wip

- Add pydantic-settings>=2.10.1 dependency for configuration management
- Update pydantic to 2.11.9 and python-dotenv to 1.1.1
- Migrate from deprecated tool.uv.dev-dependencies to dependency-groups.dev format
- Remove unnecessary dev dependencies: pillow, cairosvg
- Update all dev tooling to latest versions
- Remove duplicate python-dotenv from dev dependencies

.github/workflows/build-uv-cache.yml

@@ -0,0 +1,46 @@
+name: Build uv cache
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - "uv.lock"
+      - "pyproject.toml"
+  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@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
+
+      - name: Install dependencies and populate cache
+        run: |
+          echo "Building global UV cache for Python ${{ matrix.python-version }}..."
+          uv sync --all-groups --all-extras --no-install-project
+          echo "Cache populated successfully"
+
+      - name: Save uv caches
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.github/workflows/codeql.yml

@@ -0,0 +1,102 @@
+# 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:
+      - "src/crewai/cli/templates/**"
+  pull_request:
+    branches: [ "main" ]
+    paths-ignore:
+      - "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@v4
+
+    # 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@v3
+      with:
+        languages: ${{ matrix.language }}
+        build-mode: ${{ matrix.build-mode }}
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+
+        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+        # queries: security-extended,security-and-quality
+
+    # If the analyze step fails for one of the languages you are analyzing with
+    # "We were unable to automatically build your code", modify the matrix above
+    # to set the build mode to "manual" for that language. Then modify this step
+    # to build your code.
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+    - if: matrix.build-mode == 'manual'
+      shell: bash
+      run: |
+        echo 'If you are using a "manual" build mode for one or more of the' \
+          'languages you are analyzing, replace this with the commands to build' \
+          'your code, for example:'
+        echo '  make bootstrap'
+        echo '  make release'
+        exit 1
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v3
+      with:
+        category: "/language:${{matrix.language}}"

.github/workflows/linter.yml

@@ -2,6 +2,9 @@ name: Lint
 
 on: [pull_request]
 
+permissions:
+  contents: read
+
 jobs:
   lint:
     runs-on: ubuntu-latest
@@ -15,8 +18,27 @@ jobs:
       - name: Fetch Target Branch
         run: git fetch origin $TARGET_BRANCH --depth=1
 
-      - name: Install Ruff
-        run: pip install ruff
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        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@v6
+        with:
+          version: "0.8.4"
+          python-version: "3.11"
+          enable-cache: false
+
+      - name: Install dependencies
+        run: uv sync --all-groups --all-extras --no-install-project
 
       - name: Get Changed Python Files
         id: changed-files
@@ -33,4 +55,14 @@ jobs:
           echo "${{ steps.changed-files.outputs.files }}" \
             | tr ' ' '\n' \
             | grep -v 'src/crewai/cli/templates/' \
-            | xargs -I{} ruff check "{}"
+            | xargs -I{} uv run ruff check "{}"
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}

.github/workflows/security-checker.yml

@@ -1,23 +0,0 @@
-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/tests.yml

@@ -3,32 +3,95 @@ name: Run Tests
 on: [pull_request]
 
 permissions:
-  contents: write
+  contents: read
 
 env:
   OPENAI_API_KEY: fake-api-key
+  PYTHONUNBUFFERED: 1
 
 jobs:
   tests:
+    name: tests (${{ matrix.python-version }})
     runs-on: ubuntu-latest
     timeout-minutes: 15
     strategy:
+      fail-fast: true
       matrix:
         python-version: ['3.10', '3.11', '3.12', '3.13']
+        group: [1, 2, 3, 4, 5, 6, 7, 8]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0  # Fetch all history for proper diff
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py${{ matrix.python-version }}-
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v3
+        uses: astral-sh/setup-uv@v6
         with:
-          enable-cache: true
-
-      - name: Set up Python ${{ matrix.python-version }}
-        run: uv python install ${{ matrix.python-version }}
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
 
       - name: Install the project
-        run: uv sync --dev --all-extras
+        run: uv sync --all-groups --all-extras
 
-      - name: Run tests
-        run: uv run pytest --block-network --timeout=60 -vv
+      - name: Restore test durations
+        uses: actions/cache/restore@v4
+        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
+          
+          uv run pytest \
+            --block-network \
+            --timeout=30 \
+            -vv \
+            --splits 8 \
+            --group ${{ matrix.group }} \
+            $DURATIONS_ARG \
+            --durations=10 \
+            -n auto \
+            --maxfail=3
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.github/workflows/type-checker.yml

@@ -3,24 +3,99 @@ name: Run Type Checks
 on: [pull_request]
 
 permissions:
-  contents: write
+  contents: read
 
 jobs:
-  type-checker:
+  type-checker-matrix:
+    name: type-checker (${{ matrix.python-version }})
     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@v4
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
         with:
-          python-version: "3.11.9"
+          fetch-depth: 0 # Fetch all history for proper diff
 
-      - name: Install Requirements
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        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@v6
+        with:
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
+
+      - name: Install dependencies
+        run: uv sync --all-groups --all-extras
+
+      - name: Get changed Python files
+        id: changed-files
         run: |
-          pip install mypy
+          # Get the list of changed Python files compared to the base branch
+          echo "Fetching changed files..."
+          git diff --name-only --diff-filter=ACMRT origin/${{ github.base_ref }}...HEAD -- '*.py' > changed_files.txt
 
-      - name: Run type checks
-        run: mypy src
+          # Filter for files in src/ directory only (excluding tests/)
+          grep -E "^src/" changed_files.txt > filtered_changed_files.txt || true
+
+          # Check if there are any changed files
+          if [ -s filtered_changed_files.txt ]; then
+            echo "Changed Python files in src/:"
+            cat filtered_changed_files.txt
+            echo "has_changes=true" >> $GITHUB_OUTPUT
+            # Convert newlines to spaces for mypy command
+            echo "files=$(cat filtered_changed_files.txt | tr '\n' ' ')" >> $GITHUB_OUTPUT
+          else
+            echo "No Python files changed in src/"
+            echo "has_changes=false" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Run type checks on changed files
+        if: steps.changed-files.outputs.has_changes == 'true'
+        run: |
+          echo "Running mypy on changed files with Python ${{ matrix.python-version }}..."
+          uv run mypy ${{ steps.changed-files.outputs.files }}
+
+      - name: No files to check
+        if: steps.changed-files.outputs.has_changes == 'false'
+        run: echo "No Python files in src/ were modified - skipping type checks"
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+
+  # Summary job to provide single status for branch protection
+  type-checker:
+    name: type-checker
+    runs-on: ubuntu-latest
+    needs: type-checker-matrix
+    if: always()
+    steps:
+      - name: Check matrix results
+        run: |
+          if [ "${{ needs.type-checker-matrix.result }}" == "success" ] || [ "${{ needs.type-checker-matrix.result }}" == "skipped" ]; then
+            echo "✅ All type checks passed"
+          else
+            echo "❌ Type checks failed"
+            exit 1
+          fi

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

@@ -0,0 +1,71 @@
+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@v4
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        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@v6
+        with:
+          version: "0.8.4"
+          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@v4
+        with:
+          path: .test_durations_py*
+          key: test-durations-py${{ matrix.python-version }}
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.gitignore

@@ -21,9 +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
+build_image
+chromadb-*.lock

.pre-commit-config.yaml

@@ -1,7 +1,19 @@
 repos:
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.2
+  - repo: local
     hooks:
       - id: ruff
-        args: ["--fix"]
+        name: ruff
+        entry: uv run ruff check
+        language: system
+        types: [python]
       - id: ruff-format
+        name: ruff-format
+        entry: uv run ruff format
+        language: system
+        types: [python]
+      - id: mypy
+        name: mypy
+        entry: uv run mypy
+        language: system
+        types: [python]
+        exclude: ^tests/

.ruff.toml

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

README.md

@@ -418,10 +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/landing_page_generator)
+- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator)
 - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
-- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis)
 
 ### Quick Tutorial
 
@@ -429,19 +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/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below:
 
 [![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
 
 ### Stock Analysis
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/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")
 

TRIGGER_IMPLEMENTATION_SUMMARY.md

@@ -0,0 +1,135 @@
+# CrewAI CLI Trigger Feature Implementation
+
+## Overview
+Successfully implemented the trigger functionality for CrewAI CLI as requested, adding two main commands:
+- `crewai trigger list` - Lists all triggers grouped by provider
+- `crewai trigger <app/trigger_name>` - Runs a crew with the specified trigger payload
+
+## Implementation Details
+
+### 1. Extended PlusAPI Client (`src/crewai/cli/plus_api.py`)
+- Added `TRIGGERS_RESOURCE = "/v1/triggers"` endpoint constant
+- Implemented `list_triggers()` method for GET `/v1/triggers`
+- Implemented `get_trigger_sample_payload(trigger_identification)` method for POST `/v1/triggers/sample_payload`
+
+### 2. Created TriggerCommand Class (`src/crewai/cli/trigger_command.py`)
+- Inherits from `BaseCommand` and `PlusAPIMixin` for proper authentication
+- Implements `list_triggers()` method with:
+  - Rich table display grouped by provider
+  - Comprehensive error handling for network issues, authentication, etc.
+  - User-friendly messages and styling
+- Implements `run_trigger(trigger_identification)` method with:
+  - Trigger identification format validation (`app/trigger_name`)
+  - Sample payload retrieval from API
+  - Dynamic crew/flow execution with trigger payload injection
+  - Temporary script generation and cleanup
+  - Robust error handling and validation
+
+### 3. Integrated CLI Commands (`src/crewai/cli/cli.py`)
+- Added import for `TriggerCommand`
+- Implemented `@crewai.command()` decorator for `trigger` command
+- Supports both `crewai trigger list` and `crewai trigger <app/trigger_name>` syntax
+- Proper argument parsing and command routing
+
+### 4. Key Features
+
+#### Trigger Listing
+- Fetches triggers from `/v1/triggers` endpoint
+- Displays triggers in a formatted table grouped by provider
+- Shows trigger ID and description for each trigger
+- Provides usage instructions
+
+#### Trigger Execution
+- Validates trigger identification format
+- Fetches sample payload from `/v1/triggers/sample_payload` endpoint
+- Detects project type (crew vs flow) from `pyproject.toml`
+- Generates appropriate execution script with trigger payload injection
+- Executes crew/flow with `uv run python` command
+- Adds trigger payload to inputs as `crewai_trigger_payload`
+- Handles cleanup of temporary files
+
+#### Error Handling
+- Network connectivity issues
+- Authentication failures (401)
+- Authorization issues (403)
+- Trigger not found (404)
+- Invalid project structure
+- Subprocess execution errors
+- Comprehensive user feedback with actionable suggestions
+
+### 5. Usage Examples
+
+```bash
+# List all available triggers
+crewai trigger list
+
+# Run a specific trigger
+crewai trigger github/pull_request_opened
+crewai trigger slack/message_received
+crewai trigger webhook/user_signup
+```
+
+### 6. API Integration Points
+
+#### CrewAI Client → Rails App
+- GET `/v1/triggers` - Returns triggers grouped by provider
+- POST `/v1/triggers/sample_payload` with `{"trigger_identification": "app/trigger_name"}`
+
+#### Expected Response Format
+```json
+{
+  "github": {
+    "github/pull_request_opened": {
+      "description": "Triggered when a pull request is opened"
+    },
+    "github/issue_created": {
+      "description": "Triggered when an issue is created"
+    }
+  },
+  "slack": {
+    "slack/message_received": {
+      "description": "Triggered when a message is received"
+    }
+  }
+}
+```
+
+### 7. Crew/Flow Integration
+The trigger payload is automatically injected into the crew/flow inputs as `crewai_trigger_payload`, allowing crews to access trigger data:
+
+```python
+# In crew/flow code
+def my_crew():
+    crew = Crew(...)
+    result = crew.kickoff(inputs=inputs)  # inputs will contain 'crewai_trigger_payload'
+    return result
+```
+
+### 8. Dependencies
+- `click` - CLI framework
+- `rich` - Enhanced terminal output
+- `requests` - HTTP client
+- Existing CrewAI CLI infrastructure (authentication, configuration, etc.)
+
+## Testing
+- All imports work correctly
+- CLI command structure is properly implemented
+- Error handling is comprehensive
+- Code follows CrewAI patterns and conventions
+
+## Next Steps for Backend Implementation
+
+### Rails App Requirements
+1. Add `GET /v1/triggers` endpoint
+2. Add `POST /v1/triggers/sample_payload` endpoint
+3. Implement integration service method `summarize_triggers`
+4. Each provider service must implement:
+   - `list_triggers()` method
+   - `get_sample_payload(trigger_identification)` method
+
+### CrewAI OAuth Requirements
+1. Implement endpoint that returns sample payload for trigger identification
+2. Ensure trigger data format matches expected structure
+
+The CLI implementation is complete and ready for integration with the backend services.
+

docs/en/api-reference/inputs.mdx

@@ -0,0 +1,8 @@
+---
+title: "GET /inputs"
+description: "Get required inputs for your crew"
+openapi: "/enterprise-api.en.yaml GET /inputs"
+mode: "wide"
+---
+
+

docs/en/api-reference/introduction.mdx

@@ -2,6 +2,7 @@
 title: "Introduction"
 description: "Complete reference for the CrewAI Enterprise REST API"
 icon: "code"
+mode: "wide"
 ---
 
