tool you're using\nRESULT: Your final answer or conclusion"
+  }
+}
+```
+
+Then integrate it like so:
+
+```python
+from crewai import Agent, Crew, Task, Process
+
+# Create agents and tasks as normal
+researcher = Agent(
+    role="Research Specialist",
+    goal="Find information on quantum computing",
+    backstory="You are a quantum physics expert",
+    verbose=True
+)
+
+research_task = Task(
+    description="Research quantum computing applications",
+    expected_output="A summary of practical applications",
+    agent=researcher
+)
+
+# Create a crew with your custom prompt file
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task],
+    prompt_file="path/to/custom_prompts.json",
+    verbose=True
+)
+
+# Run the crew
+result = crew.kickoff()
+```
+
+With these few edits, you gain low-level control over how your agents communicate and solve tasks.
+
+## Optimizing for Specific Models
+
+Different models thrive on differently structured prompts. Making deeper adjustments can significantly boost performance by aligning your prompts with a model's nuances.
+
+### Example: Llama 3.3 Prompting Template
+
+For instance, when dealing with Meta's Llama 3.3, deeper-level customization may reflect the recommended structure described at:
+https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/#prompt-template
+
+Here's an example to highlight how you might fine-tune an Agent to leverage Llama 3.3 in code:
+
+```python
+from crewai import Agent, Crew, Task, Process
+from crewai_tools import DirectoryReadTool, FileReadTool
+
+# Define templates for system, user (prompt), and assistant (response) messages
+system_template = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>{{ .System }}<|eot_id|>"""
+prompt_template = """<|start_header_id|>user<|end_header_id|>{{ .Prompt }}<|eot_id|>"""
+response_template = """<|start_header_id|>assistant<|end_header_id|>{{ .Response }}<|eot_id|>"""
+
+# Create an Agent using Llama-specific layouts
+principal_engineer = Agent(
+    role="Principal Engineer",
+    goal="Oversee AI architecture and make high-level decisions",
+    backstory="You are the lead engineer responsible for critical AI systems",
+    verbose=True,
+    llm="groq/llama-3.3-70b-versatile",  # Using the Llama 3 model
+    system_template=system_template,
+    prompt_template=prompt_template,
+    response_template=response_template,
+    tools=[DirectoryReadTool(), FileReadTool()]
+)
+
+# Define a sample task
+engineering_task = Task(
+    description="Review AI implementation files for potential improvements",
+    expected_output="A summary of key findings and recommendations",
+    agent=principal_engineer
+)
+
+# Create a Crew for the task
+llama_crew = Crew(
+    agents=[principal_engineer],
+    tasks=[engineering_task],
+    process=Process.sequential,
+    verbose=True
+)
+
+# Execute the crew
+result = llama_crew.kickoff()
+print(result.raw)
+```
+
+Through this deeper configuration, you can exercise comprehensive, low-level control over your Llama-based workflows without needing a separate JSON file.
+
+## Conclusion
+
+Low-level prompt customization in CrewAI opens the door to super custom, complex use cases. By establishing well-organized prompt files (or direct inline templates), you can accommodate various models, languages, and specialized domains. This level of flexibility ensures you can craft precisely the AI behavior you need, all while knowing CrewAI still provides reliable defaults when you don't override them.
+
+<Check>
+You now have the foundation for advanced prompt customizations in CrewAI. Whether you're adapting for model-specific structures or domain-specific constraints, this low-level approach lets you shape agent interactions in highly specialized ways.
+</Check>

docs/guides/advanced/fingerprinting.mdx

@@ -0,0 +1,133 @@
+---
+title: Fingerprinting
+description: Learn how to use CrewAI's fingerprinting system to uniquely identify and track components throughout their lifecycle.
+icon: fingerprint
+---
+
+## Overview
+
+Fingerprints in CrewAI provide a way to uniquely identify and track components throughout their lifecycle. Each `Agent`, `Crew`, and `Task` automatically receives a unique fingerprint when created, which cannot be manually overridden.
+
+These fingerprints can be used for:
+- Auditing and tracking component usage
+- Ensuring component identity integrity
+- Attaching metadata to components
+- Creating a traceable chain of operations
+
+## How Fingerprints Work
+
+A fingerprint is an instance of the `Fingerprint` class from the `crewai.security` module. Each fingerprint contains:
+
+- A UUID string: A unique identifier for the component that is automatically generated and cannot be manually set
+- A creation timestamp: When the fingerprint was generated, automatically set and cannot be manually modified
+- Metadata: A dictionary of additional information that can be customized
+
+Fingerprints are automatically generated and assigned when a component is created. Each component exposes its fingerprint through a read-only property.
+
+## Basic Usage
+
+### Accessing Fingerprints
+
+```python
+from crewai import Agent, Crew, Task
+
+# Create components - fingerprints are automatically generated
+agent = Agent(
+    role="Data Scientist",
+    goal="Analyze data",
+    backstory="Expert in data analysis"
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[]
+)
+
+task = Task(
+    description="Analyze customer data",
+    expected_output="Insights from data analysis",
+    agent=agent
+)
+
+# Access the fingerprints
+agent_fingerprint = agent.fingerprint
+crew_fingerprint = crew.fingerprint
+task_fingerprint = task.fingerprint
+
+# Print the UUID strings
+print(f"Agent fingerprint: {agent_fingerprint.uuid_str}")
+print(f"Crew fingerprint: {crew_fingerprint.uuid_str}")
+print(f"Task fingerprint: {task_fingerprint.uuid_str}")
+```
+
+### Working with Fingerprint Metadata
+
+You can add metadata to fingerprints for additional context:
+
+```python
+# Add metadata to the agent's fingerprint
+agent.security_config.fingerprint.metadata = {
+    "version": "1.0",
+    "department": "Data Science",
+    "project": "Customer Analysis"
+}
+
+# Access the metadata
+print(f"Agent metadata: {agent.fingerprint.metadata}")
+```
+
+## Fingerprint Persistence
+
+Fingerprints are designed to persist and remain unchanged throughout a component's lifecycle. If you modify a component, the fingerprint remains the same:
+
+```python
+original_fingerprint = agent.fingerprint.uuid_str
+
+# Modify the agent
+agent.goal = "New goal for analysis"
+
+# The fingerprint remains unchanged
+assert agent.fingerprint.uuid_str == original_fingerprint
+```
+
+## Deterministic Fingerprints
+
+While you cannot directly set the UUID and creation timestamp, you can create deterministic fingerprints using the `generate` method with a seed:
+
+```python
+from crewai.security import Fingerprint
+
+# Create a deterministic fingerprint using a seed string
+deterministic_fingerprint = Fingerprint.generate(seed="my-agent-id")
+
+# The same seed always produces the same fingerprint
+same_fingerprint = Fingerprint.generate(seed="my-agent-id")
+assert deterministic_fingerprint.uuid_str == same_fingerprint.uuid_str
+
+# You can also set metadata
+custom_fingerprint = Fingerprint.generate(
+    seed="my-agent-id",
+    metadata={"version": "1.0"}
+)
+```
+
+## Advanced Usage
+
+### Fingerprint Structure
+
+Each fingerprint has the following structure:
+
+```python
+from crewai.security import Fingerprint
+
+fingerprint = agent.fingerprint
+
+# UUID string - the unique identifier (auto-generated)
+uuid_str = fingerprint.uuid_str  # e.g., "123e4567-e89b-12d3-a456-426614174000"
+
+# Creation timestamp (auto-generated)
+created_at = fingerprint.created_at  # A datetime object
+
+# Metadata - for additional information (can be customized)
+metadata = fingerprint.metadata  # A dictionary, defaults to {}
+```

docs/guides/agents/crafting-effective-agents.mdx

@@ -0,0 +1,452 @@
+---
+title: Crafting Effective Agents
+description: Learn best practices for designing powerful, specialized AI agents that collaborate effectively to solve complex problems.
+icon: robot
+---
+
+## The Art and Science of Agent Design
+
+At the heart of CrewAI lies the agent - a specialized AI entity designed to perform specific roles within a collaborative framework. While creating basic agents is simple, crafting truly effective agents that produce exceptional results requires understanding key design principles and best practices.
+
+This guide will help you master the art of agent design, enabling you to create specialized AI personas that collaborate effectively, think critically, and produce high-quality outputs tailored to your specific needs.
+
+### Why Agent Design Matters
+
+The way you define your agents significantly impacts:
+
+1. **Output quality**: Well-designed agents produce more relevant, high-quality results
+2. **Collaboration effectiveness**: Agents with complementary skills work together more efficiently
+3. **Task performance**: Agents with clear roles and goals execute tasks more effectively
+4. **System scalability**: Thoughtfully designed agents can be reused across multiple crews and contexts
+
+Let's explore best practices for creating agents that excel in these dimensions.
+
+## The 80/20 Rule: Focus on Tasks Over Agents
+
+When building effective AI systems, remember this crucial principle: **80% of your effort should go into designing tasks, and only 20% into defining agents**.
+
+Why? Because even the most perfectly defined agent will fail with poorly designed tasks, but well-designed tasks can elevate even a simple agent. This means:
+
+- Spend most of your time writing clear task instructions
+- Define detailed inputs and expected outputs
+- Add examples and context to guide execution
+- Dedicate the remaining time to agent role, goal, and backstory
+
+This doesn't mean agent design isn't important - it absolutely is. But task design is where most execution failures occur, so prioritize accordingly.
+
+## Core Principles of Effective Agent Design
+
+### 1. The Role-Goal-Backstory Framework
+
+The most powerful agents in CrewAI are built on a strong foundation of three key elements:
+
+#### Role: The Agent's Specialized Function
+
+The role defines what the agent does and their area of expertise. When crafting roles:
+
+- **Be specific and specialized**: Instead of "Writer," use "Technical Documentation Specialist" or "Creative Storyteller"
+- **Align with real-world professions**: Base roles on recognizable professional archetypes
+- **Include domain expertise**: Specify the agent's field of knowledge (e.g., "Financial Analyst specializing in market trends")
+
+**Examples of effective roles:**
+```yaml
+role: "Senior UX Researcher specializing in user interview analysis"
+role: "Full-Stack Software Architect with expertise in distributed systems"
+role: "Corporate Communications Director specializing in crisis management"
+```
+
+#### Goal: The Agent's Purpose and Motivation
+
+The goal directs the agent's efforts and shapes their decision-making process. Effective goals should:
+
+- **Be clear and outcome-focused**: Define what the agent is trying to achieve
+- **Emphasize quality standards**: Include expectations about the quality of work
+- **Incorporate success criteria**: Help the agent understand what "good" looks like
+
+**Examples of effective goals:**
+```yaml
+goal: "Uncover actionable user insights by analyzing interview data and identifying recurring patterns, unmet needs, and improvement opportunities"
+goal: "Design robust, scalable system architectures that balance performance, maintainability, and cost-effectiveness"
+goal: "Craft clear, empathetic crisis communications that address stakeholder concerns while protecting organizational reputation"
+```
+
+#### Backstory: The Agent's Experience and Perspective
+
+The backstory gives depth to the agent, influencing how they approach problems and interact with others. Good backstories:
+
+- **Establish expertise and experience**: Explain how the agent gained their skills
+- **Define working style and values**: Describe how the agent approaches their work
+- **Create a cohesive persona**: Ensure all elements of the backstory align with the role and goal
+
+**Examples of effective backstories:**
+```yaml
+backstory: "You have spent 15 years conducting and analyzing user research for top tech companies. You have a talent for reading between the lines and identifying patterns that others miss. You believe that good UX is invisible and that the best insights come from listening to what users don't say as much as what they do say."
+
+backstory: "With 20+ years of experience building distributed systems at scale, you've developed a pragmatic approach to software architecture. You've seen both successful and failed systems and have learned valuable lessons from each. You balance theoretical best practices with practical constraints and always consider the maintenance and operational aspects of your designs."
+
+backstory: "As a seasoned communications professional who has guided multiple organizations through high-profile crises, you understand the importance of transparency, speed, and empathy in crisis response. You have a methodical approach to crafting messages that address concerns while maintaining organizational credibility."
+```
+
+### 2. Specialists Over Generalists
+
+Agents perform significantly better when given specialized roles rather than general ones. A highly focused agent delivers more precise, relevant outputs:
+
+**Generic (Less Effective):**
+```yaml
+role: "Writer"
+```
+
+**Specialized (More Effective):**
+```yaml
+role: "Technical Blog Writer specializing in explaining complex AI concepts to non-technical audiences"
+```
+
+**Specialist Benefits:**
+- Clearer understanding of expected output
+- More consistent performance
+- Better alignment with specific tasks
+- Improved ability to make domain-specific judgments
+
