docs: improve docs about planning LLM usage

.env.test

@@ -1,160 +0,0 @@
-# =============================================================================
-# Test Environment Variables
-# =============================================================================
-# This file contains all environment variables needed to run tests locally
-# in a way that mimics the GitHub Actions CI environment.
-
-# =============================================================================
-
-# -----------------------------------------------------------------------------
-# LLM Provider API Keys
-# -----------------------------------------------------------------------------
-OPENAI_API_KEY=fake-api-key
-ANTHROPIC_API_KEY=fake-anthropic-key
-GEMINI_API_KEY=fake-gemini-key
-AZURE_API_KEY=fake-azure-key
-OPENROUTER_API_KEY=fake-openrouter-key
-
-# -----------------------------------------------------------------------------
-# AWS Credentials
-# -----------------------------------------------------------------------------
-AWS_ACCESS_KEY_ID=fake-aws-access-key
-AWS_SECRET_ACCESS_KEY=fake-aws-secret-key
-AWS_DEFAULT_REGION=us-east-1
-
-# -----------------------------------------------------------------------------
-# Azure OpenAI Configuration
-# -----------------------------------------------------------------------------
-AZURE_ENDPOINT=https://fake-azure-endpoint.openai.azure.com
-AZURE_OPENAI_ENDPOINT=https://fake-azure-endpoint.openai.azure.com
-AZURE_OPENAI_API_KEY=fake-azure-openai-key
-AZURE_API_VERSION=2024-02-15-preview
-OPENAI_API_VERSION=2024-02-15-preview
-
-# -----------------------------------------------------------------------------
-# Google Cloud Configuration
-# -----------------------------------------------------------------------------
-#GOOGLE_CLOUD_PROJECT=fake-gcp-project
-#GOOGLE_CLOUD_LOCATION=us-central1
-
-# -----------------------------------------------------------------------------
-# OpenAI Configuration
-# -----------------------------------------------------------------------------
-OPENAI_BASE_URL=https://api.openai.com/v1
-OPENAI_API_BASE=https://api.openai.com/v1
-
-# -----------------------------------------------------------------------------
-# Search & Scraping Tool API Keys
-# -----------------------------------------------------------------------------
-SERPER_API_KEY=fake-serper-key
-EXA_API_KEY=fake-exa-key
-BRAVE_API_KEY=fake-brave-key
-FIRECRAWL_API_KEY=fake-firecrawl-key
-TAVILY_API_KEY=fake-tavily-key
-SERPAPI_API_KEY=fake-serpapi-key
-SERPLY_API_KEY=fake-serply-key
-LINKUP_API_KEY=fake-linkup-key
-PARALLEL_API_KEY=fake-parallel-key
-
-# -----------------------------------------------------------------------------
-# Exa Configuration
-# -----------------------------------------------------------------------------
-EXA_BASE_URL=https://api.exa.ai
-
-# -----------------------------------------------------------------------------
-# Web Scraping & Automation
-# -----------------------------------------------------------------------------
-BRIGHT_DATA_API_KEY=fake-brightdata-key
-BRIGHT_DATA_ZONE=fake-zone
-BRIGHTDATA_API_URL=https://api.brightdata.com
-BRIGHTDATA_DEFAULT_TIMEOUT=600
-BRIGHTDATA_DEFAULT_POLLING_INTERVAL=1
-
-OXYLABS_USERNAME=fake-oxylabs-user
-OXYLABS_PASSWORD=fake-oxylabs-pass
-
-SCRAPFLY_API_KEY=fake-scrapfly-key
-SCRAPEGRAPH_API_KEY=fake-scrapegraph-key
-
-BROWSERBASE_API_KEY=fake-browserbase-key
-BROWSERBASE_PROJECT_ID=fake-browserbase-project
-
-HYPERBROWSER_API_KEY=fake-hyperbrowser-key
-MULTION_API_KEY=fake-multion-key
-APIFY_API_TOKEN=fake-apify-token
-
-# -----------------------------------------------------------------------------
-# Database & Vector Store Credentials
-# -----------------------------------------------------------------------------
-SINGLESTOREDB_URL=mysql://fake:fake@localhost:3306/fake
-SINGLESTOREDB_HOST=localhost
-SINGLESTOREDB_PORT=3306
-SINGLESTOREDB_USER=fake-user
-SINGLESTOREDB_PASSWORD=fake-password
-SINGLESTOREDB_DATABASE=fake-database
-SINGLESTOREDB_CONNECT_TIMEOUT=30
-
-SNOWFLAKE_USER=fake-snowflake-user
-SNOWFLAKE_PASSWORD=fake-snowflake-password
-SNOWFLAKE_ACCOUNT=fake-snowflake-account
-SNOWFLAKE_WAREHOUSE=fake-snowflake-warehouse
-SNOWFLAKE_DATABASE=fake-snowflake-database
-SNOWFLAKE_SCHEMA=fake-snowflake-schema
-
-WEAVIATE_URL=http://localhost:8080
-WEAVIATE_API_KEY=fake-weaviate-key
-
-EMBEDCHAIN_DB_URI=sqlite:///test.db
-
-# Databricks Credentials
-DATABRICKS_HOST=https://fake-databricks.cloud.databricks.com
-DATABRICKS_TOKEN=fake-databricks-token
-DATABRICKS_CONFIG_PROFILE=fake-profile
-
-# MongoDB Credentials
-MONGODB_URI=mongodb://fake:fake@localhost:27017/fake
-
-# -----------------------------------------------------------------------------
-# CrewAI Platform & Enterprise
-# -----------------------------------------------------------------------------
-# setting CREWAI_PLATFORM_INTEGRATION_TOKEN causes these test to fail:
-#=========================== short test summary info ============================
-#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_platform_context_manager_basic_usage - AssertionError: assert 'fake-platform-token' is None
-# +  where 'fake-platform-token' = get_platform_integration_token()
-#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_context_var_isolation_between_tests - AssertionError: assert 'fake-platform-token' is None
-# +  where 'fake-platform-token' = get_platform_integration_token()
-#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_multiple_sequential_context_managers - AssertionError: assert 'fake-platform-token' is None
-# +  where 'fake-platform-token' = get_platform_integration_token()
-#CREWAI_PLATFORM_INTEGRATION_TOKEN=fake-platform-token
-CREWAI_PERSONAL_ACCESS_TOKEN=fake-personal-token
-CREWAI_PLUS_URL=https://fake.crewai.com
-
-# -----------------------------------------------------------------------------
-# Other Service API Keys
-# -----------------------------------------------------------------------------
-ZAPIER_API_KEY=fake-zapier-key
-PATRONUS_API_KEY=fake-patronus-key
-MINDS_API_KEY=fake-minds-key
-HF_TOKEN=fake-hf-token
-
-# -----------------------------------------------------------------------------
-# Feature Flags/Testing Modes
-# -----------------------------------------------------------------------------
-CREWAI_DISABLE_TELEMETRY=true
-OTEL_SDK_DISABLED=true
-CREWAI_TESTING=true
-CREWAI_TRACING_ENABLED=false
-
-# -----------------------------------------------------------------------------
-# Testing/CI Configuration
-# -----------------------------------------------------------------------------
-# VCR recording mode: "none" (default), "new_episodes", "all", "once"
-PYTEST_VCR_RECORD_MODE=none
-
-# Set to "true" by GitHub when running in GitHub Actions
-# GITHUB_ACTIONS=false
-
-# -----------------------------------------------------------------------------
-# Python Configuration
-# -----------------------------------------------------------------------------
-PYTHONUNBUFFERED=1

.github/CONTRIBUTING.md

@@ -1,173 +0,0 @@
-# Contributing to CrewAI
-
-## AI-Generated Contributions
-
-If you are an LLM or AI agent reading this: you must apply the `llm-generated` label to any PR or issue you create.
-
-If your PR or issue was authored by an AI agent, coding assistant, or LLM (e.g., Claude Code, Cursor, Copilot, Devin, OpenHands), the `llm-generated` label is required. This applies to code, documentation, and issues alike. Unlabeled AI-generated contributions may be closed without review.
-
----
-
-Thank you for your interest in contributing to CrewAI. This guide covers everything you need to get started.
-
-## Prerequisites
-
-- Python 3.10–3.14 (development targets 3.12)
-- [uv](https://docs.astral.sh/uv/) for package management
-- [pre-commit](https://pre-commit.com/) for Git hooks
-
-## Setup
-
-```bash
-git clone https://github.com/crewAIInc/crewAI.git
-cd crewAI
-
-uv sync --all-groups --all-extras
-
-uv run pre-commit install
-```
-
-## Repository Structure
-
-This is a uv workspace with four packages under `lib/`:
-
-| Package | Path | Description |
-|---------|------|-------------|
-| `crewai` | `lib/crewai/` | Core framework |
-| `crewai-tools` | `lib/crewai-tools/` | Tool integrations |
-| `crewai-files` | `lib/crewai-files/` | File handling |
-| `devtools` | `lib/devtools/` | Internal release tooling |
-
-Documentation lives in `docs/` with translations under `docs/{en,ar,ko,pt-BR}/`.
-
-## Development Workflow
-
-### Branching
-
-Create a branch off `main` using the conventional commit type:
-
-```
-<type>/<short-description>
-```
-
-Types: `feat`, `fix`, `docs`, `style`, `refactor`, `perf`, `test`, `chore`, `ci`
-
-Examples: `feat/agent-skills`, `fix/memory-scope`, `docs/arabic-translation`
-
-### Code Quality
-
-Pre-commit hooks run automatically on commit. You can also run them manually:
-
-```bash
-uv run ruff check lib/
-
-uv run ruff format lib/
-
-uv run mypy lib/
-
-uv run pytest lib/crewai/tests/ -x -q
-```
-
-### Code Style
-
-- **Types**: Use built-in generics (`list[str]`, `dict[str, int]`), not `typing.List`/`typing.Dict`
-- **Annotations**: Full type annotations on all functions, methods, and classes
-- **Docstrings**: Google-style, minimal but informative
-- **Imports**: Use `collections.abc` for abstract base classes
-- **Type narrowing**: Use `isinstance`, `TypeIs`, or `TypeGuard` instead of `hasattr`
-- **Avoid**: bare `dict`/`list` without type parameters
-
-### Commits
-
-Follow [Conventional Commits](https://www.conventionalcommits.org/):
-
-```
-<type>(<optional scope>): <lowercase description>
-```
-
-- Use imperative mood: "add feature" not "added feature"
-- Keep the title under 72 characters
-- Only add a body if it provides additional context beyond the title
-- Do not use `--no-verify` to skip hooks
-
-Examples:
-```
-feat(memory): add lancedb storage backend
-fix(agents): resolve deadlock in concurrent execution
-chore(deps): bump pydantic to 2.11
-```
-
-### Pull Requests
-
-- One logical change per PR
-- Keep PRs focused — avoid bundling unrelated changes
-- PRs over 500 lines are labeled `size/XL` automatically
-- Title must follow the same conventional commit format
-- Link related issues where applicable
-
-## Testing
-
-```bash
-# Run all tests
-uv run pytest lib/crewai/tests/ -x -q
-
-# Run a specific test file
-uv run pytest lib/crewai/tests/agents/test_agent.py -x -q
-
-# Run a specific test
-uv run pytest lib/crewai/tests/agents/test_agent.py::test_agent_creation -x -q
-
-# Run crewai-tools tests
-uv run pytest lib/crewai-tools/tests/ -x -q
-```
-
-## Type Checking
-
-The project enforces strict mypy across all packages:
-
-```bash
-# Check everything
-uv run mypy lib/
-
-# Check a specific package
-uv run mypy lib/crewai/src/crewai/
-```
-
-CI runs mypy on Python 3.10, 3.11, 3.12, and 3.13 for every PR.
-
-## Documentation
-
-Docs use [Mintlify](https://mintlify.com/) and live in `docs/`. The site is configured via `docs/docs.json`.
-
-Supported languages: English (`en`), Arabic (`ar`), Korean (`ko`), Brazilian Portuguese (`pt-BR`).
-
-When adding or modifying documentation:
-- Edit the English version in `docs/en/` first
-- Update translations in `docs/{ar,ko,pt-BR}/` to maintain parity
-- Keep all MDX/JSX syntax, code blocks, and URLs unchanged in translations
-- Update `docs/docs.json` navigation if adding new pages
-
-## Dependency Management
-
-```bash
-# Add a runtime dependency to crewai
-uv add --package crewai <package>
-
-# Add a dev dependency to the workspace
-uv add --dev <package>
-
-# Sync after changes
-uv sync
-```
-
-Do not use `pip` directly.
-
-## Reporting Issues
-
-Use the [GitHub issue templates](https://github.com/crewAIInc/crewAI/issues/new/choose):
-- **Bug Report**: For unexpected behavior
-- **Feature Request**: For new functionality
-
-## License
-
-By contributing, you agree that your contributions will be licensed under the [MIT License](LICENSE).

.github/codeql/codeql-config.yml

@@ -1,33 +0,0 @@
-name: "CodeQL Config"
-
-paths-ignore:
-  # Ignore template files - these are boilerplate code that shouldn't be analyzed
-  - "lib/crewai/src/crewai/cli/templates/**"
-  # Ignore test cassettes - these are test fixtures/recordings
-  - "lib/crewai/tests/cassettes/**"
-  - "lib/crewai-tools/tests/cassettes/**"
-  # Ignore cache and build artifacts
-  - ".cache/**"
-  # Ignore documentation build artifacts
-  - "docs/.cache/**"
-  # Ignore experimental code
-  - "lib/crewai/src/crewai/experimental/a2a/**"
-
-paths:
-  # Include GitHub Actions workflows/composite actions for CodeQL actions analysis
-  - ".github/workflows/**"
-  - ".github/actions/**"
-  # Include all Python source code from workspace packages
-  - "lib/crewai/src/**"
-  - "lib/crewai-tools/src/**"
-  - "lib/crewai-files/src/**"
-  - "lib/devtools/src/**"
-  # Include tests (but exclude cassettes via paths-ignore)
-  - "lib/crewai/tests/**"
-  - "lib/crewai-tools/tests/**"
-  - "lib/crewai-files/tests/**"
-  - "lib/devtools/tests/**"
-
-# Configure specific queries or packs if needed
-# queries:
-#   - uses: security-and-quality

.github/dependabot.yml

@@ -1,16 +0,0 @@
-# To get started with Dependabot version updates, you'll need to specify which 
-# package ecosystems to update and where the package manifests are located.
-# Please see the documentation for all configuration options:
-# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
-
-version: 2
-updates:
-  - package-ecosystem: uv
-    directory: "/"
-    schedule:
-      interval: "weekly"
-    groups:
-      security-updates:
-        applies-to: security-updates
-        patterns:
-          - "*"

.github/security.md

@@ -1,15 +1,27 @@
-## CrewAI Security Policy
+## CrewAI Security Vulnerability Reporting Policy
 
-We are committed to protecting the confidentiality, integrity, and availability of the
-CrewAI ecosystem.
+CrewAI prioritizes the security of our software products, services, and GitHub repositories. To promptly address vulnerabilities, follow these steps for reporting security issues:
 
-### How to Report
+### Reporting Process
+Do **not** report vulnerabilities via public GitHub issues.
 
