chore: update crewAI version to 0.165.1 and tools dependency in templates (#3359) (#3359)

This commit simplifies the conditions for enabling tracing in both the Crew and Flow classes by removing the redundant call to `on_first_execution_tracing_confirmation()`. Additionally, it removes deprecated warning filters related to Pydantic in the KnowledgeStorage and RAGStorage classes, improving code clarity and maintainability.

.editorconfig

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

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,115 @@
+name: Bug report
+description: Create a report to help us improve CrewAI
+title: "[BUG]"
+labels: ["bug"]
+assignees: []
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: Provide a clear and concise description of what the bug is.
+    validations:
+      required: true
+  - type: textarea
+    id: steps-to-reproduce
+    attributes:
+      label: Steps to Reproduce
+      description: Provide a step-by-step process to reproduce the behavior.
+      placeholder: |
+        1. Go to '...'
+        2. Click on '....'
+        3. Scroll down to '....'
+        4. See error
+    validations:
+      required: true
+  - type: textarea
+    id: expected-behavior
+    attributes:
+      label: Expected behavior
+      description: A clear and concise description of what you expected to happen.
+    validations:
+      required: true
+  - type: textarea
+    id: screenshots-code
+    attributes:
+      label: Screenshots/Code snippets
+      description: If applicable, add screenshots or code snippets to help explain your problem.
+    validations:
+      required: true
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating System
+      description: Select the operating system you're using
+      options:
+        - Ubuntu 20.04
+        - Ubuntu 22.04
+        - Ubuntu 24.04
+        - macOS Catalina
+        - macOS Big Sur
+        - macOS Monterey
+        - macOS Ventura
+        - macOS Sonoma
+        - Windows 10
+        - Windows 11
+        - Other (specify in additional context)
+    validations:
+      required: true
+  - type: dropdown
+    id: python-version
+    attributes:
+      label: Python Version
+      description: Version of Python your Crew is running on
+      options:
+        - '3.10'
+        - '3.11'
+        - '3.12'
+    validations:
+      required: true
+  - type: input
+    id: crewai-version
+    attributes:
+      label: crewAI Version
+      description: What version of CrewAI are you using
+    validations:
+      required: true
+  - type: input
+    id: crewai-tools-version
+    attributes:
+      label: crewAI Tools Version
+      description: What version of CrewAI Tools are you using 
+    validations:
+      required: true
+  - type: dropdown
+    id: virtual-environment
+    attributes:
+      label: Virtual Environment
+      description: What Virtual Environment are you running your crew in.
+      options:
+        - Venv
+        - Conda
+        - Poetry
+    validations:
+      required: true
+  - type: textarea
+    id: evidence
+    attributes:
+      label: Evidence
+      description: Include relevant information, logs or error messages. These can be screenshots.
+    validations:
+      required: true
+  - type: textarea
+    id: possible-solution
+    attributes:
+      label: Possible Solution
+      description: Have a solution in mind? Please suggest it here, or write "None".
+    validations:
+      required: true
+  - type: textarea
+    id: additional-context
+    attributes:
+      label: Additional context
+      description: Add any other context about the problem here.
+    validations:
+      required: true

.github/ISSUE_TEMPLATE/config.yml

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

.github/ISSUE_TEMPLATE/feature_request.yml

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

.github/security.md

@@ -0,0 +1,27 @@
+## 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/black.yml

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

.github/workflows/linter.yml

@@ -0,0 +1,36 @@
+name: Lint
+
+on: [pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    env:
+      TARGET_BRANCH: ${{ github.event.pull_request.base.ref }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Fetch Target Branch
+        run: git fetch origin $TARGET_BRANCH --depth=1
+
+      - name: Install Ruff
+        run: pip install ruff
+
+      - name: Get Changed Python Files
+        id: changed-files
+        run: |
+          merge_base=$(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 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

@@ -1,35 +0,0 @@
-name: Deploy MkDocs
-
-on:
-  workflow_dispatch:    
-  push:
-    branches:
-      - main
-
-permissions:
-  contents: write
-
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v2
-
-      - name: Setup Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: '3.10'
-
-      - name: Install Requirements
-        run: |
-          sudo apt-get update &&
-          sudo apt-get install pngquant &&
-          pip install mkdocs-material
-
-        env:
-          GH_TOKEN: ${{ secrets.GH_TOKEN }}
-
-      - name: Build and deploy MkDocs
-        run: mkdocs gh-deploy --force

.github/workflows/notify-downstream.yml

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

.github/workflows/security-checker.yml

@@ -0,0 +1,23 @@
+name: Security Checker
+
+on: [pull_request]
+
+jobs:
+  security-check:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11.9"
+
+      - name: Install dependencies
+        run: pip install bandit
+
+      - name: Run Bandit
+        run: bandit -c pyproject.toml -r src/ -ll
+

.github/workflows/stale.yml

@@ -0,0 +1,29 @@
+name: Mark stale issues and pull requests
+
+permissions:
+  contents: write
+  issues: write
+  pull-requests: write
+
+on:
+  schedule:
+    - cron: '10 12 * * *'
+  workflow_dispatch:
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/stale@v9
+      with:
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        stale-issue-label: 'no-issue-activity'
+        stale-issue-message: 'This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
+        close-issue-message: 'This issue was closed because it has been stalled for 5 days with no activity.'
+        days-before-issue-stale: 30
+        days-before-issue-close: 5
+        stale-pr-label: 'no-pr-activity'
+        stale-pr-message: 'This PR is stale because it has been open for 45 days with no activity.'
+        days-before-pr-stale: 45
+        days-before-pr-close: -1
+        operations-per-run: 1200

.github/workflows/tests.yml

@@ -1,32 +1,50 @@
 name: Run Tests
 
-on: [push, pull_request]
+on: [pull_request]
 
 permissions:
   contents: write
 
 env:
   OPENAI_API_KEY: fake-api-key
+  PYTHONUNBUFFERED: 1
 
 jobs:
-  deploy:
+  tests:
+    name: tests (${{ matrix.python-version }})
     runs-on: ubuntu-latest
-
+    timeout-minutes: 15
+    strategy:
+      fail-fast: true
+      matrix:
+        python-version: ['3.10', '3.11', '3.12', '3.13']
+        group: [1, 2, 3, 4, 5, 6, 7, 8]
     steps:
       - name: Checkout code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
 
-      - name: Setup Python
-        uses: actions/setup-python@v4
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
         with:
-          python-version: '3.10'
+          enable-cache: true
+          cache-dependency-glob: |
+            **/pyproject.toml
+            **/uv.lock
 
-      - name: Install Requirements
+      - 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 (group ${{ matrix.group }} of 8)
         run: |
-          sudo apt-get update &&
-          pip install poetry &&
-          poetry lock &&
-          poetry install
-
-      - name: Run tests
-        run: poetry run pytest
+          uv run pytest \
+            --block-network \
+            --timeout=30 \
+            -vv \
+            --splits 8 \
+            --group ${{ matrix.group }} \
+            --durations=10 \
+            -n auto \
+            --maxfail=3