+### 3. Balancing Specialization and Versatility
+
+Effective agents strike the right balance between specialization (doing one thing extremely well) and versatility (being adaptable to various situations):
+
+- **Specialize in role, versatile in application**: Create agents with specialized skills that can be applied across multiple contexts
+- **Avoid overly narrow definitions**: Ensure agents can handle variations within their domain of expertise
+- **Consider the collaborative context**: Design agents whose specializations complement the other agents they'll work with
+
+### 4. Setting Appropriate Expertise Levels
+
+The expertise level you assign to your agent shapes how they approach tasks:
+
+- **Novice agents**: Good for straightforward tasks, brainstorming, or initial drafts
+- **Intermediate agents**: Suitable for most standard tasks with reliable execution
+- **Expert agents**: Best for complex, specialized tasks requiring depth and nuance
+- **World-class agents**: Reserved for critical tasks where exceptional quality is needed
+
+Choose the appropriate expertise level based on task complexity and quality requirements. For most collaborative crews, a mix of expertise levels often works best, with higher expertise assigned to core specialized functions.
+
+## Practical Examples: Before and After
+
+Let's look at some examples of agent definitions before and after applying these best practices:
+
+### Example 1: Content Creation Agent
+
+**Before:**
+```yaml
+role: "Writer"
+goal: "Write good content"
+backstory: "You are a writer who creates content for websites."
+```
+
+**After:**
+```yaml
+role: "B2B Technology Content Strategist"
+goal: "Create compelling, technically accurate content that explains complex topics in accessible language while driving reader engagement and supporting business objectives"
+backstory: "You have spent a decade creating content for leading technology companies, specializing in translating technical concepts for business audiences. You excel at research, interviewing subject matter experts, and structuring information for maximum clarity and impact. You believe that the best B2B content educates first and sells second, building trust through genuine expertise rather than marketing hype."
+```
+
+### Example 2: Research Agent
+
+**Before:**
+```yaml
+role: "Researcher"
+goal: "Find information"
+backstory: "You are good at finding information online."
+```
+
+**After:**
+```yaml
+role: "Academic Research Specialist in Emerging Technologies"
+goal: "Discover and synthesize cutting-edge research, identifying key trends, methodologies, and findings while evaluating the quality and reliability of sources"
+backstory: "With a background in both computer science and library science, you've mastered the art of digital research. You've worked with research teams at prestigious universities and know how to navigate academic databases, evaluate research quality, and synthesize findings across disciplines. You're methodical in your approach, always cross-referencing information and tracing claims to primary sources before drawing conclusions."
+```
+
+## Crafting Effective Tasks for Your Agents
+
+While agent design is important, task design is critical for successful execution. Here are best practices for designing tasks that set your agents up for success:
+
+### The Anatomy of an Effective Task
+
+A well-designed task has two key components that serve different purposes:
+
+#### Task Description: The Process
+The description should focus on what to do and how to do it, including:
+- Detailed instructions for execution
+- Context and background information
+- Scope and constraints
+- Process steps to follow
+
+#### Expected Output: The Deliverable
+The expected output should define what the final result should look like:
+- Format specifications (markdown, JSON, etc.)
+- Structure requirements
+- Quality criteria
+- Examples of good outputs (when possible)
+
+### Task Design Best Practices
+
+#### 1. Single Purpose, Single Output
+Tasks perform best when focused on one clear objective:
+
+**Bad Example (Too Broad):**
+```yaml
+task_description: "Research market trends, analyze the data, and create a visualization."
+```
+
+**Good Example (Focused):**
+```yaml
+# Task 1
+research_task:
+  description: "Research the top 5 market trends in the AI industry for 2024."
+  expected_output: "A markdown list of the 5 trends with supporting evidence."
+
+# Task 2
+analysis_task:
+  description: "Analyze the identified trends to determine potential business impacts."
+  expected_output: "A structured analysis with impact ratings (High/Medium/Low)."
+
+# Task 3
+visualization_task:
+  description: "Create a visual representation of the analyzed trends."
+  expected_output: "A description of a chart showing trends and their impact ratings."
+```
+
+#### 2. Be Explicit About Inputs and Outputs
+Always clearly specify what inputs the task will use and what the output should look like:
+
+**Example:**
+```yaml
+analysis_task:
+  description: >
+    Analyze the customer feedback data from the CSV file.
+    Focus on identifying recurring themes related to product usability.
+    Consider sentiment and frequency when determining importance.
+  expected_output: >
+    A markdown report with the following sections:
+    1. Executive summary (3-5 bullet points)
+    2. Top 3 usability issues with supporting data
+    3. Recommendations for improvement
+```
+
+#### 3. Include Purpose and Context
+Explain why the task matters and how it fits into the larger workflow:
+
+**Example:**
+```yaml
+competitor_analysis_task:
+  description: >
+    Analyze our three main competitors' pricing strategies.
+    This analysis will inform our upcoming pricing model revision.
+    Focus on identifying patterns in how they price premium features
+    and how they structure their tiered offerings.
+```
+
+#### 4. Use Structured Output Tools
+For machine-readable outputs, specify the format clearly:
+
+**Example:**
+```yaml
+data_extraction_task:
+  description: "Extract key metrics from the quarterly report."
+  expected_output: "JSON object with the following keys: revenue, growth_rate, customer_acquisition_cost, and retention_rate."
+```
+
+## Common Mistakes to Avoid
+
+Based on lessons learned from real-world implementations, here are the most common pitfalls in agent and task design:
+
+### 1. Unclear Task Instructions
+
+**Problem:** Tasks lack sufficient detail, making it difficult for agents to execute effectively.
+
+**Example of Poor Design:**
+```yaml
+research_task:
+  description: "Research AI trends."
+  expected_output: "A report on AI trends."
+```
+
+**Improved Version:**
+```yaml
+research_task:
+  description: >
+    Research the top emerging AI trends for 2024 with a focus on:
+    1. Enterprise adoption patterns
+    2. Technical breakthroughs in the past 6 months
+    3. Regulatory developments affecting implementation
+
+    For each trend, identify key companies, technologies, and potential business impacts.
+  expected_output: >
+    A comprehensive markdown report with:
+    - Executive summary (5 bullet points)
+    - 5-7 major trends with supporting evidence
+    - For each trend: definition, examples, and business implications
+    - References to authoritative sources
+```
+
+### 2. "God Tasks" That Try to Do Too Much
+
+**Problem:** Tasks that combine multiple complex operations into one instruction set.
+
+**Example of Poor Design:**
+```yaml
+comprehensive_task:
+  description: "Research market trends, analyze competitor strategies, create a marketing plan, and design a launch timeline."
+```
+
+**Improved Version:**
+Break this into sequential, focused tasks:
+```yaml
+# Task 1: Research
+market_research_task:
+  description: "Research current market trends in the SaaS project management space."
+  expected_output: "A markdown summary of key market trends."
+
+# Task 2: Competitive Analysis
+competitor_analysis_task:
+  description: "Analyze strategies of the top 3 competitors based on the market research."
+  expected_output: "A comparison table of competitor strategies."
+  context: [market_research_task]
+
+# Continue with additional focused tasks...
+```
+
+### 3. Misaligned Description and Expected Output
+
+**Problem:** The task description asks for one thing while the expected output specifies something different.
+
+**Example of Poor Design:**
+```yaml
+analysis_task:
+  description: "Analyze customer feedback to find areas of improvement."
+  expected_output: "A marketing plan for the next quarter."
+```
+
+**Improved Version:**
+```yaml
+analysis_task:
+  description: "Analyze customer feedback to identify the top 3 areas for product improvement."
+  expected_output: "A report listing the 3 priority improvement areas with supporting customer quotes and data points."
+```
+
+### 4. Not Understanding the Process Yourself
+
+**Problem:** Asking agents to execute tasks that you yourself don't fully understand.
+
+**Solution:**
+1. Try to perform the task manually first
+2. Document your process, decision points, and information sources
+3. Use this documentation as the basis for your task description
+
+### 5. Premature Use of Hierarchical Structures
+
+**Problem:** Creating unnecessarily complex agent hierarchies where sequential processes would work better.
+
+**Solution:** Start with sequential processes and only move to hierarchical models when the workflow complexity truly requires it.
+
+### 6. Vague or Generic Agent Definitions
+
+**Problem:** Generic agent definitions lead to generic outputs.
+
+**Example of Poor Design:**
+```yaml
+agent:
+  role: "Business Analyst"
+  goal: "Analyze business data"
+  backstory: "You are good at business analysis."
+```
+
+**Improved Version:**
+```yaml
+agent:
+  role: "SaaS Metrics Specialist focusing on growth-stage startups"
+  goal: "Identify actionable insights from business data that can directly impact customer retention and revenue growth"
+  backstory: "With 10+ years analyzing SaaS business models, you've developed a keen eye for the metrics that truly matter for sustainable growth. You've helped numerous companies identify the leverage points that turned around their business trajectory. You believe in connecting data to specific, actionable recommendations rather than general observations."
+```
+
+## Advanced Agent Design Strategies
+
+### Designing for Collaboration
+
+When creating agents that will work together in a crew, consider:
+
+- **Complementary skills**: Design agents with distinct but complementary abilities
+- **Handoff points**: Define clear interfaces for how work passes between agents
+- **Constructive tension**: Sometimes, creating agents with slightly different perspectives can lead to better outcomes through productive dialogue
+
+For example, a content creation crew might include:
+
+```yaml
+# Research Agent
+role: "Research Specialist for technical topics"
+goal: "Gather comprehensive, accurate information from authoritative sources"
+backstory: "You are a meticulous researcher with a background in library science..."
+
+# Writer Agent
+role: "Technical Content Writer"
+goal: "Transform research into engaging, clear content that educates and informs"
+backstory: "You are an experienced writer who excels at explaining complex concepts..."
+
+# Editor Agent
+role: "Content Quality Editor"
+goal: "Ensure content is accurate, well-structured, and polished while maintaining consistency"
+backstory: "With years of experience in publishing, you have a keen eye for detail..."
+```
+
+### Creating Specialized Tool Users
+
+Some agents can be designed specifically to leverage certain tools effectively:
+
+```yaml
+role: "Data Analysis Specialist"
+goal: "Derive meaningful insights from complex datasets through statistical analysis"
+backstory: "With a background in data science, you excel at working with structured and unstructured data..."
+tools: [PythonREPLTool, DataVisualizationTool, CSVAnalysisTool]
+```
+
+### Tailoring Agents to LLM Capabilities
+
+Different LLMs have different strengths. Design your agents with these capabilities in mind:
+
+```yaml
+# For complex reasoning tasks
+analyst:
+  role: "Data Insights Analyst"
+  goal: "..."
+  backstory: "..."
+  llm: openai/gpt-4o
+
+# For creative content
+writer:
+  role: "Creative Content Writer"
+  goal: "..."
+  backstory: "..."
+  llm: anthropic/claude-3-opus
+```
+
+## Testing and Iterating on Agent Design
+
+Agent design is often an iterative process. Here's a practical approach:
+
+1. **Start with a prototype**: Create an initial agent definition
+2. **Test with sample tasks**: Evaluate performance on representative tasks
+3. **Analyze outputs**: Identify strengths and weaknesses
+4. **Refine the definition**: Adjust role, goal, and backstory based on observations
+5. **Test in collaboration**: Evaluate how the agent performs in a crew setting
+
+## Conclusion
+
+Crafting effective agents is both an art and a science. By carefully defining roles, goals, and backstories that align with your specific needs, and combining them with well-designed tasks, you can create specialized AI collaborators that produce exceptional results.
+
+Remember that agent and task design is an iterative process. Start with these best practices, observe your agents in action, and refine your approach based on what you learn. And always keep in mind the 80/20 rule - focus most of your effort on creating clear, focused tasks to get the best results from your agents.
+
+<Check>
+Congratulations! You now understand the principles and practices of effective agent design. Apply these techniques to create powerful, specialized agents that work together seamlessly to accomplish complex tasks.
+</Check>
+
+## Next Steps
+
+- Experiment with different agent configurations for your specific use case
+- Learn about [building your first crew](/guides/crews/first-crew) to see how agents work together
+- Explore [CrewAI Flows](/guides/flows/first-flow) for more advanced orchestration