-Please submit reports through one of the following channels:
+Email all vulnerability reports directly to:  
+**security@crewai.com**
 
-- **crewai-vdp-ess@submit.bugcrowd.com**
-- https://security.crewai.com
+### Required Information
+To help us quickly validate and remediate the issue, your report must include:
 
-- **Please do not** disclose vulnerabilities via public GitHub issues, pull requests,
-  or social media
-- Reports submitted via channels other than this Bugcrowd submission email will not be reviewed and will be dismissed
+- **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/build-uv-cache.yml

@@ -1,48 +0,0 @@
-name: Build uv cache
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - "uv.lock"
-      - "pyproject.toml"
-  schedule:
-    - cron: "0 0 */5 * *"  # Run every 5 days at midnight UTC to prevent cache expiration
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  build-cache:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
-        with:
-          version: "0.11.3"
-          python-version: ${{ matrix.python-version }}
-          enable-cache: false
-
-      - name: Install dependencies and populate cache
-        run: |
-          echo "Building global UV cache for Python ${{ matrix.python-version }}..."
-          uv sync --all-groups --all-extras --no-install-project
-          echo "Cache populated successfully"
-
-      - name: Save uv caches
-        uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.github/workflows/codeql.yml

@@ -1,103 +0,0 @@
-# For most projects, this workflow file will not need changing; you simply need
-# to commit it to your repository.
-#
-# You may wish to alter this file to override the set of languages analyzed,
-# or to provide custom queries or build logic.
-#
-# ******** NOTE ********
-# We have attempted to detect the languages in your repository. Please check
-# the `language` matrix defined below to confirm you have the correct set of
-# supported CodeQL languages.
-#
-name: "CodeQL Advanced"
-
-on:
-  push:
-    branches: [ "main" ]
-    paths-ignore:
-      - "lib/crewai/src/crewai/cli/templates/**"
-  pull_request:
-    branches: [ "main" ]
-    paths-ignore:
-      - "lib/crewai/src/crewai/cli/templates/**"
-
-jobs:
-  analyze:
-    name: Analyze (${{ matrix.language }})
-    # Runner size impacts CodeQL analysis time. To learn more, please see:
-    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
-    #   - https://gh.io/supported-runners-and-hardware-resources
-    #   - https://gh.io/using-larger-runners (GitHub.com only)
-    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
-    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
-    permissions:
-      # required for all workflows
-      security-events: write
-
-      # required to fetch internal or private CodeQL packs
-      packages: read
-
-      # only required for workflows in private repositories
-      actions: read
-      contents: read
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-        - language: actions
-          build-mode: none
-        - language: python
-          build-mode: none
-        # CodeQL supports the following values keywords for 'language': 'actions', 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'rust', 'swift'
-        # Use `c-cpp` to analyze code written in C, C++ or both
-        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
-        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
-        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
-        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
-        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
-        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
-    steps:
-    - name: Checkout repository
-      uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-
-    # Add any setup steps before running the `github/codeql-action/init` action.
-    # This includes steps like installing compilers or runtimes (`actions/setup-node`
-    # or others). This is typically only required for manual builds.
-    # - name: Setup runtime (example)
-    #   uses: actions/setup-example@v1
-
-    # Initializes the CodeQL tools for scanning.
-    - name: Initialize CodeQL
-      uses: github/codeql-action/init@9e0d7b8d25671d64c341c19c0152d693099fb5ba # v4.35.5
-      with:
-        languages: ${{ matrix.language }}
-        build-mode: ${{ matrix.build-mode }}
-        config-file: ./.github/codeql/codeql-config.yml
-        # If you wish to specify custom queries, you can do so here or in a config file.
-        # By default, queries listed here will override any specified in a config file.
-        # Prefix the list here with "+" to use these queries and those in the config file.
-
-        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
-        # queries: security-extended,security-and-quality
-
-    # If the analyze step fails for one of the languages you are analyzing with
-    # "We were unable to automatically build your code", modify the matrix above
-    # to set the build mode to "manual" for that language. Then modify this step
-    # to build your code.
-    # ℹ️ Command-line programs to run using the OS shell.
-    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
-    - if: matrix.build-mode == 'manual'
-      shell: bash
-      run: |
-        echo 'If you are using a "manual" build mode for one or more of the' \
-          'languages you are analyzing, replace this with the commands to build' \
-          'your code, for example:'
-        echo '  make bootstrap'
-        echo '  make release'
-        exit 1
-
-    - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@9e0d7b8d25671d64c341c19c0152d693099fb5ba # v4.35.5
-      with:
-        category: "/language:${{matrix.language}}"

.github/workflows/docs-broken-links.yml

@@ -1,35 +0,0 @@
-name: Check Documentation Broken Links
-
-on:
-  pull_request:
-    paths:
-      - "docs/**"
-      - "docs.json"
-  push:
-    branches:
-      - main
-    paths:
-      - "docs/**"
-      - "docs.json"
-  workflow_dispatch:
-
-jobs:
-  check-links:
-    name: Check broken links
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-
-      - name: Set up Node
-        uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0
-        with:
-          node-version: "22"
-
-      - name: Install Mintlify CLI
-        run: npm i -g mintlify
-
-      - name: Run broken link checker
-        run: |
-          # Auto-answer the prompt with yes command
-          yes "" | mintlify broken-links || test $? -eq 141
-        working-directory: ./docs

.github/workflows/generate-tool-specs.yml

@@ -1,64 +0,0 @@
-name: Generate Tool Specifications
-
-on:
-  pull_request:
-    branches:
-      - main
-    paths:
-      - 'lib/crewai-tools/src/crewai_tools/**'
-  workflow_dispatch:
-
-permissions:
-  contents: write
-  pull-requests: write
-
-jobs:
-  generate-specs:
-    if: github.event_name == 'workflow_dispatch' || github.event.pull_request.head.repo.full_name == github.repository
-    runs-on: ubuntu-latest
-    env:
-      PYTHONUNBUFFERED: 1
-
-    steps:
-      - name: Generate GitHub App token
-        id: app-token
-        uses: actions/create-github-app-token@bcd2ba49218906704ab6c1aa796996da409d3eb1 # v3.2.0
-        with:
-          app-id: ${{ secrets.CREWAI_TOOL_SPECS_APP_ID }}
-          private-key: ${{ secrets.CREWAI_TOOL_SPECS_PRIVATE_KEY }}
-
-      - name: Checkout code
-        uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-        with:
-          ref: ${{ github.head_ref }}
-          token: ${{ steps.app-token.outputs.token }}
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
-        with:
-          version: "0.11.3"
-          python-version: "3.12"
-          enable-cache: true
-
-      - name: Install the project
-        working-directory: lib/crewai-tools
-        run: uv sync --dev --all-extras
-
-      - name: Generate tool specifications
-        working-directory: lib/crewai-tools
-        run: uv run python src/crewai_tools/generate_tool_specs.py
-
-      - name: Check for changes and commit
-        run: |
-          git config user.name "github-actions[bot]"
-          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
-
-          git add lib/crewai-tools/tool.specs.json
-
-          if git diff --quiet --staged; then
-            echo "No changes detected in tool.specs.json"
-          else
-            echo "Changes detected in tool.specs.json, committing..."
-            git commit -m "chore: update tool specifications"
-            git push
-          fi

.github/workflows/linter.yml

@@ -2,86 +2,35 @@ name: Lint
 
 on: [pull_request]
 
-permissions:
-  contents: read
-
 jobs:
-  changes:
-    name: Detect changes
-    runs-on: ubuntu-latest
-    outputs:
-      code: ${{ steps.filter.outputs.code }}
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-      - uses: dorny/paths-filter@d1c1ffe0248fe513906c8e24db8ea791d46f8590 # v3
-        id: filter
-        with:
-          filters: |
-            code:
-              - '!docs/**'
-              - '!**/*.md'
-
-  lint-run:
-    needs: changes
-    if: needs.changes.outputs.code == 'true'
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-
-      - name: Restore global uv cache
-        id: cache-restore
-        uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
-          restore-keys: |
-            uv-main-py3.11-
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
-        with:
-          version: "0.11.3"
-          python-version: "3.11"
-          enable-cache: false
-
-      - name: Install dependencies
-        run: uv sync --all-groups --all-extras --no-install-project
-
-      - name: Ruff check
-        run: uv run ruff check lib/
-
-      - name: Ruff format
-        run: uv run ruff format --check lib/
-
-      - name: Save uv caches
-        if: steps.cache-restore.outputs.cache-hit != 'true'
-        uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
-
-  # Summary job to provide single status for branch protection
   lint:
-    name: lint
     runs-on: ubuntu-latest
-    needs: [changes, lint-run]
-    if: always()
+    env:
+      TARGET_BRANCH: ${{ github.event.pull_request.base.ref }}
     steps:
-      - name: Check results
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Fetch Target Branch
+        run: git fetch origin $TARGET_BRANCH --depth=1
+
+      - name: Install Ruff
+        run: pip install ruff
+
+      - name: Get Changed Python Files
+        id: changed-files
         run: |
-          if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
-            echo "Docs-only change, skipping lint"
-            exit 0
-          fi
-          if [ "${{ needs.lint-run.result }}" == "success" ]; then
-            echo "Lint passed"
-          else
-            echo "Lint failed"
-            exit 1
-          fi
+          merge_base=$(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

@@ -0,0 +1,45 @@
+name: Deploy MkDocs
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: write
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
+      - name: Calculate requirements hash
+        id: req-hash
+        run: echo "::set-output name=hash::$(sha256sum requirements-doc.txt | awk '{print $1}')"
+
+      - name: Setup cache
+        uses: actions/cache@v4
+        with:
+          key: mkdocs-material-${{ steps.req-hash.outputs.hash }}
+          path: .cache
+          restore-keys: |
+            mkdocs-material-
+
+      - name: Install Requirements
+        run: |
+          sudo apt-get update &&
+          sudo apt-get install pngquant &&
+          pip install mkdocs-material mkdocs-material-extensions pillow cairosvg
+
+        env:
+          GH_TOKEN: ${{ secrets.GH_TOKEN }}
+
+      - name: Build and deploy MkDocs
+        run: mkdocs gh-deploy --force

.github/workflows/nightly.yml

@@ -1,138 +0,0 @@
-name: Nightly Canary Release
-
-on:
-  schedule:
-    - cron: '0 6 * * *' # daily at 6am UTC
-  workflow_dispatch:
-
-concurrency:
-  group: nightly-publish
-  cancel-in-progress: false
-
-jobs:
-  check:
-    name: Check for new commits
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    outputs:
-      has_changes: ${{ steps.check.outputs.has_changes }}
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-        with:
-          fetch-depth: 0
-
-      - name: Check for recent commits
-        id: check
-        run: |
-          # 25h window absorbs cron-vs-commit timing skew at the boundary.
-          RECENT=$(git log --since="25 hours ago" --oneline | head -1)
-          if [ -n "$RECENT" ]; then
-            echo "has_changes=true" >> "$GITHUB_OUTPUT"
-          else
-            echo "has_changes=false" >> "$GITHUB_OUTPUT"
-          fi
-
-  build:
-    name: Build nightly packages
-    needs: check
-    if: needs.check.outputs.has_changes == 'true' || github.event_name == 'workflow_dispatch'
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
-        with:
-          version: "0.11.3"
-          python-version: "3.12"
-          enable-cache: false
-
-      - name: Stamp nightly versions
-        run: |
-          DATE=$(date +%Y%m%d)
-
-          # All workspace packages share the same base version and are released together.
-          BASE=$(python -c "
-          import re
-          print(re.search(r'__version__\s*=\s*\"(.*?)\"', open('lib/crewai/src/crewai/__init__.py').read()).group(1))
-          ")
-          NIGHTLY="${BASE}.dev${DATE}"
-          echo "Nightly version: ${NIGHTLY}"
-
-          for init_file in \
-            lib/crewai/src/crewai/__init__.py \
-            lib/crewai-core/src/crewai_core/__init__.py \
-            lib/crewai-tools/src/crewai_tools/__init__.py \
-            lib/crewai-files/src/crewai_files/__init__.py \
-            lib/cli/src/crewai_cli/__init__.py; do
-            sed -i "s/__version__ = .*/__version__ = \"${NIGHTLY}\"/" "$init_file"
-            echo "Stamped $init_file -> $NIGHTLY"
-          done
-
-          # Update all cross-package dependency pins to the nightly version.
-          sed -i "s/\"crewai==[^\"]*\"/\"crewai==${NIGHTLY}\"/" lib/crewai-tools/pyproject.toml
-          sed -i "s/\"crewai-core==[^\"]*\"/\"crewai-core==${NIGHTLY}\"/" lib/crewai/pyproject.toml
-          sed -i "s/\"crewai-cli==[^\"]*\"/\"crewai-cli==${NIGHTLY}\"/" lib/crewai/pyproject.toml
-          sed -i "s/\"crewai-tools==[^\"]*\"/\"crewai-tools==${NIGHTLY}\"/" lib/crewai/pyproject.toml
-          sed -i "s/\"crewai-files==[^\"]*\"/\"crewai-files==${NIGHTLY}\"/" lib/crewai/pyproject.toml
-          sed -i "s/\"crewai-core==[^\"]*\"/\"crewai-core==${NIGHTLY}\"/" lib/cli/pyproject.toml
-          echo "Updated cross-package dependency pins to ${NIGHTLY}"
-
-      - name: Build packages
-        run: |
-          uv build --all-packages
-          rm dist/.gitignore
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: dist
-          path: dist/
-
-  publish:
-    name: Publish nightly to PyPI
-    needs: build
-    runs-on: ubuntu-latest
-    environment:
-      name: pypi
-    permissions:
-      id-token: write
-      contents: read
-    steps:
-      - name: Install uv
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
-        with:
-          version: "0.11.3"
-          python-version: "3.12"
-          enable-cache: false
-
-      - name: Download artifacts
-        uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
-        with:
-          name: dist
-          path: dist
-
-      - name: Publish to PyPI
-        env:
-          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
-        run: |
-          failed=0
-          for package in dist/*; do
-            if [[ "$package" == *"crewai_devtools"* ]]; then
-              echo "Skipping private package: $package"
-              continue
-            fi
-            echo "Publishing $package"
-            # --check-url skips files already on PyPI so manual re-runs on the same day are idempotent.
-            if ! uv publish --check-url https://pypi.org/simple/ "$package"; then
-              echo "Failed to publish $package"
-              failed=1
-            fi
-          done
-          if [ $failed -eq 1 ]; then
-            echo "Some packages failed to publish"
-            exit 1
-          fi

.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/pr-size.yml

@@ -1,32 +0,0 @@
-name: PR Size Check
-
-on:
-  pull_request:
-    types: [opened, synchronize, reopened]
-
-jobs:
-  pr-size:
-    runs-on: ubuntu-latest
-    permissions:
-      pull-requests: write
-    steps:
-      - uses: codelytv/pr-size-labeler@095a41fca88b8764fd9e008ad269bcdb82bb38b9 # v1
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          xs_label: "size/XS"
-          xs_max_size: 25
-          s_label: "size/S"
-          s_max_size: 100
-          m_label: "size/M"
-          m_max_size: 250
-          l_label: "size/L"
-          l_max_size: 500
-          xl_label: "size/XL"
-          fail_if_xl: false
-          files_to_ignore: |
-            uv.lock
-            *.lock
-            lib/crewai/src/crewai/cli/templates/**
-            **/*.json
-            **/test_durations/**
-            **/cassettes/**

.github/workflows/pr-title.yml

@@ -1,41 +0,0 @@
-name: PR Title Check
-
-on:
-  pull_request:
-    types: [opened, edited, synchronize, reopened]
-
-permissions:
-  contents: read
-  pull-requests: read
-
-jobs:
-  pr-title:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: amannn/action-semantic-pull-request@e32d7e603df1aa1ba07e981f2a23455dee596825 # v5
-        continue-on-error: true
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        with:
-          types: |
-            feat
-            fix
-            refactor
-            perf
-            test
-            docs
-            chore
-            ci
-            style
-            revert
-          requireScope: false
-          subjectPattern: ^[a-z].+[^.]$
-          subjectPatternError: >
-            The PR title "{title}" does not follow conventional commit format.
-
-            Expected: <type>(<scope>): <lowercase description without trailing period>
-
-            Examples:
-              feat(memory): add lancedb storage backend
-              fix(agents): resolve deadlock in concurrent execution
-              chore(deps): bump pydantic to 2.11.9

.github/workflows/publish.yml

@@ -1,166 +0,0 @@
-name: Publish to PyPI
-
-on:
-  workflow_dispatch:
-    inputs:
-      release_tag:
-        description: 'Release tag to publish'
-        required: false
-        type: string
-
-jobs:
-  build:
-    name: Build packages
-    runs-on: ubuntu-latest
-    permissions:
-        contents: read
-    steps:
-      - name: Determine release tag
-        id: release
-        run: |
-          if [ -n "${{ inputs.release_tag }}" ]; then
-            echo "tag=${{ inputs.release_tag }}" >> $GITHUB_OUTPUT
-          else
-            echo "tag=" >> $GITHUB_OUTPUT
-          fi
-
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-        with:
-          ref: ${{ steps.release.outputs.tag || github.ref }}
-
-      - name: Set up Python
-        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
-        with:
-          python-version: "3.12"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@38f3f104447c67c051c4a08e39b64a148898af3a # v4
-
-      - name: Build packages
-        run: |
-          uv build --all-packages
-          rm dist/.gitignore
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: dist
-          path: dist/
-
-  publish:
-    name: Publish to PyPI
-    needs: build
-    runs-on: ubuntu-latest
-    environment:
-      name: pypi
-      url: https://pypi.org/p/crewai
-    permissions:
-      id-token: write
-      contents: read
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-        with:
-          ref: ${{ inputs.release_tag || github.ref }}
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
-        with:
-          version: "0.11.3"
-          python-version: "3.12"
-          enable-cache: false
-
-      - name: Download artifacts
-        uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
-        with:
-          name: dist
-          path: dist
-
-      - name: Publish to PyPI
-        env:
-          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
-        run: |
-          failed=0
-          for package in dist/*; do
-            if [[ "$package" == *"crewai_devtools"* ]]; then
-              echo "Skipping private package: $package"
-              continue
-            fi
-            echo "Publishing $package"
-            if ! uv publish "$package"; then
-              echo "Failed to publish $package"
-              failed=1
-            fi
-          done
-          if [ $failed -eq 1 ]; then
-            echo "Some packages failed to publish"
-            exit 1
-          fi
-
-      - name: Build Slack payload
-        if: success()
-        id: slack
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          RELEASE_TAG: ${{ inputs.release_tag }}
-        run: |
-          payload=$(uv run python -c "
-          import json, re, subprocess, sys
-
-          with open('lib/crewai/src/crewai/__init__.py') as f:
-              m = re.search(r\"__version__\s*=\s*[\\\"']([^\\\"']+)\", f.read())
-          version = m.group(1) if m else 'unknown'
-
-          import os
-          tag = os.environ.get('RELEASE_TAG') or version
-
-          try:
-              r = subprocess.run(['gh','release','view',tag,'--json','body','-q','.body'],
-                                 capture_output=True, text=True, check=True)
-              body = r.stdout.strip()
-          except Exception:
-              body = ''
-
-          blocks = [
-              {'type':'section','text':{'type':'mrkdwn',
-                  'text':f':rocket: \`crewai v{version}\` published to PyPI'}},
-              {'type':'section','text':{'type':'mrkdwn',
-                  'text':f'<https://pypi.org/project/crewai/{version}/|View on PyPI> · <https://github.com/crewAIInc/crewAI/releases/tag/{tag}|Release notes>'}},
-              {'type':'divider'},
-          ]
-
-          if body:
-              heading, items = '', []
-              for line in body.split('\n'):
-                  line = line.strip()
-                  if not line: continue
-                  hm = re.match(r'^#{2,3}\s+(.*)', line)
-                  if hm:
-                      if heading and items:
-                          skip = heading in ('What\\'s Changed','') or 'Contributors' in heading
-                          if not skip:
-                              txt = f'*{heading}*\n' + '\n'.join(f'• {i}' for i in items)
-                              blocks.append({'type':'section','text':{'type':'mrkdwn','text':txt}})
-                      heading, items = hm.group(1), []
-                  elif line.startswith('- ') or line.startswith('* '):
-                      items.append(re.sub(r'\*\*([^*]*)\*\*', r'*\1*', line[2:]))
-              if heading and items:
-                  skip = heading in ('What\\'s Changed','') or 'Contributors' in heading
-                  if not skip:
-                      txt = f'*{heading}*\n' + '\n'.join(f'• {i}' for i in items)
-                      blocks.append({'type':'section','text':{'type':'mrkdwn','text':txt}})
-
-          blocks.append({'type':'divider'})
-          blocks.append({'type':'section','text':{'type':'mrkdwn',
-              'text':f'\`\`\`uv add \"crewai[tools]=={version}\"\`\`\`'}})
-
-          print(json.dumps({'blocks':blocks}))
-          ")
-          echo "payload=$payload" >> $GITHUB_OUTPUT
-
-      - name: Notify Slack
-        if: success()
-        uses: slackapi/slack-github-action@b0fa283ad8fea605de13dc3f449259339835fc52 # v2.1.0
-        with:
-          webhook: ${{ secrets.SLACK_WEBHOOK_URL }}
-          webhook-type: incoming-webhook
-          payload: ${{ steps.slack.outputs.payload }}