.github/workflows/type-checker.yml

@@ -0,0 +1,26 @@
+name: Run Type Checks
+
+on: [pull_request]
+
+permissions:
+  contents: write
+
+jobs:
+  type-checker:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11.9"
+
+      - name: Install Requirements
+        run: |
+          pip install mypy
+
+      - name: Run type checks
+        run: mypy src

.gitignore

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

.pre-commit-config.yaml

@@ -1,21 +1,7 @@
 repos:
-
-  - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.12.1
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.8.2
     hooks:
-      - id: black
-        language_version: python3.11
-        files: \.(py)$
-
-  - repo: https://github.com/pycqa/isort
-    rev: 5.13.2
-    hooks:
-      - id: isort
-        name: isort (python)
-        args: ["--profile", "black", "--filter-files"]
-
-  - repo: https://github.com/PyCQA/autoflake
-    rev: v2.2.1
-    hooks:
-      - id: autoflake
-        args: ['--in-place', '--remove-all-unused-imports', '--remove-unused-variables', '--ignore-init-module-imports']
+      - id: ruff
+        args: ["--fix"]
+      - id: ruff-format

.ruff.toml

@@ -0,0 +1,4 @@
+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

@@ -1,181 +1,566 @@
-# crewAI
+<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, tow 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>
 
-🤖 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://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](#crewai)
-  - [Why CrewAI?](#why-crewai)
-  - [Getting Started](#getting-started)
-  - [Key Features](#key-features)
-  - [Examples](#examples)
-    - [Code](#code)
-    - [Video](#video)
-      - [Quick Tutorial](#quick-tutorial)
-      - [Trip Planner](#trip-planner)
-      - [Stock Analysis](#stock-analysis)
-  - [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model)
-  - [How CrewAI Compares](#how-crewai-compares)
-  - [Contribution](#contribution)
-    - [Installing Dependencies](#installing-dependencies)
-    - [Virtual Env](#virtual-env)
-    - [Pre-commit hooks](#pre-commit-hooks)
-    - [Running Tests](#running-tests)
-    - [Packaging](#packaging)
-    - [Installing Locally](#installing-locally)
-  - [Hire CrewAI](#hire-crewai)
-  - [License](#license)
+<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>
+
+### Fast and Flexible Multi-Agent Automation Framework
+
+> CrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely **independent of LangChain or other agent frameworks**.
+> It empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario.
+
+- **CrewAI Crews**: Optimize for autonomy and collaborative intelligence.
+- **CrewAI Flows**: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively
+
+With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
+standard for enterprise-ready AI automation.
+
+# CrewAI Enterprise Suite
+
+CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
+
+You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
+
+## Crew Control Plane Key Features:
+
+- **Tracing & Observability**: Monitor and track your AI agents and workflows in real-time, including metrics, logs, and traces.
+- **Unified Control Plane**: A centralized platform for managing, monitoring, and scaling your AI agents and workflows.
+- **Seamless Integrations**: Easily connect with existing enterprise systems, data sources, and cloud infrastructure.
+- **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
+- **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
+- **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
+- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
+
+CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
+intelligent automations.
+
+## 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>
 
-- 🤖 [Talk with the Docs](https://chatg.pt/DWjSBZn)
-- 📄 [Documentation Wiki](https://joaomdmoura.github.io/crewAI/)
+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**:
+### 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
 ```
 
-The example below also uses duckduckgo, so also install that
+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 duckduckgo-search
+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 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 ./docs/llm-connections.md for more information.
-# from langchain.llms import Ollama
-# ollama_llm = Ollama(model="openhermes")
+	@agent
+	def researcher(self) -> Agent:
+		return Agent(
+			config=self.agents_config['researcher'],
+			verbose=True,
+			tools=[SerperDevTool()]
+		)
 
-# Install duckduckgo-search for this example:
-# !pip install -U duckduckgo-search
+	@agent
+	def reporting_analyst(self) -> Agent:
+		return Agent(
+			config=self.agents_config['reporting_analyst'],
+			verbose=True
+		)
 
-from langchain.tools import DuckDuckGoSearchRun
-search_tool = DuckDuckGoSearchRun()
+	@task
+	def research_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['research_task'],
+		)
 
-# Define your agents with roles and goals
-researcher = Agent(
-  role='Senior Research Analyst',
-  goal='Uncover cutting-edge developments in AI and data science',
-  backstory="""You work at a leading tech think tank.
-  Your expertise lies in identifying emerging trends.
-  You have a knack for dissecting complex data and presenting
-  actionable insights.""",
-  verbose=True,
-  allow_delegation=False,
-  tools=[search_tool]
-  # You can pass an optional llm attribute specifying what mode you wanna use.
-  # It can be a local model through Ollama / LM Studio or a remote
-  # model like OpenAI, Mistral, Antrophic or others (https://python.langchain.com/docs/integrations/llms/)
-  #
-  # Examples:
-  # llm=ollama_llm # was defined above in the file
-  # llm=OpenAI(model_name="gpt-3.5", temperature=0.7)
-  # For the OpenAI model you would need to import
-  # from langchain_openai import OpenAI
-)
-writer = Agent(
-  role='Tech Content Strategist',
-  goal='Craft compelling content on tech advancements',
-  backstory="""You are a renowned Content Strategist, known for
-  your insightful and engaging articles.
-  You transform complex concepts into compelling narratives.""",
-  verbose=True,
-  allow_delegation=True,
-  # (optional) llm=ollama_llm
-)
+	@task
+	def reporting_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['reporting_task'],
+			output_file='report.md'
+		)
 
-# Create tasks for your agents
-task1 = Task(
-  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-  Identify key trends, breakthrough technologies, and potential industry impacts.
-  Your final answer MUST be a full analysis report""",
-  agent=researcher
-)
-
-task2 = Task(
-  description="""Using the insights provided, develop an engaging blog
-  post that highlights the most significant AI advancements.
-  Your post should be informative yet accessible, catering to a tech-savvy audience.
-  Make it sound cool, avoid complex words so it doesn't sound like AI.
-  Your final answer MUST be the full blog post of at least 4 paragraphs.""",
-  agent=writer
-)
-
-# Instantiate your crew with a sequential process
-crew = Crew(
-  agents=[researcher, writer],
-  tasks=[task1, task2],
-  verbose=2, # You can set it to 1 or 2 to different logging levels
-)
-
-# Get your crew to work!
-result = crew.kickoff()
-
-print("######################")
-print(result)
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the LatestAiDevelopment crew"""
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=True,
+		)
 ```
 