docs/guides/concepts/evaluating-use-cases.mdx

@@ -0,0 +1,503 @@
+---
+title: Evaluating Use Cases for CrewAI
+description: Learn how to assess your AI application needs and choose the right approach between Crews and Flows based on complexity and precision requirements.
+icon: scale-balanced
+---
+
+## Understanding the Decision Framework
+
+When building AI applications with CrewAI, one of the most important decisions you'll make is choosing the right approach for your specific use case. Should you use a Crew? A Flow? A combination of both? This guide will help you evaluate your requirements and make informed architectural decisions.
+
+At the heart of this decision is understanding the relationship between **complexity** and **precision** in your application:
+
+<Frame caption="Complexity vs. Precision Matrix for CrewAI Applications">
+  <img src="../../images/complexity_precision.png" alt="Complexity vs. Precision Matrix" />
+</Frame>
+
+This matrix helps visualize how different approaches align with varying requirements for complexity and precision. Let's explore what each quadrant means and how it guides your architectural choices.
+
+## The Complexity-Precision Matrix Explained
+
+### What is Complexity?
+
+In the context of CrewAI applications, **complexity** refers to:
+
+- The number of distinct steps or operations required
+- The diversity of tasks that need to be performed
+- The interdependencies between different components
+- The need for conditional logic and branching
+- The sophistication of the overall workflow
+
+### What is Precision?
+
+**Precision** in this context refers to:
+
+- The accuracy required in the final output
+- The need for structured, predictable results
+- The importance of reproducibility
+- The level of control needed over each step
+- The tolerance for variation in outputs
+
+### The Four Quadrants
+
+#### 1. Low Complexity, Low Precision
+
+**Characteristics:**
+- Simple, straightforward tasks
+- Tolerance for some variation in outputs
+- Limited number of steps
+- Creative or exploratory applications
+
+**Recommended Approach:** Simple Crews with minimal agents
+
+**Example Use Cases:**
+- Basic content generation
+- Idea brainstorming
+- Simple summarization tasks
+- Creative writing assistance
+
+#### 2. Low Complexity, High Precision
+
+**Characteristics:**
+- Simple workflows that require exact, structured outputs
+- Need for reproducible results
+- Limited steps but high accuracy requirements
+- Often involves data processing or transformation
+
+**Recommended Approach:** Flows with direct LLM calls or simple Crews with structured outputs
+
+**Example Use Cases:**
+- Data extraction and transformation
+- Form filling and validation
+- Structured content generation (JSON, XML)
+- Simple classification tasks
+
+#### 3. High Complexity, Low Precision
+
+**Characteristics:**
+- Multi-stage processes with many steps
+- Creative or exploratory outputs
+- Complex interactions between components
+- Tolerance for variation in final results
+
+**Recommended Approach:** Complex Crews with multiple specialized agents
+
+**Example Use Cases:**
+- Research and analysis
+- Content creation pipelines
+- Exploratory data analysis
+- Creative problem-solving
+
+#### 4. High Complexity, High Precision
+
+**Characteristics:**
+- Complex workflows requiring structured outputs
+- Multiple interdependent steps with strict accuracy requirements
+- Need for both sophisticated processing and precise results
+- Often mission-critical applications
+
+**Recommended Approach:** Flows orchestrating multiple Crews with validation steps
+
+**Example Use Cases:**
+- Enterprise decision support systems
+- Complex data processing pipelines
+- Multi-stage document processing
+- Regulated industry applications
+
+## Choosing Between Crews and Flows
+
+### When to Choose Crews
+
+Crews are ideal when:
+
+1. **You need collaborative intelligence** - Multiple agents with different specializations need to work together
+2. **The problem requires emergent thinking** - The solution benefits from different perspectives and approaches
+3. **The task is primarily creative or analytical** - The work involves research, content creation, or analysis
+4. **You value adaptability over strict structure** - The workflow can benefit from agent autonomy
+5. **The output format can be somewhat flexible** - Some variation in output structure is acceptable
+
+```python
+# Example: Research Crew for market analysis
+from crewai import Agent, Crew, Process, Task
+
+# Create specialized agents
+researcher = Agent(
+    role="Market Research Specialist",
+    goal="Find comprehensive market data on emerging technologies",
+    backstory="You are an expert at discovering market trends and gathering data."
+)
+
+analyst = Agent(
+    role="Market Analyst",
+    goal="Analyze market data and identify key opportunities",
+    backstory="You excel at interpreting market data and spotting valuable insights."
+)
+
+# Define their tasks
+research_task = Task(
+    description="Research the current market landscape for AI-powered healthcare solutions",
+    expected_output="Comprehensive market data including key players, market size, and growth trends",
+    agent=researcher
+)
+
+analysis_task = Task(
+    description="Analyze the market data and identify the top 3 investment opportunities",
+    expected_output="Analysis report with 3 recommended investment opportunities and rationale",
+    agent=analyst,
+    context=[research_task]
+)
+
+# Create the crew
+market_analysis_crew = Crew(
+    agents=[researcher, analyst],
+    tasks=[research_task, analysis_task],
+    process=Process.sequential,
+    verbose=True
+)
+
+# Run the crew
+result = market_analysis_crew.kickoff()
+```
+
+### When to Choose Flows
+
+Flows are ideal when:
+
+1. **You need precise control over execution** - The workflow requires exact sequencing and state management
+2. **The application has complex state requirements** - You need to maintain and transform state across multiple steps
+3. **You need structured, predictable outputs** - The application requires consistent, formatted results
+4. **The workflow involves conditional logic** - Different paths need to be taken based on intermediate results
+5. **You need to combine AI with procedural code** - The solution requires both AI capabilities and traditional programming
+
+```python
+# Example: Customer Support Flow with structured processing
+from crewai.flow.flow import Flow, listen, router, start
+from pydantic import BaseModel
+from typing import List, Dict
+
+# Define structured state
+class SupportTicketState(BaseModel):
+    ticket_id: str = ""
+    customer_name: str = ""
+    issue_description: str = ""
+    category: str = ""
+    priority: str = "medium"
+    resolution: str = ""
+    satisfaction_score: int = 0
+
+class CustomerSupportFlow(Flow[SupportTicketState]):
+    @start()
+    def receive_ticket(self):
+        # In a real app, this might come from an API
+        self.state.ticket_id = "TKT-12345"
+        self.state.customer_name = "Alex Johnson"
+        self.state.issue_description = "Unable to access premium features after payment"
+        return "Ticket received"
+
+    @listen(receive_ticket)
+    def categorize_ticket(self, _):
+        # Use a direct LLM call for categorization
+        from crewai import LLM
+        llm = LLM(model="openai/gpt-4o-mini")
+
+        prompt = f"""
+        Categorize the following customer support issue into one of these categories:
+        - Billing
+        - Account Access
+        - Technical Issue
+        - Feature Request
+        - Other
+
+        Issue: {self.state.issue_description}
+
+        Return only the category name.
+        """
+
+        self.state.category = llm.call(prompt).strip()
+        return self.state.category
+
+    @router(categorize_ticket)
+    def route_by_category(self, category):
+        # Route to different handlers based on category
+        return category.lower().replace(" ", "_")
+
+    @listen("billing")
+    def handle_billing_issue(self):
+        # Handle billing-specific logic
+        self.state.priority = "high"
+        # More billing-specific processing...
+        return "Billing issue handled"
+
+    @listen("account_access")
+    def handle_access_issue(self):
+        # Handle access-specific logic
+        self.state.priority = "high"
+        # More access-specific processing...
+        return "Access issue handled"
+
+    # Additional category handlers...
+
+    @listen("billing", "account_access", "technical_issue", "feature_request", "other")
+    def resolve_ticket(self, resolution_info):
+        # Final resolution step
+        self.state.resolution = f"Issue resolved: {resolution_info}"
+        return self.state.resolution
+
+# Run the flow
+support_flow = CustomerSupportFlow()
+result = support_flow.kickoff()
+```
+
+### When to Combine Crews and Flows
+
+The most sophisticated applications often benefit from combining Crews and Flows:
+
+1. **Complex multi-stage processes** - Use Flows to orchestrate the overall process and Crews for complex subtasks
+2. **Applications requiring both creativity and structure** - Use Crews for creative tasks and Flows for structured processing
+3. **Enterprise-grade AI applications** - Use Flows to manage state and process flow while leveraging Crews for specialized work
+
+```python
+# Example: Content Production Pipeline combining Crews and Flows
+from crewai.flow.flow import Flow, listen, start
+from crewai import Agent, Crew, Process, Task
+from pydantic import BaseModel
+from typing import List, Dict
+
+class ContentState(BaseModel):
+    topic: str = ""
+    target_audience: str = ""
+    content_type: str = ""
+    outline: Dict = {}
+    draft_content: str = ""
+    final_content: str = ""
+    seo_score: int = 0
+
+class ContentProductionFlow(Flow[ContentState]):
+    @start()
+    def initialize_project(self):
+        # Set initial parameters
+        self.state.topic = "Sustainable Investing"
+        self.state.target_audience = "Millennial Investors"
+        self.state.content_type = "Blog Post"
+        return "Project initialized"
+
+    @listen(initialize_project)
+    def create_outline(self, _):
+        # Use a research crew to create an outline
+        researcher = Agent(
+            role="Content Researcher",
+            goal=f"Research {self.state.topic} for {self.state.target_audience}",
+            backstory="You are an expert researcher with deep knowledge of content creation."
+        )
+
+        outliner = Agent(
+            role="Content Strategist",
+            goal=f"Create an engaging outline for a {self.state.content_type}",
+            backstory="You excel at structuring content for maximum engagement."
+        )
+
+        research_task = Task(
+            description=f"Research {self.state.topic} focusing on what would interest {self.state.target_audience}",
+            expected_output="Comprehensive research notes with key points and statistics",
+            agent=researcher
+        )
+
+        outline_task = Task(
+            description=f"Create an outline for a {self.state.content_type} about {self.state.topic}",
+            expected_output="Detailed content outline with sections and key points",
+            agent=outliner,
+            context=[research_task]
+        )
+
+        outline_crew = Crew(
+            agents=[researcher, outliner],
+            tasks=[research_task, outline_task],
+            process=Process.sequential,
+            verbose=True
+        )
+
+        # Run the crew and store the result
+        result = outline_crew.kickoff()
+
+        # Parse the outline (in a real app, you might use a more robust parsing approach)
+        import json
+        try:
+            self.state.outline = json.loads(result.raw)
+        except:
+            # Fallback if not valid JSON
+            self.state.outline = {"sections": result.raw}
+
+        return "Outline created"
+
+    @listen(create_outline)
+    def write_content(self, _):
+        # Use a writing crew to create the content
+        writer = Agent(
+            role="Content Writer",
+            goal=f"Write engaging content for {self.state.target_audience}",
+            backstory="You are a skilled writer who creates compelling content."
+        )
+
+        editor = Agent(
+            role="Content Editor",
+            goal="Ensure content is polished, accurate, and engaging",
+            backstory="You have a keen eye for detail and a talent for improving content."
+        )
+
+        writing_task = Task(
+            description=f"Write a {self.state.content_type} about {self.state.topic} following this outline: {self.state.outline}",
+            expected_output="Complete draft content in markdown format",
+            agent=writer
+        )
+
+        editing_task = Task(
+            description="Edit and improve the draft content for clarity, engagement, and accuracy",
+            expected_output="Polished final content in markdown format",
+            agent=editor,
+            context=[writing_task]
+        )
+
+        writing_crew = Crew(
+            agents=[writer, editor],
+            tasks=[writing_task, editing_task],
+            process=Process.sequential,
+            verbose=True
+        )
+
+        # Run the crew and store the result
+        result = writing_crew.kickoff()
+        self.state.final_content = result.raw
+
+        return "Content created"
+
+    @listen(write_content)
+    def optimize_for_seo(self, _):
+        # Use a direct LLM call for SEO optimization
+        from crewai import LLM
+        llm = LLM(model="openai/gpt-4o-mini")
+
+        prompt = f"""
+        Analyze this content for SEO effectiveness for the keyword "{self.state.topic}".
+        Rate it on a scale of 1-100 and provide 3 specific recommendations for improvement.
+
+        Content: {self.state.final_content[:1000]}... (truncated for brevity)
+
+        Format your response as JSON with the following structure:
+        {{
+            "score": 85,
+            "recommendations": [
+                "Recommendation 1",
+                "Recommendation 2",
+                "Recommendation 3"
+            ]
+        }}
+        """
+
+        seo_analysis = llm.call(prompt)
+
+        # Parse the SEO analysis
+        import json
+        try:
+            analysis = json.loads(seo_analysis)
+            self.state.seo_score = analysis.get("score", 0)
+            return analysis
+        except:
+            self.state.seo_score = 50
+            return {"score": 50, "recommendations": ["Unable to parse SEO analysis"]}
+
+# Run the flow
+content_flow = ContentProductionFlow()
+result = content_flow.kickoff()
+```
+
+## Practical Evaluation Framework
+
+To determine the right approach for your specific use case, follow this step-by-step evaluation framework:
+
+### Step 1: Assess Complexity
+
+Rate your application's complexity on a scale of 1-10 by considering:
+
+1. **Number of steps**: How many distinct operations are required?
+   - 1-3 steps: Low complexity (1-3)
+   - 4-7 steps: Medium complexity (4-7)
+   - 8+ steps: High complexity (8-10)
+
+2. **Interdependencies**: How interconnected are the different parts?
+   - Few dependencies: Low complexity (1-3)
+   - Some dependencies: Medium complexity (4-7)
+   - Many complex dependencies: High complexity (8-10)
+
+3. **Conditional logic**: How much branching and decision-making is needed?
+   - Linear process: Low complexity (1-3)
+   - Some branching: Medium complexity (4-7)
+   - Complex decision trees: High complexity (8-10)
+
+4. **Domain knowledge**: How specialized is the knowledge required?
+   - General knowledge: Low complexity (1-3)
+   - Some specialized knowledge: Medium complexity (4-7)
+   - Deep expertise in multiple domains: High complexity (8-10)
+
+Calculate your average score to determine overall complexity.
+
+### Step 2: Assess Precision Requirements
+
+Rate your precision requirements on a scale of 1-10 by considering:
+
+1. **Output structure**: How structured must the output be?
+   - Free-form text: Low precision (1-3)
+   - Semi-structured: Medium precision (4-7)
+   - Strictly formatted (JSON, XML): High precision (8-10)
+
+2. **Accuracy needs**: How important is factual accuracy?
+   - Creative content: Low precision (1-3)
+   - Informational content: Medium precision (4-7)
+   - Critical information: High precision (8-10)
+
+3. **Reproducibility**: How consistent must results be across runs?
+   - Variation acceptable: Low precision (1-3)
+   - Some consistency needed: Medium precision (4-7)
+   - Exact reproducibility required: High precision (8-10)
+
+4. **Error tolerance**: What is the impact of errors?
+   - Low impact: Low precision (1-3)
+   - Moderate impact: Medium precision (4-7)
+   - High impact: High precision (8-10)
+
+Calculate your average score to determine overall precision requirements.
+
+### Step 3: Map to the Matrix
+
+Plot your complexity and precision scores on the matrix:
+
+- **Low Complexity (1-4), Low Precision (1-4)**: Simple Crews
+- **Low Complexity (1-4), High Precision (5-10)**: Flows with direct LLM calls