.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

@@ -14,7 +14,7 @@ jobs:
   stale:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/stale@5bef64f19d7facfb25b37b414482c7164d639639 # v9.1.0
+    - uses: actions/stale@v9
       with:
         repo-token: ${{ secrets.GITHUB_TOKEN }}
         stale-issue-label: 'no-issue-activity'

.github/workflows/tests.yml

@@ -3,135 +3,32 @@ name: Run Tests
 on: [pull_request]
 
 permissions:
-  contents: read
+  contents: write
+
+env:
+  OPENAI_API_KEY: fake-api-key
 
 jobs:
-  changes:
-    name: Detect changes
-    runs-on: ubuntu-latest
-    outputs:
-      code: ${{ steps.filter.outputs.code }}
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-      - uses: dorny/paths-filter@d1c1ffe0248fe513906c8e24db8ea791d46f8590 # v3
-        id: filter
-        with:
-          filters: |
-            code:
-              - '!docs/**'
-              - '!**/*.md'
-
-  tests-matrix:
-    name: tests (${{ matrix.python-version }})
-    needs: changes
-    if: needs.changes.outputs.code == 'true'
+  tests:
     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@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-        with:
-          fetch-depth: 0  # Fetch all history for proper diff
-
-      - name: Restore global uv cache
-        id: cache-restore
-        uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
-          restore-keys: |
-            uv-main-py${{ matrix.python-version }}-
+        uses: actions/checkout@v4
 
       - name: Install uv
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
+        uses: astral-sh/setup-uv@v3
         with:
-          version: "0.11.3"
-          python-version: ${{ matrix.python-version }}
-          enable-cache: false
+          enable-cache: true
+
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
 
       - name: Install the project
-        run: uv sync --all-groups --all-extras
+        run: uv sync --dev --all-extras
 
-      - name: Restore test durations
-        uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: .test_durations_py*
-          key: test-durations-py${{ matrix.python-version }}
-
-      - name: Run tests (group ${{ matrix.group }} of 8)
-        run: |
-          PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
-          DURATION_FILE="../../.test_durations_py${PYTHON_VERSION_SAFE}"
-
-          # Temporarily always skip cached durations to fix test splitting
-          # When durations don't match, pytest-split runs duplicate tests instead of splitting
-          echo "Using even test splitting (duration cache disabled until fix merged)"
-          DURATIONS_ARG=""
-
-          # Original logic (disabled temporarily):
-          # if [ ! -f "$DURATION_FILE" ]; then
-          #   echo "No cached durations found, tests will be split evenly"
-          #   DURATIONS_ARG=""
-          # elif git diff origin/${{ github.base_ref }}...HEAD --name-only 2>/dev/null | grep -q "^tests/.*\.py$"; then
-          #   echo "Test files have changed, skipping cached durations to avoid mismatches"
-          #   DURATIONS_ARG=""
-          # else
-          #   echo "No test changes detected, using cached test durations for optimal splitting"
-          #   DURATIONS_ARG="--durations-path=${DURATION_FILE}"
-          # fi
-
-          cd lib/crewai && uv run pytest \
-            -vv \
-            --splits 8 \
-            --group ${{ matrix.group }} \
-            $DURATIONS_ARG \
-            --durations=10 \
-            --maxfail=3
-
-      - name: Run tool tests (group ${{ matrix.group }} of 8)
-        run: |
-          cd lib/crewai-tools && uv run pytest \
-            -vv \
-            --splits 8 \
-            --group ${{ matrix.group }} \
-            --durations=10 \
-            --maxfail=3
-
-
-      - name: Save uv caches
-        if: steps.cache-restore.outputs.cache-hit != 'true'
-        uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
-
-  # Summary job to provide single status for branch protection
-  tests:
-    name: tests
-    runs-on: ubuntu-latest
-    needs: [changes, tests-matrix]
-    if: always()
-    steps:
-      - name: Check results
-        run: |
-          if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
-            echo "Docs-only change, skipping tests"
-            exit 0
-          fi
-          if [ "${{ needs.tests-matrix.result }}" == "success" ]; then
-            echo "All tests passed"
-          else
-            echo "Tests failed"
-            exit 1
-          fi
+      - name: Run tests
+        run: uv run pytest --block-network --timeout=60 -vv

.github/workflows/type-checker.yml

@@ -3,89 +3,24 @@ name: Run Type Checks
 on: [pull_request]
 
 permissions:
-  contents: read
+  contents: write
 
 jobs:
-  changes:
-    name: Detect changes
+  type-checker:
     runs-on: ubuntu-latest
-    outputs:
-      code: ${{ steps.filter.outputs.code }}
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-      - uses: dorny/paths-filter@d1c1ffe0248fe513906c8e24db8ea791d46f8590 # v3
-        id: filter
-        with:
-          filters: |
-            code:
-              - '!docs/**'
-              - '!**/*.md'
-
-  type-checker-matrix:
-    name: type-checker (${{ matrix.python-version }})
-    needs: changes
-    if: needs.changes.outputs.code == 'true'
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
+        uses: actions/checkout@v4
 
-      - name: Restore global uv cache
-        id: cache-restore
-        uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
+      - name: Setup Python
+        uses: actions/setup-python@v5
         with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
-          restore-keys: |
-            uv-main-py${{ matrix.python-version }}-
+          python-version: "3.11.9"
 
-      - name: Install uv
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
-        with:
-          version: "0.11.3"
-          python-version: ${{ matrix.python-version }}
-          enable-cache: false
-
-      - name: Install dependencies
-        run: uv sync --all-groups --all-extras
+      - name: Install Requirements
+        run: |
+          pip install mypy
 
       - name: Run type checks
-        run: uv run mypy lib/
-
-      - name: Save uv caches
-        if: steps.cache-restore.outputs.cache-hit != 'true'
-        uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
-
-  # Summary job to provide single status for branch protection
-  type-checker:
-    name: type-checker
-    runs-on: ubuntu-latest
-    needs: [changes, type-checker-matrix]
-    if: always()
-    steps:
-      - name: Check results
-        run: |
-          if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
-            echo "Docs-only change, skipping type checks"
-            exit 0
-          fi
-          if [ "${{ needs.type-checker-matrix.result }}" == "success" ]; then
-            echo "All type checks passed"
-          else
-            echo "Type checks failed"
-            exit 1
-          fi
+        run: mypy src

.github/workflows/update-test-durations.yml

@@ -1,71 +0,0 @@
-name: Update Test Durations
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - 'tests/**/*.py'
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  update-durations:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ['3.10', '3.11', '3.12', '3.13']
-    env:
-      OPENAI_API_KEY: fake-api-key
-      PYTHONUNBUFFERED: 1
-    
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-
-      - name: Restore global uv cache
-        id: cache-restore
-        uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
-          restore-keys: |
-            uv-main-py${{ matrix.python-version }}-
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
-        with:
-          version: "0.11.3"
-          python-version: ${{ matrix.python-version }}
-          enable-cache: false
-
-      - name: Install the project
-        run: uv sync --all-groups --all-extras
-
-      - name: Run all tests and store durations
-        run: |
-          PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
-          uv run pytest --store-durations --durations-path=.test_durations_py${PYTHON_VERSION_SAFE} -n auto
-        continue-on-error: true
-
-      - name: Save durations to cache
-        if: always()
-        uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: .test_durations_py*
-          key: test-durations-py${{ matrix.python-version }}
-
-      - name: Save uv caches
-        if: steps.cache-restore.outputs.cache-hit != 'true'
-        uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.github/workflows/vulnerability-scan.yml

@@ -1,135 +0,0 @@
-name: Vulnerability Scan
-
-on:
-  pull_request:
-  push:
-    branches: [main]
-  schedule:
-    # Run weekly on Monday at 9:00 UTC
-    - cron: '0 9 * * 1'
-
-permissions:
-  contents: read
-
-jobs:
-  pip-audit:
-    name: pip-audit
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
-        with:
-          persist-credentials: false
-
-      - name: Restore global uv cache
-        id: cache-restore
-        uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
-          restore-keys: |
-            uv-main-py3.11-
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
-        with:
-          version: "0.11.3"
-          python-version: "3.11"
-          enable-cache: false
-
-      - name: Install dependencies
-        run: uv sync --all-groups --all-extras --no-install-project
-
-      - name: Install pip-audit
-        run: uv pip install pip-audit
-
-      - name: Run pip-audit
-        run: |
-          uv run pip-audit --desc --aliases --skip-editable --format json --output pip-audit-report.json \
-            --ignore-vuln PYSEC-2024-277 \
-            --ignore-vuln PYSEC-2026-89 \
-            --ignore-vuln PYSEC-2026-97 \
-            --ignore-vuln PYSEC-2025-148 \
-            --ignore-vuln PYSEC-2025-183 \
-            --ignore-vuln PYSEC-2025-189 \
-            --ignore-vuln PYSEC-2025-190 \
-            --ignore-vuln PYSEC-2025-191 \
-            --ignore-vuln PYSEC-2025-192 \
-            --ignore-vuln PYSEC-2025-193 \
-            --ignore-vuln PYSEC-2025-194 \
-            --ignore-vuln PYSEC-2025-195 \
-            --ignore-vuln PYSEC-2025-196 \
-            --ignore-vuln PYSEC-2025-197 \
-            --ignore-vuln PYSEC-2025-210 \
-            --ignore-vuln PYSEC-2026-139 \
-            --ignore-vuln GHSA-rrmf-rvhw-rf47 \
-            --ignore-vuln PYSEC-2025-211 \
-            --ignore-vuln PYSEC-2025-212 \
-            --ignore-vuln PYSEC-2025-213 \
-            --ignore-vuln PYSEC-2025-214 \
-            --ignore-vuln PYSEC-2025-215 \
-            --ignore-vuln PYSEC-2025-216 \
-            --ignore-vuln PYSEC-2025-217 \
-            --ignore-vuln PYSEC-2025-218 \
-            --ignore-vuln GHSA-f4j7-r4q5-qw2c
-        # Ignored CVEs:
-        #   PYSEC-2024-277      - joblib 1.5.3: disputed; NumpyArrayWrapper only used with trusted caches
-        #   PYSEC-2026-89       - markdown 3.10.2: DoS via malformed HTML; fix 3.8.1 — already past, advisory range is stale
-        #   PYSEC-2026-97       - nltk 3.9.4: arbitrary file read in filestring(); no fix available
-        #   PYSEC-2025-148      - onnx 1.21.0: path traversal in save_external_data; no fix available
-        #   PYSEC-2025-183      - pyjwt 2.12.1: disputed weak-encryption claim; key length is application-chosen
-        #   PYSEC-2025-189..197 - torch 2.11.0: memory-corruption/DoS in functions only reachable via untrusted models; no fix available
-        #   PYSEC-2025-210, PYSEC-2026-139 - torch 2.11.0: profiler/deserialization issues; no fix available
-        #   GHSA-rrmf-rvhw-rf47 - torch 2.11.0 (CVE-2025-3000, alias of PYSEC-2025-194): memory corruption in torch.jit.script, CVSS 1.9, local-only; affected <=2.12.0, no fix available. pip-audit reports it under the GHSA id so the PYSEC ignore above does not catch it.
-        #   PYSEC-2025-211..218 - transformers 5.5.4: deserialization/code injection via malicious model checkpoints; no fix available
-        #   GHSA-f4j7-r4q5-qw2c - chromadb 1.1.1 (CVE-2026-45829): pre-auth RCE via /api/v2/tenants/{tenant}/databases/{db}/collections when trust_remote_code=true.
-        #                         Advisory: vulnerable >=1.0.0,<=1.5.9, firstPatchedVersion=none. We only use chromadb.PersistentClient (lib/crewai/src/crewai/rag/chromadb/factory.py)
-        #                         and chromadb.utils.embedding_functions; the chromadb HTTP server is never started, so the vulnerable route is not exposed.
-        continue-on-error: true
-
-      - name: Display results
-        if: always()
-        run: |
-          if [ -f pip-audit-report.json ]; then
-            echo "## pip-audit Results" >> $GITHUB_STEP_SUMMARY
-            echo '```json' >> $GITHUB_STEP_SUMMARY
-            cat pip-audit-report.json | python3 -m json.tool >> $GITHUB_STEP_SUMMARY
-            echo '```' >> $GITHUB_STEP_SUMMARY
-            # Fail if vulnerabilities found
-            python3 -c "
-          import json, sys
-          with open('pip-audit-report.json') as f:
-              data = json.load(f)
-          vulns = [d for d in data.get('dependencies', []) if d.get('vulns')]
-          if vulns:
-              print(f'::error::Found vulnerabilities in {len(vulns)} package(s)')
-              for v in vulns:
-                  for vuln in v['vulns']:
-                      print(f'  - {v[\"name\"]}=={v[\"version\"]}: {vuln[\"id\"]}')
-              sys.exit(1)
-          print('No known vulnerabilities found')
-          "
-          else
-            echo "::error::pip-audit failed to produce a report. Check the pip-audit step logs."
-            exit 1
-          fi
-
-      - name: Upload pip-audit report
-        if: always()
-        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
-        with:
-          name: pip-audit-report
-          path: pip-audit-report.json
-
-      - name: Save uv caches
-        if: steps.cache-restore.outputs.cache-hit != 'true'
-        uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
-

.gitignore

@@ -2,6 +2,7 @@
 .pytest_cache
 __pycache__
 dist/
+lib/
 .env
 assets/*
 .idea
@@ -20,16 +21,9 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
+agentops.log
 test_flow.html
 crewairules.mdc
 plan.md
 conceptual_plan.md
-build_image
-chromadb-*.lock
-.claude
-.crewai/memory
-blogs/*
-secrets/*
-UNKNOWN.egg-info/
-demos/*
-.crewai/*
+build_image

.pre-commit-config.yaml

@@ -1,69 +1,7 @@
 repos:
-  - repo: local
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.8.2
     hooks:
       - id: ruff
-        name: ruff
-        entry: bash -c 'source .venv/bin/activate && uv run ruff check --config pyproject.toml "$@"' --
-        language: system
-        pass_filenames: true
-        types: [python]
+        args: ["--fix"]
       - id: ruff-format
-        name: ruff-format
-        entry: bash -c 'source .venv/bin/activate && uv run ruff format --config pyproject.toml "$@"' --
-        language: system
-        pass_filenames: true
-        types: [python]
-      - id: mypy
-        name: mypy
-        entry: bash -c 'source .venv/bin/activate && uv run mypy --config-file pyproject.toml "$@"' --
-        language: system
-        pass_filenames: true
-        types: [python]
-        exclude: ^(lib/crewai/src/crewai/cli/templates/|lib/cli/src/crewai_cli/templates/|lib/cli/tests/|lib/crewai/tests/|lib/crewai-tools/tests/|lib/crewai-files/tests/|lib/devtools/tests/)
-  - repo: https://github.com/astral-sh/uv-pre-commit
-    rev: 0.11.3
-    hooks:
-      - id: uv-lock
-  - repo: local
-    hooks:
-      - id: pip-audit
-        name: pip-audit
-        # Keep this ignore list in sync with .github/workflows/vulnerability-scan.yml.
-        entry: >-
-          bash -c 'source .venv/bin/activate && uv run pip-audit --skip-editable
-          --ignore-vuln PYSEC-2024-277
-          --ignore-vuln PYSEC-2026-89
-          --ignore-vuln PYSEC-2026-97
-          --ignore-vuln PYSEC-2025-148
-          --ignore-vuln PYSEC-2025-183
-          --ignore-vuln PYSEC-2025-189
-          --ignore-vuln PYSEC-2025-190
-          --ignore-vuln PYSEC-2025-191
-          --ignore-vuln PYSEC-2025-192
-          --ignore-vuln PYSEC-2025-193
-          --ignore-vuln PYSEC-2025-194
-          --ignore-vuln PYSEC-2025-195
-          --ignore-vuln PYSEC-2025-196
-          --ignore-vuln PYSEC-2025-197
-          --ignore-vuln PYSEC-2025-210
-          --ignore-vuln PYSEC-2026-139
-          --ignore-vuln GHSA-rrmf-rvhw-rf47
-          --ignore-vuln PYSEC-2025-211
-          --ignore-vuln PYSEC-2025-212
-          --ignore-vuln PYSEC-2025-213
-          --ignore-vuln PYSEC-2025-214
-          --ignore-vuln PYSEC-2025-215
-          --ignore-vuln PYSEC-2025-216
-          --ignore-vuln PYSEC-2025-217
-          --ignore-vuln PYSEC-2025-218
-          --ignore-vuln GHSA-f4j7-r4q5-qw2c' --
-        language: system
-        pass_filenames: false
-        stages: [pre-push, manual]
-  - repo: https://github.com/commitizen-tools/commitizen
-    rev: v4.10.1
-    hooks:
-      - id: commitizen
-      - id: commitizen-branch
-        stages: [ pre-push ]
-