-Currently the only supported process is `Process.sequential`, where one task is executed after the other and the outcome of one is passed as extra content into this next.
+**main.py**
 
+```python
+#!/usr/bin/env python
+# src/my_project/main.py
+import sys
+from latest_ai_development.crew import LatestAiDevelopmentCrew
+
+def run():
+    """
+    Run the crew.
+    """
+    inputs = {
+        'topic': 'AI Agents'
+    }
+    LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
+```
+
+### 3. Running Your Crew
+
+Before running your crew, make sure you have the following keys set as environment variables in your `.env` file:
+
+- An [OpenAI API key](https://platform.openai.com/account/api-keys) (or other LLM API key): `OPENAI_API_KEY=sk-...`
+- A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`
+
+Lock the dependencies and install them by using the CLI command but first, navigate to your project directory:
+
+```shell
+cd my_project
+crewai install (Optional)
+```
+
+To run your crew, execute the following command in the root of your project:
+
+```bash
+crewai run
+```
+
+or
+
+```bash
+python src/my_project/main.py
+```
+
+If an error happens due to the usage of poetry, please run the following command to update your crewai package:
+
+```bash
+crewai update
+```
+
+You should see the output in the console and the `report.md` file should be created in the root of your project with the full final report.
+
+In addition to the sequential process, you can use the hierarchical process, which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. [See more about the processes here](https://docs.crewai.com/core-concepts/Processes/).
 
 ## Key Features
 
-- **Role-Based Agent Design**: Customize agents with specific roles, goals, and tools.
-- **Autonomous Inter-Agent Delegation**: Agents can autonomously delegate tasks and inquire amongst themselves, enhancing problem-solving efficiency.
-- **Flexible Task Management**: Define tasks with customizable tools and assign them to agents dynamically.
-- **Processes Driven**: Currently only supports `sequential` task execution but more complex processes like consensual and hierarchical being worked on.
-- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](./docs/llm-connections.md) page for details on configuring you agents' connections to models, even ones running locally!
+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 examples repo](https://github.com/joaomdmoura/crewAI-examples?tab=readme-ov-file)
 
-### Code
-- [Trip Planner](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis)
-- [Landing Page Generator](https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator)
-- [Having Human input on the execution](./docs/how-to/Human-Input-on-Execution.md)
+You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
 
-### Video
-#### Quick Tutorial
-[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/0.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
+- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
+- [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
 
-#### Trip Planner
-[![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/0.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
+### Quick Tutorial
 
-#### Stock Analysis
-[![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/0.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
+[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/maxresdefault.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
+
+### Write Job Descriptions
+
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
+
+[![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
+
+### Trip Planner
+
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
+
+[![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
+
+### Stock Analysis
+
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+
+[![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
+
+### 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](./docs/how-to/llm-connections.md) page for details on configuring you agents' connections to models.
+Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models.
 
 ## How CrewAI Compares
 
-- **Autogen**: While Autogen excels in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
+**CrewAI's Advantage**: CrewAI combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control.
 
+- **LangGraph**: While LangGraph provides a foundation for building agent workflows, its approach requires significant boilerplate code and complex state management patterns. The framework's tight coupling with LangChain can limit flexibility when implementing custom agent behaviors or integrating with external systems.
+
+*P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example ([see comparison](https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/QA%20Agent)) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example ([detailed analysis](https://github.com/crewAIInc/crewAI-examples/blob/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/Coding%20Assistant/coding_assistant_eval.ipynb)).*
+
+- **Autogen**: While Autogen excels at creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
 - **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
 
-**CrewAI's Advantage**: CrewAI is built with production in mind. It offers the flexibility of Autogen's conversational agents and the structured process approach of ChatDev, but without the rigidity. CrewAI's processes are designed to be dynamic and adaptable, fitting seamlessly into both development and production workflows.
-
 ## Contribution
 
 CrewAI is open-source and we welcome contributions. If you're looking to contribute, please:
@@ -187,14 +572,16 @@ CrewAI is open-source and we welcome contributions. If you're looking to contrib
 - We appreciate your input!
 