 # CrewAI Enterprise API

docs/en/api-reference/kickoff.mdx

@@ -0,0 +1,8 @@
+---
+title: "POST /kickoff"
+description: "Start a crew execution"
+openapi: "/enterprise-api.en.yaml POST /kickoff"
+mode: "wide"
+---
+
+

docs/en/api-reference/status.mdx

@@ -0,0 +1,8 @@
+---
+title: "GET /status/{kickoff_id}"
+description: "Get execution status"
+openapi: "/enterprise-api.en.yaml GET /status/{kickoff_id}"
+mode: "wide"
+---
+
+

docs/en/concepts/agents.mdx

@@ -2,6 +2,7 @@
 title: Agents
 description: Detailed guide on creating and managing agents within the CrewAI framework.
 icon: robot
+mode: "wide"
 ---
 
 ## Overview of an Agent
@@ -526,6 +527,103 @@ agent = Agent(
 The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
 </Note>
 
+## Direct Agent Interaction with `kickoff()`
+
+Agents can be used directly without going through a task or crew workflow using the `kickoff()` method. This provides a simpler way to interact with an agent when you don't need the full crew orchestration capabilities.
+
+### How `kickoff()` Works
+
+The `kickoff()` method allows you to send messages directly to an agent and get a response, similar to how you would interact with an LLM but with all the agent's capabilities (tools, reasoning, etc.).
+
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool
+
+# Create an agent
+researcher = Agent(
+    role="AI Technology Researcher",
+    goal="Research the latest AI developments",
+    tools=[SerperDevTool()],
+    verbose=True
+)
+
+# Use kickoff() to interact directly with the agent
+result = researcher.kickoff("What are the latest developments in language models?")
+
+# Access the raw response
+print(result.raw)
+```
+
+### Parameters and Return Values
+
+| Parameter         | Type                                | Description                                                               |
+| :---------------- | :---------------------------------- | :------------------------------------------------------------------------ |
+| `messages`        | `Union[str, List[Dict[str, str]]]`  | Either a string query or a list of message dictionaries with role/content |
+| `response_format` | `Optional[Type[Any]]`               | Optional Pydantic model for structured output                             |
+
+The method returns a `LiteAgentOutput` object with the following properties:
+
+- `raw`: String containing the raw output text
+- `pydantic`: Parsed Pydantic model (if a `response_format` was provided)
+- `agent_role`: Role of the agent that produced the output
+- `usage_metrics`: Token usage metrics for the execution
+
+### Structured Output
+
+You can get structured output by providing a Pydantic model as the `response_format`:
+
+```python Code
+from pydantic import BaseModel
+from typing import List
+
+class ResearchFindings(BaseModel):
+    main_points: List[str]
+    key_technologies: List[str]
+    future_predictions: str
+
+# Get structured output
+result = researcher.kickoff(
+    "Summarize the latest developments in AI for 2025",
+    response_format=ResearchFindings
+)
+
+# Access structured data
+print(result.pydantic.main_points)
+print(result.pydantic.future_predictions)
+```
+
+### Multiple Messages
+
+You can also provide a conversation history as a list of message dictionaries:
+
+```python Code
+messages = [
+    {"role": "user", "content": "I need information about large language models"},
+    {"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
+    {"role": "user", "content": "What are the latest developments in 2025?"}
+]
+
+result = researcher.kickoff(messages)
+```
+
+### Async Support
+
+An asynchronous version is available via `kickoff_async()` with the same parameters:
+
+```python Code
+import asyncio
+
+async def main():
+    result = await researcher.kickoff_async("What are the latest developments in AI?")
+    print(result.raw)
+
+asyncio.run(main())
+```
+
+<Note>
+The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler execution flow while preserving all of the agent's configuration (role, goal, backstory, tools, etc.).
+</Note>
+
 ## Important Considerations and Best Practices
 
 ### Security and Code Execution

docs/en/concepts/cli.mdx

@@ -2,8 +2,11 @@
 title: CLI
 description: Learn how to use the CrewAI CLI to interact with CrewAI.
 icon: terminal
+mode: "wide"
 ---
 
+<Warning>Since release 0.140.0, CrewAI Enterprise started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
+
 ## Overview
 
 The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
@@ -86,7 +89,7 @@ crewai replay [OPTIONS]
 - `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
 
 Example:
-```shell Terminal    
+```shell Terminal
 crewai replay -t task_123456
 ```
 
@@ -132,7 +135,7 @@ crewai test [OPTIONS]
 - `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
 
 Example:
-```shell Terminal    
+```shell Terminal
 crewai test -n 5 -m gpt-3.5-turbo
 ```
 
@@ -149,7 +152,7 @@ Starting from version 0.103.0, the `crewai run` command can be used to run both
 </Note>
 
 <Note>
-Make sure to run these commands from the directory where your CrewAI project is set up. 
+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>
 
@@ -186,10 +189,7 @@ def crew(self) -> Crew:
 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:
+    You can login or create an account with:
     ```shell Terminal
     crewai login
     ```
@@ -236,7 +236,7 @@ You must be authenticated to CrewAI Enterprise to use these organization managem
 - **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).
 
@@ -283,15 +283,33 @@ Watch this video tutorial for a step-by-step demonstration of deploying your cre
   allowfullscreen
 ></iframe>
 
-### 11. API Keys
+### 11. Login
 
-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.
+Authenticate with CrewAI Enterprise using a secure device code flow (no email entry required).
 
-Once you've selected an LLM provider, you will be prompted for API keys.
+```shell Terminal
+crewai login
+```
 
-#### Initial API key providers
+What happens:
+- A verification URL and short code are displayed in your terminal
+- Your browser opens to the verification URL
+- Enter/confirm the code to complete authentication
 
-The CLI will initially prompt for API keys for the following services:
+Notes:
+- The OAuth2 provider and domain are configured via `crewai config` (defaults use `login.crewai.com`)
+- After successful login, the CLI also attempts to authenticate to the Tool Repository automatically
+- If you reset your configuration, run `crewai login` again to re-authenticate
+
+### 12. API Keys
+
+When running ```crewai create crew``` command, the CLI will show you a list of available LLM providers to choose from, followed by model selection for your chosen provider.
+
+Once you've selected an LLM provider and model, you will be prompted for API keys.
+
+#### Available LLM Providers
+
+Here's a list of the most popular LLM providers suggested by the CLI:
 
 * OpenAI
 * Groq
@@ -299,11 +317,11 @@ The CLI will initially prompt for API keys for the following services:
 * Google Gemini
 * SambaNova
 
-When you select a provider, the CLI will prompt you to enter your API key.
+When you select a provider, the CLI will then show you available models for that provider and prompt you to enter your API key.
 
 #### Other Options
 
-If you select option 6, you will be able to select from a list of LiteLLM supported providers.
+If you select "other", you will be able to select from a list of LiteLLM supported providers.
 
 When you select a provider, the CLI will prompt you to enter the Key name and the API key.
 
@@ -311,5 +329,85 @@ See the following link for each provider's key name:
 
 * [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
 
+### 13. Configuration Management
 
+Manage CLI configuration settings for CrewAI.
 
+```shell Terminal
+crewai config [COMMAND] [OPTIONS]
+```
+
+#### Commands:
+
+- `list`: Display all CLI configuration parameters
+```shell Terminal
+crewai config list
+```
+
+- `set`: Set a CLI configuration parameter
+```shell Terminal
+crewai config set <key> <value>
+```
+
+- `reset`: Reset all CLI configuration parameters to default values
+```shell Terminal
+crewai config reset
+```
+
+#### Available Configuration Parameters
+
+- `enterprise_base_url`: Base URL of the CrewAI Enterprise instance
+- `oauth2_provider`: OAuth2 provider used for authentication (e.g., workos, okta, auth0)
+- `oauth2_audience`: OAuth2 audience value, typically used to identify the target API or resource
+- `oauth2_client_id`: OAuth2 client ID issued by the provider, used during authentication requests
+- `oauth2_domain`: OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens
+
+#### Examples
+
+Display current configuration:
+```shell Terminal
+crewai config list
+```
+
+Example output:
+| Setting             | Value                    | Description                                                 |
+| :------------------ | :----------------------- | :---------------------------------------------------------- |
+| enterprise_base_url | https://app.crewai.com   | Base URL of the CrewAI Enterprise instance                  |
+| org_name            | Not set                  | Name of the currently active organization                   |
+| org_uuid            | Not set                  | UUID of the currently active organization                   |
+| oauth2_provider     | workos                   | OAuth2 provider (e.g., workos, okta, auth0)                 |
+| oauth2_audience     | client_01YYY             | Audience identifying the target API/resource                |
+| oauth2_client_id    | client_01XXX             | OAuth2 client ID issued by the provider                     |
+| oauth2_domain       | login.crewai.com         | Provider domain (e.g., your-org.auth0.com)                  |
+
+Set the enterprise base URL:
+```shell Terminal
+crewai config set enterprise_base_url https://my-enterprise.crewai.com
+```
+
+Set OAuth2 provider:
+```shell Terminal
+crewai config set oauth2_provider auth0
+```
+
+Set OAuth2 domain:
+```shell Terminal
+crewai config set oauth2_domain my-company.auth0.com
+```
+
+Reset all configuration to defaults:
+```shell Terminal
+crewai config reset
+```
+
+<Tip>
+After resetting configuration, re-run `crewai login` to authenticate again.
+</Tip>
+
+<Tip>
+CrewAI CLI handles authentication to the Tool Repository automatically when adding packages to your project. Just append `crewai` before any `uv` command to use it. E.g. `crewai uv add requests`. For more information, see [Tool Repository](https://docs.crewai.com/enterprise/features/tool-repository) docs.
+</Tip>
+
+<Note>
+Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
+</Note>

docs/en/concepts/collaboration.mdx

@@ -2,6 +2,7 @@
 title: Collaboration
 description: How to enable agents to work together, delegate tasks, and communicate effectively within CrewAI teams.
 icon: screen-users
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/crews.mdx

@@ -2,6 +2,7 @@
 title: Crews
 description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
 icon: people-group
+mode: "wide"
 ---
 
 ## Overview
@@ -20,8 +21,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **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.                                                                                                                                                                                             |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |                                                                                                                                                                                       |
 | **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
 | **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
@@ -32,6 +32,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
+| **Knowledge Sources** _(optional)_    | `knowledge_sources`    | Knowledge sources available at the crew level, accessible to all the agents.                                                                                                                                                                                    |
 
 <Tip>
 **Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.

docs/en/concepts/event-listener.mdx

@@ -2,6 +2,7 @@
 title: 'Event Listeners'
 description: 'Tap into CrewAI events to build custom integrations and monitoring'
 icon: spinner
+mode: "wide"
 ---
 