+- **High Complexity (5-10), Low Precision (1-4)**: Complex Crews
+- **High Complexity (5-10), High Precision (5-10)**: Flows orchestrating Crews
+
+### Step 4: Consider Additional Factors
+
+Beyond complexity and precision, consider:
+
+1. **Development time**: Crews are often faster to prototype
+2. **Maintenance needs**: Flows provide better long-term maintainability
+3. **Team expertise**: Consider your team's familiarity with different approaches
+4. **Scalability requirements**: Flows typically scale better for complex applications
+5. **Integration needs**: Consider how the solution will integrate with existing systems
+
+## Conclusion
+
+Choosing between Crews and Flows—or combining them—is a critical architectural decision that impacts the effectiveness, maintainability, and scalability of your CrewAI application. By evaluating your use case along the dimensions of complexity and precision, you can make informed decisions that align with your specific requirements.
+
+Remember that the best approach often evolves as your application matures. Start with the simplest solution that meets your needs, and be prepared to refine your architecture as you gain experience and your requirements become clearer.
+
+<Check>
+You now have a framework for evaluating CrewAI use cases and choosing the right approach based on complexity and precision requirements. This will help you build more effective, maintainable, and scalable AI applications.
+</Check>
+
+## Next Steps
+
+- Learn more about [crafting effective agents](/guides/agents/crafting-effective-agents)
+- Explore [building your first crew](/guides/crews/first-crew)
+- Dive into [mastering flow state management](/guides/flows/mastering-flow-state)
+- Check out the [core concepts](/concepts/agents) for deeper understanding

docs/guides/crews/first-crew.mdx

@@ -0,0 +1,395 @@
+---
+title: Build Your First Crew
+description: Step-by-step tutorial to create a collaborative AI team that works together to solve complex problems.
+icon: users-gear
+---
+
+## Unleashing the Power of Collaborative AI
+
+Imagine having a team of specialized AI agents working together seamlessly to solve complex problems, each contributing their unique skills to achieve a common goal. This is the power of CrewAI - a framework that enables you to create collaborative AI systems that can accomplish tasks far beyond what a single AI could achieve alone.
+
+In this guide, we'll walk through creating a research crew that will help us research and analyze a topic, then create a comprehensive report. This practical example demonstrates how AI agents can collaborate to accomplish complex tasks, but it's just the beginning of what's possible with CrewAI.
+
+### What You'll Build and Learn
+
+By the end of this guide, you'll have:
+
+1. **Created a specialized AI research team** with distinct roles and responsibilities
+2. **Orchestrated collaboration** between multiple AI agents
+3. **Automated a complex workflow** that involves gathering information, analysis, and report generation
+4. **Built foundational skills** that you can apply to more ambitious projects
+
+While we're building a simple research crew in this guide, the same patterns and techniques can be applied to create much more sophisticated teams for tasks like:
+
+- Multi-stage content creation with specialized writers, editors, and fact-checkers
+- Complex customer service systems with tiered support agents
+- Autonomous business analysts that gather data, create visualizations, and generate insights
+- Product development teams that ideate, design, and plan implementation
+
+Let's get started building your first crew!
+
+### Prerequisites
+
+Before starting, make sure you have:
+
+1. Installed CrewAI following the [installation guide](/installation)
+2. Set up your LLM API key in your environment, following the [LLM setup
+   guide](/concepts/llms#setting-up-your-llm)
+3. Basic understanding of Python
+
+## Step 1: Create a New CrewAI Project
+
+First, let's create a new CrewAI project using the CLI. This command will set up a complete project structure with all the necessary files, allowing you to focus on defining your agents and their tasks rather than setting up boilerplate code.
+
+```bash
+crewai create crew research_crew
+cd research_crew
+```
+
+This will generate a project with the basic structure needed for your crew. The CLI automatically creates:
+
+- A project directory with the necessary files
+- Configuration files for agents and tasks
+- A basic crew implementation
+- A main script to run the crew
+
+<Frame caption="CrewAI Framework Overview">
+  <img src="../../images/crews.png" alt="CrewAI Framework Overview" />
+</Frame>
+
+
+## Step 2: Explore the Project Structure
+
+Let's take a moment to understand the project structure created by the CLI. CrewAI follows best practices for Python projects, making it easy to maintain and extend your code as your crews become more complex.
+
+```
+research_crew/
+├── .gitignore
+├── pyproject.toml
+├── README.md
+├── .env
+└── src/
+    └── research_crew/
+        ├── __init__.py
+        ├── main.py
+        ├── crew.py
+        ├── tools/
+        │   ├── custom_tool.py
+        │   └── __init__.py
+        └── config/
+            ├── agents.yaml
+            └── tasks.yaml
+```
+
+This structure follows best practices for Python projects and makes it easy to organize your code. The separation of configuration files (in YAML) from implementation code (in Python) makes it easy to modify your crew's behavior without changing the underlying code.
+
+## Step 3: Configure Your Agents
+
+Now comes the fun part - defining your AI agents! In CrewAI, agents are specialized entities with specific roles, goals, and backstories that shape their behavior. Think of them as characters in a play, each with their own personality and purpose.
+
+For our research crew, we'll create two agents:
+1. A **researcher** who excels at finding and organizing information
+2. An **analyst** who can interpret research findings and create insightful reports
+
+Let's modify the `agents.yaml` file to define these specialized agents. Be sure
+to set `llm` to the provider you are using.
+
+```yaml
+# src/research_crew/config/agents.yaml
+researcher:
+  role: >
+    Senior Research Specialist for {topic}
+  goal: >
+    Find comprehensive and accurate information about {topic}
+    with a focus on recent developments and key insights
+  backstory: >
+    You are an experienced research specialist with a talent for
+    finding relevant information from various sources. You excel at
+    organizing information in a clear and structured manner, making
+    complex topics accessible to others.
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+
+analyst:
+  role: >
+    Data Analyst and Report Writer for {topic}
+  goal: >
+    Analyze research findings and create a comprehensive, well-structured
+    report that presents insights in a clear and engaging way
+  backstory: >
+    You are a skilled analyst with a background in data interpretation
+    and technical writing. You have a talent for identifying patterns
+    and extracting meaningful insights from research data, then
+    communicating those insights effectively through well-crafted reports.
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+```
+
+Notice how each agent has a distinct role, goal, and backstory. These elements aren't just descriptive - they actively shape how the agent approaches its tasks. By crafting these carefully, you can create agents with specialized skills and perspectives that complement each other.
+
+## Step 4: Define Your Tasks
+
+With our agents defined, we now need to give them specific tasks to perform. Tasks in CrewAI represent the concrete work that agents will perform, with detailed instructions and expected outputs.
+
+For our research crew, we'll define two main tasks:
+1. A **research task** for gathering comprehensive information
+2. An **analysis task** for creating an insightful report
+
+Let's modify the `tasks.yaml` file:
+
+```yaml
+# src/research_crew/config/tasks.yaml
+research_task:
+  description: >
+    Conduct thorough research on {topic}. Focus on:
+    1. Key concepts and definitions
+    2. Historical development and recent trends
+    3. Major challenges and opportunities
+    4. Notable applications or case studies
+    5. Future outlook and potential developments
+
+    Make sure to organize your findings in a structured format with clear sections.
+  expected_output: >
+    A comprehensive research document with well-organized sections covering
+    all the requested aspects of {topic}. Include specific facts, figures,
+    and examples where relevant.
+  agent: researcher
+
+analysis_task:
+  description: >
+    Analyze the research findings and create a comprehensive report on {topic}.
+    Your report should:
+    1. Begin with an executive summary
+    2. Include all key information from the research
+    3. Provide insightful analysis of trends and patterns
+    4. Offer recommendations or future considerations
+    5. Be formatted in a professional, easy-to-read style with clear headings
+  expected_output: >
+    A polished, professional report on {topic} that presents the research
+    findings with added analysis and insights. The report should be well-structured
+    with an executive summary, main sections, and conclusion.
+  agent: analyst
+  context:
+    - research_task
+  output_file: output/report.md
+```
+
+Note the `context` field in the analysis task - this is a powerful feature that allows the analyst to access the output of the research task. This creates a workflow where information flows naturally between agents, just as it would in a human team.
+
+## Step 5: Configure Your Crew
+
+Now it's time to bring everything together by configuring our crew. The crew is the container that orchestrates how agents work together to complete tasks.
+
+Let's modify the `crew.py` file:
+
+```python
+# src/research_crew/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
+
+@CrewBase
+class ResearchCrew():
+    """Research crew for comprehensive topic analysis and reporting"""
+
+    agents: List[BaseAgent]
+    tasks: List[Task]
+
+    @agent
+    def researcher(self) -> Agent:
+        return Agent(
+            config=self.agents_config['researcher'], # type: ignore[index]
+            verbose=True,
+            tools=[SerperDevTool()]
+        )
+
+    @agent
+    def analyst(self) -> Agent:
+        return Agent(
+            config=self.agents_config['analyst'], # type: ignore[index]
+            verbose=True
+        )
+
+    @task
+    def research_task(self) -> Task:
+        return Task(
+            config=self.tasks_config['research_task'] # type: ignore[index]
+        )
+
+    @task
+    def analysis_task(self) -> Task:
+        return Task(
+            config=self.tasks_config['analysis_task'], # type: ignore[index]
+            output_file='output/report.md'
+        )
+
+    @crew
+    def crew(self) -> Crew:
+        """Creates the research crew"""
+        return Crew(
+            agents=self.agents,
+            tasks=self.tasks,
+            process=Process.sequential,
+            verbose=True,
+        )
+```
+
+In this code, we're:
+1. Creating the researcher agent and equipping it with the SerperDevTool to search the web
+2. Creating the analyst agent
+3. Setting up the research and analysis tasks
+4. Configuring the crew to run tasks sequentially (the analyst will wait for the researcher to finish)
+
+This is where the magic happens - with just a few lines of code, we've defined a collaborative AI system where specialized agents work together in a coordinated process.
+
+## Step 6: Set Up Your Main Script
+
+Now, let's set up the main script that will run our crew. This is where we provide the specific topic we want our crew to research.
+
+```python
+#!/usr/bin/env python
+# src/research_crew/main.py
+import os
+from research_crew.crew import ResearchCrew
+
+# Create output directory if it doesn't exist
+os.makedirs('output', exist_ok=True)
+
+def run():
+    """
+    Run the research crew.
+    """
+    inputs = {
+        'topic': 'Artificial Intelligence in Healthcare'
+    }
+
+    # Create and run the crew
+    result = ResearchCrew().crew().kickoff(inputs=inputs)
+
+    # Print the result
+    print("\n\n=== FINAL REPORT ===\n\n")
+    print(result.raw)
+
+    print("\n\nReport has been saved to output/report.md")
+
+if __name__ == "__main__":
+    run()
+```
+
+This script prepares the environment, specifies our research topic, and kicks off the crew's work. The power of CrewAI is evident in how simple this code is - all the complexity of managing multiple AI agents is handled by the framework.
+
+## Step 7: Set Up Your Environment Variables
+
+Create a `.env` file in your project root with your API keys:
+
+```sh
+SERPER_API_KEY=your_serper_api_key
+# Add your provider's API key here too.
+```
+
+See the [LLM Setup guide](/concepts/llms#setting-up-your-llm) for details on configuring your provider of choice. You can get a Serper API key from [Serper.dev](https://serper.dev/).
+
+## Step 8: Install Dependencies
+
+Install the required dependencies using the CrewAI CLI:
+
+```bash
+crewai install
+```
+
+This command will:
+1. Read the dependencies from your project configuration
+2. Create a virtual environment if needed
+3. Install all required packages
+
+## Step 9: Run Your Crew
+
+Now for the exciting moment - it's time to run your crew and see AI collaboration in action!
+
+```bash
+crewai run
+```
+
+When you run this command, you'll see your crew spring to life. The researcher will gather information about the specified topic, and the analyst will then create a comprehensive report based on that research. You'll see the agents' thought processes, actions, and outputs in real-time as they work together to complete their tasks.
+
+## Step 10: Review the Output
+
+Once the crew completes its work, you'll find the final report in the `output/report.md` file. The report will include:
+
+1. An executive summary
+2. Detailed information about the topic
+3. Analysis and insights
+4. Recommendations or future considerations
+
+Take a moment to appreciate what you've accomplished - you've created a system where multiple AI agents collaborated on a complex task, each contributing their specialized skills to produce a result that's greater than what any single agent could achieve alone.
+
+## Exploring Other CLI Commands
+
+CrewAI offers several other useful CLI commands for working with crews:
+
+```bash
+# View all available commands
+crewai --help
+
+# Run the crew
+crewai run
+
+# Test the crew
+crewai test
+
+# Reset crew memories
+crewai reset-memories
+
+# Replay from a specific task
+crewai replay -t <task_id>
+```
+
+## The Art of the Possible: Beyond Your First Crew
+
+What you've built in this guide is just the beginning. The skills and patterns you've learned can be applied to create increasingly sophisticated AI systems. Here are some ways you could extend this basic research crew:
+
+### Expanding Your Crew
+
+You could add more specialized agents to your crew:
+- A **fact-checker** to verify research findings
+- A **data visualizer** to create charts and graphs
+- A **domain expert** with specialized knowledge in a particular area
+- A **critic** to identify weaknesses in the analysis
+
+### Adding Tools and Capabilities
+
+You could enhance your agents with additional tools:
+- Web browsing tools for real-time research
+- CSV/database tools for data analysis
+- Code execution tools for data processing
+- API connections to external services
+
+### Creating More Complex Workflows
+
+You could implement more sophisticated processes:
+- Hierarchical processes where manager agents delegate to worker agents
+- Iterative processes with feedback loops for refinement
+- Parallel processes where multiple agents work simultaneously
+- Dynamic processes that adapt based on intermediate results
+
+### Applying to Different Domains
+
+The same patterns can be applied to create crews for:
+- **Content creation**: Writers, editors, fact-checkers, and designers working together
+- **Customer service**: Triage agents, specialists, and quality control working together
+- **Product development**: Researchers, designers, and planners collaborating
+- **Data analysis**: Data collectors, analysts, and visualization specialists
+
+## Next Steps
+
+Now that you've built your first crew, you can:
+
+1. Experiment with different agent configurations and personalities
+2. Try more complex task structures and workflows
+3. Implement custom tools to give your agents new capabilities
+4. Apply your crew to different topics or problem domains
+5. Explore [CrewAI Flows](/guides/flows/first-flow) for more advanced workflows with procedural programming
+
+<Check>
+Congratulations! You've successfully built your first CrewAI crew that can research and analyze any topic you provide. This foundational experience has equipped you with the skills to create increasingly sophisticated AI systems that can tackle complex, multi-stage problems through collaborative intelligence.
+</Check>