 ### Installing Dependencies
+
 ```bash
-poetry lock
-poetry install
+uv lock
+uv sync
 ```
 
 ### Virtual Env
+
 ```bash
-poetry shell
+uv venv
 ```
 
 ### Pre-commit hooks
@@ -204,25 +591,187 @@ pre-commit install
 ```
 
 ### Running Tests
+
 ```bash
-poetry run pytest
+uv run pytest .
+```
+
+### Running static type checks
+
+```bash
+uvx mypy src
 ```
 
 ### Packaging
+
 ```bash
-poetry build
+uv build
 ```
 
 ### Installing Locally
+
 ```bash
 pip install dist/*.tar.gz
 ```
 
-## Hire CrewAI
-We're a company developing crewAI and crewAI Enterprise, we for a limited time are offer consulting with selected customers, to get them early access to our enterprise solution
-If you are interested on having access to it and hiring weekly hours with our team, feel free to email us at [sales@crewai.io](mailto:sales@crewai.io)
+## Telemetry
+
+CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
+
+It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. Users can disable telemetry by setting the environment variable OTEL_SDK_DISABLED to true.
+
+Data collected includes:
+
+- Version of CrewAI
+  - So we can understand how many users are using the latest version
+- Version of Python
+  - So we can decide on what versions to better support
+- General OS (e.g. number of CPUs, macOS/Windows/Linux)
+  - So we know what OS we should focus on and if we could build specific OS related features
+- Number of agents and tasks in a crew
+  - So we make sure we are testing internally with similar use cases and educate people on the best practices
+- Crew Process being used
+  - Understand where we should focus our efforts
+- If Agents are using memory or allowing delegation
+  - Understand if we improved the features or maybe even drop them
+- If Tasks are being executed in parallel or sequentially
+  - Understand if we should focus more on parallel execution
+- Language model being used
+  - Improved support on most used languages
+- Roles of agents in a crew
+  - Understand high level use cases so we can build better tools, integrations and examples about it
+- Tools names available
+  - Understand out of the publicly available tools, which ones are being used the most so we can improve them
+
+Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
 
 ## License
-CrewAI is released under the MIT License
 
+CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/blob/main/LICENSE).
 
+## Frequently Asked Questions (FAQ)
+
+### General
+
+- [What exactly is CrewAI?](#q-what-exactly-is-crewai)
+- [How do I install CrewAI?](#q-how-do-i-install-crewai)
+- [Does CrewAI depend on LangChain?](#q-does-crewai-depend-on-langchain)
+- [Is CrewAI open-source?](#q-is-crewai-open-source)
+- [Does CrewAI collect data from users?](#q-does-crewai-collect-data-from-users)
+
+### Features and Capabilities
+
+- [Can CrewAI handle complex use cases?](#q-can-crewai-handle-complex-use-cases)
+- [Can I use CrewAI with local AI models?](#q-can-i-use-crewai-with-local-ai-models)
+- [What makes Crews different from Flows?](#q-what-makes-crews-different-from-flows)
+- [How is CrewAI better than LangChain?](#q-how-is-crewai-better-than-langchain)
+- [Does CrewAI support fine-tuning or training custom models?](#q-does-crewai-support-fine-tuning-or-training-custom-models)
+
+### Resources and Community
+
+- [Where can I find real-world CrewAI examples?](#q-where-can-i-find-real-world-crewai-examples)
+- [How can I contribute to CrewAI?](#q-how-can-i-contribute-to-crewai)
+
+### Enterprise Features
+
+- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
+- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
+- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
+
+### Q: What exactly is CrewAI?
+
+A: CrewAI is a standalone, lean, and fast Python framework built specifically for orchestrating autonomous AI agents. Unlike frameworks like LangChain, CrewAI does not rely on external dependencies, making it leaner, faster, and simpler.
+
+### Q: How do I install CrewAI?
+
+A: Install CrewAI using pip:
+
+```shell
+pip install crewai
+```
+
+For additional tools, use:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+### Q: Does CrewAI depend on LangChain?
+
+A: No. CrewAI is built entirely from the ground up, with no dependencies on LangChain or other agent frameworks. This ensures a lean, fast, and flexible experience.
+
+### Q: Can CrewAI handle complex use cases?
+
+A: Yes. CrewAI excels at both simple and highly complex real-world scenarios, offering deep customization options at both high and low levels, from internal prompts to sophisticated workflow orchestration.
+
+### Q: Can I use CrewAI with local AI models?
+
+A: Absolutely! CrewAI supports various language models, including local ones. Tools like Ollama and LM Studio allow seamless integration. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
+
+### Q: What makes Crews different from Flows?
+
+A: Crews provide autonomous agent collaboration, ideal for tasks requiring flexible decision-making and dynamic interaction. Flows offer precise, event-driven control, ideal for managing detailed execution paths and secure state management. You can seamlessly combine both for maximum effectiveness.
+
+### Q: How is CrewAI better than LangChain?
+
+A: CrewAI provides simpler, more intuitive APIs, faster execution speeds, more reliable and consistent results, robust documentation, and an active community—addressing common criticisms and limitations associated with LangChain.
+
+### Q: Is CrewAI open-source?
+
+A: Yes, CrewAI is open-source and actively encourages community contributions and collaboration.
+
+### Q: Does CrewAI collect data from users?
+
+A: CrewAI collects anonymous telemetry data strictly for improvement purposes. Sensitive data such as prompts, tasks, or API responses are never collected unless explicitly enabled by the user.
+
+### Q: Where can I find real-world CrewAI examples?
+
+A: Check out practical examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), covering use cases like trip planners, stock analysis, and job postings.
+
+### Q: How can I contribute to CrewAI?
+
+A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
+
+### Q: What additional features does CrewAI Enterprise offer?
+
+A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
+
+### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
+
+A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
+
+### Q: Can I try CrewAI Enterprise for free?
+
+A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
+
+### Q: Does CrewAI support fine-tuning or training custom models?
+
+A: Yes, CrewAI can integrate with custom-trained or fine-tuned models, allowing you to enhance your agents with domain-specific knowledge and accuracy.
+
+### Q: Can CrewAI agents interact with external tools and APIs?
+
+A: Absolutely! CrewAI agents can easily integrate with external tools, APIs, and databases, empowering them to leverage real-world data and resources.
+
+### Q: Is CrewAI suitable for production environments?
+
+A: Yes, CrewAI is explicitly designed with production-grade standards, ensuring reliability, stability, and scalability for enterprise deployments.
+
+### Q: How scalable is CrewAI?
+
+A: CrewAI is highly scalable, supporting simple automations and large-scale enterprise workflows involving numerous agents and complex tasks simultaneously.
+
+### Q: Does CrewAI offer debugging and monitoring tools?
+
+A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
+
+### Q: What programming languages does CrewAI support?
+
+A: CrewAI is primarily Python-based but easily integrates with services and APIs written in any programming language through its flexible API integration capabilities.
+
+### Q: Does CrewAI offer educational resources for beginners?
+
+A: Yes, CrewAI provides extensive beginner-friendly tutorials, courses, and documentation through learn.crewai.com, supporting developers at all skill levels.
+
+### Q: Can CrewAI automate human-in-the-loop workflows?
+
+A: Yes, CrewAI fully supports human-in-the-loop workflows, allowing seamless collaboration between human experts and AI agents for enhanced decision-making.

crewAI.excalidraw

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

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/core-concepts/Agent-Tools.md

@@ -1,90 +0,0 @@
-# What is a Tool?
-
-A tool in CrewAI is a function or capability that an agent can utilize to perform actions, gather information, or interact with external systems, behind the scenes tools are [LangChain Tools](https://python.langchain.com/docs/modules/agents/tools/).
-These tools can be as straightforward as a search function or as sophisticated as integrations with other chains or APIs.
-
-## Key Characteristics of Tools
-
-- **Utility**: Tools are designed to serve specific purposes, such as searching the web, analyzing data, or generating content.
-- **Integration**: Tools can be integrated into agents to extend their capabilities beyond their basic functions.
-- **Customizability**: Developers can create custom tools tailored to the specific needs of their agents or use pre-built LangChain ones available in the ecosystem.