 ## Overview
@@ -44,12 +45,12 @@ To create a custom event listener, you need to:
 Here's a simple example of a custom event listener class:
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     CrewKickoffStartedEvent,
     CrewKickoffCompletedEvent,
     AgentExecutionCompletedEvent,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 
 class MyCustomListener(BaseEventListener):
     def __init__(self):
@@ -146,7 +147,7 @@ my_project/
 
 ```python
 # my_custom_listener.py
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 # ... import events ...
 
 class MyCustomListener(BaseEventListener):
@@ -177,14 +178,7 @@ 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.
+This is how third-party event listeners are registered in the CrewAI codebase.
 
 ## Available Event Types
 
@@ -255,6 +249,17 @@ CrewAI provides a wide range of events that you can listen for:
 - **LLMCallFailedEvent**: Emitted when an LLM call fails
 - **LLMStreamChunkEvent**: Emitted for each chunk received during streaming LLM responses
 
+### Memory Events
+
+- **MemoryQueryStartedEvent**: Emitted when a memory query is started. Contains the query, limit, and optional score threshold.
+- **MemoryQueryCompletedEvent**: Emitted when a memory query is completed successfully. Contains the query, results, limit, score threshold, and query execution time.
+- **MemoryQueryFailedEvent**: Emitted when a memory query fails. Contains the query, limit, score threshold, and error message.
+- **MemorySaveStartedEvent**: Emitted when a memory save operation is started. Contains the value to be saved, metadata, and optional agent role.
+- **MemorySaveCompletedEvent**: Emitted when a memory save operation is completed successfully. Contains the saved value, metadata, agent role, and save execution time.
+- **MemorySaveFailedEvent**: Emitted when a memory save operation fails. Contains the value, metadata, agent role, and error message.
+- **MemoryRetrievalStartedEvent**: Emitted when memory retrieval for a task prompt starts. Contains the optional task ID.
+- **MemoryRetrievalCompletedEvent**: Emitted when memory retrieval for a task prompt completes successfully. Contains the task ID, memory content, and retrieval execution time.
+
 ## Event Handler Structure
 
 Each event handler receives two parameters:
@@ -269,84 +274,13 @@ The structure of the event object depends on the event type, but all events inhe
 
 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
+from crewai.events import crewai_event_bus, CrewKickoffStartedEvent
 
 with crewai_event_bus.scoped_handlers():
     @crewai_event_bus.on(CrewKickoffStartedEvent)

docs/en/concepts/flows.mdx

@@ -2,6 +2,7 @@
 title: Flows
 description: Learn how to create and manage AI workflows using CrewAI Flows.
 icon: arrow-progress
+mode: "wide"
 ---
 
 ## Overview
@@ -97,7 +98,13 @@ The state's unique ID and stored data can be useful for tracking flow executions
 
 ### @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.
+The `@start()` decorator marks entry points for a Flow. You can:
+
+- Declare multiple unconditional starts: `@start()`
+- Gate a start on a prior method or router label: `@start("method_or_label")`
+- Provide a callable condition to control when a start should fire
+
+All satisfied `@start()` methods will execute (often in parallel) when the Flow begins or resumes.
 
 ### @listen()
 

docs/en/concepts/knowledge.mdx

@@ -2,6 +2,7 @@
 title: Knowledge
 description: What is knowledge in CrewAI and how to use it.
 icon: book
+mode: "wide"
 ---
 
 ## Overview
@@ -24,6 +25,41 @@ For file-based Knowledge Sources, make sure to place your files in a `knowledge`
 Also, use relative paths from the `knowledge` directory when creating the source.
 </Tip>
 
+### Vector store (RAG) client configuration
+
+CrewAI exposes a provider-neutral RAG client abstraction for vector stores. The default provider is ChromaDB, and Qdrant is supported as well. You can switch providers using configuration utilities.
+
+Supported today:
+- ChromaDB (default)
+- Qdrant
+
+```python Code
+from crewai.rag.config.utils import set_rag_config, get_rag_client, clear_rag_config
+
+# ChromaDB (default)
+from crewai.rag.chromadb.config import ChromaDBConfig
+set_rag_config(ChromaDBConfig())
+chromadb_client = get_rag_client()
+
+# Qdrant
+from crewai.rag.qdrant.config import QdrantConfig
+set_rag_config(QdrantConfig())
+qdrant_client = get_rag_client()
+
+# Example operations (same API for any provider)
+client = qdrant_client  # or chromadb_client
+client.create_collection(collection_name="docs")
+client.add_documents(
+    collection_name="docs",
+    documents=[{"id": "1", "content": "CrewAI enables collaborative AI agents."}],
+)
+results = client.search(collection_name="docs", query="collaborative agents", limit=3)
+
+clear_rag_config()  # optional reset
+```
+
+This RAG client is separate from Knowledge’s built-in storage. Use it when you need direct vector-store control or custom retrieval pipelines.
+
 ### Basic String Knowledge Example
 
 ```python Code
@@ -681,11 +717,11 @@ CrewAI emits events during the knowledge retrieval process that you can listen f
 #### Example: Monitoring Knowledge Retrieval
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     KnowledgeRetrievalStartedEvent,
     KnowledgeRetrievalCompletedEvent,
+    BaseEventListener,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
 
 class KnowledgeMonitorListener(BaseEventListener):
     def setup_listeners(self, crewai_event_bus):

docs/en/concepts/llms.mdx

@@ -2,6 +2,7 @@
 title: 'LLMs'
 description: 'A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects'
 icon: 'microchip-ai'
+mode: "wide"
 ---
 
 ## Overview
@@ -270,7 +271,7 @@ In this section, you'll find detailed examples that help you select, configure,
     from crewai import LLM
 
     llm = LLM(
-        model="gemini/gemini-1.5-pro-latest",
+        model="gemini-1.5-pro-latest", # or vertex_ai/gemini-1.5-pro-latest
         temperature=0.7,
         vertex_credentials=vertex_credentials_json
     )
@@ -684,6 +685,28 @@ In this section, you'll find detailed examples that help you select, configure,
       - openrouter/deepseek/deepseek-chat
     </Info>
   </Accordion>
+
+  <Accordion title="Nebius AI Studio">
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    NEBIUS_API_KEY=<your-api-key>
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="nebius/Qwen/Qwen3-30B-A3B"
+    )
+    ```
+
+    <Info>
+      Nebius AI Studio features:
+      - Large collection of open source models
+      - Higher rate limits
+      - Competitive pricing
+      - Good balance of speed and quality
+    </Info>
+  </Accordion>
 </AccordionGroup>
 
 ## Streaming Responses
@@ -711,10 +734,10 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     CrewAI emits events for each chunk received during streaming:
 
     ```python
-    from crewai.utilities.events import (
+    from crewai.events import (
       LLMStreamChunkEvent
     )
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai.events import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):
@@ -727,9 +750,58 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     ```
 
     <Tip>
-      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details 
+      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details
     </Tip>
   </Tab>
+
+  <Tab title="Agent & Task Tracking">
+    All LLM events in CrewAI include agent and task information, allowing you to track and filter LLM interactions by specific agents or tasks:
+
+    ```python
+    from crewai import LLM, Agent, Task, Crew
+    from crewai.events import LLMStreamChunkEvent
+    from crewai.events import BaseEventListener
+
+    class MyCustomListener(BaseEventListener):
+        def setup_listeners(self, crewai_event_bus):
+            @crewai_event_bus.on(LLMStreamChunkEvent)
+            def on_llm_stream_chunk(source, event):
+                if researcher.id == event.agent_id:
+                    print("\n==============\n Got event:", event, "\n==============\n")
+
+
+    my_listener = MyCustomListener()
+
+    llm = LLM(model="gpt-4o-mini", temperature=0, stream=True)
+
+    researcher = Agent(
+        role="About User",
+        goal="You know everything about the user.",
+        backstory="""You are a master at understanding people and their preferences.""",
+        llm=llm,
+    )
+
+    search = Task(
+        description="Answer the following questions about the user: {question}",
+        expected_output="An answer to the question.",
+        agent=researcher,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[search])
+
+    result = crew.kickoff(
+        inputs={"question": "..."}
+    )
+    ```
+
+    <Info>
+      This feature is particularly useful for:
+      - Debugging specific agent behaviors
+      - Logging LLM usage by task type
+      - Auditing which agents are making what types of LLM calls
+      - Performance monitoring of specific tasks
+    </Info>
+  </Tab>
 </Tabs>
 
 ## Structured LLM Calls
@@ -825,7 +897,7 @@ Learn how to get the most out of your LLM configuration:
       Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
     </Info>
   </Accordion>
-  
+
   <Accordion title="Drop Additional Parameters">
     CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
     For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:

docs/en/concepts/memory.mdx

@@ -2,15 +2,15 @@
 title: Memory
 description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
 icon: database
+mode: "wide"
 ---
 
 ## Overview
 
-The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **three distinct memory approaches** that serve different use cases:
+The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **two distinct memory approaches** that serve different use cases:
 
 1. **Basic Memory System** - Built-in short-term, long-term, and entity memory
-2. **User Memory** - User-specific memory with Mem0 integration (legacy approach)  
-3. **External Memory** - Standalone external memory providers (new approach)
+2. **External Memory** - Standalone external memory providers
 
 ## Memory System Components
 
@@ -19,7 +19,7 @@ The CrewAI framework provides a sophisticated memory system designed to signific
 | **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.|
 | **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
 | **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
-| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
+| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, `ExternalMemory` and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
 
 ## 1. Basic Memory System (Recommended)
 
@@ -62,7 +62,7 @@ By default, CrewAI uses the `appdirs` library to determine storage locations fol
 ```
 ~/Library/Application Support/CrewAI/{project_name}/
 ├── knowledge/           # Knowledge base ChromaDB files
-├── short_term_memory/   # Short-term memory ChromaDB files  
+├── short_term_memory/   # Short-term memory ChromaDB files
 ├── long_term_memory/    # Long-term memory ChromaDB files
 ├── entities/            # Entity memory ChromaDB files
 └── long_term_memory_storage.db  # SQLite database
@@ -202,7 +202,7 @@ crew = Crew(
     tasks=[task],
     memory=True,
     embedder={
-        "provider": "anthropic",  # Match your LLM provider
+        "provider": "anthropic", # Match your LLM provider
         "config": {
             "api_key": "your-anthropic-key",
             "model": "text-embedding-3-small"
@@ -252,7 +252,7 @@ chroma_path = os.path.join(storage_path, "knowledge")
 if os.path.exists(chroma_path):
     client = chromadb.PersistentClient(path=chroma_path)
     collections = client.list_collections()
-    
+
     print("ChromaDB Collections:")
     for collection in collections:
         print(f"  - {collection.name}: {collection.count()} documents")
@@ -269,7 +269,7 @@ crew = Crew(agents=[...], tasks=[...], memory=True)
 
 # Reset specific memory types
 crew.reset_memories(command_type='short')     # Short-term memory
-crew.reset_memories(command_type='long')      # Long-term memory  
+crew.reset_memories(command_type='long')      # Long-term memory
 crew.reset_memories(command_type='entity')    # Entity memory
 crew.reset_memories(command_type='knowledge') # Knowledge storage
 ```
@@ -540,16 +540,71 @@ crew = Crew(
 )
 ```
 
+### Mem0 Provider
+
+Short-Term Memory and Entity Memory both supports a tight integration with both Mem0 OSS and Mem0 Client as a provider. Here is how you can use Mem0 as a provider.
+
+```python
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.memory.entity_entity_memory import EntityMemory
+
+mem0_oss_embedder_config = {
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "local_mem0_config": {
+                "vector_store": {"provider": "qdrant","config": {"host": "localhost", "port": 6333}},
+                "llm": {"provider": "openai","config": {"api_key": "your-api-key", "model": "gpt-4"}},
+                "embedder": {"provider": "openai","config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}}
+            },
+            "infer": True # Optional defaults to True
+        },
+    }
+
+
+mem0_client_embedder_config = {
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "org_id": "my_org_id",        # Optional
+            "project_id": "my_project_id", # Optional
+            "api_key": "custom-api-key"    # Optional - overrides env var
+            "run_id": "my_run_id",        # Optional - for short-term memory
+            "includes": "include1",       # Optional 
+            "excludes": "exclude1",       # Optional
+            "infer": True                 # Optional defaults to True
+            "custom_categories": new_categories  # Optional - custom categories for user memory
+        },
+    }
+
+
+short_term_memory_mem0_oss = ShortTermMemory(embedder_config=mem0_oss_embedder_config) # Short Term Memory with Mem0 OSS
+short_term_memory_mem0_client = ShortTermMemory(embedder_config=mem0_client_embedder_config) # Short Term Memory with Mem0 Client
+entity_memory_mem0_oss = EntityMemory(embedder_config=mem0_oss_embedder_config) # Entity Memory with Mem0 OSS
+entity_memory_mem0_client = EntityMemory(embedder_config=mem0_client_embedder_config) # Short Term Memory with Mem0 Client
+
+crew = Crew(
+    memory=True,
+    short_term_memory=short_term_memory_mem0_oss, # or short_term_memory_mem0_client
+    entity_memory=entity_memory_mem0_oss # or entity_memory_mem0_client
+)
+```
+
 ### Choosing the Right Embedding Provider
 
-| Provider | Best For | Pros | Cons |
-|:---------|:----------|:------|:------|
-| **OpenAI** | General use, reliability | High quality, well-tested | Cost, requires API key |
-| **Ollama** | Privacy, cost savings | Free, local, private | Requires local setup |
-| **Google AI** | Google ecosystem | Good performance | Requires Google account |
-| **Azure OpenAI** | Enterprise, compliance | Enterprise features | Complex setup |
-| **Cohere** | Multilingual content | Great language support | Specialized use case |
-| **VoyageAI** | Retrieval tasks | Optimized for search | Newer provider |
+When selecting an embedding provider, consider factors like performance, privacy, cost, and integration needs.  
+Below is a comparison to help you decide:
+
+| Provider       | Best For                       | Pros                              | Cons                      |
+| -------------- | ------------------------------ | --------------------------------- | ------------------------- |
+| **OpenAI**     | General use, high reliability  | High quality, widely tested       | Paid service, API key required |
+| **Ollama**     | Privacy-focused, cost savings  | Free, runs locally, fully private | Requires local installation/setup |
+| **Google AI**  | Integration in Google ecosystem| Strong performance, good support  | Google account required   |
+| **Azure OpenAI** | Enterprise & compliance needs| Enterprise-grade features, security | More complex setup process |
+| **Cohere**     | Multilingual content handling  | Excellent language support        | More niche use cases      |
+| **VoyageAI**   | Information retrieval & search | Optimized for retrieval tasks     | Relatively new provider   |
+| **Mem0**       | Per-user personalization       | Search-optimized embeddings       | Paid service, API key required |
+
 