docs/guides/flows/first-flow.mdx

@@ -0,0 +1,622 @@
+---
+title: Build Your First Flow
+description: Learn how to create structured, event-driven workflows with precise control over execution.
+icon: diagram-project
+---
+
+## Taking Control of AI Workflows with Flows
+
+CrewAI Flows represent the next level in AI orchestration - combining the collaborative power of AI agent crews with the precision and flexibility of procedural programming. While crews excel at agent collaboration, flows give you fine-grained control over exactly how and when different components of your AI system interact.
+
+In this guide, we'll walk through creating a powerful CrewAI Flow that generates a comprehensive learning guide on any topic. This tutorial will demonstrate how Flows provide structured, event-driven control over your AI workflows by combining regular code, direct LLM calls, and crew-based processing.
+
+### What Makes Flows Powerful
+
+Flows enable you to:
+
+1. **Combine different AI interaction patterns** - Use crews for complex collaborative tasks, direct LLM calls for simpler operations, and regular code for procedural logic
+2. **Build event-driven systems** - Define how components respond to specific events and data changes
+3. **Maintain state across components** - Share and transform data between different parts of your application
+4. **Integrate with external systems** - Seamlessly connect your AI workflow with databases, APIs, and user interfaces
+5. **Create complex execution paths** - Design conditional branches, parallel processing, and dynamic workflows
+
+### What You'll Build and Learn
+
+By the end of this guide, you'll have:
+
+1. **Created a sophisticated content generation system** that combines user input, AI planning, and multi-agent content creation
+2. **Orchestrated the flow of information** between different components of your system
+3. **Implemented event-driven architecture** where each step responds to the completion of previous steps
+4. **Built a foundation for more complex AI applications** that you can expand and customize
+
+This guide creator flow demonstrates fundamental patterns that can be applied to create much more advanced applications, such as:
+
+- Interactive AI assistants that combine multiple specialized subsystems
+- Complex data processing pipelines with AI-enhanced transformations
+- Autonomous agents that integrate with external services and APIs
+- Multi-stage decision-making systems with human-in-the-loop processes
+
+Let's dive in and build your first flow!
+
+## Prerequisites
+
+Before starting, make sure you have:
+
+1. Installed CrewAI following the [installation guide](/installation)
+2. Set up your LLM API key in your environment, following the [LLM setup
+   guide](/concepts/llms#setting-up-your-llm)
+3. Basic understanding of Python
+
+## Step 1: Create a New CrewAI Flow Project
+
+First, let's create a new CrewAI Flow project using the CLI. This command sets up a scaffolded project with all the necessary directories and template files for your flow.
+
+```bash
+crewai create flow guide_creator_flow
+cd guide_creator_flow
+```
+
+This will generate a project with the basic structure needed for your flow.
+
+<Frame caption="CrewAI Framework Overview">
+  <img src="../../images/flows.png" alt="CrewAI Framework Overview" />
+</Frame>
+
+## Step 2: Understanding the Project Structure
+
+The generated project has the following structure. Take a moment to familiarize yourself with it, as understanding this structure will help you create more complex flows in the future.
+
+```
+guide_creator_flow/
+├── .gitignore
+├── pyproject.toml
+├── README.md
+├── .env
+├── main.py
+├── crews/
+│   └── poem_crew/
+│       ├── config/
+│       │   ├── agents.yaml
+│       │   └── tasks.yaml
+│       └── poem_crew.py
+└── tools/
+    └── custom_tool.py
+```
+
+This structure provides a clear separation between different components of your flow:
+- The main flow logic in the `main.py` file
+- Specialized crews in the `crews` directory
+- Custom tools in the `tools` directory
+
+We'll modify this structure to create our guide creator flow, which will orchestrate the process of generating comprehensive learning guides.
+
+## Step 3: Add a Content Writer Crew
+
+Our flow will need a specialized crew to handle the content creation process. Let's use the CrewAI CLI to add a content writer crew:
+
+```bash
+crewai flow add-crew content-crew
+```
+
+This command automatically creates the necessary directories and template files for your crew. The content writer crew will be responsible for writing and reviewing sections of our guide, working within the overall flow orchestrated by our main application.
+
+## Step 4: Configure the Content Writer Crew
+
+Now, let's modify the generated files for the content writer crew. We'll set up two specialized agents - a writer and a reviewer - that will collaborate to create high-quality content for our guide.
+
+1. First, update the agents configuration file to define our content creation team:
+
+   Remember to set `llm` to the provider you are using.
+
+```yaml
+# src/guide_creator_flow/crews/content_crew/config/agents.yaml
+content_writer:
+  role: >
+    Educational Content Writer
+  goal: >
+    Create engaging, informative content that thoroughly explains the assigned topic
+    and provides valuable insights to the reader
+  backstory: >
+    You are a talented educational writer with expertise in creating clear, engaging
+    content. You have a gift for explaining complex concepts in accessible language
+    and organizing information in a way that helps readers build their understanding.
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+
+content_reviewer:
+  role: >
+    Educational Content Reviewer and Editor
+  goal: >
+    Ensure content is accurate, comprehensive, well-structured, and maintains
+    consistency with previously written sections
+  backstory: >
+    You are a meticulous editor with years of experience reviewing educational
+    content. You have an eye for detail, clarity, and coherence. You excel at
+    improving content while maintaining the original author's voice and ensuring
+    consistent quality across multiple sections.
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+```
+
+These agent definitions establish the specialized roles and perspectives that will shape how our AI agents approach content creation. Notice how each agent has a distinct purpose and expertise.
+
+2. Next, update the tasks configuration file to define the specific writing and reviewing tasks:
+
+```yaml
+# src/guide_creator_flow/crews/content_crew/config/tasks.yaml
+write_section_task:
+  description: >
+    Write a comprehensive section on the topic: "{section_title}"
+
+    Section description: {section_description}
+    Target audience: {audience_level} level learners
+
+    Your content should:
+    1. Begin with a brief introduction to the section topic
+    2. Explain all key concepts clearly with examples
+    3. Include practical applications or exercises where appropriate
+    4. End with a summary of key points
+    5. Be approximately 500-800 words in length
+
+    Format your content in Markdown with appropriate headings, lists, and emphasis.
+
+    Previously written sections:
+    {previous_sections}
+
+    Make sure your content maintains consistency with previously written sections
+    and builds upon concepts that have already been explained.
+  expected_output: >
+    A well-structured, comprehensive section in Markdown format that thoroughly
+    explains the topic and is appropriate for the target audience.
+  agent: content_writer
+
+review_section_task:
+  description: >
+    Review and improve the following section on "{section_title}":
+
+    {draft_content}
+
+    Target audience: {audience_level} level learners
+
+    Previously written sections:
+    {previous_sections}
+
+    Your review should:
+    1. Fix any grammatical or spelling errors
+    2. Improve clarity and readability
+    3. Ensure content is comprehensive and accurate
+    4. Verify consistency with previously written sections
+    5. Enhance the structure and flow
+    6. Add any missing key information
+
+    Provide the improved version of the section in Markdown format.
+  expected_output: >
+    An improved, polished version of the section that maintains the original
+    structure but enhances clarity, accuracy, and consistency.
+  agent: content_reviewer
+  context:
+    - write_section_task
+```
+
+These task definitions provide detailed instructions to our agents, ensuring they produce content that meets our quality standards. Note how the `context` parameter in the review task creates a workflow where the reviewer has access to the writer's output.
+
+3. Now, update the crew implementation file to define how our agents and tasks work together:
+
+```python
+# src/guide_creator_flow/crews/content_crew/content_crew.py
+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
+
+@CrewBase
+class ContentCrew():
+    """Content writing crew"""
+
+    agents: List[BaseAgent]
+    tasks: List[Task]
+
+    @agent
+    def content_writer(self) -> Agent:
+        return Agent(
+            config=self.agents_config['content_writer'], # type: ignore[index]
+            verbose=True
+        )
+
+    @agent
+    def content_reviewer(self) -> Agent:
+        return Agent(
+            config=self.agents_config['content_reviewer'], # type: ignore[index]
+            verbose=True
+        )
+
+    @task
+    def write_section_task(self) -> Task:
+        return Task(
+            config=self.tasks_config['write_section_task'] # type: ignore[index]
+        )
+
+    @task
+    def review_section_task(self) -> Task:
+        return Task(
+            config=self.tasks_config['review_section_task'], # type: ignore[index]
+            context=[self.write_section_task()]
+        )
+
+    @crew
+    def crew(self) -> Crew:
+        """Creates the content writing crew"""
+        return Crew(
+            agents=self.agents,
+            tasks=self.tasks,
+            process=Process.sequential,
+            verbose=True,
+        )
+```
+
+This crew definition establishes the relationship between our agents and tasks, setting up a sequential process where the content writer creates a draft and then the reviewer improves it. While this crew can function independently, in our flow it will be orchestrated as part of a larger system.
+
+## Step 5: Create the Flow
+
+Now comes the exciting part - creating the flow that will orchestrate the entire guide creation process. This is where we'll combine regular Python code, direct LLM calls, and our content creation crew into a cohesive system.
+
+Our flow will:
+1. Get user input for a topic and audience level
+2. Make a direct LLM call to create a structured guide outline
+3. Process each section sequentially using the content writer crew
+4. Combine everything into a final comprehensive document
+
+Let's create our flow in the `main.py` file:
+
+```python
+#!/usr/bin/env python
+import json
+import os
+from typing import List, Dict
+from pydantic import BaseModel, Field
+from crewai import LLM
+from crewai.flow.flow import Flow, listen, start
+from guide_creator_flow.crews.content_crew.content_crew import ContentCrew
+
+# Define our models for structured data
+class Section(BaseModel):
+    title: str = Field(description="Title of the section")
+    description: str = Field(description="Brief description of what the section should cover")
+
+class GuideOutline(BaseModel):
+    title: str = Field(description="Title of the guide")
+    introduction: str = Field(description="Introduction to the topic")
+    target_audience: str = Field(description="Description of the target audience")
+    sections: List[Section] = Field(description="List of sections in the guide")
+    conclusion: str = Field(description="Conclusion or summary of the guide")
+
+# Define our flow state
+class GuideCreatorState(BaseModel):
+    topic: str = ""
+    audience_level: str = ""
+    guide_outline: GuideOutline = None
+    sections_content: Dict[str, str] = {}
+
+class GuideCreatorFlow(Flow[GuideCreatorState]):
+    """Flow for creating a comprehensive guide on any topic"""
+
+    @start()
+    def get_user_input(self):
+        """Get input from the user about the guide topic and audience"""
+        print("\n=== Create Your Comprehensive Guide ===\n")
+
+        # Get user input
+        self.state.topic = input("What topic would you like to create a guide for? ")
+
+        # Get audience level with validation
+        while True:
+            audience = input("Who is your target audience? (beginner/intermediate/advanced) ").lower()
+            if audience in ["beginner", "intermediate", "advanced"]:
+                self.state.audience_level = audience
+                break
+            print("Please enter 'beginner', 'intermediate', or 'advanced'")
+
+        print(f"\nCreating a guide on {self.state.topic} for {self.state.audience_level} audience...\n")
+        return self.state
+
+    @listen(get_user_input)
+    def create_guide_outline(self, state):
+        """Create a structured outline for the guide using a direct LLM call"""
+        print("Creating guide outline...")
+
+        # Initialize the LLM
+        llm = LLM(model="openai/gpt-4o-mini", response_format=GuideOutline)
+
+        # Create the messages for the outline
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
+            {"role": "user", "content": f"""
+            Create a detailed outline for a comprehensive guide on "{state.topic}" for {state.audience_level} level learners.
+
+            The outline should include:
+            1. A compelling title for the guide
+            2. An introduction to the topic
+            3. 4-6 main sections that cover the most important aspects of the topic
+            4. A conclusion or summary
+
+            For each section, provide a clear title and a brief description of what it should cover.
+            """}
+        ]
+
+        # Make the LLM call with JSON response format
+        response = llm.call(messages=messages)
+
+        # Parse the JSON response
+        outline_dict = json.loads(response)
+        self.state.guide_outline = GuideOutline(**outline_dict)
+
+        # Ensure output directory exists before saving
+        os.makedirs("output", exist_ok=True)
+
+        # Save the outline to a file
+        with open("output/guide_outline.json", "w") as f:
+            json.dump(outline_dict, f, indent=2)
+
+        print(f"Guide outline created with {len(self.state.guide_outline.sections)} sections")
+        return self.state.guide_outline
+
+    @listen(create_guide_outline)
+    def write_and_compile_guide(self, outline):
+        """Write all sections and compile the guide"""
+        print("Writing guide sections and compiling...")
+        completed_sections = []
+
+        # Process sections one by one to maintain context flow
+        for section in outline.sections:
+            print(f"Processing section: {section.title}")
+
+            # Build context from previous sections
+            previous_sections_text = ""
+            if completed_sections:
+                previous_sections_text = "# Previously Written Sections\n\n"
+                for title in completed_sections:
+                    previous_sections_text += f"## {title}\n\n"
+                    previous_sections_text += self.state.sections_content.get(title, "") + "\n\n"
+            else:
+                previous_sections_text = "No previous sections written yet."
+
+            # Run the content crew for this section
+            result = ContentCrew().crew().kickoff(inputs={
+                "section_title": section.title,
+                "section_description": section.description,
+                "audience_level": self.state.audience_level,
+                "previous_sections": previous_sections_text,
+                "draft_content": ""
+            })
+
+            # Store the content
+            self.state.sections_content[section.title] = result.raw
+            completed_sections.append(section.title)
+            print(f"Section completed: {section.title}")
+
+        # Compile the final guide
+        guide_content = f"# {outline.title}\n\n"
+        guide_content += f"## Introduction\n\n{outline.introduction}\n\n"
+
+        # Add each section in order
+        for section in outline.sections:
+            section_content = self.state.sections_content.get(section.title, "")
+            guide_content += f"\n\n{section_content}\n\n"
+
+        # Add conclusion
+        guide_content += f"## Conclusion\n\n{outline.conclusion}\n\n"
+
+        # Save the guide
+        with open("output/complete_guide.md", "w") as f:
+            f.write(guide_content)
+
+        print("\nComplete guide compiled and saved to output/complete_guide.md")
+        return "Guide creation completed successfully"
+
+def kickoff():
+    """Run the guide creator flow"""
+    GuideCreatorFlow().kickoff()
+    print("\n=== Flow Complete ===")
+    print("Your comprehensive guide is ready in the output directory.")
+    print("Open output/complete_guide.md to view it.")
+
+def plot():
+    """Generate a visualization of the flow"""
+    flow = GuideCreatorFlow()
+    flow.plot("guide_creator_flow")
+    print("Flow visualization saved to guide_creator_flow.html")
+
+if __name__ == "__main__":
+    kickoff()
+```
+
+Let's analyze what's happening in this flow:
+
+1. We define Pydantic models for structured data, ensuring type safety and clear data representation
+2. We create a state class to maintain data across different steps of the flow
+3. We implement three main flow steps:
+   - Getting user input with the `@start()` decorator
+   - Creating a guide outline with a direct LLM call
+   - Processing sections with our content crew
+4. We use the `@listen()` decorator to establish event-driven relationships between steps
+
+This is the power of flows - combining different types of processing (user interaction, direct LLM calls, crew-based tasks) into a coherent, event-driven system.
+
+## Step 6: Set Up Your Environment Variables
+
+Create a `.env` file in your project root with your API keys. See the [LLM setup
+guide](/concepts/llms#setting-up-your-llm) for details on configuring a provider.
+
+```sh .env
+OPENAI_API_KEY=your_openai_api_key
+# or
+GEMINI_API_KEY=your_gemini_api_key
+# or
+ANTHROPIC_API_KEY=your_anthropic_api_key
+```
+
+## Step 7: Install Dependencies
+
+Install the required dependencies:
+
+```bash
+crewai install
+```
+
+## Step 8: Run Your Flow
+
+Now it's time to see your flow in action! Run it using the CrewAI CLI:
+
+```bash
+crewai flow kickoff
+```
+
+When you run this command, you'll see your flow spring to life:
+1. It will prompt you for a topic and audience level
+2. It will create a structured outline for your guide
+3. It will process each section, with the content writer and reviewer collaborating on each
+4. Finally, it will compile everything into a comprehensive guide
+
+This demonstrates the power of flows to orchestrate complex processes involving multiple components, both AI and non-AI.
+
+## Step 9: Visualize Your Flow
+
+One of the powerful features of flows is the ability to visualize their structure:
+
+```bash
+crewai flow plot
+```
+
+This will create an HTML file that shows the structure of your flow, including the relationships between different steps and the data that flows between them. This visualization can be invaluable for understanding and debugging complex flows.
+
+## Step 10: Review the Output
+
+Once the flow completes, you'll find two files in the `output` directory:
+
+1. `guide_outline.json`: Contains the structured outline of the guide
+2. `complete_guide.md`: The comprehensive guide with all sections
+
+Take a moment to review these files and appreciate what you've built - a system that combines user input, direct AI interactions, and collaborative agent work to produce a complex, high-quality output.
+
+## The Art of the Possible: Beyond Your First Flow
+
+What you've learned in this guide provides a foundation for creating much more sophisticated AI systems. Here are some ways you could extend this basic flow:
+
+### Enhancing User Interaction
+
+You could create more interactive flows with:
+- Web interfaces for input and output
+- Real-time progress updates
+- Interactive feedback and refinement loops
+- Multi-stage user interactions
+
+### Adding More Processing Steps
+
+You could expand your flow with additional steps for:
+- Research before outline creation
+- Image generation for illustrations
+- Code snippet generation for technical guides
+- Final quality assurance and fact-checking
+
+### Creating More Complex Flows
+
+You could implement more sophisticated flow patterns:
+- Conditional branching based on user preferences or content type
+- Parallel processing of independent sections
+- Iterative refinement loops with feedback
+- Integration with external APIs and services
+
+### Applying to Different Domains
+
+The same patterns can be applied to create flows for:
+- **Interactive storytelling**: Create personalized stories based on user input
+- **Business intelligence**: Process data, generate insights, and create reports
+- **Product development**: Facilitate ideation, design, and planning
+- **Educational systems**: Create personalized learning experiences
+
+## Key Features Demonstrated
+
+This guide creator flow demonstrates several powerful features of CrewAI:
+
+1. **User interaction**: The flow collects input directly from the user
+2. **Direct LLM calls**: Uses the LLM class for efficient, single-purpose AI interactions
+3. **Structured data with Pydantic**: Uses Pydantic models to ensure type safety
+4. **Sequential processing with context**: Writes sections in order, providing previous sections for context
+5. **Multi-agent crews**: Leverages specialized agents (writer and reviewer) for content creation
+6. **State management**: Maintains state across different steps of the process
+7. **Event-driven architecture**: Uses the `@listen` decorator to respond to events
+
+## Understanding the Flow Structure
+
+Let's break down the key components of flows to help you understand how to build your own:
+
+### 1. Direct LLM Calls
+
+Flows allow you to make direct calls to language models when you need simple, structured responses:
+
+```python
+llm = LLM(
+    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
+    response_format=GuideOutline
+)
+response = llm.call(messages=messages)
+```
+
+This is more efficient than using a crew when you need a specific, structured output.
+
+### 2. Event-Driven Architecture
+
+Flows use decorators to establish relationships between components:
+
+```python
+@start()
+def get_user_input(self):
+    # First step in the flow
+    # ...
+
+@listen(get_user_input)
+def create_guide_outline(self, state):
+    # This runs when get_user_input completes
+    # ...
+```
+
+This creates a clear, declarative structure for your application.
+
+### 3. State Management
+
+Flows maintain state across steps, making it easy to share data:
+
+```python
+class GuideCreatorState(BaseModel):
+    topic: str = ""
+    audience_level: str = ""
+    guide_outline: GuideOutline = None
+    sections_content: Dict[str, str] = {}
+```
+
+This provides a type-safe way to track and transform data throughout your flow.
+
+### 4. Crew Integration
+
+Flows can seamlessly integrate with crews for complex collaborative tasks:
+
+```python
+result = ContentCrew().crew().kickoff(inputs={
+    "section_title": section.title,
+    # ...
+})
+```
+
+This allows you to use the right tool for each part of your application - direct LLM calls for simple tasks and crews for complex collaboration.
+
+## Next Steps
+
+Now that you've built your first flow, you can:
+
+1. Experiment with more complex flow structures and patterns
+2. Try using `@router()` to create conditional branches in your flows
+3. Explore the `and_` and `or_` functions for more complex parallel execution
+4. Connect your flow to external APIs, databases, or user interfaces
+5. Combine multiple specialized crews in a single flow
+
+<Check>
+Congratulations! You've successfully built your first CrewAI Flow that combines regular code, direct LLM calls, and crew-based processing to create a comprehensive guide. These foundational skills enable you to create increasingly sophisticated AI applications that can tackle complex, multi-stage problems through a combination of procedural control and collaborative intelligence.
+</Check>