-
-# Creating your own Tools
-
-You can easily create your own tool using [LangChain Tool Custom Tool Creation](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
-
-Example:
-```python
-import json
-import requests
-
-from crewai import Agent
-from langchain.tools import tool
-from unstructured.partition.html import partition_html
-
-class BrowserTools():
-  @tool("Scrape website content")
-  def scrape_website(website):
-    """Useful to scrape a website content"""
-    url = f"https://chrome.browserless.io/content?token={config('BROWSERLESS_API_KEY')}"
-    payload = json.dumps({"url": website})
-    headers = {
-      'cache-control': 'no-cache',
-      'content-type': 'application/json'
-    }
-    response = requests.request("POST", url, headers=headers, data=payload)
-    elements = partition_html(text=response.text)
-    content = "\n\n".join([str(el) for el in elements])
-
-    # Return only the first 5k characters
-    return content[:5000]
-
-
-# Create an agent and assign the scrapping tool
-agent = Agent(
-  role='Research Analyst',
-  goal='Provide up-to-date market analysis',
-  backstory='An expert analyst with a keen eye for market trends.',
-  tools=[BrowserTools().scrape_website]
-)
-```
-
-# Using Existing Tools
-
-Check [LangChain Integration](https://python.langchain.com/docs/integrations/tools/) for a set of useful existing tools.
-To assign a tool to an agent, you'd provide it as part of the agent's properties during initialization.
-
-```python
-from crewai import Agent
-from langchain.agents import Tool
-from langchain.utilities import GoogleSerperAPIWrapper
-
-# Initialize SerpAPI tool with your API key
-os.environ["OPENAI_API_KEY"] = "Your Key"
-os.environ["SERPER_API_KEY"] = "Your Key"
-
-search = GoogleSerperAPIWrapper()
-
-# Create tool to be used by agent
-serper_tool = Tool(
-  name="Intermediate Answer",
-  func=search.run,
-  description="useful for when you need to ask with search",
-)
-
-# Create an agent and assign the search tool
-agent = Agent(
-  role='Research Analyst',
-  goal='Provide up-to-date market analysis',
-  backstory='An expert analyst with a keen eye for market trends.',
-  tools=[serper_tool]
-)
-```
-
-# Tool Interaction
-
-Tools enhance an agent's ability to perform tasks autonomously or in collaboration with other agents. For instance, an agent might use a search tool to gather information, then pass that data to another agent specialized in analysis.
-
-# Conclusion
-
-Tools are vital components that expand the functionality of agents within the CrewAI framework. They enable agents to perform a wide range of actions and collaborate effectively with one another. As you build with CrewAI, consider the array of tools you can leverage to empower your agents and how they can be interwoven to create a robust AI ecosystem.

docs/core-concepts/Creating-Tasks.md

@@ -1,50 +0,0 @@
-# What is a Task?
-
-A Task in CrewAI is essentially a job or an assignment that an AI agent needs to complete. It's defined by what needs to be done and can include additional information like which agent should do it and what tools they might need.
-
-# Task Properties
-
-- **Description**: A clear, concise statement of what the task entails.
-- **Agent**: Optionally, you can specify which agent is responsible for the task. If not, the crew's process will determine who takes it on.
-- **Tools**: These are the functions or capabilities the agent can utilize to perform the task. They can be anything from simple actions like 'search' to more complex interactions with other agents or APIs.
-
-# Integrating Tools with Tasks
-
-In CrewAI, tools are functions from the `langchain` toolkit that agents can use to interact with the world. These can be generic utilities or specialized functions designed for specific actions. When you assign tools to a task, they empower the agent to perform its duties more effectively.
-
-## Example of Creating a Task with Tools
-
-```python
-from crewai import Task
-from langchain.agents import Tool
-from langchain.utilities import GoogleSerperAPIWrapper
-
-# Initialize SerpAPI tool with your API key
-os.environ["OPENAI_API_KEY"] = "Your Key"
-os.environ["SERPER_API_KEY"] = "Your Key"
-
-search = GoogleSerperAPIWrapper()
-
-# Create tool to be used by agent
-serper_tool = Tool(
-  name="Intermediate Answer",
-  func=search.run,
-  description="useful for when you need to ask with search",
-)
-
-# Create a task with a description and the search tool
-task = Task(
-  description='Find and summarize the latest and most relevant news on AI',
-  tools=[serper_tool]
-)
-```
-
-When the task is executed by an agent, the tools specified in the task will override the agent's default tools. This means that for the duration of this task, the agent will use the search tool provided, even if it has other tools assigned to it.
-
-# Tool Override Mechanism
-
-The ability to override an agent's tools with those specified in a task allows for greater flexibility. An agent might generally use a set of standard tools, but for certain tasks, you may want it to use a particular tool that is more suited to the task at hand.
-
-# Conclusion
-
-Creating tasks with the right tools is crucial in CrewAI. It ensures that your agents are not only aware of what they need to do but are also equipped with the right functions to do it effectively. This feature underlines the flexibility and power of the CrewAI system, where tasks can be tailored with specific tools to achieve the best outcome.

docs/core-concepts/Defining-Tasks.md

@@ -1,42 +0,0 @@
-# Overview of a Task
-
-In the CrewAI framework, tasks are the individual assignments that agents are responsible for completing. They are the fundamental units of work that your AI crew will undertake. Understanding how to define and manage tasks is key to leveraging the full potential of CrewAI.
-
-A task in CrewAI encapsulates all the information needed for an agent to execute it, including a description, the agent assigned to it, and any specific tools required. Tasks are designed to be flexible, allowing for both simple and complex actions depending on your needs.
-
-# Properties of a Task
-
-Every task in CrewAI has several properties:
-
-- **Description**: A clear and concise statement of what needs to be done.
-- **Agent**: The agent assigned to the task (optional). If no agent is specified, the task can be picked up by any agent based on the process defined.
-- **Tools**: A list of tools (optional) that the agent can use to complete the task. These can override the agent's default tools if necessary.
-
-# Creating a Task
-
-Creating a task is straightforward. You define what needs to be done and, optionally, who should do it and what tools they should use. Here’s a conceptual guide:
-
-```python
-from crewai import Task
-
-# Define a task with a designated agent and specific tools
-task = Task(description='Generate monthly sales report', agent=sales_agent, tools=[reporting_tool])
-```
-
-# Task Assignment
-
-Tasks can be assigned to agents in several ways:
-
-- Directly, by specifying the agent when creating the task.
-- [WIP] Through the Crew's process, which can assign tasks based on agent roles, availability, or other criteria.
-
-# Task Execution
-
-Once a task has been defined and assigned, it's ready to be executed. Execution is typically handled by the Crew object, which manages the workflow and ensures that tasks are completed according to the defined process.
-
-# Task Collaboration
-
-Tasks in CrewAI can be designed to require collaboration between agents. For example, one agent might gather data while another analyzes it. This collaborative approach can be defined within the task properties and managed by the Crew's process.
-
-# Conclusion
-Tasks are the driving force behind the actions of agents in CrewAI. By properly defining tasks, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. In the following sections, we will explore how tasks fit into the larger picture of processes and crew management.

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

@@ -1,26 +0,0 @@
-# How Agents Collaborate:
-
-In CrewAI, collaboration is the cornerstone of agent interaction. Agents are designed to work together by sharing information, requesting assistance, and combining their skills to complete tasks more efficiently.
-
-- **Information Sharing**: Agents can share findings and data amongst themselves to ensure all members are informed and can contribute effectively.
-- **Task Assistance**: If an agent encounters a task that requires additional expertise, it can seek the help of another agent with the necessary skill set.
-- **Resource Allocation**: Agents can share or allocate resources such as tools or processing power to optimize task execution.
-
-Collaboration is embedded in the DNA of CrewAI, enabling a dynamic and adaptive approach to problem-solving.
-
-# Delegation: Dividing to Conquer
-
-Delegation is the process by which an agent assigns a task to another agent, or just ask another agent, it's an intelligent decision-making process that enhances the crew's functionality.