 ### Environment Variable Configuration
 
@@ -596,7 +651,7 @@ providers_to_test = [
     {
         "name": "Ollama",
         "config": {
-            "provider": "ollama", 
+            "provider": "ollama",
             "config": {"model": "mxbai-embed-large"}
         }
     }
@@ -604,7 +659,7 @@ providers_to_test = [
 
 for provider in providers_to_test:
     print(f"\nTesting {provider['name']} embeddings...")
-    
+
     # Create crew with specific embedder
     crew = Crew(
         agents=[...],
@@ -612,7 +667,7 @@ for provider in providers_to_test:
         memory=True,
         embedder=provider['config']
     )
-    
+
     # Run your test and measure performance
     result = crew.kickoff()
     print(f"{provider['name']} completed successfully")
@@ -623,7 +678,7 @@ for provider in providers_to_test:
 **Model not found errors:**
 ```python
 # Verify model availability
-from crewai.utilities.embedding_configurator import EmbeddingConfigurator
+from crewai.rag.embeddings.configurator import EmbeddingConfigurator
 
 configurator = EmbeddingConfigurator()
 try:
@@ -655,17 +710,17 @@ import time
 
 def test_embedding_performance(embedder_config, test_text="This is a test document"):
     start_time = time.time()
-    
+
     crew = Crew(
         agents=[...],
         tasks=[...],
         memory=True,
         embedder=embedder_config
     )
-    
+
     # Simulate memory operation
     crew.kickoff()
-    
+
     end_time = time.time()
     return end_time - start_time
 
@@ -676,7 +731,7 @@ openai_time = test_embedding_performance({
 })
 
 ollama_time = test_embedding_performance({
-    "provider": "ollama", 
+    "provider": "ollama",
     "config": {"model": "mxbai-embed-large"}
 })
 
@@ -684,67 +739,29 @@ print(f"OpenAI: {openai_time:.2f}s")
 print(f"Ollama: {ollama_time:.2f}s")
 ```
 
-## 2. User Memory with Mem0 (Legacy)
+### Entity Memory batching behavior
 
-<Warning>
-**Legacy Approach**: While fully functional, this approach is considered legacy. For new projects requiring user-specific memory, consider using External Memory instead.
-</Warning>
+Entity Memory supports batching when saving multiple entities at once. When you pass a list of `EntityMemoryItem`, the system:
 
-User Memory integrates with [Mem0](https://mem0.ai/) to provide user-specific memory that persists across sessions and integrates with the crew's contextual memory system.
+- Emits a single MemorySaveStartedEvent with `entity_count`
+- Saves each entity internally, collecting any partial errors
+- Emits MemorySaveCompletedEvent with aggregate metadata (saved count, errors)
+- Raises a partial-save exception if some entities failed (includes counts)
 
-### Prerequisites
-```bash
-pip install mem0ai
-```
+This improves performance and observability when writing many entities in one operation.
 
-### Mem0 Cloud Configuration
+## 2. External Memory 
+External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing.
+
+### Basic External Memory with Mem0
 ```python
 import os
-from crewai import Crew, Process
+from crewai import Agent, Crew, Process, Task
+from crewai.memory.external.external_memory import ExternalMemory
 
-# Set your Mem0 API key
-os.environ["MEM0_API_KEY"] = "m0-your-api-key"
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,  # Required for contextual memory integration
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john"},
-        "user_memory": {}  # Required - triggers user memory initialization
-    },
-    process=Process.sequential,
-    verbose=True
-)
-```
-
-### Advanced Mem0 Configuration
-```python
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {
-            "user_id": "john",
-            "org_id": "my_org_id",        # Optional
-            "project_id": "my_project_id", # Optional
-            "api_key": "custom-api-key"    # Optional - overrides env var
-        },
-        "user_memory": {}
-    }
-)
-```
-
-### Local Mem0 Configuration
-```python
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,
-    memory_config={
+# Create external memory instance with local Mem0 Configuration
+external_memory = ExternalMemory(
+    embedder_config={
         "provider": "mem0",
         "config": {
             "user_id": "john",
@@ -761,37 +778,60 @@ crew = Crew(
                     "provider": "openai",
                     "config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}
                 }
-            }
+            },
+            "infer": True # Optional defaults to True
         },
-        "user_memory": {}
-    }
-)
-```
-
-## 3. External Memory (New Approach)
-
-External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing.
-
-### Basic External Memory with Mem0
-```python
-import os
-from crewai import Agent, Crew, Process, Task
-from crewai.memory.external.external_memory import ExternalMemory
-
-os.environ["MEM0_API_KEY"] = "your-api-key"
-
-# Create external memory instance
-external_memory = ExternalMemory(
-    embedder_config={
-        "provider": "mem0", 
-        "config": {"user_id": "U-123"}
     }
 )
 
 crew = Crew(
     agents=[...],
     tasks=[...],
-    external_memory=external_memory,  # Separate from basic memory
+    external_memory=external_memory, # Separate from basic memory
+    process=Process.sequential,
+    verbose=True
+)
+```
+
+### Advanced External Memory with Mem0 Client
+When using Mem0 Client, you can customize the memory configuration further, by using parameters like 'includes', 'excludes', 'custom_categories', 'infer' and 'run_id' (this is only for short-term memory).
+You can find more details in the [Mem0 documentation](https://docs.mem0.ai/).
+
+```python
+import os
+from crewai import Agent, Crew, Process, Task
+from crewai.memory.external.external_memory import ExternalMemory
+
+new_categories = [
+    {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"},
+    {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"},
+    {"personal_information": "Basic information about the user including name, preferences, and personality traits"}
+]
+
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+# Create external memory instance with Mem0 Client
+external_memory = ExternalMemory(
+    embedder_config={
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "org_id": "my_org_id",        # Optional
+            "project_id": "my_project_id", # Optional
+            "api_key": "custom-api-key"    # Optional - overrides env var
+            "run_id": "my_run_id",        # Optional - for short-term memory
+            "includes": "include1",       # Optional 
+            "excludes": "exclude1",       # Optional
+            "infer": True                 # Optional defaults to True
+            "custom_categories": new_categories  # Optional - custom categories for user memory
+        },
+    }
+)
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    external_memory=external_memory, # Separate from basic memory
     process=Process.sequential,
     verbose=True
 )
@@ -808,8 +848,8 @@ class CustomStorage(Storage):
 
     def save(self, value, metadata=None, agent=None):
         self.memories.append({
-            "value": value, 
-            "metadata": metadata, 
+            "value": value,
+            "metadata": metadata,
             "agent": agent
         })
 
@@ -830,17 +870,18 @@ crew = Crew(
 )
 ```
 
-## Memory System Comparison
+## 🧠 Memory System Comparison
+
+| **Category**        | **Feature**            | **Basic Memory**            | **External Memory**         |
+|---------------------|------------------------|-----------------------------|------------------------------|
+| **Ease of Use**     | Setup Complexity       | Simple                      | Moderate                     |
+|                     | Integration            | Built-in (contextual)       | Standalone                   |
+| **Persistence**     | Storage                | Local files                 | Custom / Mem0                |
+|                     | Cross-session Support  | ✅                           | ✅                           |
+| **Personalization** | User-specific Memory   | ❌                          |  ✅                           |
+|                     | Custom Providers       | Limited                     | Any provider                 |
+| **Use Case Fit**    | Recommended For        | Most general use cases      | Specialized / custom needs   |
 
-| Feature | Basic Memory | User Memory (Legacy) | External Memory |
-|---------|-------------|---------------------|----------------|
-| **Setup Complexity** | Simple | Medium | Medium |
-| **Integration** | Built-in contextual | Contextual + User-specific | Standalone |
-| **Storage** | Local files | Mem0 Cloud/Local | Custom/Mem0 |
-| **Cross-session** | ✅ | ✅ | ✅ |
-| **User-specific** | ❌ | ✅ | ✅ |
-| **Custom providers** | Limited | Mem0 only | Any provider |
-| **Recommended for** | Most use cases | Legacy projects | Specialized needs |
 
 ## Supported Embedding Providers
 
@@ -986,7 +1027,201 @@ crew = Crew(
 - 🫡 **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
 - 🧠 **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
 
+## Memory Events
+
+CrewAI's event system provides powerful insights into memory operations. By leveraging memory events, you can monitor, debug, and optimize your memory system's performance and behavior.
+
+### Available Memory Events
+
+CrewAI emits the following memory-related events:
+
+| Event | Description | Key Properties |
+| :---- | :---------- | :------------- |
+| **MemoryQueryStartedEvent** | Emitted when a memory query begins | `query`, `limit`, `score_threshold` |
+| **MemoryQueryCompletedEvent** | Emitted when a memory query completes successfully | `query`, `results`, `limit`, `score_threshold`, `query_time_ms` |
+| **MemoryQueryFailedEvent** | Emitted when a memory query fails | `query`, `limit`, `score_threshold`, `error` |
+| **MemorySaveStartedEvent** | Emitted when a memory save operation begins | `value`, `metadata`, `agent_role` |
+| **MemorySaveCompletedEvent** | Emitted when a memory save operation completes successfully | `value`, `metadata`, `agent_role`, `save_time_ms` |
+| **MemorySaveFailedEvent** | Emitted when a memory save operation fails | `value`, `metadata`, `agent_role`, `error` |
+| **MemoryRetrievalStartedEvent** | Emitted when memory retrieval for a task prompt starts | `task_id` |
+| **MemoryRetrievalCompletedEvent** | Emitted when memory retrieval completes successfully | `task_id`, `memory_content`, `retrieval_time_ms` |
+
+### Practical Applications
+
+#### 1. Memory Performance Monitoring
+
+Track memory operation timing to optimize your application:
+
+```python
+from crewai.events import (
+    BaseEventListener,
+    MemoryQueryCompletedEvent,
+    MemorySaveCompletedEvent
+)
+import time
+
+class MemoryPerformanceMonitor(BaseEventListener):
+    def __init__(self):
+        super().__init__()
+        self.query_times = []
+        self.save_times = []
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemoryQueryCompletedEvent)
+        def on_memory_query_completed(source, event: MemoryQueryCompletedEvent):
+            self.query_times.append(event.query_time_ms)
+            print(f"Memory query completed in {event.query_time_ms:.2f}ms. Query: '{event.query}'")
+            print(f"Average query time: {sum(self.query_times)/len(self.query_times):.2f}ms")
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_memory_save_completed(source, event: MemorySaveCompletedEvent):
+            self.save_times.append(event.save_time_ms)
+            print(f"Memory save completed in {event.save_time_ms:.2f}ms")
+            print(f"Average save time: {sum(self.save_times)/len(self.save_times):.2f}ms")
+
+# Create an instance of your listener
+memory_monitor = MemoryPerformanceMonitor()
+```
+
+#### 2. Memory Content Logging
+
+Log memory operations for debugging and insights:
+
+```python
+from crewai.events import (
+    BaseEventListener,
+    MemorySaveStartedEvent,
+    MemoryQueryStartedEvent,
+    MemoryRetrievalCompletedEvent
+)
+import logging
+
+# Configure logging
+logger = logging.getLogger('memory_events')
+
+class MemoryLogger(BaseEventListener):
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemorySaveStartedEvent)
+        def on_memory_save_started(source, event: MemorySaveStartedEvent):
+            if event.agent_role:
+                logger.info(f"Agent '{event.agent_role}' saving memory: {event.value[:50]}...")
+            else:
+                logger.info(f"Saving memory: {event.value[:50]}...")
+
+        @crewai_event_bus.on(MemoryQueryStartedEvent)
+        def on_memory_query_started(source, event: MemoryQueryStartedEvent):
+            logger.info(f"Memory query started: '{event.query}' (limit: {event.limit})")
+
+        @crewai_event_bus.on(MemoryRetrievalCompletedEvent)
+        def on_memory_retrieval_completed(source, event: MemoryRetrievalCompletedEvent):
+            if event.task_id:
+                logger.info(f"Memory retrieved for task {event.task_id} in {event.retrieval_time_ms:.2f}ms")
+            else:
+                logger.info(f"Memory retrieved in {event.retrieval_time_ms:.2f}ms")
+            logger.debug(f"Memory content: {event.memory_content}")
+
+# Create an instance of your listener
+memory_logger = MemoryLogger()
+```
+
+#### 3. Error Tracking and Notifications
+
+Capture and respond to memory errors:
+
+```python
+from crewai.events import (
+    BaseEventListener,
+    MemorySaveFailedEvent,
+    MemoryQueryFailedEvent
+)
+import logging
+from typing import Optional
+
+# Configure logging
+logger = logging.getLogger('memory_errors')
+
+class MemoryErrorTracker(BaseEventListener):
+    def __init__(self, notify_email: Optional[str] = None):
+        super().__init__()
+        self.notify_email = notify_email
+        self.error_count = 0
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemorySaveFailedEvent)
+        def on_memory_save_failed(source, event: MemorySaveFailedEvent):
+            self.error_count += 1
+            agent_info = f"Agent '{event.agent_role}'" if event.agent_role else "Unknown agent"
+            error_message = f"Memory save failed: {event.error}. {agent_info}"
+            logger.error(error_message)
+
+            if self.notify_email and self.error_count % 5 == 0:
+                self._send_notification(error_message)
+
+        @crewai_event_bus.on(MemoryQueryFailedEvent)
+        def on_memory_query_failed(source, event: MemoryQueryFailedEvent):
+            self.error_count += 1
+            error_message = f"Memory query failed: {event.error}. Query: '{event.query}'"
+            logger.error(error_message)
+
+            if self.notify_email and self.error_count % 5 == 0:
+                self._send_notification(error_message)
+
+    def _send_notification(self, message):
+        # Implement your notification system (email, Slack, etc.)
+        print(f"[NOTIFICATION] Would send to {self.notify_email}: {message}")
+
+# Create an instance of your listener
+error_tracker = MemoryErrorTracker(notify_email="admin@example.com")
+```
+
+### Integrating with Analytics Platforms
+
+Memory events can be forwarded to analytics and monitoring platforms to track performance metrics, detect anomalies, and visualize memory usage patterns:
+
+```python
+from crewai.events import (
+    BaseEventListener,
+    MemoryQueryCompletedEvent,
+    MemorySaveCompletedEvent
+)
+
+class MemoryAnalyticsForwarder(BaseEventListener):
+    def __init__(self, analytics_client):
+        super().__init__()
+        self.client = analytics_client
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(MemoryQueryCompletedEvent)
+        def on_memory_query_completed(source, event: MemoryQueryCompletedEvent):
+            # Forward query metrics to analytics platform
+            self.client.track_metric({
+                "event_type": "memory_query",
+                "query": event.query,
+                "duration_ms": event.query_time_ms,
+                "result_count": len(event.results) if hasattr(event.results, "__len__") else 0,
+                "timestamp": event.timestamp
+            })
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_memory_save_completed(source, event: MemorySaveCompletedEvent):
+            # Forward save metrics to analytics platform
+            self.client.track_metric({
+                "event_type": "memory_save",
+                "agent_role": event.agent_role,
+                "duration_ms": event.save_time_ms,
+                "timestamp": event.timestamp
+            })
+```
+
+### Best Practices for Memory Event Listeners
+
+1. **Keep handlers lightweight**: Avoid complex processing in event handlers to prevent performance impacts
+2. **Use appropriate logging levels**: Use INFO for normal operations, DEBUG for details, ERROR for issues
+3. **Batch metrics when possible**: Accumulate metrics before sending to external systems
+4. **Handle exceptions gracefully**: Ensure your event handlers don't crash due to unexpected data
+5. **Consider memory consumption**: Be mindful of storing large amounts of event data
+
 ## Conclusion
 
-Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, 
+Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations,
 you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

docs/en/concepts/planning.mdx

@@ -2,6 +2,7 @@
 title: Planning
 description: Learn how to add planning to your CrewAI Crew and improve their performance.
 icon: ruler-combined
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/processes.mdx

@@ -2,6 +2,7 @@
 title: Processes
 description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
 icon: bars-staggered
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/reasoning.mdx

@@ -2,6 +2,7 @@
 title: Reasoning
 description: "Learn how to enable and use agent reasoning to improve task execution."
 icon: brain
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/tasks.mdx

@@ -2,6 +2,7 @@
 title: Tasks
 description: Detailed guide on managing and creating tasks within the CrewAI framework.
 icon: list-check
+mode: "wide"
 ---
 
 ## Overview
@@ -54,9 +55,17 @@ crew = Crew(
 | **Markdown** _(optional)_        | `markdown`        | `Optional[bool]`              | Whether the task should instruct the agent to return the final answer formatted in Markdown. Defaults to False.      |
 | **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Task-specific configuration parameters.                                                                              |
 | **Output File** _(optional)_     | `output_file`     | `Optional[str]`               | File path for storing the task output.                                                                               |
+| **Create Directory** _(optional)_ | `create_directory` | `Optional[bool]`             | Whether to create the directory for output_file if it doesn't exist. Defaults to True.                               |
 | **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
+| **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
+| **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]`     | Maximum number of retries when guardrail validation fails. Defaults to 3.                                         |
+
+<Note type="warning" title="Deprecated: max_retries">
+  The task attribute `max_retries` is deprecated and will be removed in v1.0.0.
+  Use `guardrail_max_retries` instead to control retry attempts when a guardrail fails.
+</Note>
 
 ## Creating Tasks
 
@@ -332,9 +341,11 @@ Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
 feedback to agents when their output doesn't meet specific criteria.
 
-### Using Task Guardrails
+Guardrails are implemented as Python functions that contain custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
 
-To add a guardrail to a task, provide a validation function through the `guardrail` parameter:
+### Function-Based Guardrails
+
+To add a function-based guardrail to a task, provide a validation function through the `guardrail` parameter:
 
 ```python Code
 from typing import Tuple, Union, Dict, Any
@@ -372,9 +383,7 @@ blog_task = Task(
    - On success: it returns a tuple of `(bool, Any)`. For example: `(True, validated_result)`
    - On Failure: it returns a tuple of `(bool, str)`. For example: `(False, "Error message explain the failure")`
 
-### LLMGuardrail
 
-The `LLMGuardrail` class offers a robust mechanism for validating task outputs.
 
 ### Error Handling Best Practices
 
@@ -429,7 +438,7 @@ When a guardrail returns `(False, error)`:
 2. The agent attempts to fix the issue
 3. The process repeats until:
    - The guardrail returns `(True, result)`
-   - Maximum retries are reached
+   - Maximum retries are reached (`guardrail_max_retries`)
 
 Example with retry handling:
 ```python Code
@@ -450,7 +459,7 @@ task = Task(
     expected_output="A valid JSON object",
     agent=analyst,
     guardrail=validate_json_output,
-    max_retries=3  # Limit retry attempts
+    guardrail_max_retries=3  # Limit retry attempts
 )
 ```
 
@@ -798,184 +807,91 @@ While creating and executing tasks, certain validation mechanisms are in place t
 
 These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
 
-## Task Guardrails
 
-Task guardrails provide a powerful way to validate, transform, or filter task outputs before they are passed to the next task. Guardrails are optional functions that execute before the next task starts, allowing you to ensure that task outputs meet specific requirements or formats.
-
-### Basic Usage
-
-#### Define your own logic to validate
-
-```python Code
-from typing import Tuple, Union
-from crewai import Task
-
-def validate_json_output(result: str) -> Tuple[bool, Union[dict, str]]:
-    """Validate that the output is valid JSON."""
-    try:
-        json_data = json.loads(result)
-        return (True, json_data)
-    except json.JSONDecodeError:
-        return (False, "Output must be valid JSON")
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail=validate_json_output
-)
-```
-
-#### Leverage a no-code approach for validation
-
-```python Code
-from crewai import Task
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail="Ensure the response is a valid JSON object"
-)
-```
-
-#### Using YAML
-
-```yaml
-research_task:
-  ...
-  guardrail: make sure each bullet contains a minimum of 100 words
-  ...
-```
-
-```python Code
-@CrewBase
-class InternalCrew:
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
-
-    ...
-    @task
-    def research_task(self):
-        return Task(config=self.tasks_config["research_task"])  # type: ignore[index]
-    ...
-```
-
-
-#### Use custom models for code generation
-
-```python Code
-from crewai import Task
-from crewai.llm import LLM
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail=LLMGuardrail(
-        description="Ensure the response is a valid JSON object",
-        llm=LLM(model="gpt-4o-mini"),
-    )
-)
-```
-
-### How Guardrails Work
-
-1. **Optional Attribute**: Guardrails are an optional attribute at the task level, allowing you to add validation only where needed.
-2. **Execution Timing**: The guardrail function is executed before the next task starts, ensuring valid data flow between tasks.
-3. **Return Format**: Guardrails must return a tuple of `(success, data)`:
-   - If `success` is `True`, `data` is the validated/transformed result
-   - If `success` is `False`, `data` is the error message
-4. **Result Routing**:
-   - On success (`True`), the result is automatically passed to the next task
-   - On failure (`False`), the error is sent back to the agent to generate a new answer
-
-### Common Use Cases
-
-#### Data Format Validation
-```python Code
-def validate_email_format(result: str) -> Tuple[bool, Union[str, str]]:
-    """Ensure the output contains a valid email address."""
-    import re
-    email_pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
-    if re.match(email_pattern, result.strip()):
-        return (True, result.strip())
-    return (False, "Output must be a valid email address")
-```
-
-#### Content Filtering
-```python Code
-def filter_sensitive_info(result: str) -> Tuple[bool, Union[str, str]]:
-    """Remove or validate sensitive information."""
-    sensitive_patterns = ['SSN:', 'password:', 'secret:']
-    for pattern in sensitive_patterns:
-        if pattern.lower() in result.lower():
-            return (False, f"Output contains sensitive information ({pattern})")
-    return (True, result)
-```
-
-#### Data Transformation
-```python Code
-def normalize_phone_number(result: str) -> Tuple[bool, Union[str, str]]:
-    """Ensure phone numbers are in a consistent format."""
-    import re
-    digits = re.sub(r'\D', '', result)
-    if len(digits) == 10:
-        formatted = f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
-        return (True, formatted)
-    return (False, "Output must be a 10-digit phone number")
-```
-
-### Advanced Features
-
-#### Chaining Multiple Validations
-```python Code
-def chain_validations(*validators):
-    """Chain multiple validators together."""
-    def combined_validator(result):
-        for validator in validators:
-            success, data = validator(result)
-            if not success:
-                return (False, data)
-            result = data
-        return (True, result)
-    return combined_validator
-
-# Usage
-task = Task(
-    description="Get user contact info",
-    expected_output="Email and phone",
-    guardrail=chain_validations(
-        validate_email_format,
-        filter_sensitive_info
-    )
-)
-```
-
-#### Custom Retry Logic
-```python Code
-task = Task(
-    description="Generate data",
-    expected_output="Valid data",
-    guardrail=validate_data,
-    max_retries=5  # Override default retry limit
-)
-```
 
 ## Creating Directories when Saving Files
 
-You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
+The `create_directory` parameter controls whether CrewAI should automatically create directories when saving task outputs to files. This feature is particularly useful for organizing outputs and ensuring that file paths are correctly structured, especially when working with complex project hierarchies.
+
+### Default Behavior
+
+By default, `create_directory=True`, which means CrewAI will automatically create any missing directories in the output file path:
 
 ```python Code
-# ...
-
-save_output_task = Task(
-    description='Save the summarized AI news to a file',
-    expected_output='File saved successfully',
-    agent=research_agent,
-    tools=[file_save_tool],
-    output_file='outputs/ai_news_summary.txt',
-    create_directory=True
+# Default behavior - directories are created automatically
+report_task = Task(
+    description='Generate a comprehensive market analysis report',
+    expected_output='A detailed market analysis with charts and insights',
+    agent=analyst_agent,
+    output_file='reports/2025/market_analysis.md',  # Creates 'reports/2025/' if it doesn't exist
+    markdown=True
 )
+```
 
-#...
+### Disabling Directory Creation
+
+If you want to prevent automatic directory creation and ensure that the directory already exists, set `create_directory=False`:
+
+```python Code
+# Strict mode - directory must already exist
+strict_output_task = Task(
+    description='Save critical data that requires existing infrastructure',
+    expected_output='Data saved to pre-configured location',
+    agent=data_agent,
+    output_file='secure/vault/critical_data.json',
+    create_directory=False  # Will raise RuntimeError if 'secure/vault/' doesn't exist
+)
+```
+
+### YAML Configuration
+
+You can also configure this behavior in your YAML task definitions:
+
+```yaml tasks.yaml
+analysis_task:
+  description: >
+    Generate quarterly financial analysis
+  expected_output: >
+    A comprehensive financial report with quarterly insights
+  agent: financial_analyst
+  output_file: reports/quarterly/q4_2024_analysis.pdf
+  create_directory: true  # Automatically create 'reports/quarterly/' directory
+
+audit_task:
+  description: >
+    Perform compliance audit and save to existing audit directory
+  expected_output: >
+    A compliance audit report
+  agent: auditor
+  output_file: audit/compliance_report.md
+  create_directory: false  # Directory must already exist
+```
+
+### Use Cases
+
+**Automatic Directory Creation (`create_directory=True`):**
+- Development and prototyping environments
+- Dynamic report generation with date-based folders
+- Automated workflows where directory structure may vary
+- Multi-tenant applications with user-specific folders
+
+**Manual Directory Management (`create_directory=False`):**
+- Production environments with strict file system controls
+- Security-sensitive applications where directories must be pre-configured
+- Systems with specific permission requirements
+- Compliance environments where directory creation is audited
+
+### Error Handling
+
+When `create_directory=False` and the directory doesn't exist, CrewAI will raise a `RuntimeError`:
+
+```python Code
+try:
+    result = crew.kickoff()
+except RuntimeError as e:
+    # Handle missing directory error
+    print(f"Directory creation failed: {e}")
+    # Create directory manually or use fallback location
 ```
 
 Check out the video below to see how to use structured outputs in CrewAI:

docs/en/concepts/testing.mdx

@@ -2,6 +2,7 @@
 title: Testing
 description: Learn how to test your CrewAI Crew and evaluate their performance.
 icon: vial
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/tools.mdx

@@ -2,6 +2,7 @@
 title: Tools
 description: Understanding and leveraging tools within the CrewAI framework for agent collaboration and task execution.
 icon: screwdriver-wrench
+mode: "wide"
 ---
 
 ## Overview

docs/en/concepts/training.mdx

@@ -2,14 +2,15 @@
 title: Training
 description: Learn how to train your CrewAI agents by giving them feedback early on and get consistent results.
 icon: dumbbell
+mode: "wide"
 ---
 
 ## Overview
 
-The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
+The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI).
 By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
 
-During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. 
+During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback.
 This helps the agents improve their understanding, decision-making, and problem-solving abilities.
 
 ### Training Your Crew Using the CLI
@@ -21,13 +22,17 @@ To use the training feature, follow these steps:
 3. Run the following command:
 