docs/guides/flows/mastering-flow-state.mdx

@@ -0,0 +1,771 @@
+---
+title: Mastering Flow State Management
+description: A comprehensive guide to managing, persisting, and leveraging state in CrewAI Flows for building robust AI applications.
+icon: diagram-project
+---
+
+## Understanding the Power of State in Flows
+
+State management is the backbone of any sophisticated AI workflow. In CrewAI Flows, the state system allows you to maintain context, share data between steps, and build complex application logic. Mastering state management is essential for creating reliable, maintainable, and powerful AI applications.
+
+This guide will walk you through everything you need to know about managing state in CrewAI Flows, from basic concepts to advanced techniques, with practical code examples along the way.
+
+### Why State Management Matters
+
+Effective state management enables you to:
+
+1. **Maintain context across execution steps** - Pass information seamlessly between different stages of your workflow
+2. **Build complex conditional logic** - Make decisions based on accumulated data
+3. **Create persistent applications** - Save and restore workflow progress
+4. **Handle errors gracefully** - Implement recovery patterns for more robust applications
+5. **Scale your applications** - Support complex workflows with proper data organization
+6. **Enable conversational applications** - Store and access conversation history for context-aware AI interactions
+
+Let's explore how to leverage these capabilities effectively.
+
+## State Management Fundamentals
+
+### The Flow State Lifecycle
+
+In CrewAI Flows, the state follows a predictable lifecycle:
+
+1. **Initialization** - When a flow is created, its state is initialized (either as an empty dictionary or a Pydantic model instance)
+2. **Modification** - Flow methods access and modify the state as they execute
+3. **Transmission** - State is passed automatically between flow methods
+4. **Persistence** (optional) - State can be saved to storage and later retrieved
+5. **Completion** - The final state reflects the cumulative changes from all executed methods
+
+Understanding this lifecycle is crucial for designing effective flows.
+
+### Two Approaches to State Management
+
+CrewAI offers two ways to manage state in your flows:
+
+1. **Unstructured State** - Using dictionary-like objects for flexibility
+2. **Structured State** - Using Pydantic models for type safety and validation
+
+Let's examine each approach in detail.
+
+## Unstructured State Management
+
+Unstructured state uses a dictionary-like approach, offering flexibility and simplicity for straightforward applications.
+
+### How It Works
+
+With unstructured state:
+- You access state via `self.state` which behaves like a dictionary
+- You can freely add, modify, or remove keys at any point
+- All state is automatically available to all flow methods
+
+### Basic Example
+
+Here's a simple example of unstructured state management:
+
+```python
+from crewai.flow.flow import Flow, listen, start
+
+class UnstructuredStateFlow(Flow):
+    @start()
+    def initialize_data(self):
+        print("Initializing flow data")
+        # Add key-value pairs to state
+        self.state["user_name"] = "Alex"
+        self.state["preferences"] = {
+            "theme": "dark",
+            "language": "English"
+        }
+        self.state["items"] = []
+
+        # The flow state automatically gets a unique ID
+        print(f"Flow ID: {self.state['id']}")
+
+        return "Initialized"
+
+    @listen(initialize_data)
+    def process_data(self, previous_result):
+        print(f"Previous step returned: {previous_result}")
+
+        # Access and modify state
+        user = self.state["user_name"]
+        print(f"Processing data for {user}")
+
+        # Add items to a list in state
+        self.state["items"].append("item1")
+        self.state["items"].append("item2")
+
+        # Add a new key-value pair
+        self.state["processed"] = True
+
+        return "Processed"
+
+    @listen(process_data)
+    def generate_summary(self, previous_result):
+        # Access multiple state values
+        user = self.state["user_name"]
+        theme = self.state["preferences"]["theme"]
+        items = self.state["items"]
+        processed = self.state.get("processed", False)
+
+        summary = f"User {user} has {len(items)} items with {theme} theme. "
+        summary += "Data is processed." if processed else "Data is not processed."
+
+        return summary
+
+# Run the flow
+flow = UnstructuredStateFlow()
+result = flow.kickoff()
+print(f"Final result: {result}")
+print(f"Final state: {flow.state}")
+```
+
+### When to Use Unstructured State
+
+Unstructured state is ideal for:
+- Quick prototyping and simple flows
+- Dynamically evolving state needs
+- Cases where the structure may not be known in advance
+- Flows with simple state requirements
+
+While flexible, unstructured state lacks type checking and schema validation, which can lead to errors in complex applications.
+
+## Structured State Management
+
+Structured state uses Pydantic models to define a schema for your flow's state, providing type safety, validation, and better developer experience.
+
+### How It Works
+
+With structured state:
+- You define a Pydantic model that represents your state structure
+- You pass this model type to your Flow class as a type parameter
+- You access state via `self.state`, which behaves like a Pydantic model instance
+- All fields are validated according to their defined types
+- You get IDE autocompletion and type checking support
+
+### Basic Example
+
+Here's how to implement structured state management:
+
+```python
+from crewai.flow.flow import Flow, listen, start
+from pydantic import BaseModel, Field
+from typing import List, Dict, Optional
+
+# Define your state model
+class UserPreferences(BaseModel):
+    theme: str = "light"
+    language: str = "English"
+
+class AppState(BaseModel):
+    user_name: str = ""
+    preferences: UserPreferences = UserPreferences()
+    items: List[str] = []
+    processed: bool = False
+    completion_percentage: float = 0.0
+
+# Create a flow with typed state
+class StructuredStateFlow(Flow[AppState]):
+    @start()
+    def initialize_data(self):
+        print("Initializing flow data")
+        # Set state values (type-checked)
+        self.state.user_name = "Taylor"
+        self.state.preferences.theme = "dark"
+
+        # The ID field is automatically available
+        print(f"Flow ID: {self.state.id}")
+
+        return "Initialized"
+
+    @listen(initialize_data)
+    def process_data(self, previous_result):
+        print(f"Processing data for {self.state.user_name}")
+
+        # Modify state (with type checking)
+        self.state.items.append("item1")
+        self.state.items.append("item2")
+        self.state.processed = True
+        self.state.completion_percentage = 50.0
+
+        return "Processed"
+
+    @listen(process_data)
+    def generate_summary(self, previous_result):
+        # Access state (with autocompletion)
+        summary = f"User {self.state.user_name} has {len(self.state.items)} items "
+        summary += f"with {self.state.preferences.theme} theme. "
+        summary += "Data is processed." if self.state.processed else "Data is not processed."
+        summary += f" Completion: {self.state.completion_percentage}%"
+
+        return summary
+
+# Run the flow
+flow = StructuredStateFlow()
+result = flow.kickoff()
+print(f"Final result: {result}")
+print(f"Final state: {flow.state}")
+```
+
+### Benefits of Structured State
+
+Using structured state provides several advantages:
+
+1. **Type Safety** - Catch type errors at development time
+2. **Self-Documentation** - The state model clearly documents what data is available
+3. **Validation** - Automatic validation of data types and constraints
+4. **IDE Support** - Get autocomplete and inline documentation
+5. **Default Values** - Easily define fallbacks for missing data
+
+### When to Use Structured State
+
+Structured state is recommended for:
+- Complex flows with well-defined data schemas
+- Team projects where multiple developers work on the same code
+- Applications where data validation is important
+- Flows that need to enforce specific data types and constraints
+
+## The Automatic State ID
+
+Both unstructured and structured states automatically receive a unique identifier (UUID) to help track and manage state instances.
+
+### How It Works
+
+- For unstructured state, the ID is accessible as `self.state["id"]`
+- For structured state, the ID is accessible as `self.state.id`
+- This ID is generated automatically when the flow is created
+- The ID remains the same throughout the flow's lifecycle
+- The ID can be used for tracking, logging, and retrieving persisted states
+
+This UUID is particularly valuable when implementing persistence or tracking multiple flow executions.
+
+## Dynamic State Updates
+
+Regardless of whether you're using structured or unstructured state, you can update state dynamically throughout your flow's execution.
+
+### Passing Data Between Steps
+
+Flow methods can return values that are then passed as arguments to listening methods:
+
+```python
+from crewai.flow.flow import Flow, listen, start
+
+class DataPassingFlow(Flow):
+    @start()
+    def generate_data(self):
+        # This return value will be passed to listening methods
+        return "Generated data"
+
+    @listen(generate_data)
+    def process_data(self, data_from_previous_step):
+        print(f"Received: {data_from_previous_step}")
+        # You can modify the data and pass it along
+        processed_data = f"{data_from_previous_step} - processed"
+        # Also update state
+        self.state["last_processed"] = processed_data
+        return processed_data
+
+    @listen(process_data)
+    def finalize_data(self, processed_data):
+        print(f"Received processed data: {processed_data}")
+        # Access both the passed data and state
+        last_processed = self.state.get("last_processed", "")
+        return f"Final: {processed_data} (from state: {last_processed})"
+```
+
+This pattern allows you to combine direct data passing with state updates for maximum flexibility.
+
+## Persisting Flow State
+
+One of CrewAI's most powerful features is the ability to persist flow state across executions. This enables workflows that can be paused, resumed, and even recovered after failures.
+
+### The @persist() Decorator
+
+The `@persist()` decorator automates state persistence, saving your flow's state at key points in execution.
+
+#### Class-Level Persistence
+
+When applied at the class level, `@persist()` saves state after every method execution:
+
+```python
+from crewai.flow.flow import Flow, listen, start
+from crewai.flow.persistence import persist
+from pydantic import BaseModel
+
+class CounterState(BaseModel):
+    value: int = 0
+
+@persist()  # Apply to the entire flow class
+class PersistentCounterFlow(Flow[CounterState]):
+    @start()
+    def increment(self):
+        self.state.value += 1
+        print(f"Incremented to {self.state.value}")
+        return self.state.value
+
+    @listen(increment)
+    def double(self, value):
+        self.state.value = value * 2
+        print(f"Doubled to {self.state.value}")
+        return self.state.value
+
+# First run
+flow1 = PersistentCounterFlow()
+result1 = flow1.kickoff()
+print(f"First run result: {result1}")
+
+# Second run - state is automatically loaded
+flow2 = PersistentCounterFlow()
+result2 = flow2.kickoff()
+print(f"Second run result: {result2}")  # Will be higher due to persisted state
+```
+
+#### Method-Level Persistence
+
+For more granular control, you can apply `@persist()` to specific methods:
+
+```python
+from crewai.flow.flow import Flow, listen, start
+from crewai.flow.persistence import persist
+
+class SelectivePersistFlow(Flow):
+    @start()
+    def first_step(self):
+        self.state["count"] = 1
+        return "First step"
+
+    @persist()  # Only persist after this method
+    @listen(first_step)
+    def important_step(self, prev_result):
+        self.state["count"] += 1
+        self.state["important_data"] = "This will be persisted"
+        return "Important step completed"
+
+    @listen(important_step)
+    def final_step(self, prev_result):
+        self.state["count"] += 1
+        return f"Complete with count {self.state['count']}"
+```
+
+
+## Advanced State Patterns
+
+### State-Based Conditional Logic
+
+You can use state to implement complex conditional logic in your flows:
+
+```python
+from crewai.flow.flow import Flow, listen, router, start
+from pydantic import BaseModel
+
+class PaymentState(BaseModel):
+    amount: float = 0.0
+    is_approved: bool = False
+    retry_count: int = 0
+
+class PaymentFlow(Flow[PaymentState]):
+    @start()
+    def process_payment(self):
+        # Simulate payment processing
+        self.state.amount = 100.0
+        self.state.is_approved = self.state.amount < 1000
+        return "Payment processed"
+
+    @router(process_payment)
+    def check_approval(self, previous_result):
+        if self.state.is_approved:
+            return "approved"
+        elif self.state.retry_count < 3:
+            return "retry"
+        else:
+            return "rejected"
+
+    @listen("approved")
+    def handle_approval(self):
+        return f"Payment of ${self.state.amount} approved!"
+
+    @listen("retry")
+    def handle_retry(self):
+        self.state.retry_count += 1
+        print(f"Retrying payment (attempt {self.state.retry_count})...")
+        # Could implement retry logic here
+        return "Retry initiated"
+
+    @listen("rejected")
+    def handle_rejection(self):
+        return f"Payment of ${self.state.amount} rejected after {self.state.retry_count} retries."
+```
+
+### Handling Complex State Transformations
+
+For complex state transformations, you can create dedicated methods:
+
+```python
+from crewai.flow.flow import Flow, listen, start
+from pydantic import BaseModel
+from typing import List, Dict
+
+class UserData(BaseModel):
+    name: str
+    active: bool = True
+    login_count: int = 0
+
+class ComplexState(BaseModel):
+    users: Dict[str, UserData] = {}
+    active_user_count: int = 0
+
+class TransformationFlow(Flow[ComplexState]):
+    @start()
+    def initialize(self):
+        # Add some users
+        self.add_user("alice", "Alice")
+        self.add_user("bob", "Bob")
+        self.add_user("charlie", "Charlie")
+        return "Initialized"
+
+    @listen(initialize)
+    def process_users(self, _):
+        # Increment login counts
+        for user_id in self.state.users:
+            self.increment_login(user_id)
+
+        # Deactivate one user
+        self.deactivate_user("bob")
+
+        # Update active count
+        self.update_active_count()
+
+        return f"Processed {len(self.state.users)} users"
+
+    # Helper methods for state transformations
+    def add_user(self, user_id: str, name: str):
+        self.state.users[user_id] = UserData(name=name)
+        self.update_active_count()
+
+    def increment_login(self, user_id: str):
+        if user_id in self.state.users:
+            self.state.users[user_id].login_count += 1
+
+    def deactivate_user(self, user_id: str):
+        if user_id in self.state.users:
+            self.state.users[user_id].active = False
+            self.update_active_count()
+
+    def update_active_count(self):
+        self.state.active_user_count = sum(
+            1 for user in self.state.users.values() if user.active
+        )
+```
+
+This pattern of creating helper methods keeps your flow methods clean while enabling complex state manipulations.
+
+## State Management with Crews
+
+One of the most powerful patterns in CrewAI is combining flow state management with crew execution.
+
+### Passing State to Crews
+
+You can use flow state to parameterize crews:
+
+```python
+from crewai.flow.flow import Flow, listen, start
+from crewai import Agent, Crew, Process, Task
+from pydantic import BaseModel
+
+class ResearchState(BaseModel):
+    topic: str = ""
+    depth: str = "medium"
+    results: str = ""
+
+class ResearchFlow(Flow[ResearchState]):
+    @start()
+    def get_parameters(self):
+        # In a real app, this might come from user input
+        self.state.topic = "Artificial Intelligence Ethics"
+        self.state.depth = "deep"
+        return "Parameters set"
+
+    @listen(get_parameters)
+    def execute_research(self, _):
+        # Create agents
+        researcher = Agent(
+            role="Research Specialist",
+            goal=f"Research {self.state.topic} in {self.state.depth} detail",
+            backstory="You are an expert researcher with a talent for finding accurate information."
+        )
+
+        writer = Agent(
+            role="Content Writer",
+            goal="Transform research into clear, engaging content",
+            backstory="You excel at communicating complex ideas clearly and concisely."
+        )
+
+        # Create tasks
+        research_task = Task(
+            description=f"Research {self.state.topic} with {self.state.depth} analysis",
+            expected_output="Comprehensive research notes in markdown format",
+            agent=researcher
+        )
+
+        writing_task = Task(
+            description=f"Create a summary on {self.state.topic} based on the research",
+            expected_output="Well-written article in markdown format",
+            agent=writer,
+            context=[research_task]
+        )
+
+        # Create and run crew
+        research_crew = Crew(
+            agents=[researcher, writer],
+            tasks=[research_task, writing_task],
+            process=Process.sequential,
+            verbose=True
+        )
+
+        # Run crew and store result in state
+        result = research_crew.kickoff()
+        self.state.results = result.raw
+
+        return "Research completed"
+
+    @listen(execute_research)
+    def summarize_results(self, _):
+        # Access the stored results
+        result_length = len(self.state.results)
+        return f"Research on {self.state.topic} completed with {result_length} characters of results."
+```
+
+### Handling Crew Outputs in State
+
+When a crew completes, you can process its output and store it in your flow state:
+
+```python
+@listen(execute_crew)
+def process_crew_results(self, _):
+    # Parse the raw results (assuming JSON output)
+    import json
+    try:
+        results_dict = json.loads(self.state.raw_results)
+        self.state.processed_results = {
+            "title": results_dict.get("title", ""),
+            "main_points": results_dict.get("main_points", []),
+            "conclusion": results_dict.get("conclusion", "")
+        }
+        return "Results processed successfully"
+    except json.JSONDecodeError:
+        self.state.error = "Failed to parse crew results as JSON"
+        return "Error processing results"
+```
+
+## Best Practices for State Management
+
+### 1. Keep State Focused
+
+Design your state to contain only what's necessary:
+
+```python
+# Too broad
+class BloatedState(BaseModel):
+    user_data: Dict = {}
+    system_settings: Dict = {}
+    temporary_calculations: List = []
+    debug_info: Dict = {}
+    # ...many more fields
+
+# Better: Focused state
+class FocusedState(BaseModel):
+    user_id: str
+    preferences: Dict[str, str]
+    completion_status: Dict[str, bool]
+```
+
+### 2. Use Structured State for Complex Flows
+
+As your flows grow in complexity, structured state becomes increasingly valuable:
+
+```python
+# Simple flow can use unstructured state
+class SimpleGreetingFlow(Flow):
+    @start()
+    def greet(self):
+        self.state["name"] = "World"
+        return f"Hello, {self.state['name']}!"
+
+# Complex flow benefits from structured state
+class UserRegistrationState(BaseModel):
+    username: str
+    email: str
+    verification_status: bool = False
+    registration_date: datetime = Field(default_factory=datetime.now)
+    last_login: Optional[datetime] = None
+
+class RegistrationFlow(Flow[UserRegistrationState]):
+    # Methods with strongly-typed state access
+```
+
+### 3. Document State Transitions
+
+For complex flows, document how state changes throughout the execution:
+
+```python
+@start()
+def initialize_order(self):
+    """
+    Initialize order state with empty values.
+
+    State before: {}
+    State after: {order_id: str, items: [], status: 'new'}
+    """
+    self.state.order_id = str(uuid.uuid4())
+    self.state.items = []
+    self.state.status = "new"
+    return "Order initialized"
+```
+
+### 4. Handle State Errors Gracefully
+
+Implement error handling for state access:
+
+```python
+@listen(previous_step)
+def process_data(self, _):
+    try:
+        # Try to access a value that might not exist
+        user_preference = self.state.preferences.get("theme", "default")
+    except (AttributeError, KeyError):
+        # Handle the error gracefully
+        self.state.errors = self.state.get("errors", [])
+        self.state.errors.append("Failed to access preferences")
+        user_preference = "default"
+
+    return f"Used preference: {user_preference}"
+```
+
+### 5. Use State for Progress Tracking
+
+Leverage state to track progress in long-running flows:
+
+```python
+class ProgressTrackingFlow(Flow):
+    @start()
+    def initialize(self):
+        self.state["total_steps"] = 3
+        self.state["current_step"] = 0
+        self.state["progress"] = 0.0
+        self.update_progress()
+        return "Initialized"
+
+    def update_progress(self):
+        """Helper method to calculate and update progress"""
+        if self.state.get("total_steps", 0) > 0:
+            self.state["progress"] = (self.state.get("current_step", 0) /
+                                    self.state["total_steps"]) * 100
+            print(f"Progress: {self.state['progress']:.1f}%")
+
+    @listen(initialize)
+    def step_one(self, _):
+        # Do work...
+        self.state["current_step"] = 1
+        self.update_progress()
+        return "Step 1 complete"
+
+    # Additional steps...
+```
+
+### 6. Use Immutable Operations When Possible
+
+Especially with structured state, prefer immutable operations for clarity:
+
+```python
+# Instead of modifying lists in place:
+self.state.items.append(new_item)  # Mutable operation
+
+# Consider creating new state:
+from pydantic import BaseModel
+from typing import List
+
+class ItemState(BaseModel):
+    items: List[str] = []
+
+class ImmutableFlow(Flow[ItemState]):
+    @start()
+    def add_item(self):
+        # Create new list with the added item
+        self.state.items = [*self.state.items, "new item"]
+        return "Item added"
+```
+
+## Debugging Flow State
+
+### Logging State Changes
+
+When developing, add logging to track state changes:
+
+```python
+import logging
+logging.basicConfig(level=logging.INFO)
+
+class LoggingFlow(Flow):
+    def log_state(self, step_name):
+        logging.info(f"State after {step_name}: {self.state}")
+
+    @start()
+    def initialize(self):
+        self.state["counter"] = 0
+        self.log_state("initialize")
+        return "Initialized"
+
+    @listen(initialize)
+    def increment(self, _):
+        self.state["counter"] += 1
+        self.log_state("increment")
+        return f"Incremented to {self.state['counter']}"
+```
+
+### State Visualization
+
+You can add methods to visualize your state for debugging:
+
+```python
+def visualize_state(self):
+    """Create a simple visualization of the current state"""
+    import json
+    from rich.console import Console
+    from rich.panel import Panel
+
+    console = Console()
+
+    if hasattr(self.state, "model_dump"):
+        # Pydantic v2
+        state_dict = self.state.model_dump()
+    elif hasattr(self.state, "dict"):
+        # Pydantic v1
+        state_dict = self.state.dict()
+    else:
+        # Unstructured state
+        state_dict = dict(self.state)
+
+    # Remove id for cleaner output
+    if "id" in state_dict:
+        state_dict.pop("id")
+
+    state_json = json.dumps(state_dict, indent=2, default=str)
+    console.print(Panel(state_json, title="Current Flow State"))
+```
+
+## Conclusion
+
+Mastering state management in CrewAI Flows gives you the power to build sophisticated, robust AI applications that maintain context, make complex decisions, and deliver consistent results.
+
+Whether you choose unstructured or structured state, implementing proper state management practices will help you create flows that are maintainable, extensible, and effective at solving real-world problems.
+
+As you develop more complex flows, remember that good state management is about finding the right balance between flexibility and structure, making your code both powerful and easy to understand.
+
+<Check>
+You've now mastered the concepts and practices of state management in CrewAI Flows! With this knowledge, you can create robust AI workflows that effectively maintain context, share data between steps, and build sophisticated application logic.
+</Check>
+
+## Next Steps
+
+- Experiment with both structured and unstructured state in your flows
+- Try implementing state persistence for long-running workflows
+- Explore [building your first crew](/guides/crews/first-crew) to see how crews and flows can work together
+- Check out the [Flow reference documentation](/concepts/flows) for more advanced features