-By default all agents can delegate work and ask questions, so if you want an agent to work alone make sure to set that option when initializing an Agent, this is useful to prevent deviations if the task is supposed to be straightforward.
-
-## Implementing Collaboration and Delegation
-
-When setting up your crew, you'll define the roles and capabilities of each agent. CrewAI's infrastructure takes care of the rest, managing the complex interplay of agents as they work together.
-
-## Example Scenario:
-
-Imagine a scenario where you have a researcher agent that gathers data and a writer agent that compiles reports. The writer can autonomously ask question or delegate more in depth research work depending on its needs as it tries to complete its task.
-
-# Conclusion
-
-Collaboration and delegation are what transform a collection of AI agents into a unified, intelligent crew. With CrewAI, you have a framework that not only simplifies these interactions but also makes them more effective, paving the way for sophisticated AI systems that can tackle complex, multi-dimensional tasks.

docs/core-concepts/Managing-Processes.md

@@ -1,49 +0,0 @@
-# Managing Processes in CrewAI
-
-Processes are the heart of CrewAI's workflow management, akin to the way a human team organizes its work. In CrewAI, processes define the sequence and manner in which tasks are executed by agents, mirroring the coordination you'd expect in a well-functioning team of people.
-
-## Understanding Processes
-
-A process in CrewAI can be thought of as the game plan for how your AI agents will handle their workload. Just as a project manager assigns tasks to team members based on their skills and the project timeline, CrewAI processes assign tasks to agents to ensure efficient workflow.
-
-## Process Implementations
-
-- **Sequential (Supported)**: This is the only process currently implemented in CrewAI. It ensures tasks are handled one at a time, in a given order, much like a relay race where one runner passes the baton to the next.
-- **Consensual (WIP)**: Envisioned for a future update, the consensual process will enable agents to make joint decisions on task execution, similar to a team consensus in a meeting before proceeding.
-- **Hierarchical (WIP)**: Also in the pipeline, this process will introduce a chain of command to task execution, where some agents may have the authority to prioritize tasks or delegate them, akin to a traditional corporate hierarchy.
-These additional processes, once implemented, will offer more nuanced and sophisticated ways for agents to interact and complete tasks, much like teams in complex organizational structures.
-
-
-## Defining a Sequential Process
-
-Creating a sequential process in CrewAI is straightforward and reflects the simplicity of coordinating a team's efforts step by step. In this process the outcome of the previous task is sent into the next one as context that I should use to accomplish it's task
-
-```python
-from crewai import Process
-
-# Define a sequential process
-sequential_process = Process.sequential
-```
-
-# The Magic of Sequential Processes
-
-The sequential process is where much of CrewAI's magic happens. It ensures that tasks are approached with the same thoughtful progression that a human team would use, fostering a natural and logical flow of work while passing on task outcome into the next.
-
-## Assigning Processes to a Crew
-
-To assign a process to a crew, simply set it during the crew's creation. The process will dictate the crew's approach to task execution.
-
-```python
-from crewai import Crew
-
-# Create a crew with a sequential process
-crew = Crew(agents=my_agents, tasks=my_tasks, process=sequential_process)
-```
-
-## The Role of Processes in Teamwork
-
-The process you choose for your crew is critical. It's what transforms a group of individual agents into a cohesive unit that can tackle complex projects with the precision and harmony you'd find in a team of skilled humans.
-
-## Conclusion
-
-Processes bring structure and order to the CrewAI ecosystem, allowing agents to collaborate effectively and accomplish goals systematically. As CrewAI evolves, additional process types will be introduced to enhance the framework's versatility, much like a team that grows and adapts over time.

docs/core-concepts/Understanding-Agents.md

@@ -1,41 +0,0 @@
-# What is an Agent?
-
-In CrewAI, an agent is an autonomous unit programmed to perform tasks, make decisions, and communicate with other agents. Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.
-
-# Key Properties of an Agent
-
-- **Role**: Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for.
-- **Goal**: The individual objective that the agent aims to achieve. It guides the agent's decision-making process.
-- **Backstory**: Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics.
-- **Tools**: A set of capabilities or functions that the agent can use to perform tasks. Tools can be shared or exclusive to specific agents.
-- **Verbose**: This allow you to actually see what is going on during the Crew execution.
-- **Allow Delegation**: Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent.
-
-# Agent Lifecycle
-
-1. **Initialization**: An agent is created with a defined role, goal, backstory, and set of tools.
-2. **Task Assignment**: The agent is assigned tasks either directly or through the crew's process management.
-3. **Execution**: The agent performs the task using its available tools and in accordance with its role and goal.
-4. **Collaboration**: Throughout the execution, the agent can communicate with other agents to delegate, inquire, or assist.
-
-# Creating an Agent
-
-To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example:
-
-```python
-from crewai import Agent
-
-# Create an agent with a role and a goal
-agent = Agent(
-  role='Data Analyst',
-  goal='Extract actionable insights',
-  verbose=True,
-  backstory="You'er a data analyst at a large company. I am responsible for analyzing data and providing insights to the business. I am currently working on a project to analyze the performance of our marketing campaigns. I have been asked to provide insights on how to improve the performance of our marketing campaigns."
-)
-```
-
-# Agent Interaction
-Agents can interact with each other using the CrewAI's built-in delegation and communication mechanisms. This allows for dynamic task management and problem-solving within the crew.
-
-# Conclusion
-Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

docs/en/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/en/concepts/agents.mdx

@@ -0,0 +1,690 @@
+---
+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](/en/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>
+
+## Direct Agent Interaction with `kickoff()`
+
+Agents can be used directly without going through a task or crew workflow using the `kickoff()` method. This provides a simpler way to interact with an agent when you don't need the full crew orchestration capabilities.
+
+### How `kickoff()` Works
+
+The `kickoff()` method allows you to send messages directly to an agent and get a response, similar to how you would interact with an LLM but with all the agent's capabilities (tools, reasoning, etc.).
+
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool
+
+# Create an agent
+researcher = Agent(
+    role="AI Technology Researcher",
+    goal="Research the latest AI developments",
+    tools=[SerperDevTool()],
+    verbose=True
+)
+
+# Use kickoff() to interact directly with the agent
+result = researcher.kickoff("What are the latest developments in language models?")
+
+# Access the raw response
+print(result.raw)
+```
+
+### Parameters and Return Values
+
+| Parameter         | Type                                | Description                                                               |
+| :---------------- | :---------------------------------- | :------------------------------------------------------------------------ |
+| `messages`        | `Union[str, List[Dict[str, str]]]`  | Either a string query or a list of message dictionaries with role/content |
+| `response_format` | `Optional[Type[Any]]`               | Optional Pydantic model for structured output                             |
+
+The method returns a `LiteAgentOutput` object with the following properties:
+
+- `raw`: String containing the raw output text
+- `pydantic`: Parsed Pydantic model (if a `response_format` was provided)
+- `agent_role`: Role of the agent that produced the output
+- `usage_metrics`: Token usage metrics for the execution
+
+### Structured Output
+
+You can get structured output by providing a Pydantic model as the `response_format`:
+
+```python Code
+from pydantic import BaseModel
+from typing import List
+
+class ResearchFindings(BaseModel):
+    main_points: List[str]
+    key_technologies: List[str]
+    future_predictions: str
+
+# Get structured output
+result = researcher.kickoff(
+    "Summarize the latest developments in AI for 2025",
+    response_format=ResearchFindings
+)
+
+# Access structured data
+print(result.pydantic.main_points)
+print(result.pydantic.future_predictions)