 ```shell
-crewai train -n <n_iterations> <filename> (optional)
+crewai train -n <n_iterations> -f <filename.pkl>
 ```
 <Tip>
   Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
 </Tip>
 
-### Training Your Crew Programmatically
+<Note>
+  If you omit `-f`, the output defaults to `trained_agents_data.pkl` in the current working directory. You can pass an absolute path to control where the file is written.
+</Note>
+
+### Training your Crew programmatically
 
 To train your crew programmatically, use the following steps:
 
@@ -42,8 +47,8 @@ filename = "your_model.pkl"
 
 try:
     YourCrewName_Crew().crew().train(
-      n_iterations=n_iterations, 
-      inputs=inputs, 
+      n_iterations=n_iterations,
+      inputs=inputs,
       filename=filename
     )
 
@@ -51,17 +56,142 @@ except Exception as e:
     raise Exception(f"An error occurred while training the crew: {e}")
 ```
 
+## How trained data is used by agents
+
+CrewAI uses the training artifacts in two ways: during training to incorporate your human feedback, and after training to guide agents with consolidated suggestions.
+
+### Training data flow
+
+```mermaid
+flowchart TD
+  A["Start training<br/>CLI: crewai train -n -f<br/>or Python: crew.train(...)"] --> B["Setup training mode<br/>- task.human_input = true<br/>- disable delegation<br/>- init training_data.pkl + trained file"]
+
+  subgraph "Iterations"
+    direction LR
+    C["Iteration i<br/>initial_output"] --> D["User human_feedback"]
+    D --> E["improved_output"]
+    E --> F["Append to training_data.pkl<br/>by agent_id and iteration"]
+  end
+
+  B --> C
+  F --> G{"More iterations?"}
+  G -- "Yes" --> C
+  G -- "No" --> H["Evaluate per agent<br/>aggregate iterations"]
+
+  H --> I["Consolidate<br/>suggestions[] + quality + final_summary"]
+  I --> J["Save by agent role to trained file<br/>(default: trained_agents_data.pkl)"]
+
+  J --> K["Normal (non-training) runs"]
+  K --> L["Auto-load suggestions<br/>from trained_agents_data.pkl"]
+  L --> M["Append to prompt<br/>for consistent improvements"]
+```
+
+### During training runs
+
+- On each iteration, the system records for every agent:
+  - `initial_output`: the agent’s first answer
+  - `human_feedback`: your inline feedback when prompted
+  - `improved_output`: the agent’s follow-up answer after feedback
+- This data is stored in a working file named `training_data.pkl` keyed by the agent’s internal ID and iteration.
+- While training is active, the agent automatically appends your prior human feedback to its prompt to enforce those instructions on subsequent attempts within the training session.
+  Training is interactive: tasks set `human_input = true`, so running in a non-interactive environment will block on user input.
+
+### After training completes
+
+- When `train(...)` finishes, CrewAI evaluates the collected training data per agent and produces a consolidated result containing:
+  - `suggestions`: clear, actionable instructions distilled from your feedback and the difference between initial/improved outputs
+  - `quality`: a 0–10 score capturing improvement
+  - `final_summary`: a step-by-step set of action items for future tasks
+- These consolidated results are saved to the filename you pass to `train(...)` (default via CLI is `trained_agents_data.pkl`). Entries are keyed by the agent’s `role` so they can be applied across sessions.
+- During normal (non-training) execution, each agent automatically loads its consolidated `suggestions` and appends them to the task prompt as mandatory instructions. This gives you consistent improvements without changing your agent definitions.
+
+### File summary
+
+- `training_data.pkl` (ephemeral, per-session):
+  - Structure: `agent_id -> { iteration_number: { initial_output, human_feedback, improved_output } }`
+  - Purpose: capture raw data and human feedback during training
+  - Location: saved in the current working directory (CWD)
+- `trained_agents_data.pkl` (or your custom filename):
+  - Structure: `agent_role -> { suggestions: string[], quality: number, final_summary: string }`
+  - Purpose: persist consolidated guidance for future runs
+  - Location: written to the CWD by default; use `-f` to set a custom (including absolute) path
+
+## Small Language Model Considerations
+
+<Warning>
+  When using smaller language models (≤7B parameters) for training data evaluation, be aware that they may face challenges with generating structured outputs and following complex instructions.
+</Warning>
+
+### Limitations of Small Models in Training Evaluation
+
+<CardGroup cols={2}>
+  <Card title="JSON Output Accuracy" icon="triangle-exclamation">
+    Smaller models often struggle with producing valid JSON responses needed for structured training evaluations, leading to parsing errors and incomplete data.
+  </Card>
+  <Card title="Evaluation Quality" icon="chart-line">
+    Models under 7B parameters may provide less nuanced evaluations with limited reasoning depth compared to larger models.
+  </Card>
+  <Card title="Instruction Following" icon="list-check">
+    Complex training evaluation criteria may not be fully followed or considered by smaller models.
+  </Card>
+  <Card title="Consistency" icon="rotate">
+    Evaluations across multiple training iterations may lack consistency with smaller models.
+  </Card>
+</CardGroup>
+
+### Recommendations for Training
+
+<Tabs>
+  <Tab title="Best Practice">
+    For optimal training quality and reliable evaluations, we strongly recommend using models with at least 7B parameters or larger:
+
+    ```python
+    from crewai import Agent, Crew, Task, LLM
+
+    # Recommended minimum for training evaluation
+    llm = LLM(model="mistral/open-mistral-7b")
+
+    # Better options for reliable training evaluation
+    llm = LLM(model="anthropic/claude-3-sonnet-20240229-v1:0")
+    llm = LLM(model="gpt-4o")
+
+    # Use this LLM with your agents
+    agent = Agent(
+        role="Training Evaluator",
+        goal="Provide accurate training feedback",
+        llm=llm
+    )
+    ```
+
+    <Tip>
+      More powerful models provide higher quality feedback with better reasoning, leading to more effective training iterations.
+    </Tip>
+  </Tab>
+  <Tab title="Small Model Usage">
+    If you must use smaller models for training evaluation, be aware of these constraints:
+
+    ```python
+    # Using a smaller model (expect some limitations)
+    llm = LLM(model="huggingface/microsoft/Phi-3-mini-4k-instruct")
+    ```
+
+    <Warning>
+      While CrewAI includes optimizations for small models, expect less reliable and less nuanced evaluation results that may require more human intervention during training.
+    </Warning>
+  </Tab>
+</Tabs>
+
 ### Key Points to Note
 
 - **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
 - **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
 - **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
+- Trained guidance is applied at prompt time; it does not modify your Python/YAML agent configuration.
+- Agents automatically load trained suggestions from a file named `trained_agents_data.pkl` located in the current working directory. If you trained to a different filename, either rename it to `trained_agents_data.pkl` before running, or adjust the loader in code.
+- You can change the output filename when calling `crewai train` with `-f/--filename`. Absolute paths are supported if you want to save outside the CWD.
 
 It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
 
 Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
 
 Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
-
-Happy training with CrewAI! 🚀
-  

docs/en/enterprise/features/agent-repositories.mdx

@@ -0,0 +1,156 @@
+---
+title: 'Agent Repositories'
+description: 'Learn how to use Agent Repositories to share and reuse your agents across teams and projects'
+icon: 'database'
+mode: "wide"
+---
+
+Agent Repositories allow enterprise users to store, share, and reuse agent definitions across teams and projects. This feature enables organizations to maintain a centralized library of standardized agents, promoting consistency and reducing duplication of effort.
+
+## Benefits of Agent Repositories
+
+- **Standardization**: Maintain consistent agent definitions across your organization
+- **Reusability**: Create an agent once and use it in multiple crews and projects
+- **Governance**: Implement organization-wide policies for agent configurations
+- **Collaboration**: Enable teams to share and build upon each other's work
+
+## Using Agent Repositories
+
+### Prerequisites
+
+1. You must have an account at CrewAI, try the [free plan](https://app.crewai.com).
+2. You need to be authenticated using the CrewAI CLI.
+3. If you have more than one organization, make sure you are switched to the correct organization using the CLI command:
+
+```bash
+crewai org switch <org_id>
+```
+
+### Creating and Managing Agents in Repositories
+
+To create and manage agents in repositories,Enterprise Dashboard.
+
+### Loading Agents from Repositories
+
+You can load agents from repositories in your code using the `from_repository` parameter:
+
+```python
+from crewai import Agent
+
+# Create an agent by loading it from a repository
+# The agent is loaded with all its predefined configurations
+researcher = Agent(
+    from_repository="market-research-agent"
+)
+
+```
+
+### Overriding Repository Settings
+
+You can override specific settings from the repository by providing them in the configuration:
+
+```python
+researcher = Agent(
+    from_repository="market-research-agent",
+    goal="Research the latest trends in AI development",  # Override the repository goal
+    verbose=True  # Add a setting not in the repository
+)
+```
+
+### Example: Creating a Crew with Repository Agents
+
+```python
+from crewai import Crew, Agent, Task
+
+# Load agents from repositories
+researcher = Agent(
+    from_repository="market-research-agent"
+)
+
+writer = Agent(
+    from_repository="content-writer-agent"
+)
+
+# Create tasks
+research_task = Task(
+    description="Research the latest trends in AI",
+    agent=researcher
+)
+
+writing_task = Task(
+    description="Write a comprehensive report based on the research",
+    agent=writer
+)
+
+# Create the crew
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, writing_task],
+    verbose=True
+)
+
+# Run the crew
+result = crew.kickoff()
+```
+
+### Example: Using `kickoff()` with Repository Agents
+
+You can also use repository agents directly with the `kickoff()` method for simpler interactions:
+
+```python
+from crewai import Agent
+from pydantic import BaseModel
+from typing import List
+
+# Define a structured output format
+class MarketAnalysis(BaseModel):
+    key_trends: List[str]
+    opportunities: List[str]
+    recommendation: str
+
+# Load an agent from repository
+analyst = Agent(
+    from_repository="market-analyst-agent",
+    verbose=True
+)
+
+# Get a free-form response
+result = analyst.kickoff("Analyze the AI market in 2025")
+print(result.raw)  # Access the raw response
+
+# Get structured output
+structured_result = analyst.kickoff(
+    "Provide a structured analysis of the AI market in 2025",
+    response_format=MarketAnalysis
+)
+
+# Access structured data
+print(f"Key Trends: {structured_result.pydantic.key_trends}")
+print(f"Recommendation: {structured_result.pydantic.recommendation}")
+```
+
+## Best Practices
+
+1. **Naming Convention**: Use clear, descriptive names for your repository agents
+2. **Documentation**: Include comprehensive descriptions for each agent
+3. **Tool Management**: Ensure that tools referenced by repository agents are available in your environment
+4. **Access Control**: Manage permissions to ensure only authorized team members can modify repository agents
+
+## Organization Management
+
+To switch between organizations or see your current organization, use the CrewAI CLI:
+
+```bash
+# View current organization
+crewai org current
+
+# Switch to a different organization
+crewai org switch <org_id>
+
+# List all available organizations
+crewai org list
+```
+
+<Note>
+When loading agents from repositories, you must be authenticated and switched to the correct organization. If you receive errors, check your authentication status and organization settings using the CLI commands above.
+</Note>

docs/en/enterprise/features/hallucination-guardrail.mdx

@@ -2,6 +2,7 @@
 title: Hallucination Guardrail
 description: "Prevent and detect AI hallucinations in your CrewAI tasks"
 icon: "shield-check"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/features/integrations.mdx

@@ -2,6 +2,7 @@
 title: Integrations
 description: "Connected applications for your agents to take actions."
 icon: "plug"
+mode: "wide"
 ---
 
 ## Overview
@@ -59,7 +60,7 @@ Before using Authentication Integrations, ensure you have:
 3. Click **Connect** on your desired service from the Authentication Integrations section
 4. Complete the OAuth authentication flow
 5. Grant necessary permissions for your use case
-6. Get your Enterprise Token from your [CrewAI Enterprise](https://app.crewai.com) account page - https://app.crewai.com/crewai_plus/settings/account
+6. All set! Get your Enterprise Token from your [CrewAI Enterprise](https://app.crewai.com) in **Integration** tab
 