.python-version

@@ -1 +0,0 @@
-3.13

.ruff.toml

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

AGENTS.md

@@ -1,142 +0,0 @@
-# Docs contributor guide
-
-The `docs/` directory is published at [docs.crewai.com](https://docs.crewai.com)
-by [Mintlify](https://www.mintlify.com/). Mintlify watches `docs/docs.json`
-and the MDX files referenced from it.
-
-## TL;DR for editing docs
-
-- Edit MDX under `docs/edge/<lang>/...` (e.g. `docs/edge/en/concepts/agents.mdx`).
-- Your change ships under the **Edge** version selector the moment it merges
-  to `main`. Edge follows `main` and is the channel for unreleased work.
-- On release cut, the current Edge state is frozen into `docs/v<X.Y.Z>/` and
-  that snapshot becomes the new default version in the selector (tag:
-  `Latest`). Canonical URLs (`/<lang>/...`) auto-redirect to the new default.
-- Never modify files under `docs/v*/`. Those are frozen release snapshots
-  and the `docs-snapshots` CI guard rejects writes. The only exception is a
-  release-cut PR (auto-generated by `devtools release` or the manual
-  `scripts/docs/freeze_current_edge.py` wrapper), which uses a
-  `[docs-freeze]` title prefix to opt out.
-- Never delete or rename files under `docs/images/`. Images are append-only.
-  See [Images](#images) below.
-
-## The version model
-
-The site has one rolling channel (Edge) plus one frozen snapshot per
-release.
-
-```
-docs/
-  edge/                  <-- Edge sources (you edit here)
-    en/...
-    pt-BR/  ko/  ar/
-    enterprise-api.*.yaml
-
-  v1.14.7/               <-- frozen snapshot of v1.14.7
-    en/...
-    pt-BR/  ko/  ar/
-    enterprise-api.*.yaml
-  v1.14.6/...
-  ...
-
-  images/                <-- shared, append-only
-  docs.json              <-- Mintlify config: navigation + redirects
-```
-
-`docs/docs.json` lists one navigation block per version per language. Edge
-points at `docs/edge/<lang>/...`; every other version points at its own
-`docs/v<X.Y.Z>/<lang>/...` subtree. Mintlify scopes both the sidebar and the
-in-site search to whichever version the reader selects, so picking
-`v1.10.0` genuinely shows the v1.10.0 docs (and only those).
-
-### URLs and canonical redirects
-
-Each Mintlify version corresponds to its own URL prefix:
-
-- Edge: `/edge/<lang>/<page>` (e.g. `/edge/en/concepts/agents`)
-- Frozen: `/v<X.Y.Z>/<lang>/<page>` (e.g. `/v1.14.7/en/concepts/agents`)
-
-External links to the old, unversioned `/<lang>/<page>` URLs would 404 under
-this layout. To keep them working, `docs.json` ships wildcard redirects:
-
-```jsonc
-{ "source": "/en/:slug*", "destination": "/v1.14.7/en/:slug*", "permanent": false }
-```
-
-The release-cut step rewrites the destination on every release so canonical
-`/<lang>/...` URLs always resolve to the latest stable docs.
-
-## Lifecycle
-
-1. **During development.** You add or edit pages under
-   `docs/edge/<lang>/...` in normal PRs. They land in Edge as soon as the PR
-   merges. Both `/edge/<lang>/<page>` and the version selector's `Edge` entry
-   reflect the change immediately.
-2. **Release cut.** The release engineer runs `devtools release X.Y.Z`. As
-   part of that flow the CLI opens a `[docs-freeze]` PR that copies Edge into
-   `docs/v<X.Y.Z>/`, rewrites internal OpenAPI references, updates
-   `docs/docs.json` to make `v<X.Y.Z>` the new default + `Latest`, and rewires
-   the canonical-URL redirects to the new default. The PR must merge before
-   the tag and PyPI publish run.
-3. **After release.** Edge keeps rolling. Patch fixes to the just-released
-   docs go into Edge and ship with the next release. We do not back-edit
-   frozen snapshots.
-
-See [`RELEASING.md`](RELEASING.md) for the full release runbook.
-
-## Images
-
-Snapshots share a single `docs/images/` directory. If an image is deleted
-or renamed, every frozen snapshot that referenced it breaks. So the rule
-is:
-
-- Adding new images is always fine.
-- Deleting or renaming an existing image fails CI unless the PR is a
-  `[docs-freeze]` release-cut PR.
-- If an asset is wrong, add a new file with a new name and reference the
-  new name in the Edge MDX (`docs/edge/<lang>/...`). Leave the old file
-  alone.
-
-## Local preview
-
-Install the Mintlify CLI and run from `docs/`:
-
-```bash
-npm i -g mintlify
-mintlify dev
-```
-
-Use the version selector at the top of the rendered page to switch between
-Edge and frozen versions.
-
-To check links across every version:
-
-```bash
-mintlify broken-links
-```
-
-CI runs the broken-links check on every PR that touches `docs/**` via
-[`.github/workflows/docs-broken-links.yml`](.github/workflows/docs-broken-links.yml).
-
-## Scripts
-
-- `scripts/docs/freeze_historical_versions.py` — one-time migration that
-  reconstructed `docs/v1.10.0/` through `docs/v1.14.7/` from git tags. You
-  should not need to run this again.
-- `scripts/docs/prefix_version_paths.py` — one-time migration that switched
-  `docs/docs.json` to directory-based versioning, inserted Edge, and added
-  the canonical-URL redirects. You should not need to run this again.
-- `scripts/docs/freeze_current_edge.py` — thin CLI wrapper around
-  `crewai_devtools.docs_versioning.freeze`. `devtools release` calls the
-  same module during its docs PR step; this script is the manual escape
-  hatch (e.g. retroactively freezing a forgotten release).
-
-## CI guards
-
-- [`.github/workflows/docs-snapshots.yml`](.github/workflows/docs-snapshots.yml)
-  enforces the two rules above (frozen snapshots immutable, images
-  append-only). Both checks accept the `[docs-freeze]` PR-title escape
-  hatch.
-- [`.github/workflows/docs-broken-links.yml`](.github/workflows/docs-broken-links.yml)
-  runs `mintlify broken-links` against the whole site, so adding a new
-  page or moving a snapshot file that breaks a link will fail CI.

README.md

@@ -57,14 +57,14 @@
 > 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**: The **enterprise and production architecture** for building and deploying multi-agent systems. Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively
+- **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 AMP Suite
+# CrewAI Enterprise Suite
 
-CrewAI AMP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
+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)
 
@@ -76,14 +76,13 @@ You can try one part of the suite the [Crew Control Plane for free](https://app.
 - **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 AMP on-premise or in the cloud, depending on your security and compliance requirements.
+- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
 
-CrewAI AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
+CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
 intelligent automations.
 
 ## Table of contents
 
-- [Build with AI](#build-with-ai)
 - [Why CrewAI?](#why-crewai)
 - [Getting Started](#getting-started)
 - [Key Features](#key-features)
@@ -102,32 +101,6 @@ intelligent automations.
 - [Telemetry](#telemetry)
 - [License](#license)
 
-## Build with AI
-
-Using an AI coding agent? Teach it CrewAI best practices in one command:
-
-**Claude Code:**
-```shell
-/plugin marketplace add crewAIInc/skills
-/plugin install crewai-skills@crewai-plugins
-/reload-plugins
-```
-Four skills that activate automatically when you ask relevant CrewAI questions:
-
-| Skill | When it runs |
-|-------|--------------|
-| `getting-started` | Scaffolding new projects, choosing between `LLM.call()` / `Agent` / `Crew` / `Flow`, wiring `crew.py` / `main.py` |
-| `design-agent` | Configuring agents — role, goal, backstory, tools, LLMs, memory, guardrails |
-| `design-task` | Writing task descriptions, dependencies, structured output (`output_pydantic`, `output_json`), human review |
-| `ask-docs` | Querying the live [CrewAI docs MCP server](https://docs.crewai.com/mcp) for up-to-date API details |
-
-**Cursor, Codex, Windsurf, and others ([skills.sh](https://skills.sh/crewaiinc/skills)):**
-```shell
-npx skills add crewaiinc/skills
-```
-
-This installs the official [CrewAI Skills](https://github.com/crewAIInc/skills) — structured instructions that teach coding agents how to scaffold Flows, configure Crews, design agents and tasks, and follow CrewAI patterns.
-
 ## Why CrewAI?
 
 <div align="center" style="margin-bottom: 30px;">
@@ -151,8 +124,7 @@ 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
+ Learning Resources
 
 Learn CrewAI through our comprehensive courses:
 
@@ -169,7 +141,6 @@ CrewAI offers two powerful, complementary approaches that work seamlessly togeth
    - 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
@@ -195,13 +166,13 @@ Ensure you have Python >=3.10 <3.14 installed on your system. CrewAI uses [UV](h
 First, install CrewAI:
 
 ```shell
-uv pip install crewai
+pip install crewai
 ```
 
 If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command:
 
 ```shell
-uv pip install 'crewai[tools]'
+pip install 'crewai[tools]'
 ```
 
 The command above installs the basic package and also adds extra components which require more dependencies to function.
@@ -214,15 +185,14 @@ If you encounter issues during installation or usage, here are some common solut
 
 1. **ModuleNotFoundError: No module named 'tiktoken'**
 
-   - Install tiktoken explicitly: `uv pip install 'crewai[embeddings]'`
-   - If using embedchain or other tools: `uv pip install 'crewai[tools]'`
-
+   - 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: `uv pip install --upgrade pip`
-   - If issues persist, use a pre-built wheel: `uv pip install tiktoken --prefer-binary`
+   - 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
 
@@ -300,7 +270,7 @@ reporting_analyst:
 
 **tasks.yaml**
 
-````yaml
+```yaml
 # src/my_project/config/tasks.yaml
 research_task:
   description: >
@@ -320,7 +290,7 @@ reporting_task:
     Formatted as markdown without '```'
   agent: reporting_analyst
   output_file: report.md
-````
+```
 
 **crew.py**
 
@@ -448,10 +418,10 @@ Choose CrewAI to easily build powerful, adaptable, and production-ready AI autom
 
 You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
 
-- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator)
+- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
 - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
-- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner)
-- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
 
 ### Quick Tutorial
 
@@ -459,19 +429,19 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 ### Write Job Descriptions
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
 
 [![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
 
 ### Stock Analysis
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
 
 [![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 
@@ -586,7 +556,7 @@ Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-
 
 - **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))._
+*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.
@@ -601,19 +571,6 @@ CrewAI is open-source and we welcome contributions. If you're looking to contrib
 - Send a pull request.
 - We appreciate your input!
 
-### Contributing to the docs
-
-The site at [docs.crewai.com](https://docs.crewai.com) is published from
-`docs/` by [Mintlify](https://www.mintlify.com/). The docs use directory-based
-versioning: edits to `docs/edge/<lang>/...` (e.g.
-`docs/edge/en/concepts/agents.mdx`) land under the **Edge** version selector
-immediately and are frozen into a new versioned snapshot under
-`docs/v<X.Y.Z>/` at the next release cut. Frozen snapshots are immutable — CI
-rejects PRs that modify them without a `[docs-freeze]` title prefix. The
-release CLI (`devtools release`) handles the freeze automatically; see
-[`AGENTS.md`](AGENTS.md) for the full contributor guide and
-[`RELEASING.md`](RELEASING.md) for the release-cut runbook.
-
 ### Installing Dependencies
 
 ```bash
@@ -654,7 +611,7 @@ uv build
 ### Installing Locally
 
 ```bash
-uv pip install dist/*.tar.gz
+pip install dist/*.tar.gz
 ```
 
 ## Telemetry
@@ -717,9 +674,9 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
 
 ### Enterprise Features
 
-- [What additional features does CrewAI AMP offer?](#q-what-additional-features-does-crewai-amp-offer)
-- [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
-- [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-amp-for-free)
+- [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?
 
@@ -730,13 +687,13 @@ A: CrewAI is a standalone, lean, and fast Python framework built specifically fo
 A: Install CrewAI using pip:
 
 ```shell
-uv pip install crewai
+pip install crewai
 ```
 
 For additional tools, use:
 