+```
+
+### Multiple Messages
+
+You can also provide a conversation history as a list of message dictionaries:
+
+```python Code
+messages = [
+    {"role": "user", "content": "I need information about large language models"},
+    {"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
+    {"role": "user", "content": "What are the latest developments in 2025?"}
+]
+
+result = researcher.kickoff(messages)
+```
+
+### Async Support
+
+An asynchronous version is available via `kickoff_async()` with the same parameters:
+
+```python Code
+import asyncio
+
+async def main():
+    result = await researcher.kickoff_async("What are the latest developments in AI?")
+    print(result.raw)
+
+asyncio.run(main())
+```
+
+<Note>
+The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler execution flow while preserving all of the agent's configuration (role, goal, backstory, tools, etc.).
+</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/en/concepts/cli.mdx

@@ -0,0 +1,390 @@
+---
+title: CLI
+description: Learn how to use the CrewAI CLI to interact with CrewAI.
+icon: terminal
+---
+
+<Warning>Since release 0.140.0, CrewAI Enterprise started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
+
+## 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.
+    You can login or create an account 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 show you a list of available LLM providers to choose from, followed by model selection for your chosen provider.
+
+Once you've selected an LLM provider and model, you will be prompted for API keys.
+
+#### Available LLM Providers
+
+Here's a list of the most popular LLM providers suggested by the CLI:
+
+* OpenAI
+* Groq
+* Anthropic
+* Google Gemini
+* SambaNova
+
+When you select a provider, the CLI will then show you available models for that provider and prompt you to enter your API key.
+
+#### Other Options
+
+If you select "other", 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)
+
+### 12. Configuration Management
+
+Manage CLI configuration settings for CrewAI.
+
+```shell Terminal
+crewai config [COMMAND] [OPTIONS]
+```
+
+#### Commands:
+
+- `list`: Display all CLI configuration parameters
+```shell Terminal
+crewai config list
+```
+
+- `set`: Set a CLI configuration parameter
+```shell Terminal
+crewai config set <key> <value>
+```
+
+- `reset`: Reset all CLI configuration parameters to default values
+```shell Terminal
+crewai config reset
+```
+
+#### Available Configuration Parameters
+
+- `enterprise_base_url`: Base URL of the CrewAI Enterprise instance
+- `oauth2_provider`: OAuth2 provider used for authentication (e.g., workos, okta, auth0)
+- `oauth2_audience`: OAuth2 audience value, typically used to identify the target API or resource
+- `oauth2_client_id`: OAuth2 client ID issued by the provider, used during authentication requests
+- `oauth2_domain`: OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens
+
+#### Examples
+
+Display current configuration:
+```shell Terminal
+crewai config list
+```
+
+Example output:
+```
+CrewAI CLI Configuration
+┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Setting            ┃ Value                              ┃ Description                                                                                      ┃
+┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ enterprise_base_url│ https://app.crewai.com             │ Base URL of the CrewAI Enterprise instance                                                       │
+│ org_name           │ Not set                            │ Name of the currently active organization                                                        │
+│ org_uuid           │ Not set                            │ UUID of the currently active organization                                                        │
+│ oauth2_provider    │ workos                             │ OAuth2 provider used for authentication (e.g., workos, okta, auth0).                             │
+│ oauth2_audience    │ client_01YYY                       │ OAuth2 audience value, typically used to identify the target API or resource.                    │
+│ oauth2_client_id   │ client_01XXX                       │ OAuth2 client ID issued by the provider, used during authentication requests.                    │
+│ oauth2_domain      │ login.crewai.com                   │ OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens.                     │
+```
+
+Set the enterprise base URL:
+```shell Terminal
+crewai config set enterprise_base_url https://my-enterprise.crewai.com
+```
+
+Set OAuth2 provider:
+```shell Terminal
+crewai config set oauth2_provider auth0
+```
+
+Set OAuth2 domain:
+```shell Terminal
+crewai config set oauth2_domain my-company.auth0.com
+```
+
+Reset all configuration to defaults:
+```shell Terminal
+crewai config reset
+```
+
+<Note>
+Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
+</Note>

docs/en/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/en/concepts/crews.mdx

@@ -0,0 +1,368 @@
+---
+title: Crews
+description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
+icon: people-group
+---
+
+## 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.
+
+## Crew Attributes
+
+| Attribute                             | Parameters             | Description                                                                                                                                                                                                                                               |
+| :------------------------------------ | :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Tasks**                             | `tasks`                | A list of tasks assigned to the crew.                                                                                                                                                                                                                     |
+| **Agents**                            | `agents`               | A list of agents that are part of the crew.                                                                                                                                                                                                               |
+| **Process** _(optional)_              | `process`              | The process flow (e.g., sequential, hierarchical) the crew follows. Default is `sequential`.                                                                                                                                                              |
+| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution. Defaults to `False`.                                                                                                                                                                                    |
+| **Manager LLM** _(optional)_          | `manager_llm`          | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.**                                                                                                                                   |
+| **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
+| **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
+| **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
+| **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. 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`      | 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.                                                                                                                                                                                             |
+| **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.                                                                                                                                                                                        |
+| **Knowledge Sources** _(optional)_    | `knowledge_sources`    | Knowledge sources available at the crew level, accessible to all the agents.                                                                                                                                                                                    |
+
+<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 Crews
+
+There are two ways to create crews 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 crews and is consistent with how agents and tasks are defined in CrewAI projects.
+
+After creating your CrewAI project as outlined in the [Installation](/en/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.
+
+#### Example Crew Class with Decorators
+
+```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
+
+@CrewBase
+class YourCrewName:
+    """Description of your crew"""
+
+    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
+
+The output of a crew in the CrewAI framework is encapsulated within the `CrewOutput` class.
+This class provides a structured way to access results of the crew's execution, including various formats such as raw strings, JSON, and Pydantic models.
+The `CrewOutput` includes the results from the final task output, token usage, and individual task outputs.