 <Frame>
   ![Integrations](/images/enterprise/enterprise_action_auth_token.png)

docs/en/enterprise/features/rbac.mdx

@@ -0,0 +1,104 @@
+---
+title: "Role-Based Access Control (RBAC)"
+description: "Control access to crews, tools, and data with roles, scopes, and granular permissions."
+icon: "shield"
+mode: "wide"
+---
+
+## Overview
+
+RBAC in CrewAI Enterprise enables secure, scalable access management through a combination of organization‑level roles and automation‑level visibility controls.
+
+<Frame>
+  <img src="/images/enterprise/users_and_roles.png" alt="RBAC overview in CrewAI Enterprise" />
+  
+</Frame>
+
+## Users and Roles
+
+Each member in your CrewAI workspace is assigned a role, which determines their access across various features. 
+
+You can:
+
+- Use predefined roles (Owner, Member)
+- Create custom roles tailored to specific permissions
+- Assign roles at any time through the settings panel
+
+You can configure users and roles in Settings → Roles.
+
+<Steps>
+  <Step title="Open Roles settings">
+    Go to <b>Settings → Roles</b> in CrewAI Enterprise.
+  </Step>
+  <Step title="Choose a role type">
+    Use a predefined role (<b>Owner</b>, <b>Member</b>) or click <b>Create role</b> to define a custom one.
+  </Step>
+  <Step title="Assign to members">
+    Select users and assign the role. You can change this anytime.
+  </Step>
+</Steps>
+
+### Configuration summary
+
+| Area | Where to configure | Options |
+|:---|:---|:---|
+| Users & Roles | Settings → Roles | Predefined: Owner, Member; Custom roles |
+| Automation visibility | Automation → Settings → Visibility | Private; Whitelist users/roles |
+
+## Automation‑level Access Control
+
+In addition to organization‑wide roles, CrewAI Automations support fine‑grained visibility settings that let you restrict access to specific automations by user or role.
+
+This is useful for:
+
+- Keeping sensitive or experimental automations private
+- Managing visibility across large teams or external collaborators
+- Testing automations in isolated contexts
+
+Deployments can be configured as private, meaning only whitelisted users and roles will be able to:
+
+- View the deployment
+- Run it or interact with its API
+- Access its logs, metrics, and settings
+
+The organization owner always has access, regardless of visibility settings.
+
+You can configure automation‑level access control in Automation → Settings → Visibility tab.
+
+<Steps>
+  <Step title="Open Visibility tab">
+    Navigate to <b>Automation → Settings → Visibility</b>.
+  </Step>
+  <Step title="Set visibility">
+    Choose <b>Private</b> to restrict access. The organization owner always retains access.
+  </Step>
+  <Step title="Whitelist access">
+    Add specific users and roles allowed to view, run, and access logs/metrics/settings.
+  </Step>
+  <Step title="Save and verify">
+    Save changes, then confirm that non‑whitelisted users cannot view or run the automation.
+  </Step>
+</Steps>
+
+### Private visibility: access outcomes
+
+| Action | Owner | Whitelisted user/role | Not whitelisted |
+|:---|:---|:---|:---|
+| View automation | ✓ | ✓ | ✗ |
+| Run automation/API | ✓ | ✓ | ✗ |
+| Access logs/metrics/settings | ✓ | ✓ | ✗ |
+
+<Tip>
+The organization owner always has access. In private mode, only whitelisted users and roles can view, run, and access logs/metrics/settings.
+</Tip>
+
+<Frame>
+  <img src="/images/enterprise/visibility.png" alt="Automation Visibility settings in CrewAI Enterprise" />
+  
+</Frame>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with RBAC questions.
+</Card>
+
+

docs/en/enterprise/features/tool-repository.mdx

@@ -2,6 +2,7 @@
 title: Tool Repository
 description: "Using the Tool Repository to manage your tools"
 icon: "toolbox"
+mode: "wide"
 ---
 
 ## Overview
@@ -35,6 +36,52 @@ crewai tool install <tool-name>
 
 This installs the tool and adds it to `pyproject.toml`.
 
+You can use the tool by importing it and adding it to your agents:
+
+```python
+from your_tool.tool import YourTool
+
+custom_tool = YourTool()
+
+researcher = Agent(
+    role='Market Research Analyst',
+    goal='Provide up-to-date market analysis of the AI industry',
+    backstory='An expert analyst with a keen eye for market trends.',
+    tools=[custom_tool],
+    verbose=True
+)
+```
+
+## Adding other packages after installing a tool
+
+After installing a tool from the CrewAI Enterprise Tool Repository, you need to use the `crewai uv` command to add other packages to your project.
+Using pure `uv` commands will fail due to authentication to tool repository being handled by the CLI. By using the `crewai uv` command, you can add other packages to your project without having to worry about authentication.
+Any `uv` command can be used with the `crewai uv` command, making it a powerful tool for managing your project's dependencies without the hassle of managing authentication through environment variables or other methods.
+
+Say that you have installed a custom tool from the CrewAI Enterprise Tool Repository called "my-tool":
+
+```bash
+crewai tool install my-tool
+```
+
+And now you want to add another package to your project, you can use the following command:
+
+```bash
+crewai uv add requests
+```
+
+Other commands like `uv sync` or `uv remove` can also be used with the `crewai uv` command:
+
+```bash
+crewai uv sync
+```
+
+```bash
+crewai uv remove requests
+```
+
+This will add the package to your project and update `pyproject.toml` accordingly.
+
 ## Creating and Publishing Tools
 
 To create a new tool project:

docs/en/enterprise/features/traces.mdx

@@ -2,6 +2,7 @@
 title: Traces
 description: "Using Traces to monitor your Crews"
 icon: "timeline"
+mode: "wide"
 ---
 
 ## Overview
@@ -141,6 +142,16 @@ Traces are invaluable for troubleshooting issues with your crews:
   </Step>
 </Steps>
 
+## Performance and batching
+
+CrewAI batches trace uploads to reduce overhead on high-volume runs:
+
+- A TraceBatchManager buffers events and sends them in batches via the Plus API client
+- Reduces network chatter and improves reliability on flaky connections
+- Automatically enabled in the default trace listener; no configuration needed
+
+This yields more stable tracing under load while preserving detailed task/agent telemetry.
+
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
   Contact our support team for assistance with trace analysis or any other CrewAI Enterprise features.
 </Card>

docs/en/enterprise/features/webhook-streaming.mdx

@@ -2,6 +2,7 @@
 title: Webhook Streaming
 description: "Using Webhook Streaming to stream events to your webhook"
 icon: "webhook"
+mode: "wide"
 ---
 
 ## Overview
@@ -62,16 +63,96 @@ As requests are sent over HTTP, the order of events can't be guaranteed. If you
 
 CrewAI supports both system events and custom events in Enterprise Event Streaming. These events are sent to your configured webhook endpoint during crew and flow execution.
 
-- `crew_kickoff_started`
-- `crew_step_started`
-- `crew_step_completed`
-- `crew_execution_completed`
-- `llm_call_started`
-- `llm_call_completed`
-- `tool_usage_started`
-- `tool_usage_completed`
-- `crew_test_failed`
-- *...and others*
+### Flow Events:
+
+  - flow_created
+  - flow_started
+  - flow_finished
+  - flow_plot
+  - method_execution_started
+  - method_execution_finished
+  - method_execution_failed
+
+### Agent Events:
+
+  - agent_execution_started
+  - agent_execution_completed
+  - agent_execution_error
+  - lite_agent_execution_started
+  - lite_agent_execution_completed
+  - lite_agent_execution_error
+  - agent_logs_started
+  - agent_logs_execution
+  - agent_evaluation_started
+  - agent_evaluation_completed
+  - agent_evaluation_failed
+
+### Crew Events:
+
+  - crew_kickoff_started
+  - crew_kickoff_completed
+  - crew_kickoff_failed
+  - crew_train_started
+  - crew_train_completed
+  - crew_train_failed
+  - crew_test_started
+  - crew_test_completed
+  - crew_test_failed
+  - crew_test_result
+
+### Task Events:
+
+  - task_started
+  - task_completed
+  - task_failed
+  - task_evaluation
+
+### Tool Usage Events:
+
+  - tool_usage_started
+  - tool_usage_finished
+  - tool_usage_error
+  - tool_validate_input_error
+  - tool_selection_error
+  - tool_execution_error
+
+### LLM Events:
+
+  - llm_call_started
+  - llm_call_completed
+  - llm_call_failed
+  - llm_stream_chunk
+
+### LLM Guardrail Events:
+
+  - llm_guardrail_started
+  - llm_guardrail_completed
+
+### Memory Events:
+
+  - memory_query_started
+  - memory_query_completed
+  - memory_query_failed
+  - memory_save_started
+  - memory_save_completed
+  - memory_save_failed
+  - memory_retrieval_started
+  - memory_retrieval_completed
+
+### Knowledge Events:
+
+  - knowledge_search_query_started
+  - knowledge_search_query_completed
+  - knowledge_search_query_failed
+  - knowledge_query_started
+  - knowledge_query_completed
+  - knowledge_query_failed
+
+### Reasoning Events:
+
+  - agent_reasoning_started
+  - agent_reasoning_completed
+  - agent_reasoning_failed
 
 Event names match the internal event bus. See [GitHub source](https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events) for the full list.
 

docs/en/enterprise/guides/automation-triggers.mdx

@@ -0,0 +1,179 @@
+---
+title: "Automation Triggers"
+description: "Automatically execute your CrewAI workflows when specific events occur in connected integrations"
+icon: "bolt"
+mode: "wide"
+---
+
+Automation triggers enable you to automatically run your CrewAI deployments when specific events occur in your connected integrations, creating powerful event-driven workflows that respond to real-time changes in your business systems.
+
+## Overview
+
+With automation triggers, you can:
+
+- **Respond to real-time events** - Automatically execute workflows when specific conditions are met
+- **Integrate with external systems** - Connect with platforms like Gmail, Outlook, OneDrive, JIRA, Slack, Stripe and more
+- **Scale your automation** - Handle high-volume events without manual intervention
+- **Maintain context** - Access trigger data within your crews and flows
+
+## Managing Automation Triggers
+
+### Viewing Available Triggers
+
+To access and manage your automation triggers:
+
+1. Navigate to your deployment in the CrewAI dashboard
+2. Click on the **Triggers** tab to view all available trigger integrations
+
+<Frame>
+  <img src="/images/enterprise/list-available-triggers.png" alt="List of available automation triggers" />
+</Frame>
+
+This view shows all the trigger integrations available for your deployment, along with their current connection status.
+
+### Enabling and Disabling Triggers
+
+Each trigger can be easily enabled or disabled using the toggle switch:
+
+<Frame>
+  <img src="/images/enterprise/trigger-selected.png" alt="Enable or disable triggers with toggle" />
+</Frame>
+
+- **Enabled (blue toggle)**: The trigger is active and will automatically execute your deployment when the specified events occur
+- **Disabled (gray toggle)**: The trigger is inactive and will not respond to events
+
+Simply click the toggle to change the trigger state. Changes take effect immediately.
+
+### Monitoring Trigger Executions
+
+Track the performance and history of your triggered executions:
+
+<Frame>
+  <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
+</Frame>
+
+## Building Automation
+
+Before building your automation, it's helpful to understand the structure of trigger payloads that your crews and flows will receive.
+
+### Payload Samples Repository
+
+We maintain a comprehensive repository with sample payloads from various trigger sources to help you build and test your automations:
+
+**🔗 [CrewAI Enterprise Trigger Payload Samples](https://github.com/crewAIInc/crewai-enterprise-trigger-payload-samples)**
+
+This repository contains:
+
+- **Real payload examples** from different trigger sources (Gmail, Google Drive, etc.)
+- **Payload structure documentation** showing the format and available fields
+
+### Triggers with Crew
+
+Your existing crew definitions work seamlessly with triggers, you just need to have a task to parse the received payload:
+
+```python
+@CrewBase
+class MyAutomatedCrew:
+    @agent
+    def researcher(self) -> Agent:
+        return Agent(
+            config=self.agents_config['researcher'],
+        )
+
+    @task
+    def parse_trigger_payload(self) -> Task:
+        return Task(
+            config=self.tasks_config['parse_trigger_payload'],
+            agent=self.researcher(),
+        )
+
+    @task
+    def analyze_trigger_content(self) -> Task:
+        return Task(
+            config=self.tasks_config['analyze_trigger_data'],
+            agent=self.researcher(),
+        )
+```
+
+The crew will automatically receive and can access the trigger payload through the standard CrewAI context mechanisms.
+
+<Note>
+  Crew and Flow inputs can include `crewai_trigger_payload`. CrewAI automatically injects this payload:
+  - Tasks: appended to the first task's description by default ("Trigger Payload: {crewai_trigger_payload}")
+  - Control via `allow_crewai_trigger_context`: set `True` to always inject, `False` to never inject
+  - Flows: any `@start()` method that accepts a `crewai_trigger_payload` parameter will receive it
+</Note>
+
+### Integration with Flows
+
+For flows, you have more control over how trigger data is handled:
+
+#### Accessing Trigger Payload
+
+All `@start()` methods in your flows will accept an additional parameter called `crewai_trigger_payload`:
+
+```python
+from crewai.flow import Flow, start, listen
+
+class MyAutomatedFlow(Flow):
+    @start()
+    def handle_trigger(self, crewai_trigger_payload: dict = None):
+        """
+        This start method can receive trigger data
+        """
+        if crewai_trigger_payload:
+            # Process the trigger data
+            trigger_id = crewai_trigger_payload.get('id')
+            event_data = crewai_trigger_payload.get('payload', {})
+
+            # Store in flow state for use by other methods
+            self.state.trigger_id = trigger_id
+            self.state.trigger_type = event_data
+
+            return event_data
+
+        # Handle manual execution
+        return None
+
+    @listen(handle_trigger)
+    def process_data(self, trigger_data):
+        """
+        Process the data from the trigger
+        """
+        # ... process the trigger
+```
+
+#### Triggering Crews from Flows
+
+When kicking off a crew within a flow that was triggered, pass the trigger payload as it:
+
+```python
+@start()
+def delegate_to_crew(self, crewai_trigger_payload: dict = None):
+    """
+    Delegate processing to a specialized crew
+    """
+    crew = MySpecializedCrew()
+
+    # Pass the trigger payload to the crew
+    result = crew.crew().kickoff(
+        inputs={
+            'a_custom_parameter': "custom_value",
+            'crewai_trigger_payload': crewai_trigger_payload
+        },
+    )
+
+    return result
+```
+
+## Troubleshooting
+
+**Trigger not firing:**
+- Verify the trigger is enabled
+- Check integration connection status
+
+**Execution failures:**
+- Check the execution logs for error details
+- If you are developing, make sure the inputs include the `crewai_trigger_payload` parameter with the correct payload
+
+Automation triggers transform your CrewAI deployments into responsive, event-driven systems that can seamlessly integrate with your existing business processes and tools.