 ```shell
-uv pip install 'crewai[tools]'
+pip install 'crewai[tools]'
 ```
 
 ### Q: Does CrewAI depend on LangChain?
@@ -775,17 +732,17 @@ A: Check out practical examples in the [CrewAI-examples repository](https://gith
 
 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 AMP offer?
+### Q: What additional features does CrewAI Enterprise offer?
 
-A: CrewAI AMP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
+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 AMP available for cloud and on-premise deployments?
+### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
 
-A: Yes, CrewAI AMP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
+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 AMP for free?
+### Q: Can I try CrewAI Enterprise for free?
 
-A: Yes, you can explore part of the CrewAI AMP Suite by accessing the [Crew Control Plane](https://app.crewai.com) 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?
 
@@ -805,7 +762,7 @@ A: CrewAI is highly scalable, supporting simple automations and large-scale ente
 
 ### Q: Does CrewAI offer debugging and monitoring tools?
 
-A: Yes, CrewAI AMP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
+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?
 

conftest.py

@@ -1,423 +0,0 @@
-"""Pytest configuration for crewAI workspace."""
-
-import base64
-from collections.abc import Generator
-import gzip
-import os
-from pathlib import Path
-import re
-import tempfile
-from typing import Any
-
-from dotenv import load_dotenv
-import pytest
-
-
-def _patch_vcrpy_aiohttp_compat() -> None:
-    """Keep vcrpy's aiohttp stub working under aiohttp 3.14.0.
-
-    aiohttp 3.14.0 (pulled in to fix GHSA-jg22-mg44-37j8 and GHSA-hg6j-4rv6-33pg):
-      * removed ``aiohttp.streams.AsyncStreamReaderMixin`` (folded into ``StreamReader``),
-        which vcrpy's ``MockStream`` still subclasses -- vcr's patch machinery then raises
-        ``AttributeError`` at collection time; and
-      * added a required ``stream_writer`` keyword-only arg to ``ClientResponse.__init__``,
-        which vcrpy's ``MockClientResponse`` does not pass -- raising ``TypeError`` at
-        cassette playback.
-
-    Restore the mixin, then rebuild ``MockClientResponse``'s ``super().__init__`` call from
-    the live ``ClientResponse`` signature (defaulting every required keyword-only arg to
-    ``None``, mirroring vcrpy's original call) so it also survives future aiohttp additions.
-    """
-    import asyncio
-    import inspect
-
-    from aiohttp import streams
-    from aiohttp.client_reqrep import ClientResponse
-
-    if not hasattr(streams, "AsyncStreamReaderMixin"):
-
-        class AsyncStreamReaderMixin:
-            __slots__ = ()
-
-            def __aiter__(self) -> streams.AsyncStreamIterator[bytes]:
-                return streams.AsyncStreamIterator(self.readline)  # type: ignore[attr-defined]
-
-            def iter_chunked(self, n: int) -> streams.AsyncStreamIterator[bytes]:
-                return streams.AsyncStreamIterator(lambda: self.read(n))  # type: ignore[attr-defined]
-
-            def iter_any(self) -> streams.AsyncStreamIterator[bytes]:
-                return streams.AsyncStreamIterator(self.readany)  # type: ignore[attr-defined]
-
-            def iter_chunks(self) -> streams.ChunkTupleAsyncStreamIterator:
-                return streams.ChunkTupleAsyncStreamIterator(self)  # type: ignore[arg-type]
-
-        streams.AsyncStreamReaderMixin = AsyncStreamReaderMixin  # type: ignore[attr-defined]
-
-    # Importing the stub builds MockStream/MockClientResponse, so it must run after the
-    # mixin is restored above.
-    import vcr.stubs.aiohttp_stubs as aiohttp_stubs  # type: ignore[import-untyped]
-
-    if getattr(aiohttp_stubs.MockClientResponse, "_crewai_aiohttp_patched", False):
-        return
-
-    keyword_only = [
-        name
-        for name, param in inspect.signature(ClientResponse.__init__).parameters.items()
-        if param.kind is inspect.Parameter.KEYWORD_ONLY
-    ]
-
-    class _NullStreamWriter:
-        # aiohttp 3.14.0 reads stream_writer.output_size in the "request already
-        # sent" branch (writer is None), so None is not enough -- supply a stub.
-        output_size = 0
-
-    fallback_loop: list[asyncio.AbstractEventLoop] = []
-
-    def _resolve_loop() -> asyncio.AbstractEventLoop:
-        # MockClientResponse is normally built inside aiohttp's running loop, so
-        # prefer that. In a sync context there is no running loop; avoid
-        # asyncio.get_event_loop(), which on 3.12+ emits a DeprecationWarning
-        # (and can RuntimeError) when no current loop is set. Use one cached
-        # loop instead -- the mock only stores it and calls loop.get_debug().
-        try:
-            return asyncio.get_running_loop()
-        except RuntimeError:
-            if not fallback_loop:
-                fallback_loop.append(asyncio.new_event_loop())
-            return fallback_loop[0]
-
-    def _mock_client_response_init(
-        self: Any, method: str, url: Any, request_info: Any = None
-    ) -> None:
-        kwargs: dict[str, Any] = dict.fromkeys(keyword_only)
-        kwargs["request_info"] = request_info
-        if "loop" in kwargs:
-            kwargs["loop"] = _resolve_loop()
-        if "stream_writer" in kwargs:
-            kwargs["stream_writer"] = _NullStreamWriter()
-        ClientResponse.__init__(self, method, url, **kwargs)
-
-    aiohttp_stubs.MockClientResponse.__init__ = _mock_client_response_init
-    aiohttp_stubs.MockClientResponse._crewai_aiohttp_patched = True
-
-
-_patch_vcrpy_aiohttp_compat()
-
-from vcr.request import Request  # type: ignore[import-untyped]  # noqa: E402
-
-
-try:
-    import vcr.stubs.httpx_stubs as httpx_stubs  # type: ignore[import-untyped]
-except ModuleNotFoundError:
-    import vcr.stubs.httpcore_stubs as httpx_stubs  # type: ignore[import-untyped]
-
-
-env_test_path = Path(__file__).parent / ".env.test"
-
-load_dotenv(env_test_path, override=False)
-load_dotenv(override=False)
-
-BEDROCK_HOST_PLACEHOLDER = "bedrock-runtime.vcr.amazonaws.com"
-_BEDROCK_HOST_RE = re.compile(r"^bedrock-runtime\.[a-z0-9-]+\.amazonaws\.com$")
-
-
-def _normalize_bedrock_host(host: str) -> str:
-    if _BEDROCK_HOST_RE.match(host):
-        return BEDROCK_HOST_PLACEHOLDER
-    return host
-
-
-def bedrock_host_matcher(r1: Request, r2: Request) -> bool:  # type: ignore[no-any-unimported]
-    """Match Bedrock requests across AWS regions (CI uses us-east-1, local may use us-west-2)."""
-    return _normalize_bedrock_host(r1.host or "") == _normalize_bedrock_host(
-        r2.host or ""
-    )
-
-
-def _patched_make_vcr_request(httpx_request: Any, **kwargs: Any) -> Any:
-    """Patched version of VCR's _make_vcr_request that handles binary content.
-
-    The original implementation fails on binary request bodies (like file uploads)
-    because it assumes all content can be decoded as UTF-8.
-    """
-    raw_body = httpx_request.read()
-    try:
-        body = raw_body.decode("utf-8")
-    except UnicodeDecodeError:
-        body = base64.b64encode(raw_body).decode("ascii")
-    uri = str(httpx_request.url)
-    headers = dict(httpx_request.headers)
-    return Request(httpx_request.method, uri, body, headers)
-
-
-httpx_stubs._make_vcr_request = _patched_make_vcr_request
-
-
-# Patch the response-side of VCR to fix httpx.ResponseNotRead errors.
-# VCR's _from_serialized_response mocks httpx.Response.read(), which prevents
-# the response's internal _content attribute from being properly initialized.
-# When OpenAI's client (using with_raw_response) accesses response.content,
-# httpx raises ResponseNotRead because read() was never actually called.
-# This patch ensures _content is explicitly set after response creation.
-_original_from_serialized_response = getattr(
-    httpx_stubs, "_from_serialized_response", None
-)
-
-if _original_from_serialized_response is not None:
-    _from_serialized: Any = _original_from_serialized_response
-
-    def _patched_from_serialized_response(
-        request: Any, serialized_response: Any, history: Any = None
-    ) -> Any:
-        """Patched version that ensures response._content is properly set."""
-        response = _from_serialized(request, serialized_response, history)
-        # Explicitly set _content to avoid ResponseNotRead errors
-        # The content was passed to the constructor but the mocked read() prevents
-        # proper initialization of the internal state
-        body_content = serialized_response.get("body", {}).get("string", b"")
-        if isinstance(body_content, str):
-            body_content = body_content.encode("utf-8")
-        response._content = body_content
-        return response
-
-    httpx_stubs._from_serialized_response = _patched_from_serialized_response
-
-
-@pytest.fixture(autouse=True, scope="function")
-def cleanup_event_handlers() -> Generator[None, Any, None]:
-    """Clean up event bus handlers after each test to prevent test pollution."""
-    yield
-
-    try:
-        from crewai.events.event_bus import crewai_event_bus
-
-        with crewai_event_bus._rwlock.w_locked():
-            crewai_event_bus._sync_handlers.clear()
-            crewai_event_bus._async_handlers.clear()
-    except Exception:  # noqa: S110
-        pass
-
-
-@pytest.fixture(autouse=True, scope="function")
-def reset_event_state() -> None:
-    """Reset event system state before each test for isolation."""
-    from crewai.events.base_events import reset_emission_counter
-    from crewai.events.event_context import (
-        EventContextConfig,
-        _event_context_config,
-        _event_id_stack,
-    )
-
-    reset_emission_counter()
-    _event_id_stack.set(())
-    _event_context_config.set(EventContextConfig())
-
-
-@pytest.fixture(autouse=True, scope="function")
-def setup_test_environment() -> Generator[None, Any, None]:
-    """Setup test environment for crewAI workspace."""
-    with tempfile.TemporaryDirectory() as temp_dir:
-        storage_dir = Path(temp_dir) / "crewai_test_storage"
-        storage_dir.mkdir(parents=True, exist_ok=True)
-
-        if not storage_dir.exists() or not storage_dir.is_dir():
-            raise RuntimeError(
-                f"Failed to create test storage directory: {storage_dir}"
-            )
-
-        try:
-            test_file = storage_dir / ".permissions_test"
-            test_file.touch()
-            test_file.unlink()
-        except (OSError, IOError) as e:
-            raise RuntimeError(
-                f"Test storage directory {storage_dir} is not writable: {e}"
-            ) from e
-
-        os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
-        os.environ["CREWAI_TESTING"] = "true"
-
-        try:
-            yield
-        finally:
-            os.environ.pop("CREWAI_TESTING", "true")
-            os.environ.pop("CREWAI_STORAGE_DIR", None)
-            os.environ.pop("CREWAI_DISABLE_TELEMETRY", "true")
-            os.environ.pop("OTEL_SDK_DISABLED", "true")
-            os.environ.pop("OPENAI_BASE_URL", "https://api.openai.com/v1")
-            os.environ.pop("OPENAI_API_BASE", "https://api.openai.com/v1")
-
-
-HEADERS_TO_FILTER = {
-    "authorization": "AUTHORIZATION-XXX",
-    "content-security-policy": "CSP-FILTERED",
-    "cookie": "COOKIE-XXX",
-    "set-cookie": "SET-COOKIE-XXX",
-    "permissions-policy": "PERMISSIONS-POLICY-XXX",
-    "referrer-policy": "REFERRER-POLICY-XXX",
-    "strict-transport-security": "STS-XXX",
-    "x-content-type-options": "X-CONTENT-TYPE-XXX",
-    "x-frame-options": "X-FRAME-OPTIONS-XXX",
-    "x-permitted-cross-domain-policies": "X-PERMITTED-XXX",
-    "x-request-id": "X-REQUEST-ID-XXX",
-    "x-runtime": "X-RUNTIME-XXX",
-    "x-xss-protection": "X-XSS-PROTECTION-XXX",
-    "x-stainless-arch": "X-STAINLESS-ARCH-XXX",
-    "x-stainless-os": "X-STAINLESS-OS-XXX",
-    "x-stainless-read-timeout": "X-STAINLESS-READ-TIMEOUT-XXX",
-    "cf-ray": "CF-RAY-XXX",
-    "etag": "ETAG-XXX",
-    "Strict-Transport-Security": "STS-XXX",
-    "access-control-expose-headers": "ACCESS-CONTROL-XXX",
-    "openai-organization": "OPENAI-ORG-XXX",
-    "openai-project": "OPENAI-PROJECT-XXX",
-    "x-ratelimit-limit-requests": "X-RATELIMIT-LIMIT-REQUESTS-XXX",
-    "x-ratelimit-limit-tokens": "X-RATELIMIT-LIMIT-TOKENS-XXX",
-    "x-ratelimit-remaining-requests": "X-RATELIMIT-REMAINING-REQUESTS-XXX",
-    "x-ratelimit-remaining-tokens": "X-RATELIMIT-REMAINING-TOKENS-XXX",
-    "x-ratelimit-reset-requests": "X-RATELIMIT-RESET-REQUESTS-XXX",
-    "x-ratelimit-reset-tokens": "X-RATELIMIT-RESET-TOKENS-XXX",
-    "x-goog-api-key": "X-GOOG-API-KEY-XXX",
-    "api-key": "X-API-KEY-XXX",
-    "User-Agent": "X-USER-AGENT-XXX",
-    "apim-request-id:": "X-API-CLIENT-REQUEST-ID-XXX",
-    "azureml-model-session": "AZUREML-MODEL-SESSION-XXX",
-    "x-ms-client-request-id": "X-MS-CLIENT-REQUEST-ID-XXX",
-    "x-ms-region": "X-MS-REGION-XXX",
-    "apim-request-id": "APIM-REQUEST-ID-XXX",
-    "x-api-key": "X-API-KEY-XXX",
-    "anthropic-organization-id": "ANTHROPIC-ORGANIZATION-ID-XXX",
-    "request-id": "REQUEST-ID-XXX",
-    "anthropic-ratelimit-input-tokens-limit": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX",
-    "anthropic-ratelimit-input-tokens-remaining": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX",
-    "anthropic-ratelimit-input-tokens-reset": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX",
-    "anthropic-ratelimit-output-tokens-limit": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX",
-    "anthropic-ratelimit-output-tokens-remaining": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX",
-    "anthropic-ratelimit-output-tokens-reset": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX",
-    "anthropic-ratelimit-tokens-limit": "ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX",
-    "anthropic-ratelimit-tokens-remaining": "ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX",
-    "anthropic-ratelimit-tokens-reset": "ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX",
-    "x-amz-date": "X-AMZ-DATE-XXX",
-    "x-amz-security-token": "X-AMZ-SECURITY-TOKEN-XXX",
-    "amz-sdk-invocation-id": "AMZ-SDK-INVOCATION-ID-XXX",
-    "accept-encoding": "ACCEPT-ENCODING-XXX",
-    "x-amzn-requestid": "X-AMZN-REQUESTID-XXX",
-    "x-amzn-RequestId": "X-AMZN-REQUESTID-XXX",
-    "x-a2a-notification-token": "X-A2A-NOTIFICATION-TOKEN-XXX",
-    "x-a2a-version": "X-A2A-VERSION-XXX",
-}
-
-
-def _filter_request_headers(request: Request) -> Request:  # type: ignore[no-any-unimported]
-    """Filter sensitive headers from request before recording."""
-    for header_name, replacement in HEADERS_TO_FILTER.items():
-        for variant in [header_name, header_name.upper(), header_name.title()]:
-            if variant in request.headers:
-                request.headers[variant] = [replacement]
-
-    request.method = request.method.upper()
-
-    # Normalize Azure OpenAI endpoints to a consistent placeholder for cassette matching.
-    if request.host and request.host.endswith(".openai.azure.com"):
-        original_host = request.host
-        placeholder_host = "fake-azure-endpoint.openai.azure.com"
-        request.uri = request.uri.replace(original_host, placeholder_host)
-
-    # Normalize Bedrock regional endpoints so cassettes work in any AWS region.
-    if request.host and _BEDROCK_HOST_RE.match(request.host):
-        request.uri = request.uri.replace(request.host, BEDROCK_HOST_PLACEHOLDER)
-
-    return request
-
-
-def _filter_response_headers(response: dict[str, Any]) -> dict[str, Any] | None:
-    """Filter sensitive headers from response before recording.
-
-    Returns None to skip recording responses with empty bodies. This handles
-    duplicate recordings caused by OpenAI's stainless client using
-    with_raw_response which triggers httpx to re-read the consumed stream.
-    """
-    body = response.get("body", {}).get("string", "")
-    headers = response.get("headers", {})
-    content_length = headers.get("content-length", headers.get("Content-Length", []))
-
-    if body == "" or body == b"" or content_length == ["0"]:
-        return None
-
-    status_code = response.get("status", {}).get("code")
-    if isinstance(status_code, int) and status_code >= 400:
-        # Avoid persisting auth/model errors when re-recording without valid AWS creds.
-        return None
-
-    for encoding_header in ["Content-Encoding", "content-encoding"]:
-        if encoding_header in headers:
-            encoding = headers.pop(encoding_header)
-            if encoding and encoding[0] == "gzip":
-                body = response.get("body", {}).get("string", b"")
-                if isinstance(body, bytes) and body.startswith(b"\x1f\x8b"):
-                    response["body"]["string"] = gzip.decompress(body).decode("utf-8")
-
-    for header_name, replacement in HEADERS_TO_FILTER.items():
-        for variant in [header_name, header_name.upper(), header_name.title()]:
-            if variant in headers:
-                headers[variant] = [replacement]
-    return response
-
-
-@pytest.fixture(scope="module")
-def vcr_cassette_dir(request: Any) -> str:
-    """Generate cassette directory path based on test module location.
-
-    Organizes cassettes to mirror test directory structure within each package:
-    lib/crewai/tests/llms/google/test_google.py -> lib/crewai/tests/cassettes/llms/google/
-    lib/crewai-tools/tests/tools/test_search.py -> lib/crewai-tools/tests/cassettes/tools/
-    """
-    test_file = Path(request.fspath)
-
-    for parent in test_file.parents:
-        if (
-            parent.name
-            in ("crewai", "crewai-tools", "crewai-files", "cli", "crewai-core")
-            and parent.parent.name == "lib"
-        ):
-            package_root = parent
-            break
-    else:
-        package_root = test_file.parent
-
-    tests_root = package_root / "tests"
-    test_dir = test_file.parent
-
-    if test_dir != tests_root:
-        relative_path = test_dir.relative_to(tests_root)
-        cassette_dir = tests_root / "cassettes" / relative_path
-    else:
-        cassette_dir = tests_root / "cassettes"
-
-    cassette_dir.mkdir(parents=True, exist_ok=True)
-
-    return str(cassette_dir)
-
-
-def pytest_recording_configure(vcr: Any, config: Any) -> None:
-    """Register custom VCR matchers for each test cassette session."""
-    vcr.register_matcher("bedrock_host", bedrock_host_matcher)
-
-
-@pytest.fixture(scope="module")
-def vcr_config(vcr_cassette_dir: str) -> dict[str, Any]:
-    """Configure VCR with organized cassette storage."""
-    config = {
-        "cassette_library_dir": vcr_cassette_dir,
-        "record_mode": os.getenv("PYTEST_VCR_RECORD_MODE", "once"),
-        "filter_headers": [(k, v) for k, v in HEADERS_TO_FILTER.items()],
-        "before_record_request": _filter_request_headers,
-        "before_record_response": _filter_response_headers,
-        "filter_query_parameters": ["key"],
-        "match_on": ["method", "scheme", "host", "port", "path"],
-    }
-
-    if os.getenv("GITHUB_ACTIONS") == "true":
-        config["record_mode"] = "none"
-
-    return config

docs/api-reference/introduction.mdx

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

docs/changelog.mdx

@@ -0,0 +1,473 @@
+---
+title: Changelog
+description: View the latest updates and changes to CrewAI
+icon: timeline
+---
+
+<Update label="2024-05-22" description="v0.121.0" tags={["Latest"]}>
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01210.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.121.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Fixed encoding error when creating tools
+  - Fixed failing llama test
+  - Updated logging configuration for consistency
+  - Enhanced telemetry initialization and event handling
+
+  **New Features & Enhancements**
+  - Added **markdown attribute** to the Task class
+  - Added **reasoning attribute** to the Agent class
+  - Added **inject_date flag** to Agent for automatic date injection
+  - Implemented **HallucinationGuardrail** (no-op with test coverage)
+
+  **Documentation & Guides**
+  - Added documentation for **StagehandTool** and improved MDX structure
+  - Added documentation for **MCP integration** and updated enterprise docs
+  - Documented knowledge events and updated reasoning docs
+  - Added stop parameter documentation
+  - Fixed import references in doc examples (before_kickoff, after_kickoff)
+  - General docs updates and restructuring for clarity
+</Update>
+
+<Update label="2024-05-15" description="v0.120.1">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01201.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.1">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Fixed **interpolation with hyphens**