+
+### Crew Output Attributes
+
+| Attribute        | Parameters     | Type                       | Description                                                                                          |
+| :--------------- | :------------- | :------------------------- | :--------------------------------------------------------------------------------------------------- |
+| **Raw**          | `raw`          | `str`                      | The raw output of the crew. This is the default format for the output.                               |
+| **Pydantic**     | `pydantic`     | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the crew.                              |
+| **JSON Dict**    | `json_dict`    | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the crew.                                               |
+| **Tasks Output** | `tasks_output` | `List[TaskOutput]`         | A list of `TaskOutput` objects, each representing the output of a task in the crew.                  |
+| **Token Usage**  | `token_usage`  | `Dict[str, Any]`           | A summary of token usage, providing insights into the language model's performance during execution. |
+
+### Crew Output Methods and Properties
+
+| Method/Property | Description                                                                                       |
+| :-------------- | :------------------------------------------------------------------------------------------------ |
+| **json**        | Returns the JSON string representation of the crew output if the output format is JSON.           |
+| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
+| \***\*str\*\*** | Returns the string representation of the crew output, prioritizing Pydantic, then JSON, then raw. |
+
+### Accessing Crew Outputs
+
+Once a crew has been executed, its output can be accessed through the `output` attribute of the `Crew` object. The `CrewOutput` class provides various ways to interact with and present this output.
+
+#### Example
+
+```python Code
+# Example crew execution
+crew = Crew(
+    agents=[research_agent, writer_agent],
+    tasks=[research_task, write_article_task],
+    verbose=True
+)
+
+crew_output = crew.kickoff()
+
+# Accessing the crew output
+print(f"Raw Output: {crew_output.raw}")
+if crew_output.json_dict:
+    print(f"JSON Output: {json.dumps(crew_output.json_dict, indent=2)}")
+if crew_output.pydantic:
+    print(f"Pydantic Output: {crew_output.pydantic}")
+print(f"Tasks Output: {crew_output.tasks_output}")
+print(f"Token Usage: {crew_output.token_usage}")
+```
+
+## 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.
+
+## Cache Utilization
+
+Caches can be employed to store the results of tools' execution, making the process more efficient by reducing the need to re-execute identical tasks.
+
+## Crew Usage Metrics
+
+After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.
+
+```python Code
+# Access the crew's usage metrics
+crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
+crew.kickoff()
+print(crew.usage_metrics)
+```
+
+## Crew Execution Process
+
+- **Sequential Process**: Tasks are executed one after another, allowing for a linear flow of work.
+- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. **Note**: A `manager_llm` or `manager_agent` is required for this process and it's essential for validating the process flow.
+
+### Kicking Off a Crew
+
+Once your crew is assembled, initiate the workflow with the `kickoff()` method. This starts the execution process according to the defined process flow.
+
+```python Code
+# Start the crew's task execution
+result = my_crew.kickoff()
+print(result)
+```
+
+### Different Ways to Kick Off a Crew
+
+Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
+
+- `kickoff()`: Starts the execution process according to the defined process flow.
+- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
+- `kickoff_async()`: Initiates the workflow asynchronously.
+- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.
+
+```python Code
+# Start the crew's task execution
+result = my_crew.kickoff()
+print(result)
+
+# Example of using kickoff_for_each
+inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
+results = my_crew.kickoff_for_each(inputs=inputs_array)
+for result in results:
+    print(result)
+
+# Example of using kickoff_async
+inputs = {'topic': 'AI in healthcare'}
+async_result = 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 = await my_crew.kickoff_for_each_async(inputs=inputs_array)
+for async_result in async_results:
+    print(async_result)
+```
+
+These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs.
+
+### Replaying from a Specific Task
+
+You can now replay from a specific task using our CLI command `replay`.
+
+The replay feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the `task_id` for the replay process.
+
+Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
+
+### Replaying from a Specific Task Using the CLI
+
+To use the replay feature, follow these steps:
+
+1. Open your terminal or command prompt.
+2. Navigate to the directory where your CrewAI project is located.
+3. Run the following command:
+
+To view the latest kickoff task IDs, use:
+
+```shell
+crewai log-tasks-outputs
+```
+
+Then, to replay from a specific task, use:
+
+```shell
+crewai replay -t <task_id>
+```
+
+These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

docs/en/concepts/event-listener.mdx

@@ -0,0 +1,312 @@
+---
+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 how third-party event listeners are registered in the CrewAI codebase.
+
+## 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
+
+### Memory Events
+
+- **MemoryQueryStartedEvent**: Emitted when a memory query is started. Contains the query, limit, and optional score threshold.
+- **MemoryQueryCompletedEvent**: Emitted when a memory query is completed successfully. Contains the query, results, limit, score threshold, and query execution time.
+- **MemoryQueryFailedEvent**: Emitted when a memory query fails. Contains the query, limit, score threshold, and error message.
+- **MemorySaveStartedEvent**: Emitted when a memory save operation is started. Contains the value to be saved, metadata, and optional agent role.
+- **MemorySaveCompletedEvent**: Emitted when a memory save operation is completed successfully. Contains the saved value, metadata, agent role, and save execution time.
+- **MemorySaveFailedEvent**: Emitted when a memory save operation fails. Contains the value, metadata, agent role, and error message.
+- **MemoryRetrievalStartedEvent**: Emitted when memory retrieval for a task prompt starts. Contains the optional task ID.
+- **MemoryRetrievalCompletedEvent**: Emitted when memory retrieval for a task prompt completes successfully. Contains the task ID, memory content, and retrieval execution time.
+
+## 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.
+
+
+## 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/en/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/en/concepts/llms.mdx

@@ -0,0 +1,958 @@
+---
+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-1.5-pro-latest", # or vertex_ai/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>
+
+  <Accordion title="Nebius AI Studio">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    NEBIUS_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="nebius/Qwen/Qwen3-30B-A3B"
+    )
+    ```
+
+    <Info>
+      Nebius AI Studio features:
+      - Large collection of open source models
+      - Higher rate limits
+      - Competitive pricing
+      - Good balance of speed and quality
+    </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>
+
+  <Tab title="Agent & Task Tracking">
+    All LLM events in CrewAI include agent and task information, allowing you to track and filter LLM interactions by specific agents or tasks:
+
+    ```python
+    from crewai import LLM, Agent, Task, Crew
+    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(source, event):
+                if researcher.id == event.agent_id:
+                    print("\n==============\n Got event:", event, "\n==============\n")
+
+
+    my_listener = MyCustomListener()
+
+    llm = LLM(model="gpt-4o-mini", temperature=0, stream=True)
+
+    researcher = Agent(
+        role="About User",
+        goal="You know everything about the user.",
+        backstory="""You are a master at understanding people and their preferences.""",
+        llm=llm,
+    )
+
+    search = Task(
+        description="Answer the following questions about the user: {question}",
+        expected_output="An answer to the question.",
+        agent=researcher,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[search])
+
+    result = crew.kickoff(
+        inputs={"question": "..."}
+    )
+    ```
+
+    <Info>
+      This feature is particularly useful for:
+      - Debugging specific agent behaviors
+      - Logging LLM usage by task type
+      - Auditing which agents are making what types of LLM calls
+      - Performance monitoring of specific tasks
+    </Info>
+  </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>