docs/how-to/AgentOps-Observability.md

@@ -1,86 +0,0 @@
----
-title: Agent Monitoring with AgentOps
-description: Understanding and logging your agent performance with AgentOps.
----
-
-# Intro
-Observability is a key aspect of developing and deploying conversational AI agents. It allows developers to understand how their agents are performing, how their agents are interacting with users, and how their agents use external tools and APIs. AgentOps is a product independent of CrewAI that provides a comprehensive observability solution for agents.
-
-## AgentOps
-
-[AgentOps](https://agentops.ai/?=crew) provides session replays, metrics, and monitoring for agents.
-
-At a high level, AgentOps gives you the ability to monitor cost, token usage, latency, agent failures, session-wide statistics, and more. For more info, check out the [AgentOps Repo](https://github.com/AgentOps-AI/agentops).
-
-### Overview
-AgentOps provides monitoring for agents in development and production. It provides a dashboard for tracking agent performance, session replays, and custom reporting.
-
-Additionally, AgentOps provides session drilldowns for viewing Crew agent interactions, LLM calls, and tool usage in real-time. This feature is useful for debugging and understanding how agents interact with users as well as other agents.
-
-![Overview of a select series of agent session runs](..%2Fassets%2Fagentops-overview.png)
-![Overview of session drilldowns for examining agent runs](..%2Fassets%2Fagentops-session.png)
-![Viewing a step-by-step agent replay execution graph](..%2Fassets%2Fagentops-replay.png)
-
-### Features
-- **LLM Cost Management and Tracking**: Track spend with foundation model providers.
-- **Replay Analytics**: Watch step-by-step agent execution graphs.
-- **Recursive Thought Detection**: Identify when agents fall into infinite loops.
-- **Custom Reporting**: Create custom analytics on agent performance.
-- **Analytics Dashboard**: Monitor high-level statistics about agents in development and production.
-- **Public Model Testing**: Test your agents against benchmarks and leaderboards.
-- **Custom Tests**: Run your agents against domain-specific tests.
-- **Time Travel Debugging**: Restart your sessions from checkpoints.
-- **Compliance and Security**: Create audit logs and detect potential threats such as profanity and PII leaks.
-- **Prompt Injection Detection**: Identify potential code injection and secret leaks.
-
-### Using AgentOps
-
-1. **Create an API Key:**
-   Create a user API key here: [Create API Key](https://app.agentops.ai/account)
-
-2. **Configure Your Environment:**
-   Add your API key to your environment variables
-
-   ```bash
-   AGENTOPS_API_KEY=<YOUR_AGENTOPS_API_KEY>
-   ```
-
-3. **Install AgentOps:**
-   Install AgentOps with:
-   ```bash
-   pip install crewai[agentops]
-   ```
-   or
-   ```bash
-   pip install agentops
-   ```
-
-   Before using `Crew` in your script, include these lines:
-
-   ```python
-   import agentops
-   agentops.init()
-   ```
-
-   This will initiate an AgentOps session as well as automatically track Crew agents. For further info on how to outfit more complex agentic systems, check out the [AgentOps documentation](https://docs.agentops.ai) or join the [Discord](https://discord.gg/j4f3KbeH).
-
-### Crew + AgentOps Examples
-- [Job Posting](https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting)
-- [Markdown Validator](https://github.com/joaomdmoura/crewAI-examples/tree/main/markdown_validator)
-- [Instagram Post](https://github.com/joaomdmoura/crewAI-examples/tree/main/instagram_post)
-
-### Further Information
-
-To get started, create an [AgentOps account](https://agentops.ai/?=crew).
-
-For feature requests or bug reports, please reach out to the AgentOps team on the [AgentOps Repo](https://github.com/AgentOps-AI/agentops).
-
-#### Extra links
-
-<a href="https://twitter.com/agentopsai/">🐦 Twitter</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://discord.gg/JHPt4C7r">📢 Discord</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://app.agentops.ai/?=crew">🖇️ AgentOps Dashboard</a>
-<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
-<a href="https://docs.agentops.ai/introduction">📙 Documentation</a>

docs/how-to/Customize-Prompts.md

@@ -1,94 +0,0 @@
----
-title: Initial Support to Bring Your Own Prompts in CrewAI
-description: Enhancing customization and internationalization by allowing users to bring their own prompts in CrewAI.
-
----
-
-# Initial Support to Bring Your Own Prompts in CrewAI
-
-CrewAI now supports the ability to bring your own prompts, enabling extensive customization and internationalization. This feature allows users to tailor the inner workings of their agents to better suit specific needs, including support for multiple languages.
-
-## Internationalization and Customization Support
-
-### Custom Prompts with `prompt_file`
-
-The `prompt_file` attribute facilitates full customization of the agent prompts, enhancing the global usability of CrewAI. Users can specify their prompt templates, ensuring that the agents communicate in a manner that aligns with specific project requirements or language preferences.
-
-#### Example of a Custom Prompt File
-
-The custom prompts can be defined in a JSON file, similar to the example provided [here](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json).
-
-### Supported Languages
-
-CrewAI's custom prompt support includes internationalization, allowing prompts to be written in different languages. This is particularly useful for global teams or projects that require multilingual support.
-
-## How to Use the `prompt_file` Attribute
-
-To utilize the `prompt_file` attribute, include it in your crew definition. Below is an example demonstrating how to set up agents and tasks with custom prompts.
-
-### Example
-
-```python
-import os
-from crewai import Agent, Task, Crew
-
-# Define your agents
-researcher = Agent(
-    role="Researcher",
-    goal="Make the best research and analysis on content about AI and AI agents",
-    backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-    allow_delegation=False,
-)
-
-writer = Agent(
-    role="Senior Writer",
-    goal="Write the best content about AI and AI agents.",
-    backstory="You're a senior writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer.",
-    allow_delegation=False,
-)
-
-# Define your tasks
-tasks = [
-    Task(
-        description="Say Hi",
-        expected_output="The word: Hi",
-        agent=researcher,
-    )
-]
-
-# Instantiate your crew with custom prompts
-crew = Crew(
-    agents=[researcher],
-    tasks=tasks,
-    prompt_file="prompt.json",  # Path to your custom prompt file
-)
-
-# Get your crew to work!
-crew.kickoff()
-```
-
-## Advanced Customization Features
-
-### `language` Attribute
-
-In addition to `prompt_file`, the `language` attribute can be used to specify the language for the agent's prompts. This ensures that the prompts are generated in the desired language, further enhancing the internationalization capabilities of CrewAI.
-
-### Creating Custom Prompt Files
-
-Custom prompt files should be structured in JSON format and include all necessary prompt templates. Below is a simplified example of a prompt JSON file:
-
-```json
-{
-    "system": "You are a system template.",
-    "prompt": "Here is your prompt template.",
-    "response": "Here is your response template."
-}
-```
-
-### Benefits of Custom Prompts
-
-- **Enhanced Flexibility**: Tailor agent communication to specific project needs.
-- **Improved Usability**: Supports multiple languages, making it suitable for global projects.
-- **Consistency**: Ensures uniform prompt structures across different agents and tasks.
-
-By incorporating these updates, CrewAI provides users with the ability to fully customize and internationalize their agent prompts, making the platform more versatile and user-friendly.

docs/how-to/Customizing-Agents.md

@@ -1,88 +0,0 @@
----
-title: Customizing Agents in CrewAI
-description: A comprehensive guide to tailoring agents for specific roles, tasks, and advanced customizations within the CrewAI framework.
----
-
-## Customizable Attributes
-Crafting an efficient CrewAI team hinges on the ability to dynamically tailor your AI agents to meet the unique requirements of any project. This section covers the foundational attributes you can customize.
-
-### Key Attributes for Customization
-- **Role**: Specifies the agent's job within the crew, such as 'Analyst' or 'Customer Service Rep'.
-- **Goal**: Defines what the agent aims to achieve, in alignment with its role and the overarching objectives of the crew.
-- **Backstory**: Provides depth to the agent's persona, enriching its motivations and engagements within the crew.
-- **Tools** *(Optional)*: Represents the capabilities or methods the agent uses to perform tasks, from simple functions to intricate integrations.
-- **Cache** *(Optional)*: Determines whether the agent should use a cache for tool usage.
-- **Max RPM**: Sets the maximum number of requests per minute (`max_rpm`). This attribute is optional and can be set to `None` for no limit, allowing for unlimited queries to external services if needed.
-- **Verbose** *(Optional)*: Enables detailed logging of an agent's actions, useful for debugging and optimization. Specifically, it provides insights into agent execution processes, aiding in the optimization of performance.
-- **Allow Delegation** *(Optional)*: `allow_delegation` controls whether the agent is allowed to delegate tasks to other agents.
-- **Max Iter** *(Optional)*: The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. The default value is set to 25, providing a balance between thoroughness and efficiency. Once the agent approaches this number, it will try its best to give a good answer.
-- **Max Execution Time** *(Optional)*: `max_execution_time` Sets the maximum execution time for an agent to complete a task.
-- **System Template** *(Optional)*: `system_template` defines the system format for the agent.
-- **Prompt Template** *(Optional)*: `prompt_template` defines the prompt format for the agent.
-- **Response Template** *(Optional)*: `response_template` defines the response format for the agent.
-
-## Advanced Customization Options
-Beyond the basic attributes, CrewAI allows for deeper customization to enhance an agent's behavior and capabilities significantly.
-
-### Language Model Customization
-Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`), offering advanced control over their processing and decision-making abilities. It's important to note that setting the `function_calling_llm` allows for overriding the default crew function-calling language model, providing a greater degree of customization.
-
-## Performance and Debugging Settings
-Adjusting an agent's performance and monitoring its operations are crucial for efficient task execution.
-
-### Verbose Mode and RPM Limit
-- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization. Specifically, it provides insights into agent execution processes, aiding in the optimization of performance.
-- **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`). This attribute is optional and can be set to `None` for no limit, allowing for unlimited queries to external services if needed.
-
-### Maximum Iterations for Task Execution
-The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. The default value is set to 25, providing a balance between thoroughness and efficiency. Once the agent approaches this number, it will try its best to give a good answer.
-
-## Customizing Agents and Tools
-Agents are customized by defining their attributes and tools during initialization. Tools are critical for an agent's functionality, enabling them to perform specialized tasks. The `tools` attribute should be an array of tools the agent can utilize, and it's initialized as an empty list by default. Tools can be added or modified post-agent initialization to adapt to new requirements.
-
-```shell
-pip install 'crewai[tools]'
-```
-
-### Example: Assigning Tools to an Agent
-```python
-import os
-from crewai import Agent
-from crewai_tools import SerperDevTool
-
-# Set API keys for tool initialization
-os.environ["OPENAI_API_KEY"] = "Your Key"
-os.environ["SERPER_API_KEY"] = "Your Key"
-
-# Initialize a search tool
-search_tool = SerperDevTool()
-
-# Initialize the agent with advanced options
-agent = Agent(
-  role='Research Analyst',
-  goal='Provide up-to-date market analysis',
-  backstory='An expert analyst with a keen eye for market trends.',
-  tools=[search_tool],
-  memory=True, # Enable memory
-  verbose=True,
-  max_rpm=None, # No limit on requests per minute
-  max_iter=25, # Default value for maximum iterations
-  allow_delegation=False
-)
-```
-
-## Delegation and Autonomy
-Controlling an agent's ability to delegate tasks or ask questions is vital for tailoring its autonomy and collaborative dynamics within the CrewAI framework. By default, the `allow_delegation` attribute is set to `True`, enabling agents to seek assistance or delegate tasks as needed. This default behavior promotes collaborative problem-solving and efficiency within the CrewAI ecosystem. If needed, delegation can be disabled to suit specific operational requirements.
-
-### Example: Disabling Delegation for an Agent
-```python
-agent = Agent(
-  role='Content Writer',
-  goal='Write engaging content on market trends',
-  backstory='A seasoned writer with expertise in market analysis.',
-  allow_delegation=False # Disabling delegation
-)
-```
-
-## Conclusion
-Customizing agents in CrewAI by setting their roles, goals, backstories, and tools, alongside advanced options like language model customization, memory, performance settings, and delegation preferences, equips a nuanced and capable AI team ready for complex challenges.

docs/how-to/Force-Tool-Ouput-as-Result.md

@@ -1,32 +0,0 @@
----
-title: Forcing Tool Output as Result
-description: Learn how to force tool output as the result in of an Agent's task in CrewAI.
----
-
-## Introduction
-In CrewAI, you can force the output of a tool as the result of an agent's task. This feature is useful when you want to ensure that the tool output is captured and returned as the task result, and avoid the agent modifying the output during the task execution.
-
-## Forcing Tool Output as Result
-To force the tool output as the result of an agent's task, you can set the `result_as_answer` parameter to `True` when creating the agent. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
-
-Here's an example of how to force the tool output as the result of an agent's task:
-
-```python
-# ...
-from crewai.agent import Agent
-
-# Define a custom tool that returns the result as the answer
-coding_agent = Agent(
-        role="Data Scientist",
-        goal="Produce amazing reports on AI",
-        backstory="You work with data and AI",
-        tools=[MyCustomTool(result_as_answer=True)],
-    )
-```
-
-## Workflow in Action
-
-1. **Task Execution**: The agent executes the task using the tool provided.
-2. **Tool Output**: The tool generates the output, which is captured as the task result.
-3. **Agent Interaction**: The agent may reflect and take learnings from the tool but the output is not modified.
-4. **Result Return**: The tool output is returned as the task result without any modifications.

docs/how-to/Hierarchical.md

@@ -1,69 +0,0 @@
----
-title: Implementing the Hierarchical Process in CrewAI
-description: A comprehensive guide to understanding and applying the hierarchical process within your CrewAI projects, updated to reflect the latest coding practices and functionalities.
----
-
-## Introduction
-The hierarchical process in CrewAI introduces a structured approach to task management, simulating traditional organizational hierarchies for efficient task delegation and execution. This systematic workflow enhances project outcomes by ensuring tasks are handled with optimal efficiency and accuracy.
-
-!!! note "Complexity and Efficiency"
-    The hierarchical process is designed to leverage advanced models like GPT-4, optimizing token usage while handling complex tasks with greater efficiency.
-
-## Hierarchical Process Overview
-By default, tasks in CrewAI are managed through a sequential process. However, adopting a hierarchical approach allows for a clear hierarchy in task management, where a 'manager' agent coordinates the workflow, delegates tasks, and validates outcomes for streamlined and effective execution. This manager agent can now be either automatically created by CrewAI or explicitly set by the user.
-
-### Key Features
-- **Task Delegation**: A manager agent allocates tasks among crew members based on their roles and capabilities.
-- **Result Validation**: The manager evaluates outcomes to ensure they meet the required standards.
-- **Efficient Workflow**: Emulates corporate structures, providing an organized approach to task management.
-
-## Implementing the Hierarchical Process
-To utilize the hierarchical process, it's essential to explicitly set the process attribute to `Process.hierarchical`, as the default behavior is `Process.sequential`. Define a crew with a designated manager and establish a clear chain of command.
-
-!!! note "Tools and Agent Assignment"
-    Assign tools at the agent level to facilitate task delegation and execution by the designated agents under the manager's guidance. Tools can also be specified at the task level for precise control over tool availability during task execution.
-
-!!! note "Manager LLM Requirement"
-    Configuring the `manager_llm` parameter is crucial for the hierarchical process. The system requires a manager LLM to be set up for proper function, ensuring tailored decision-making.
-
-```python
-from langchain_openai import ChatOpenAI
-from crewai import Crew, Process, Agent
-
-# Agents are defined with attributes for backstory, cache, and verbose mode
-researcher = Agent(
-    role='Researcher',
-    goal='Conduct in-depth analysis',
-    backstory='Experienced data analyst with a knack for uncovering hidden trends.',
-    cache=True,
-    verbose=False,
-    # tools=[]  # This can be optionally specified; defaults to an empty list
-)
-writer = Agent(
-    role='Writer',
-    goal='Create engaging content',
-    backstory='Creative writer passionate about storytelling in technical domains.',
-    cache=True,
-    verbose=False,
-    # tools=[]  # Optionally specify tools; defaults to an empty list
-)
-
-# Establishing the crew with a hierarchical process and additional configurations
-project_crew = Crew(
-    tasks=[...],  # Tasks to be delegated and executed under the manager's supervision
-    agents=[researcher, writer],
-    manager_llm=ChatOpenAI(temperature=0, model="gpt-4"),  # Mandatory if manager_agent is not set
-    process=Process.hierarchical,  # Specifies the hierarchical management approach
-    memory=True,  # Enable memory usage for enhanced task execution
-    manager_agent=None,  # Optional: explicitly set a specific agent as manager instead of the manager_llm
-    planning=True,  # Enable planning feature for pre-execution strategy
-)
-```
-
-### Workflow in Action
-1. **Task Assignment**: The manager assigns tasks strategically, considering each agent's capabilities and available tools.
-2. **Execution and Review**: Agents complete their tasks with the option for asynchronous execution and callback functions for streamlined workflows.
-3. **Sequential Task Progression**: Despite being a hierarchical process, tasks follow a logical order for smooth progression, facilitated by the manager's oversight.
-
-## Conclusion
-Adopting the hierarchical process in CrewAI, with the correct configurations and understanding of the system's capabilities, facilitates an organized and efficient approach to project management. Utilize the advanced features and customizations to tailor the workflow to your specific needs, ensuring optimal task execution and project success.