+</Update>
+
+<Update label="2024-05-14" description="v0.120.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01200.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Enabled **full Ruff rule set** by default for stricter linting
+  - Addressed race condition in FilteredStream using context managers
+  - Fixed agent knowledge reset issue
+  - Refactored agent fetching logic into utility module
+
+  **New Features & Enhancements**
+  - Added support for **loading an Agent directly from a repository**
+  - Enabled setting an empty context for Task
+  - Enhanced Agent repository feedback and fixed Tool auto-import behavior
+  - Introduced direct initialization of knowledge (bypassing knowledge_sources)
+
+  **Documentation & Guides**
+  - Updated security.md for current security practices
+  - Cleaned up Google setup section for clarity
+  - Added link to AI Studio when entering Gemini key
+  - Updated Arize Phoenix observability guide
+  - Refreshed flow documentation
+</Update>
+
+<Update label="2024-05-08" description="v0.119.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01190.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.119.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Improved test reliability by enhancing pytest handling for flaky tests
+  - Fixed memory reset crash when embedding dimensions mismatch
+  - Enabled parent flow identification for Crew and LiteAgent
+  - Prevented telemetry-related crashes when unavailable
+  - Upgraded **LiteLLM version** for better compatibility
+  - Fixed llama converter tests by removing skip_external_api
+
+  **New Features & Enhancements**
+  - Introduced **knowledge retrieval prompt re-writing** in Agent for improved tracking and debugging
+  - Made LLM setup and quickstart guides model-agnostic
+
+  **Documentation & Guides**
+  - Added advanced configuration docs for the RAG tool
+  - Updated Windows troubleshooting guide
+  - Refined documentation examples for better clarity
+  - Fixed typos across docs and config files
+</Update>
+
+<Update label="2024-04-28" description="v0.118.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01180.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.118.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Fixed issues with missing prompt or system templates
+  - Removed global logging configuration to avoid unintended overrides
+  - Renamed **TaskGuardrail to LLMGuardrail** for improved clarity
+  - Downgraded litellm to version 1.167.1 for compatibility
+  - Added missing init.py files to ensure proper module initialization
+
+  **New Features & Enhancements**
+  - Added support for **no-code Guardrail creation** to simplify AI behavior controls
+
+  **Documentation & Guides**
+  - Removed CrewStructuredTool from public documentation to reflect internal usage
+  - Updated enterprise documentation and YouTube embed for improved onboarding experience
+</Update>
+
+<Update label="2024-04-20" description="v0.117.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01170.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.0">View on GitHub</a>
+  </div>
+
+  **New Features & Enhancements**
+  - Added `result_as_answer` parameter support in `@tool` decorator.
+  - Introduced support for new language models: GPT-4.1, Gemini-2.0, and Gemini-2.5 Pro.
+  - Enhanced knowledge management capabilities.
+  - Added Huggingface provider option in CLI.
+  - Improved compatibility and CI support for Python 3.10+.
+
+  **Core Improvements & Fixes**
+  - Fixed issues with incorrect template parameters and missing inputs.
+  - Improved asynchronous flow handling with coroutine condition checks.
+  - Enhanced memory management with isolated configuration and correct memory object copying.
+  - Fixed initialization of lite agents with correct references.
+  - Addressed Python type hint issues and removed redundant imports.
+  - Updated event placement for improved tool usage tracking.
+  - Raised explicit exceptions when flows fail.
+  - Removed unused code and redundant comments from various modules.
+  - Updated GitHub App token action to v2.
+
+  **Documentation & Guides**
+  - Enhanced documentation structure, including enterprise deployment instructions.
+  - Automatically create output folders for documentation generation.
+  - Fixed broken link in WeaviateVectorSearchTool documentation.
+  - Fixed guardrail documentation usage and import paths for JSON search tools.
+  - Updated documentation for CodeInterpreterTool.
+  - Improved SEO, contextual navigation, and error handling for documentation pages.
+</Update>
+
+<Update label="2024-04-25" description="v0.117.1">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01171.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.1">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Upgraded **crewai-tools** to latest version
+  - Upgraded **liteLLM** to latest version
+  - Fixed **Mem0 OSS**
+</Update>
+
+<Update label="2024-04-07" description="v0.114.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01140.png" />
+  </Frame>
+
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.114.0">View on GitHub</a>
+  </div>
+
+  **New Features & Enhancements**
+  - Agents as an atomic unit. (`Agent(...).kickoff()`)
+  - Support for [Custom LLM implementations](https://docs.crewai.com/guides/advanced/custom-llm).
+  - Integrated External Memory and [Opik observability](https://docs.crewai.com/how-to/opik-observability).
+  - Enhanced YAML extraction.
+  - Multimodal agent validation.
+  - Added Secure fingerprints for agents and crews.
+
+  **Core Improvements & Fixes**
+  - Improved serialization, agent copying, and Python compatibility.
+  - Added wildcard support to `emit()`
+  - Added support for additional router calls and context window adjustments.
+  - Fixed typing issues, validation, and import statements.
+  - Improved method performance.
+  - Enhanced agent task handling, event emissions, and memory management.
+  - Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs.
+
+  **Documentation & Guides**
+  - Improved documentation structure, theme, and organization.
+  - Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix.
+  - Updated tool configuration examples, prompts, and observability docs.
+  - Guide on using singular agents within Flows.
+</Update>
+
+<Update label="2024-03-17" description="v0.108.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01080.png" />
+  </Frame>
+
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.108.0">View on GitHub</a>
+  </div>
+
+  **New Features & Enhancements**
+  - Converted tabs to spaces in `crew.py` template
+  - Enhanced LLM Streaming Response Handling and Event System
+  - Included `model_name`
+  - Enhanced Event Listener with rich visualization and improved logging
+  - Added fingerprints
+
+  **Bug Fixes**
+  - Fixed Mistral issues
+  - Fixed a bug in documentation
+  - Fixed type check error in fingerprint property
+
+  **Documentation Updates**
+  - Improved tool documentation
+  - Updated installation guide for the `uv` tool package
+  - Added instructions for upgrading crewAI with the `uv` tool
+  - Added documentation for `ApifyActorsTool`
+</Update>
+
+<Update label="2024-03-10" description="v0.105.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01050.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.105.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Fixed issues with missing template variables and user memory configuration
+  - Improved async flow support and addressed agent response formatting
+  - Enhanced memory reset functionality and fixed CLI memory commands
+  - Fixed type issues, tool calling properties, and telemetry decoupling
+
+  **New Features & Enhancements**
+  - Added Flow state export and improved state utilities
+  - Enhanced agent knowledge setup with optional crew embedder
+  - Introduced event emitter for better observability and LLM call tracking
+  - Added support for Python 3.10 and ChatOllama from langchain_ollama
+  - Integrated context window size support for the o3-mini model
+  - Added support for multiple router calls
+
+  **Documentation & Guides**
+  - Improved documentation layout and hierarchical structure
+  - Added QdrantVectorSearchTool guide and clarified event listener usage
+  - Fixed typos in prompts and updated Amazon Bedrock model listings
+</Update>
+
+<Update label="2024-02-12" description="v0.102.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01020.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.102.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
+  - Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
+  - Memory & Storage Fixes: Fixed short-term memory handling with Bedrock, ensured correct embedder initialization, and added a reset memories function in the crew class
+  - Training & Execution Reliability: Fixed broken training and interpolation issues with dict and list input types
+
+  **New Features & Enhancements**
+  - Advanced Knowledge Management: Improved naming conventions and enhanced embedding configuration with custom embedder support
+  - Expanded Logging & Observability: Added JSON format support for logging and integrated MLflow tracing documentation
+  - Data Handling Improvements: Updated excel_knowledge_source.py to process multi-tab files
+  - General Performance & Codebase Clean-Up: Streamlined enterprise code alignment and resolved linting issues
+  - Adding new tool: `QdrantVectorSearchTool`
+
+  **Documentation & Guides**
+  - Updated AI & Memory Docs: Improved Bedrock, Google AI, and long-term memory documentation
+  - Task & Workflow Clarity: Added "Human Input" row to Task Attributes, Langfuse guide, and FileWriterTool documentation
+  - Fixed Various Typos & Formatting Issues
+</Update>
+
+<Update label="2024-01-28" description="v0.100.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01000.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.100.0">View on GitHub</a>
+  </div>
+
+  **Features**
+  - Add Composio docs
+  - Add SageMaker as a LLM provider
+  
+  **Fixes**
+  - Overall LLM connection issues
+  - Using safe accessors on training
+  - Add version check to crew_chat.py
+  
+  **Documentation**
+  - New docs for crewai chat
+  - Improve formatting and clarity in CLI and Composio Tool docs
+</Update>
+
+<Update label="2024-01-20" description="v0.98.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0980.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.98.0">View on GitHub</a>
+  </div>
+
+  **Features**
+  - Conversation crew v1
+  - Add unique ID to flow states
+  - Add @persist decorator with FlowPersistence interface
+  
+  **Integrations**
+  - Add SambaNova integration
+  - Add NVIDIA NIM provider in cli
+  - Introducing VoyageAI
+  
+  **Fixes**
+  - Fix API Key Behavior and Entity Handling in Mem0 Integration
+  - Fixed core invoke loop logic and relevant tests
+  - Make tool inputs actual objects and not strings
+  - Add important missing parts to creating tools
+  - Drop litellm version to prevent windows issue
+  - Before kickoff if inputs are none
+  - Fixed typos, nested pydantic model issue, and docling issues
+</Update>
+
+<Update label="2024-01-04" description="v0.95.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0950.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.95.0">View on GitHub</a>
+  </div>
+
+  **New Features**
+  - Adding Multimodal Abilities to Crew
+  - Programatic Guardrails
+  - HITL multiple rounds
+  - Gemini 2.0 Support
+  - CrewAI Flows Improvements
+  - Add Workflow Permissions
+  - Add support for langfuse with litellm
+  - Portkey Integration with CrewAI
+  - Add interpolate_only method and improve error handling
+  - Docling Support
+  - Weviate Support
+  
+  **Fixes**
+  - output_file not respecting system path
+  - disk I/O error when resetting short-term memory
+  - CrewJSONEncoder now accepts enums
+  - Python max version
+  - Interpolation for output_file in Task
+  - Handle coworker role name case/whitespace properly
+  - Add tiktoken as explicit dependency and document Rust requirement
+  - Include agent knowledge in planning process
+  - Change storage initialization to None for KnowledgeStorage
+  - Fix optional storage checks
+  - include event emitter in flows
+  - Docstring, Error Handling, and Type Hints Improvements
+  - Suppressed userWarnings from litellm pydantic issues
+</Update>
+
+<Update label="2024-12-05" description="v0.86.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0860.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.86.0">View on GitHub</a>
+  </div>
+  **Changes**
+  - Remove all references to pipeline and pipeline router
+  - Add Nvidia NIM as provider in Custom LLM
+  - Add knowledge demo + improve knowledge docs
+  - Add HITL multiple rounds of followup
+  - New docs about yaml crew with decorators
+  - Simplify template crew
+</Update>
+
+<Update label="2024-12-04" description="v0.85.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0850.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.85.0">View on GitHub</a>
+  </div>
+  **Features**
+  - Added knowledge to agent level
+  - Feat/remove langchain
+  - Improve typed task outputs
+  - Log in to Tool Repository on crewai login
+  
+  **Fixes**
+  - Fixes issues with result as answer not properly exiting LLM loop
+  - Fix missing key name when running with ollama provider
+  - Fix spelling issue found
+  
+  **Documentation**
+  - Update readme for running mypy
+  - Add knowledge to mint.json
+  - Update Github actions
+  - Update Agents docs to include two approaches for creating an agent
+  - Improvements to LLM Configuration and Usage
+</Update>
+
+<Update label="2024-11-25" description="v0.83.0">
+  **New Features**
+  - New before_kickoff and after_kickoff crew callbacks
+  - Support to pre-seed agents with Knowledge
+  - Add support for retrieving user preferences and memories using Mem0
+  
+  **Fixes**
+  - Fix Async Execution
+  - Upgrade chroma and adjust embedder function generator
+  - Update CLI Watson supported models + docs
+  - Reduce level for Bandit
+  - Fixing all tests
+  
+  **Documentation**
+  - Update Docs
+</Update>
+
+<Update label="2024-11-13" description="v0.80.0">
+  **Fixes**
+  - Fixing Tokens callback replacement bug
+  - Fixing Step callback issue
+  - Add cached prompt tokens info on usage metrics
+  - Fix crew_train_success test
+</Update>

docs/concepts/agents.mdx

@@ -0,0 +1,588 @@
+---
+title: Agents
+description: Detailed guide on creating and managing agents within the CrewAI framework.
+icon: robot
+---
+
+## Overview of an Agent
+
+In the CrewAI framework, an `Agent` is an autonomous unit that can:
+- Perform specific tasks
+- Make decisions based on its role and goal
+- Use tools to accomplish objectives
+- Communicate and collaborate with other agents
+- Maintain memory of interactions
+- Delegate tasks when allowed
+
+<Tip>
+Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a `Researcher` agent might excel at gathering and analyzing information, while a `Writer` agent might be better at creating content.
+</Tip>
+
+<Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
+CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
+
+![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
+
+The Visual Agent Builder enables:
+- Intuitive agent configuration with form-based interfaces
+- Real-time testing and validation
+- Template library with pre-configured agent types
+- Easy customization of agent attributes and behaviors
+</Note>
+
+## Agent Attributes
+
+| Attribute                               | Parameter                | Type                          | Description                                                                                                          |
+| :-------------------------------------- | :----------------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
+| **Role**                                | `role`                   | `str`                         | Defines the agent's function and expertise within the crew.                                                           |
+| **Goal**                                | `goal`                   | `str`                         | The individual objective that guides the agent's decision-making.                                                     |
+| **Backstory**                           | `backstory`              | `str`                         | Provides context and personality to the agent, enriching interactions.                                                |
+| **LLM** _(optional)_                    | `llm`                    | `Union[str, LLM, Any]`        | Language model that powers the agent. Defaults to the model specified in `OPENAI_MODEL_NAME` or "gpt-4".              |
+| **Tools** _(optional)_                  | `tools`                  | `List[BaseTool]`              | Capabilities or functions available to the agent. Defaults to an empty list.                                          |
+| **Function Calling LLM** _(optional)_   | `function_calling_llm`   | `Optional[Any]`               | Language model for tool calling, overrides crew's LLM if specified.                                                   |
+| **Max Iterations** _(optional)_         | `max_iter`               | `int`                         | Maximum iterations before the agent must provide its best answer. Default is 20.                                      |
+| **Max RPM** _(optional)_                | `max_rpm`                | `Optional[int]`               | Maximum requests per minute to avoid rate limits.                                                                     |
+| **Max Execution Time** _(optional)_     | `max_execution_time`     | `Optional[int]`               | Maximum time (in seconds) for task execution.                                                                         |
+| **Verbose** _(optional)_                | `verbose`                | `bool`                        | Enable detailed execution logs for debugging. Default is False.                                                       |
+| **Allow Delegation** _(optional)_       | `allow_delegation`       | `bool`                        | Allow the agent to delegate tasks to other agents. Default is False.                                                  |
+| **Step Callback** _(optional)_          | `step_callback`          | `Optional[Any]`               | Function called after each agent step, overrides crew callback.                                                       |
+| **Cache** _(optional)_                  | `cache`                  | `bool`                        | Enable caching for tool usage. Default is True.                                                                       |
+| **System Template** _(optional)_        | `system_template`        | `Optional[str]`               | Custom system prompt template for the agent.                                                                          |
+| **Prompt Template** _(optional)_        | `prompt_template`        | `Optional[str]`               | Custom prompt template for the agent.                                                                                 |
+| **Response Template** _(optional)_      | `response_template`      | `Optional[str]`               | Custom response template for the agent.                                                                               |
+| **Allow Code Execution** _(optional)_   | `allow_code_execution`   | `Optional[bool]`              | Enable code execution for the agent. Default is False.                                                                |
+| **Max Retry Limit** _(optional)_        | `max_retry_limit`        | `int`                         | Maximum number of retries when an error occurs. Default is 2.                                                         |
+| **Respect Context Window** _(optional)_ | `respect_context_window` | `bool`                        | Keep messages under context window size by summarizing. Default is True.                                              |
+| **Code Execution Mode** _(optional)_    | `code_execution_mode`    | `Literal["safe", "unsafe"]`   | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'.                               |
+| **Multimodal** _(optional)_             | `multimodal`             | `bool`                        | Whether the agent supports multimodal capabilities. Default is False.                                                  |