docs/how-to/Kickoff-async.md

@@ -1,54 +0,0 @@
----
-title: Kickoff Async
-description: Kickoff a Crew Asynchronously
----
-
-## Introduction
-CrewAI provides the ability to kickoff a crew asynchronously, allowing you to start the crew execution in a non-blocking manner. This feature is particularly useful when you want to run multiple crews concurrently or when you need to perform other tasks while the crew is executing.
-
-## Asynchronous Crew Execution
-To kickoff a crew asynchronously, use the `kickoff_async()` method. This method initiates the crew execution in a separate thread, allowing the main thread to continue executing other tasks.
-
-### Method Signature
-
-```python
-def kickoff_async(self, inputs: dict) -> CrewOutput:
-```
-
-### Parameters
-
-- `inputs` (dict): A dictionary containing the input data required for the tasks.
-
-### Returns
-
-- `CrewOutput`: An object representing the result of the crew execution.
-
-## Example
-Here's an example of how to kickoff a crew asynchronously:
-
-```python
-from crewai import Crew, Agent, Task
-
-# Create an agent with code execution enabled
-coding_agent = Agent(
-    role="Python Data Analyst",
-    goal="Analyze data and provide insights using Python",
-    backstory="You are an experienced data analyst with strong Python skills.",
-    allow_code_execution=True
-)
-
-# Create a task that requires code execution
-data_analysis_task = Task(
-    description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
-    agent=coding_agent
-)
-
-# Create a crew and add the task
-analysis_crew = Crew(
-    agents=[coding_agent],
-    tasks=[data_analysis_task]
-)
-
-# Execute the crew asynchronously
-result = analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
-```

docs/how-to/LLM-Connections.md

@@ -1,197 +0,0 @@
----
-title: Connect CrewAI to LLMs
-description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs), including detailed class attributes, methods, and configuration options.
----
-
-## Connect CrewAI to LLMs
-
-!!! note "Default LLM"
-    By default, CrewAI uses OpenAI's GPT-4o model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4o") for language processing. You can configure your agents to use a different model or API as described in this guide.
-    By default, CrewAI uses OpenAI's GPT-4 model (specifically, the model specified by the OPENAI_MODEL_NAME environment variable, defaulting to "gpt-4") for language processing. You can configure your agents to use a different model or API as described in this guide.
-
-CrewAI provides extensive versatility in integrating with various Language Models (LLMs), including local options through Ollama such as Llama and Mixtral to cloud-based solutions like Azure. Its compatibility extends to all [LangChain LLM components](https://python.langchain.com/v0.2/docs/integrations/llms/), offering a wide range of integration possibilities for customized AI applications.
-
-The platform supports connections to an array of Generative AI models, including:
-
- - OpenAI's suite of advanced language models
- - Anthropic's cutting-edge AI offerings
- - Ollama's diverse range of locally-hosted generative model & embeddings
- - LM Studio's diverse range of locally hosted generative models & embeddings
- - Groq's Super Fast LLM offerings
- - Azures' generative AI offerings
- - HuggingFace's generative AI offerings
-
-This broad spectrum of LLM options enables users to select the most suitable model for their specific needs, whether prioritizing local deployment, specialized capabilities, or cloud-based scalability.
-
-## Changing the default LLM
-The default LLM is provided through the `langchain openai` package, which is installed by default when you install CrewAI. You can change this default LLM to a different model or API by setting the `OPENAI_MODEL_NAME` environment variable. This straightforward process allows you to harness the power of different OpenAI models, enhancing the flexibility and capabilities of your CrewAI implementation.
-```python
-# Required
-os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"
-
-# Agent will automatically use the model defined in the environment variable
-example_agent = Agent(
-  role='Local Expert',
-  goal='Provide insights about the city',
-  backstory="A knowledgeable local guide.",
-  verbose=True
-)
-```
-
-## Ollama Local Integration
-Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, you will need the `langchain-ollama` package. You can then set the following environment variables to connect to your Ollama instance running locally on port 11434.
-
-```sh
-os.environ[OPENAI_API_BASE]='http://localhost:11434'
-os.environ[OPENAI_MODEL_NAME]='llama2'  # Adjust based on available model
-os.environ[OPENAI_API_KEY]='' # No API Key required for Ollama
-```
-
-## Ollama Integration Step by Step (ex. for using Llama 3.1 8B locally)
-1. [Download and install Ollama](https://ollama.com/download).
-2. After setting up the Ollama, Pull the Llama3.1 8B model by typing following lines into your terminal ```ollama run llama3.1```.
-3. Llama3.1 should now be served locally on `http://localhost:11434`
-```
-from crewai import Agent, Task, Crew
-from langchain_ollama import ChatOllama
-import os
-os.environ["OPENAI_API_KEY"] = "NA"
-
-llm = ChatOllama(
-    model = "llama3.1",
-    base_url = "http://localhost:11434")
-
-general_agent = Agent(role = "Math Professor",
-                      goal = """Provide the solution to the students that are asking mathematical questions and give them the answer.""",
-                      backstory = """You are an excellent math professor that likes to solve math questions in a way that everyone can understand your solution""",
-                      allow_delegation = False,
-                      verbose = True,
-                      llm = llm)
-
-task = Task(description="""what is 3 + 5""",
-             agent = general_agent,
-             expected_output="A numerical answer.")
-
-crew = Crew(
-            agents=[general_agent],
-            tasks=[task],
-            verbose=True
-        )
-
-result = crew.kickoff()
-
-print(result)
-```
-
-## HuggingFace Integration
-There are a couple of different ways you can use HuggingFace to host your LLM.
-
-### Your own HuggingFace endpoint
-```python
-from langchain_huggingface import HuggingFaceEndpoint,
-
-llm = HuggingFaceEndpoint(
-    repo_id="microsoft/Phi-3-mini-4k-instruct",
-    task="text-generation",
-    max_new_tokens=512,
-    do_sample=False,
-    repetition_penalty=1.03,
-)
-
-agent = Agent(
-    role="HuggingFace Agent",
-    goal="Generate text using HuggingFace",
-    backstory="A diligent explorer of GitHub docs.",
-    llm=llm
-)
-```
-
-## OpenAI Compatible API Endpoints
-Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, Groq, and Mistral AI.
-
-### Configuration Examples
-#### FastChat
-```sh
-os.environ[OPENAI_API_BASE]="http://localhost:8001/v1"
-os.environ[OPENAI_MODEL_NAME]='oh-2.5m7b-q51'
-os.environ[OPENAI_API_KEY]=NA
-```
-
-#### LM Studio
-Launch [LM Studio](https://lmstudio.ai) and go to the Server tab. Then select a model from the dropdown menu and wait for it to load. Once it's loaded, click the green Start Server button and use the URL, port, and API key that's shown (you can modify them). Below is an example of the default settings as of LM Studio 0.2.19:
-```sh
-os.environ[OPENAI_API_BASE]="http://localhost:1234/v1"
-os.environ[OPENAI_API_KEY]="lm-studio"
-```
-
-#### Groq API
-```sh
-os.environ[OPENAI_API_KEY]=your-groq-api-key
-os.environ[OPENAI_MODEL_NAME]='llama3-8b-8192'
-os.environ[OPENAI_API_BASE]=https://api.groq.com/openai/v1
-```
-
-#### Mistral API
-```sh
-os.environ[OPENAI_API_KEY]=your-mistral-api-key
-os.environ[OPENAI_API_BASE]=https://api.mistral.ai/v1
-os.environ[OPENAI_MODEL_NAME]="mistral-small"
-```
-
-### Solar
-```sh
-from langchain_community.chat_models.solar import SolarChat
-```
-```sh
-os.environ[SOLAR_API_BASE]="https://api.upstage.ai/v1/solar"
-os.environ[SOLAR_API_KEY]="your-solar-api-key"
-```
-
-# Free developer API key available here: https://console.upstage.ai/services/solar
-# Langchain Example: https://github.com/langchain-ai/langchain/pull/18556
-
-
-### Cohere
-```python
-from langchain_cohere import ChatCohere
-# Initialize language model
-os.environ["COHERE_API_KEY"] = "your-cohere-api-key"
-llm = ChatCohere()
-
-# Free developer API key available here: https://cohere.com/
-# Langchain Documentation: https://python.langchain.com/docs/integrations/chat/cohere
-```
-
-### Azure Open AI Configuration
-For Azure OpenAI API integration, set the following environment variables:
-```sh
-
-os.environ[AZURE_OPENAI_DEPLOYMENT] = "Your deployment"
-os.environ["OPENAI_API_VERSION"] = "2023-12-01-preview"
-os.environ["AZURE_OPENAI_ENDPOINT"] = "Your Endpoint"
-os.environ["AZURE_OPENAI_API_KEY"] = "<Your API Key>"
-```
-
-### Example Agent with Azure LLM
-```python
-from dotenv import load_dotenv
-from crewai import Agent
-from langchain_openai import AzureChatOpenAI
-
-load_dotenv()
-
-azure_llm = AzureChatOpenAI(
-    azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
-    api_key=os.environ.get("AZURE_OPENAI_KEY")
-)
-
-azure_agent = Agent(
-  role='Example Agent',
-  goal='Demonstrate custom LLM configuration',
-  backstory='A diligent explorer of GitHub docs.',
-  llm=azure_llm
-)
-```
-
-## Conclusion
-Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.

docs/how-to/Langtrace-Observability.md

@@ -1,89 +0,0 @@
----
-title: CrewAI Agent Monitoring with Langtrace
-description: How to monitor cost, latency, and performance of CrewAI Agents using Langtrace, an external observability tool.
----
-
-# Langtrace Overview
-
-Langtrace is an open-source, external tool that helps you set up observability and evaluations for Large Language Models (LLMs), LLM frameworks, and Vector Databases. While not built directly into CrewAI, Langtrace can be used alongside CrewAI to gain deep visibility into the cost, latency, and performance of your CrewAI Agents. This integration allows you to log hyperparameters, monitor performance regressions, and establish a process for continuous improvement of your Agents.
-
-## Setup Instructions
-
-1. Sign up for [Langtrace](https://langtrace.ai/) by visiting [https://langtrace.ai/signup](https://langtrace.ai/signup).
-2. Create a project and generate an API key.
-3. Install Langtrace in your CrewAI project using the following commands:
-
-```bash
-# Install the SDK
-pip install langtrace-python-sdk
-```
-
-## Using Langtrace with CrewAI
-
-To integrate Langtrace with your CrewAI project, follow these steps:
-
-1. Import and initialize Langtrace at the beginning of your script, before any CrewAI imports:
-
-```python
-from langtrace_python_sdk import langtrace
-langtrace.init(api_key='<LANGTRACE_API_KEY>')
-
-# Now import CrewAI modules
-from crewai import Agent, Task, Crew
-```
-
-2. Create your CrewAI agents and tasks as usual.
-
-3. Use Langtrace's tracking functions to monitor your CrewAI operations. For example:
-
-```python
-with langtrace.trace("CrewAI Task Execution"):
-    result = crew.kickoff()
-```
-
-### Features and Their Application to CrewAI
-
-1. **LLM Token and Cost Tracking**
-   - Monitor the token usage and associated costs for each CrewAI agent interaction.
-   - Example:
-     ```python
-     with langtrace.trace("Agent Interaction"):
-         agent_response = agent.execute(task)
-     ```
-
-2. **Trace Graph for Execution Steps**
-   - Visualize the execution flow of your CrewAI tasks, including latency and logs.
-   - Useful for identifying bottlenecks in your agent workflows.
-
-3. **Dataset Curation with Manual Annotation**
-   - Create datasets from your CrewAI task outputs for future training or evaluation.
-   - Example:
-     ```python
-     langtrace.log_dataset_item(task_input, agent_output, {"task_type": "research"})
-     ```
-
-4. **Prompt Versioning and Management**
-   - Keep track of different versions of prompts used in your CrewAI agents.
-   - Useful for A/B testing and optimizing agent performance.
-
-5. **Prompt Playground with Model Comparisons**
-   - Test and compare different prompts and models for your CrewAI agents before deployment.
-
-6. **Testing and Evaluations**
-   - Set up automated tests for your CrewAI agents and tasks.
-   - Example:
-     ```python
-     langtrace.evaluate(agent_output, expected_output, "accuracy")
-     ```
-
-## Monitoring New CrewAI Features
-
-CrewAI has introduced several new features that can be monitored using Langtrace:
-
-1. **Code Execution**: Monitor the performance and output of code executed by agents.
-   ```python
-   with langtrace.trace("Agent Code Execution"):
-       code_output = agent.execute_code(code_snippet)
-   ```
-
-2. **Third-party Agent Integration**: Track interactions with LlamaIndex, LangChain, and Autogen agents.

docs/how-to/Replay-tasks-from-latest-Crew-Kickoff.md

@@ -1,52 +0,0 @@
----
-title: Replay Tasks from Latest Crew Kickoff
-description: Replay tasks from the latest crew.kickoff(...)
----
-
-## Introduction
-CrewAI provides the ability to replay from a task specified from the latest crew kickoff. This feature is particularly useful when you've finished a kickoff and may want to retry certain tasks or don't need to refetch data over and your agents already have the context saved from the kickoff execution so you just need to replay the tasks you want to.
-
-## Note:
-You must run `crew.kickoff()` before you can replay a task. Currently, only the latest kickoff is supported, so if you use `kickoff_for_each`, it will only allow you to replay from the most recent crew run.
-
-Here's an example of how to replay from a task:
-
-### Replaying from Specific Task Using the CLI
-To use the replay feature, follow these steps:
-
-1. Open your terminal or command prompt.
-2. Navigate to the directory where your CrewAI project is located.
-3. Run the following command:
-To view the latest kickoff task_ids use:
-```shell
-crewai log-tasks-outputs
-```
-
-Once you have your task_id to replay from use:
-```shell
-crewai replay -t <task_id>
-```
-
-
-### Replaying from a Task Programmatically
-To replay from a task programmatically, use the following steps:
-
-1. Specify the task_id and input parameters for the replay process.
-2. Execute the replay command within a try-except block to handle potential errors.
-
-```python
-  def replay():
-    """
-    Replay the crew execution from a specific task.
-    """
-    task_id = '<task_id>'
-    inputs = {"topic": "CrewAI Training"}  # This is optional; you can pass in the inputs you want to replay; otherwise, it uses the previous kickoff's inputs.
-    try:
-        YourCrewName_Crew().crew().replay(task_id=task_id, inputs=inputs)
-
-    except subprocess.CalledProcessError as e:
-        raise Exception(f"An error occurred while replaying the crew: {e}")
-
-    except Exception as e:
-        raise Exception(f"An unexpected error occurred: {e}")
-```