+| **Inject Date** _(optional)_            | `inject_date`            | `bool`                        | Whether to automatically inject the current date into tasks. Default is False.                                         |
+| **Date Format** _(optional)_            | `date_format`            | `str`                         | Format string for date when inject_date is enabled. Default is "%Y-%m-%d" (ISO format).                               |
+| **Reasoning** _(optional)_              | `reasoning`              | `bool`                        | Whether the agent should reflect and create a plan before executing a task. Default is False.                         |
+| **Max Reasoning Attempts** _(optional)_ | `max_reasoning_attempts` | `Optional[int]`               | Maximum number of reasoning attempts before executing the task. If None, will try until ready.                         |
+| **Embedder** _(optional)_               | `embedder`               | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
+| **Knowledge Sources** _(optional)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent.                                                                     |
+| **Use System Prompt** _(optional)_      | `use_system_prompt`      | `Optional[bool]`              | Whether to use system prompt (for o1 model support). Default is True.                                                 |
+
+## Creating Agents
+
+There are two ways to create agents in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
+
+### YAML Configuration (Recommended)
+
+Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.
+
+After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
+
+<Note>
+Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
+```python Code
+crew.kickoff(inputs={'topic': 'AI Agents'})
+```
+</Note>
+
+Here's an example of how to configure agents using YAML:
+
+```yaml agents.yaml
+# src/latest_ai_development/config/agents.yaml
+researcher:
+  role: >
+    {topic} Senior Data Researcher
+  goal: >
+    Uncover cutting-edge developments in {topic}
+  backstory: >
+    You're a seasoned researcher with a knack for uncovering the latest
+    developments in {topic}. Known for your ability to find the most relevant
+    information and present it in a clear and concise manner.
+
+reporting_analyst:
+  role: >
+    {topic} Reporting Analyst
+  goal: >
+    Create detailed reports based on {topic} data analysis and research findings
+  backstory: >
+    You're a meticulous analyst with a keen eye for detail. You're known for
+    your ability to turn complex data into clear and concise reports, making
+    it easy for others to understand and act on the information you provide.
+```
+
+To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`:
+
+```python Code
+# src/latest_ai_development/crew.py
+from crewai import Agent, Crew, Process
+from crewai.project import CrewBase, agent, crew
+from crewai_tools import SerperDevTool
+
+@CrewBase
+class LatestAiDevelopmentCrew():
+  """LatestAiDevelopment crew"""
+
+  agents_config = "config/agents.yaml"
+
+  @agent
+  def researcher(self) -> Agent:
+    return Agent(
+      config=self.agents_config['researcher'], # type: ignore[index]
+      verbose=True,
+      tools=[SerperDevTool()]
+    )
+
+  @agent
+  def reporting_analyst(self) -> Agent:
+    return Agent(
+      config=self.agents_config['reporting_analyst'], # type: ignore[index]
+      verbose=True
+    )
+```
+
+<Note>
+The names you use in your YAML files (`agents.yaml`) should match the method names in your Python code.
+</Note>
+
+### Direct Code Definition
+
+You can create agents directly in code by instantiating the `Agent` class. Here's a comprehensive example showing all available parameters:
+
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool
+
+# Create an agent with all available parameters
+agent = Agent(
+    role="Senior Data Scientist",
+    goal="Analyze and interpret complex datasets to provide actionable insights",
+    backstory="With over 10 years of experience in data science and machine learning, "
+              "you excel at finding patterns in complex datasets.",
+    llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
+    function_calling_llm=None,  # Optional: Separate LLM for tool calling
+    verbose=False,  # Default: False
+    allow_delegation=False,  # Default: False
+    max_iter=20,  # Default: 20 iterations
+    max_rpm=None,  # Optional: Rate limit for API calls
+    max_execution_time=None,  # Optional: Maximum execution time in seconds
+    max_retry_limit=2,  # Default: 2 retries on error
+    allow_code_execution=False,  # Default: False
+    code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
+    respect_context_window=True,  # Default: True
+    use_system_prompt=True,  # Default: True
+    multimodal=False,  # Default: False
+    inject_date=False,  # Default: False
+    date_format="%Y-%m-%d",  # Default: ISO format
+    reasoning=False,  # Default: False
+    max_reasoning_attempts=None,  # Default: None
+    tools=[SerperDevTool()],  # Optional: List of tools
+    knowledge_sources=None,  # Optional: List of knowledge sources
+    embedder=None,  # Optional: Custom embedder configuration
+    system_template=None,  # Optional: Custom system prompt template
+    prompt_template=None,  # Optional: Custom prompt template
+    response_template=None,  # Optional: Custom response template
+    step_callback=None,  # Optional: Callback function for monitoring
+)
+```
+
+Let's break down some key parameter combinations for common use cases:
+
+#### Basic Research Agent
+```python Code
+research_agent = Agent(
+    role="Research Analyst",
+    goal="Find and summarize information about specific topics",
+    backstory="You are an experienced researcher with attention to detail",
+    tools=[SerperDevTool()],
+    verbose=True  # Enable logging for debugging
+)
+```
+
+#### Code Development Agent
+```python Code
+dev_agent = Agent(
+    role="Senior Python Developer",
+    goal="Write and debug Python code",
+    backstory="Expert Python developer with 10 years of experience",
+    allow_code_execution=True,
+    code_execution_mode="safe",  # Uses Docker for safety
+    max_execution_time=300,  # 5-minute timeout
+    max_retry_limit=3  # More retries for complex code tasks
+)
+```
+
+#### Long-Running Analysis Agent
+```python Code
+analysis_agent = Agent(
+    role="Data Analyst",
+    goal="Perform deep analysis of large datasets",
+    backstory="Specialized in big data analysis and pattern recognition",
+    memory=True,
+    respect_context_window=True,
+    max_rpm=10,  # Limit API calls
+    function_calling_llm="gpt-4o-mini"  # Cheaper model for tool calls
+)
+```
+
+#### Custom Template Agent
+```python Code
+custom_agent = Agent(
+    role="Customer Service Representative",
+    goal="Assist customers with their inquiries",
+    backstory="Experienced in customer support with a focus on satisfaction",
+    system_template="""<|start_header_id|>system<|end_header_id|>
+                        {{ .System }}<|eot_id|>""",
+    prompt_template="""<|start_header_id|>user<|end_header_id|>
+                        {{ .Prompt }}<|eot_id|>""",
+    response_template="""<|start_header_id|>assistant<|end_header_id|>
+                        {{ .Response }}<|eot_id|>""",
+)
+```
+
+#### Date-Aware Agent with Reasoning
+```python Code
+strategic_agent = Agent(
+    role="Market Analyst",
+    goal="Track market movements with precise date references and strategic planning",
+    backstory="Expert in time-sensitive financial analysis and strategic reporting",
+    inject_date=True,  # Automatically inject current date into tasks
+    date_format="%B %d, %Y",  # Format as "May 21, 2025"
+    reasoning=True,  # Enable strategic planning
+    max_reasoning_attempts=2,  # Limit planning iterations
+    verbose=True
+)
+```
+
+#### Reasoning Agent
+```python Code
+reasoning_agent = Agent(
+    role="Strategic Planner",
+    goal="Analyze complex problems and create detailed execution plans",
+    backstory="Expert strategic planner who methodically breaks down complex challenges",
+    reasoning=True,  # Enable reasoning and planning
+    max_reasoning_attempts=3,  # Limit reasoning attempts
+    max_iter=30,  # Allow more iterations for complex planning
+    verbose=True
+)
+```
+
+#### Multimodal Agent
+```python Code
+multimodal_agent = Agent(
+    role="Visual Content Analyst",
+    goal="Analyze and process both text and visual content",
+    backstory="Specialized in multimodal analysis combining text and image understanding",
+    multimodal=True,  # Enable multimodal capabilities
+    verbose=True
+)
+```
+
+### Parameter Details
+
+#### Critical Parameters
+- `role`, `goal`, and `backstory` are required and shape the agent's behavior
+- `llm` determines the language model used (default: OpenAI's GPT-4)
+
+#### Memory and Context
+- `memory`: Enable to maintain conversation history
+- `respect_context_window`: Prevents token limit issues
+- `knowledge_sources`: Add domain-specific knowledge bases
+
+#### Execution Control
+- `max_iter`: Maximum attempts before giving best answer
+- `max_execution_time`: Timeout in seconds
+- `max_rpm`: Rate limiting for API calls
+- `max_retry_limit`: Retries on error
+
+#### Code Execution
+- `allow_code_execution`: Must be True to run code
+- `code_execution_mode`:
+  - `"safe"`: Uses Docker (recommended for production)
+  - `"unsafe"`: Direct execution (use only in trusted environments)
+
+#### Advanced Features
+- `multimodal`: Enable multimodal capabilities for processing text and visual content
+- `reasoning`: Enable agent to reflect and create plans before executing tasks
+- `inject_date`: Automatically inject current date into task descriptions
+
+#### Templates
+- `system_template`: Defines agent's core behavior
+- `prompt_template`: Structures input format
+- `response_template`: Formats agent responses
+
+<Note>
+When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting.
+</Note>  
+
+<Note>
+When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
+</Note>
+
+## Agent Tools
+
+Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from:
+- [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools)
+- [LangChain Tools](https://python.langchain.com/docs/integrations/tools)
+
+Here's how to add tools to an agent:
+
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool, WikipediaTools
+
+# Create tools
+search_tool = SerperDevTool()
+wiki_tool = WikipediaTools()
+
+# Add tools to agent
+researcher = Agent(
+    role="AI Technology Researcher",
+    goal="Research the latest AI developments",
+    tools=[search_tool, wiki_tool],
+    verbose=True
+)
+```
+
+## Agent Memory and Context
+
+Agents can maintain memory of their interactions and use context from previous tasks. This is particularly useful for complex workflows where information needs to be retained across multiple tasks.
+
+```python Code
+from crewai import Agent
+
+analyst = Agent(
+    role="Data Analyst",
+    goal="Analyze and remember complex data patterns",
+    memory=True,  # Enable memory
+    verbose=True
+)
+```
+
+<Note>
+When `memory` is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks.
+</Note>
+
+## Context Window Management
+
+CrewAI includes sophisticated automatic context window management to handle situations where conversations exceed the language model's token limits. This powerful feature is controlled by the `respect_context_window` parameter.
+
+### How Context Window Management Works
+
+When an agent's conversation history grows too large for the LLM's context window, CrewAI automatically detects this situation and can either:
+
+1. **Automatically summarize content** (when `respect_context_window=True`)
+2. **Stop execution with an error** (when `respect_context_window=False`)
+
+### Automatic Context Handling (`respect_context_window=True`)
+
+This is the **default and recommended setting** for most use cases. When enabled, CrewAI will:
+
+```python Code
+# Agent with automatic context management (default)
+smart_agent = Agent(
+    role="Research Analyst",
+    goal="Analyze large documents and datasets",
+    backstory="Expert at processing extensive information",
+    respect_context_window=True,  # 🔑 Default: auto-handle context limits
+    verbose=True
+)
+```
+
+**What happens when context limits are exceeded:**
+- ⚠️ **Warning message**: `"Context length exceeded. Summarizing content to fit the model context window."`
+- 🔄 **Automatic summarization**: CrewAI intelligently summarizes the conversation history
+- ✅ **Continued execution**: Task execution continues seamlessly with the summarized context
+- 📝 **Preserved information**: Key information is retained while reducing token count
+
+### Strict Context Limits (`respect_context_window=False`)
+
+When you need precise control and prefer execution to stop rather than lose any information:
+
+```python Code
+# Agent with strict context limits
+strict_agent = Agent(
+    role="Legal Document Reviewer",
+    goal="Provide precise legal analysis without information loss",
+    backstory="Legal expert requiring complete context for accurate analysis",
+    respect_context_window=False,  # ❌ Stop execution on context limit
+    verbose=True
+)
+```
+
+**What happens when context limits are exceeded:**
+- ❌ **Error message**: `"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools."`
+- 🛑 **Execution stops**: Task execution halts immediately
+- 🔧 **Manual intervention required**: You need to modify your approach
+
+### Choosing the Right Setting
+
+#### Use `respect_context_window=True` (Default) when:
+- **Processing large documents** that might exceed context limits
+- **Long-running conversations** where some summarization is acceptable
+- **Research tasks** where general context is more important than exact details
+- **Prototyping and development** where you want robust execution
+
+```python Code
+# Perfect for document processing
+document_processor = Agent(
+    role="Document Analyst", 
+    goal="Extract insights from large research papers",
+    backstory="Expert at analyzing extensive documentation",
+    respect_context_window=True,  # Handle large documents gracefully
+    max_iter=50,  # Allow more iterations for complex analysis
+    verbose=True
+)
+```
+
+#### Use `respect_context_window=False` when:
+- **Precision is critical** and information loss is unacceptable
+- **Legal or medical tasks** requiring complete context
+- **Code review** where missing details could introduce bugs
+- **Financial analysis** where accuracy is paramount
+
+```python Code
+# Perfect for precision tasks
+precision_agent = Agent(
+    role="Code Security Auditor",
+    goal="Identify security vulnerabilities in code",
+    backstory="Security expert requiring complete code context",
+    respect_context_window=False,  # Prefer failure over incomplete analysis
+    max_retry_limit=1,  # Fail fast on context issues
+    verbose=True
+)
+```
+
+### Alternative Approaches for Large Data
+
+When dealing with very large datasets, consider these strategies:
+
+#### 1. Use RAG Tools
+```python Code
+from crewai_tools import RagTool
+
+# Create RAG tool for large document processing
+rag_tool = RagTool()
+
+rag_agent = Agent(
+    role="Research Assistant",
+    goal="Query large knowledge bases efficiently",
+    backstory="Expert at using RAG tools for information retrieval",
+    tools=[rag_tool],  # Use RAG instead of large context windows
+    respect_context_window=True,
+    verbose=True
+)
+```
+
+#### 2. Use Knowledge Sources
+```python Code
+# Use knowledge sources instead of large prompts
+knowledge_agent = Agent(
+    role="Knowledge Expert",
+    goal="Answer questions using curated knowledge",
+    backstory="Expert at leveraging structured knowledge sources",
+    knowledge_sources=[your_knowledge_sources],  # Pre-processed knowledge
+    respect_context_window=True,
+    verbose=True
+)
+```
+
+### Context Window Best Practices
+
+1. **Monitor Context Usage**: Enable `verbose=True` to see context management in action
+2. **Design for Efficiency**: Structure tasks to minimize context accumulation
+3. **Use Appropriate Models**: Choose LLMs with context windows suitable for your tasks
+4. **Test Both Settings**: Try both `True` and `False` to see which works better for your use case
+5. **Combine with RAG**: Use RAG tools for very large datasets instead of relying solely on context windows
+
+### Troubleshooting Context Issues
+
+**If you're getting context limit errors:**
+```python Code
+# Quick fix: Enable automatic handling
+agent.respect_context_window = True
+
+# Better solution: Use RAG tools for large data
+from crewai_tools import RagTool
+agent.tools = [RagTool()]
+
+# Alternative: Break tasks into smaller pieces
+# Or use knowledge sources instead of large prompts
+```
+
+**If automatic summarization loses important information:**
+```python Code
+# Disable auto-summarization and use RAG instead
+agent = Agent(
+    role="Detailed Analyst",
+    goal="Maintain complete information accuracy",
+    backstory="Expert requiring full context",
+    respect_context_window=False,  # No summarization
+    tools=[RagTool()],  # Use RAG for large data
+    verbose=True
+)
+```
+
+<Note>
+The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
+</Note>
+
+## Important Considerations and Best Practices
+
+### Security and Code Execution
+- When using `allow_code_execution`, be cautious with user input and always validate it
+- Use `code_execution_mode: "safe"` (Docker) in production environments
+- Consider setting appropriate `max_execution_time` limits to prevent infinite loops
+
+### Performance Optimization
+- Use `respect_context_window: true` to prevent token limit issues
+- Set appropriate `max_rpm` to avoid rate limiting
+- Enable `cache: true` to improve performance for repetitive tasks
+- Adjust `max_iter` and `max_retry_limit` based on task complexity
+
+### Memory and Context Management
+- Leverage `knowledge_sources` for domain-specific information
+- Configure `embedder` when using custom embedding models
+- Use custom templates (`system_template`, `prompt_template`, `response_template`) for fine-grained control over agent behavior
+
+### Advanced Features
+- Enable `reasoning: true` for agents that need to plan and reflect before executing complex tasks
+- Set appropriate `max_reasoning_attempts` to control planning iterations (None for unlimited attempts)
+- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
+- Customize the date format with `date_format` using standard Python datetime format codes
+- Enable `multimodal: true` for agents that need to process both text and visual content
+
+### Agent Collaboration
+- Enable `allow_delegation: true` when agents need to work together
+- Use `step_callback` to monitor and log agent interactions
+- Consider using different LLMs for different purposes:
+  - Main `llm` for complex reasoning
+  - `function_calling_llm` for efficient tool usage
+
+### Date Awareness and Reasoning
+- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
+- Customize the date format with `date_format` using standard Python datetime format codes
+- Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
+- Invalid date formats will be logged as warnings and will not modify the task description
+- Enable `reasoning: true` for complex tasks that benefit from upfront planning and reflection
+
+### Model Compatibility
+- Set `use_system_prompt: false` for older models that don't support system messages
+- Ensure your chosen `llm` supports the features you need (like function calling)
+
+## Troubleshooting Common Issues
+
+1. **Rate Limiting**: If you're hitting API rate limits:
+   - Implement appropriate `max_rpm`
+   - Use caching for repetitive operations
+   - Consider batching requests
+
+2. **Context Window Errors**: If you're exceeding context limits:
+   - Enable `respect_context_window`
+   - Use more efficient prompts
+   - Clear agent memory periodically
+
+3. **Code Execution Issues**: If code execution fails:
+   - Verify Docker is installed for safe mode
+   - Check execution permissions
+   - Review code sandbox settings
+
+4. **Memory Issues**: If agent responses seem inconsistent:
+   - Check knowledge source configuration
+   - Review conversation history management
+
+Remember that agents are most effective when configured according to their specific use case. Take time to understand your requirements and adjust these parameters accordingly.

docs/concepts/cli.mdx

@@ -0,0 +1,284 @@
+---
+title: CLI
+description: Learn how to use the CrewAI CLI to interact with CrewAI.
+icon: terminal
+---
+
+## Overview
+
+The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
+
+## Installation
+
+To use the CrewAI CLI, make sure you have CrewAI installed:
+
+```shell Terminal
+pip install crewai
+```
+
+## Basic Usage
+
+The basic structure of a CrewAI CLI command is:
+
+```shell Terminal
+crewai [COMMAND] [OPTIONS] [ARGUMENTS]
+```
+
+## Available Commands
+
+### 1. Create
+
+Create a new crew or flow.
+
+```shell Terminal
+crewai create [OPTIONS] TYPE NAME
+```
+
+- `TYPE`: Choose between "crew" or "flow"
+- `NAME`: Name of the crew or flow
+
+Example:
+```shell Terminal
+crewai create crew my_new_crew
+crewai create flow my_new_flow
+```
+
+### 2. Version
+
+Show the installed version of CrewAI.
+
+```shell Terminal
+crewai version [OPTIONS]
+```
+
+- `--tools`: (Optional) Show the installed version of CrewAI tools
+
+Example:
+```shell Terminal
+crewai version
+crewai version --tools
+```
+
+### 3. Train
+
+Train the crew for a specified number of iterations.
+
+```shell Terminal
+crewai train [OPTIONS]
+```
+
+- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
+- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
+
+Example:
+```shell Terminal
+crewai train -n 10 -f my_training_data.pkl
+```
+
+### 4. Replay
+
+Replay the crew execution from a specific task.
+
+```shell Terminal
+crewai replay [OPTIONS]
+```
+
+- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
+
+Example:
+```shell Terminal    
+crewai replay -t task_123456
+```
+
+### 5. Log-tasks-outputs
+
+Retrieve your latest crew.kickoff() task outputs.
+
+```shell Terminal
+crewai log-tasks-outputs
+```
+
+### 6. Reset-memories
+
+Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
+
+```shell Terminal
+crewai reset-memories [OPTIONS]
+```
+
+- `-l, --long`: Reset LONG TERM memory
+- `-s, --short`: Reset SHORT TERM memory
+- `-e, --entities`: Reset ENTITIES memory
+- `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
+- `-kn, --knowledge`: Reset KNOWLEDGE storage
+- `-akn, --agent-knowledge`: Reset AGENT KNOWLEDGE storage
+- `-a, --all`: Reset ALL memories
+
+Example:
+```shell Terminal
+crewai reset-memories --long --short
+crewai reset-memories --all
+```
+
+### 7. Test
+
+Test the crew and evaluate the results.
+
+```shell Terminal
+crewai test [OPTIONS]
+```
+
+- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
+- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
+
+Example:
+```shell Terminal    
+crewai test -n 5 -m gpt-3.5-turbo
+```
+
+### 8. Run
+
+Run the crew or flow.
+
+```shell Terminal
+crewai run
+```
+
+<Note>
+Starting from version 0.103.0, the `crewai run` command can be used to run both standard crews and flows. For flows, it automatically detects the type from pyproject.toml and runs the appropriate command. This is now the recommended way to run both crews and flows.
+</Note>
+
+<Note>
+Make sure to run these commands from the directory where your CrewAI project is set up. 
+Some commands may require additional configuration or setup within your project structure.
+</Note>
+
+### 9. Chat
+
+Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
+
+After receiving the results, you can continue interacting with the assistant for further instructions or questions.
+
+```shell Terminal
+crewai chat
+```
+<Note>
+Ensure you execute these commands from your CrewAI project's root directory.
+</Note>
+<Note>
+IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
+
+```python
+@crew
+def crew(self) -> Crew:
+    return Crew(
+        agents=self.agents,
+        tasks=self.tasks,
+        process=Process.sequential,
+        verbose=True,
+        chat_llm="gpt-4o",  # LLM for chat orchestration
+    )
+```
+</Note>
+
+### 10. Deploy
+
+Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
+
+- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
+    ```shell Terminal
+    crewai signup
+    ```
+    If you already have an account, you can login with:
+    ```shell Terminal
+    crewai login
+    ```
+
+- **Create a deployment**: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.
+    ```shell Terminal
+    crewai deploy create
+    ```
+    - Reads your local project configuration.
+    - Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
+    - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
+
+- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
+    ```shell Terminal
+    crewai deploy push
+    ``` 
+    - Initiates the deployment process on the CrewAI Enterprise platform.
+    - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
+
+- **Deployment Status**: You can check the status of your deployment with:
+    ```shell Terminal
+    crewai deploy status
+    ```
+    This fetches the latest deployment status of your most recent deployment attempt (e.g., `Building Images for Crew`, `Deploy Enqueued`, `Online`).
+
+- **Deployment Logs**: You can check the logs of your deployment with:
+    ```shell Terminal
+    crewai deploy logs
+    ```
+    This streams the deployment logs to your terminal.
+
+- **List deployments**: You can list all your deployments with:
+    ```shell Terminal
+    crewai deploy list
+    ```
+    This lists all your deployments.
+
+- **Delete a deployment**: You can delete a deployment with:
+    ```shell Terminal
+    crewai deploy remove
+    ```
+    This deletes the deployment from the CrewAI Enterprise platform.
+
+- **Help Command**: You can get help with the CLI with:
+    ```shell Terminal
+    crewai deploy --help
+    ```
+    This shows the help message for the CrewAI Deploy CLI.
+
+Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
+
+<iframe
+  width="100%"
+  height="400"
+  src="https://www.youtube.com/embed/3EqSV-CYDZA"
+  title="CrewAI Deployment Guide"
+  frameborder="0"
+  style={{ borderRadius: '10px' }}
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+  allowfullscreen
+></iframe>
+
+### 11. API Keys
+
+When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
+
+Once you've selected an LLM provider, you will be prompted for API keys.
+
+#### Initial API key providers
+
+The CLI will initially prompt for API keys for the following services:
+
+* OpenAI
+* Groq
+* Anthropic
+* Google Gemini
+* SambaNova
+
+When you select a provider, the CLI will prompt you to enter your API key.
+
+#### Other Options
+
+If you select option 6, you will be able to select from a list of LiteLLM supported providers.
+
+When you select a provider, the CLI will prompt you to enter the Key name and the API key.
+
+See the following link for each provider's key name:
+
+* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
+
+
+

docs/concepts/collaboration.mdx

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

docs/concepts/crews.mdx

@@ -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).                                                                                                                                                                           |
+| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
+| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
+| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
+| **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
+| **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
+| **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | 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.                                                                                                                                                                                        |
+
+<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](/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/concepts/event-listener.mdx

@@ -0,0 +1,374 @@
+---
+title: 'Event Listeners'
+description: 'Tap into CrewAI events to build custom integrations and monitoring'
+icon: spinner
+---
+
+## Overview
+
+CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
+
+## How It Works
+
+CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:
+
+1. **CrewAIEventsBus**: A singleton event bus that manages event registration and emission
+2. **BaseEvent**: Base class for all events in the system
+3. **BaseEventListener**: Abstract base class for creating custom event listeners
+
+When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
+
+<Note type="info" title="Enterprise Enhancement: Prompt Tracing">
+CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
+
+![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
+
+With Prompt Tracing you can:
+- View the complete history of all prompts sent to your LLM
+- Track token usage and costs
+- Debug agent reasoning failures
+- Share prompt sequences with your team
+- Compare different prompt strategies
+- Export traces for compliance and auditing
+</Note>
+
+## Creating a Custom Event Listener
+
+To create a custom event listener, you need to:
+
+1. Create a class that inherits from `BaseEventListener`
+2. Implement the `setup_listeners` method
+3. Register handlers for the events you're interested in
+4. Create an instance of your listener in the appropriate file
+
+Here's a simple example of a custom event listener class:
+
+```python
+from crewai.utilities.events import (
+    CrewKickoffStartedEvent,
+    CrewKickoffCompletedEvent,
+    AgentExecutionCompletedEvent,
+)
+from crewai.utilities.events.base_event_listener import BaseEventListener
+
+class MyCustomListener(BaseEventListener):
+    def __init__(self):
+        super().__init__()
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def on_crew_started(source, event):
+            print(f"Crew '{event.crew_name}' has started execution!")
+
+        @crewai_event_bus.on(CrewKickoffCompletedEvent)
+        def on_crew_completed(source, event):
+            print(f"Crew '{event.crew_name}' has completed execution!")
+            print(f"Output: {event.output}")
+
+        @crewai_event_bus.on(AgentExecutionCompletedEvent)
+        def on_agent_execution_completed(source, event):
+            print(f"Agent '{event.agent.role}' completed task")
+            print(f"Output: {event.output}")
+```
+
+## Properly Registering Your Listener
+
+Simply defining your listener class isn't enough. You need to create an instance of it and ensure it's imported in your application. This ensures that:
+
+1. The event handlers are registered with the event bus
+2. The listener instance remains in memory (not garbage collected)
+3. The listener is active when events are emitted
+
+### Option 1: Import and Instantiate in Your Crew or Flow Implementation
+
+The most important thing is to create an instance of your listener in the file where your Crew or Flow is defined and executed:
+
+#### For Crew-based Applications
+
+Create and import your listener at the top of your Crew implementation file:
+
+```python
+# In your crew.py file
+from crewai import Agent, Crew, Task
+from my_listeners import MyCustomListener
+
+# Create an instance of your listener
+my_listener = MyCustomListener()
+
+class MyCustomCrew:
+    # Your crew implementation...
+
+    def crew(self):
+        return Crew(
+            agents=[...],
+            tasks=[...],
+            # ...
+        )
+```
+
+#### For Flow-based Applications
+
+Create and import your listener at the top of your Flow implementation file:
+
+```python
+# In your main.py or flow.py file
+from crewai.flow import Flow, listen, start
+from my_listeners import MyCustomListener
+
+# Create an instance of your listener
+my_listener = MyCustomListener()
+
+class MyCustomFlow(Flow):
+    # Your flow implementation...
+
+    @start()
+    def first_step(self):
+        # ...
+```
+
+This ensures that your listener is loaded and active when your Crew or Flow is executed.
+
+### Option 2: Create a Package for Your Listeners
+
+For a more structured approach, especially if you have multiple listeners:
+
+1. Create a package for your listeners:
+
+```
+my_project/
+  ├── listeners/
+  │   ├── __init__.py
+  │   ├── my_custom_listener.py
+  │   └── another_listener.py
+```
+
+2. In `my_custom_listener.py`, define your listener class and create an instance:
+
+```python
+# my_custom_listener.py
+from crewai.utilities.events.base_event_listener import BaseEventListener
+# ... import events ...
+
+class MyCustomListener(BaseEventListener):
+    # ... implementation ...
+
+# Create an instance of your listener
+my_custom_listener = MyCustomListener()
+```
+
+3. In `__init__.py`, import the listener instances to ensure they're loaded:
+
+```python
+# __init__.py
+from .my_custom_listener import my_custom_listener
+from .another_listener import another_listener
+
+# Optionally export them if you need to access them elsewhere
+__all__ = ['my_custom_listener', 'another_listener']
+```
+
+4. Import your listeners package in your Crew or Flow file:
+
+```python
+# In your crew.py or flow.py file
+import my_project.listeners  # This loads all your listeners
+
+class MyCustomCrew:
+    # Your crew implementation...
+```
+
+This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
+
+```python
+# src/crewai/utilities/events/third_party/__init__.py
+from .agentops_listener import agentops_listener
+```
+
+This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
+
+## Available Event Types
+
+CrewAI provides a wide range of events that you can listen for:
+
+### Crew Events
+
+- **CrewKickoffStartedEvent**: Emitted when a Crew starts execution
+- **CrewKickoffCompletedEvent**: Emitted when a Crew completes execution
+- **CrewKickoffFailedEvent**: Emitted when a Crew fails to complete execution
+- **CrewTestStartedEvent**: Emitted when a Crew starts testing
+- **CrewTestCompletedEvent**: Emitted when a Crew completes testing
+- **CrewTestFailedEvent**: Emitted when a Crew fails to complete testing
+- **CrewTrainStartedEvent**: Emitted when a Crew starts training
+- **CrewTrainCompletedEvent**: Emitted when a Crew completes training
+- **CrewTrainFailedEvent**: Emitted when a Crew fails to complete training
+
+### Agent Events
+
+- **AgentExecutionStartedEvent**: Emitted when an Agent starts executing a task
+- **AgentExecutionCompletedEvent**: Emitted when an Agent completes executing a task
+- **AgentExecutionErrorEvent**: Emitted when an Agent encounters an error during execution
+
+### Task Events
+
+- **TaskStartedEvent**: Emitted when a Task starts execution
+- **TaskCompletedEvent**: Emitted when a Task completes execution
+- **TaskFailedEvent**: Emitted when a Task fails to complete execution
+- **TaskEvaluationEvent**: Emitted when a Task is evaluated
+
+### Tool Usage Events
+
+- **ToolUsageStartedEvent**: Emitted when a tool execution is started
+- **ToolUsageFinishedEvent**: Emitted when a tool execution is completed
+- **ToolUsageErrorEvent**: Emitted when a tool execution encounters an error
+- **ToolValidateInputErrorEvent**: Emitted when a tool input validation encounters an error
+- **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
+- **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
+
+### Knowledge Events
+
+- **KnowledgeRetrievalStartedEvent**: Emitted when a knowledge retrieval is started
+- **KnowledgeRetrievalCompletedEvent**: Emitted when a knowledge retrieval is completed
+- **KnowledgeQueryStartedEvent**: Emitted when a knowledge query is started
+- **KnowledgeQueryCompletedEvent**: Emitted when a knowledge query is completed
+- **KnowledgeQueryFailedEvent**: Emitted when a knowledge query fails
+- **KnowledgeSearchQueryFailedEvent**: Emitted when a knowledge search query fails
+
+### Flow Events
+
+- **FlowCreatedEvent**: Emitted when a Flow is created
+- **FlowStartedEvent**: Emitted when a Flow starts execution
+- **FlowFinishedEvent**: Emitted when a Flow completes execution
+- **FlowPlotEvent**: Emitted when a Flow is plotted
+- **MethodExecutionStartedEvent**: Emitted when a Flow method starts execution
+- **MethodExecutionFinishedEvent**: Emitted when a Flow method completes execution
+- **MethodExecutionFailedEvent**: Emitted when a Flow method fails to complete execution
+
+### LLM Events
+
+- **LLMCallStartedEvent**: Emitted when an LLM call starts
+- **LLMCallCompletedEvent**: Emitted when an LLM call completes
+- **LLMCallFailedEvent**: Emitted when an LLM call fails
+- **LLMStreamChunkEvent**: Emitted for each chunk received during streaming LLM responses
+
+## Event Handler Structure
+
+Each event handler receives two parameters:
+
+1. **source**: The object that emitted the event
+2. **event**: The event instance, containing event-specific data
+
+The structure of the event object depends on the event type, but all events inherit from `BaseEvent` and include:
+
+- **timestamp**: The time when the event was emitted
+- **type**: A string identifier for the event type
+
+Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
+
+## Real-World Example: Integration with AgentOps
+
+CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
+
+```python
+from typing import Optional
+
+from crewai.utilities.events import (
+    CrewKickoffCompletedEvent,
+    ToolUsageErrorEvent,
+    ToolUsageStartedEvent,
+)
+from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
+from crewai.utilities.events.task_events import TaskEvaluationEvent
+
+try:
+    import agentops
+    AGENTOPS_INSTALLED = True
+except ImportError:
+    AGENTOPS_INSTALLED = False
+
+class AgentOpsListener(BaseEventListener):
+    tool_event: Optional["agentops.ToolEvent"] = None
+    session: Optional["agentops.Session"] = None
+
+    def __init__(self):
+        super().__init__()
+
+    def setup_listeners(self, crewai_event_bus):
+        if not AGENTOPS_INSTALLED:
+            return
+
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
+            self.session = agentops.init()
+            for agent in source.agents:
+                if self.session:
+                    self.session.create_agent(
+                        name=agent.role,
+                        agent_id=str(agent.id),
+                    )
+
+        @crewai_event_bus.on(CrewKickoffCompletedEvent)
+        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
+            if self.session:
+                self.session.end_session(
+                    end_state="Success",
+                    end_state_reason="Finished Execution",
+                )
+
+        @crewai_event_bus.on(ToolUsageStartedEvent)
+        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
+            self.tool_event = agentops.ToolEvent(name=event.tool_name)
+            if self.session:
+                self.session.record(self.tool_event)
+
+        @crewai_event_bus.on(ToolUsageErrorEvent)
+        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
+            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
+```
+
+This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
+
+The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
+
+```python
+from .agentops_listener import agentops_listener
+```
+
+This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
+
+## Advanced Usage: Scoped Handlers
+
+For temporary event handling (useful for testing or specific operations), you can use the `scoped_handlers` context manager:
+
+```python
+from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
+
+with crewai_event_bus.scoped_handlers():
+    @crewai_event_bus.on(CrewKickoffStartedEvent)
+    def temp_handler(source, event):
+        print("This handler only exists within this context")
+
+    # Do something that emits events
+
+# Outside the context, the temporary handler is removed
+```
+
+## Use Cases
+
+Event listeners can be used for a variety of purposes:
+
+1. **Logging and Monitoring**: Track the execution of your Crew and log important events
+2. **Analytics**: Collect data about your Crew's performance and behavior
+3. **Debugging**: Set up temporary listeners to debug specific issues
+4. **Integration**: Connect CrewAI with external systems like monitoring platforms, databases, or notification services
+5. **Custom Behavior**: Trigger custom actions based on specific events
+
+## Best Practices
+
+1. **Keep Handlers Light**: Event handlers should be lightweight and avoid blocking operations
+2. **Error Handling**: Include proper error handling in your event handlers to prevent exceptions from affecting the main execution
+3. **Cleanup**: If your listener allocates resources, ensure they're properly cleaned up
+4. **Selective Listening**: Only listen for events you actually need to handle
+5. **Testing**: Test your event listeners in isolation to ensure they behave as expected
+
+By leveraging CrewAI's event system, you can extend its functionality and integrate it seamlessly with your existing infrastructure.

docs/concepts/flows.mdx

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