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

@@ -2,6 +2,7 @@
 title: "Azure OpenAI Setup"
 description: "Configure Azure OpenAI with Crew Studio for enterprise LLM connections"
 icon: "microsoft"
+mode: "wide"
 ---
 
 This guide walks you through connecting Azure OpenAI with Crew Studio for seamless enterprise AI operations.
@@ -9,12 +10,12 @@ This guide walks you through connecting Azure OpenAI with Crew Studio for seamle
 ## Setup Process
 
 <Steps>
-    <Step title="Access Azure OpenAI Studio">
-        1. In Azure, go to `Azure AI Services > select your deployment > open Azure OpenAI Studio`.
+    <Step title="Access Azure AI Foundry">
+        1. In Azure, go to [Azure AI Foundry](https://ai.azure.com/) > select your Azure OpenAI deployment.
         2. On the left menu, click `Deployments`. If you don't have one, create a deployment with your desired model.
         3. Once created, select your deployment and locate the `Target URI` and `Key` on the right side of the page. Keep this page open, as you'll need this information.
             <Frame>
-                <img src="/images/enterprise/azure-openai-studio.png" alt="Azure OpenAI Studio" />
+                <img src="/images/enterprise/azure-openai-studio.png" alt="Azure AI Foundry" />
             </Frame>
     </Step>
 
@@ -48,4 +49,4 @@ If you encounter issues:
 - Verify the Target URI format matches the expected pattern
 - Check that the API key is correct and has proper permissions
 - Ensure network access is configured to allow CrewAI connections
-- Confirm the deployment model matches what you've configured in CrewAI 
+- Confirm the deployment model matches what you've configured in CrewAI 

docs/en/enterprise/guides/build-crew.mdx

@@ -2,6 +2,7 @@
 title: "Build Crew"
 description: "A Crew is a group of agents that work together to complete a task."
 icon: "people-arrows"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/guides/deploy-crew.mdx

@@ -2,6 +2,7 @@
 title: "Deploy Crew"
 description: "Deploying a Crew on CrewAI Enterprise"
 icon: "rocket"
+mode: "wide"
 ---
 
 <Note>
@@ -41,11 +42,8 @@ The CLI provides the fastest way to deploy locally developed crews to the Enterp
     First, you need to authenticate your CLI with the CrewAI Enterprise platform:
 
     ```bash
-    # If you already have a CrewAI Enterprise account
+    # If you already have a CrewAI Enterprise account, or want to create one:
     crewai login
-
-    # If you're creating a new account
-    crewai signup
     ```
 
     When you run either command, the CLI will:

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

@@ -2,6 +2,7 @@
 title: "Enable Crew Studio"
 description: "Enabling Crew Studio on CrewAI Enterprise"
 icon: "comments"
+mode: "wide"
 ---
 
 <Tip>

docs/en/enterprise/guides/hubspot-trigger.mdx

@@ -2,6 +2,7 @@
 title: "HubSpot Trigger"
 description: "Trigger CrewAI crews directly from HubSpot Workflows"
 icon: "hubspot"
+mode: "wide"
 ---
 
 This guide provides a step-by-step process to set up HubSpot triggers for CrewAI Enterprise, enabling you to initiate crews directly from HubSpot Workflows.

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

@@ -2,6 +2,7 @@
 title: "HITL Workflows"
 description: "Learn how to implement Human-In-The-Loop workflows in CrewAI for enhanced decision-making"
 icon: "user-check"
+mode: "wide"
 ---
 
 Human-In-The-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. This guide shows you how to implement HITL within CrewAI.

docs/en/enterprise/guides/kickoff-crew.mdx

@@ -2,6 +2,7 @@
 title: "Kickoff Crew"
 description: "Kickoff a Crew on CrewAI Enterprise"
 icon: "flag-checkered"
+mode: "wide"
 ---
 
 ## Overview

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

@@ -2,6 +2,7 @@
 title: "React Component Export"
 description: "Learn how to export and integrate CrewAI Enterprise React components into your applications"
 icon: "react"
+mode: "wide"
 ---
 
 This guide explains how to export CrewAI Enterprise crews as React components and integrate them into your own applications.

docs/en/enterprise/guides/salesforce-trigger.mdx

@@ -2,6 +2,7 @@
 title: "Salesforce Trigger"
 description: "Trigger CrewAI crews from Salesforce workflows for CRM automation"
 icon: "salesforce"
+mode: "wide"
 ---
 
 CrewAI Enterprise can be triggered from Salesforce to automate customer relationship management workflows and enhance your sales operations.

docs/en/enterprise/guides/slack-trigger.mdx

@@ -2,6 +2,7 @@
 title: "Slack Trigger"
 description: "Trigger CrewAI crews directly from Slack using slash commands"
 icon: "slack"
+mode: "wide"
 ---
 
 This guide explains how to start a crew directly from Slack using CrewAI triggers.

docs/en/enterprise/guides/team-management.mdx

@@ -2,6 +2,7 @@
 title: "Team Management"
 description: "Learn how to invite and manage team members in your CrewAI Enterprise organization"
 icon: "users"
+mode: "wide"
 ---
 
 As an administrator of a CrewAI Enterprise account, you can easily invite new team members to join your organization. This guide will walk you through the process step-by-step.

docs/en/enterprise/guides/update-crew.mdx

@@ -2,6 +2,7 @@
 title: "Update Crew"
 description: "Updating a Crew on CrewAI Enterprise"
 icon: "pencil"
+mode: "wide"
 ---
 
 <Note>

docs/en/enterprise/guides/webhook-automation.mdx

@@ -2,6 +2,7 @@
 title: "Webhook Automation"
 description: "Automate CrewAI Enterprise workflows using webhooks with platforms like ActivePieces, Zapier, and Make.com"
 icon: "webhook"
+mode: "wide"
 ---
 
 CrewAI Enterprise allows you to automate your workflow using webhooks. This article will guide you through the process of setting up and using webhooks to kickoff your crew execution, with a focus on integration with ActivePieces, a workflow automation platform similar to Zapier and Make.com.
@@ -79,14 +80,24 @@ CrewAI Enterprise allows you to automate your workflow using webhooks. This arti
 
 ## Webhook Output Examples
 
+**Note:** Any `meta` object provided in your kickoff request will be included in all webhook payloads, allowing you to track requests and maintain context across the entire crew execution lifecycle.
+
 <Tabs>
     <Tab title="Step Webhook">
         `stepWebhookUrl` - Callback that will be executed upon each agent inner thought
 
         ```json
         {
-            "action": "**Preliminary Research Report on the Financial Industry for crewai Enterprise Solution**\n1. Industry Overview and Trends\nThe financial industry in ....\nConclusion:\nThe financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance. Further engagement with the lead is recommended to better tailor the crewai solution to their specific needs and scale.",
-            "task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0"
+            "prompt": "Research the financial industry for potential AI solutions",
+            "thought": "I need to conduct preliminary research on the financial industry",
+            "tool": "research_tool",
+            "tool_input": "financial industry AI solutions",
+            "result": "**Preliminary Research Report on the Financial Industry for crewai Enterprise Solution**\n1. Industry Overview and Trends\nThe financial industry in ....\nConclusion:\nThe financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance. Further engagement with the lead is recommended to better tailor the crewai solution to their specific needs and scale.",
+            "kickoff_id": "97eba64f-958c-40a0-b61c-625fe635a3c0",
+            "meta": {
+                "requestId": "travel-req-123",
+                "source": "web-app"
+            }
         }
         ```
     </Tab>
@@ -95,8 +106,21 @@ CrewAI Enterprise allows you to automate your workflow using webhooks. This arti
 
         ```json
         {
-            "description": "Using the information gathered from the lead's data, conduct preliminary research on the lead's industry, company background, and potential use cases for crewai. Focus on finding relevant data that can aid in scoring the lead and planning a strategy to pitch them crewai.The financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance. Further engagement with the lead is recommended to better tailor the crewai solution to their specific needs and scale.",
-            "task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0"
+            "description": "Using the information gathered from the lead's data, conduct preliminary research on the lead's industry, company background, and potential use cases for crewai. Focus on finding relevant data that can aid in scoring the lead and planning a strategy to pitch them crewai.",
+            "name": "Industry Research Task",
+            "expected_output": "Detailed research report on the financial industry",
+            "summary": "The financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance. Further engagement with the lead is recommended to better tailor the crewai solution to their specific needs and scale.",
+            "agent": "Research Agent",
+            "output": "**Preliminary Research Report on the Financial Industry for crewai Enterprise Solution**\n1. Industry Overview and Trends\nThe financial industry in ....\nConclusion:\nThe financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance.",
+            "output_json": {
+                "industry": "financial",
+                "key_opportunities": ["digital customer engagement", "risk management", "regulatory compliance"]
+            },
+            "kickoff_id": "97eba64f-958c-40a0-b61c-625fe635a3c0",
+            "meta": {
+                "requestId": "travel-req-123",
+                "source": "web-app"
+            }
         }
         ```
     </Tab>
@@ -105,8 +129,9 @@ CrewAI Enterprise allows you to automate your workflow using webhooks. This arti
 
         ```json
         {
-            "task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0",
-            "result": {
+            "kickoff_id": "97eba64f-958c-40a0-b61c-625fe635a3c0",
+            "result": "**Final Analysis Report**\n\nLead Score: Customer service enhancement and compliance are particularly relevant.\n\nTalking Points:\n- Highlight how crewai's AI solutions can transform customer service\n- Discuss crewai's potential for sustainability goals\n- Emphasize compliance capabilities\n- Stress adaptability for various operation scales",
+            "result_json": {
                 "lead_score": "Customer service enhancement, and compliance are particularly relevant.",
                 "talking_points": [
                     "Highlight how crewai's AI solutions can transform customer service with automated, personalized experiences and 24/7 support, improving both customer satisfaction and operational efficiency.",
@@ -114,6 +139,15 @@ CrewAI Enterprise allows you to automate your workflow using webhooks. This arti
                     "Emphasize crewai's ability to enhance compliance with evolving regulations through efficient data processing and reporting, reducing the risk of non-compliance penalties.",
                     "Stress the adaptability of crewai to support both extensive multinational operations and smaller, targeted projects, ensuring the solution grows with the institution's needs."
                 ]
+            },
+            "token_usage": {
+                "total_tokens": 1250,
+                "prompt_tokens": 800,
+                "completion_tokens": 450
+            },
+            "meta": {
+                "requestId": "travel-req-123",
+                "source": "web-app"
             }
         }
         ```

docs/en/enterprise/guides/zapier-trigger.mdx

@@ -2,6 +2,7 @@
 title: "Zapier Trigger"
 description: "Trigger CrewAI crews from Zapier workflows to automate cross-app workflows"
 icon: "bolt"
+mode: "wide"
 ---
 
 This guide will walk you through the process of setting up Zapier triggers for CrewAI Enterprise, allowing you to automate workflows between CrewAI Enterprise and other applications.

docs/en/enterprise/integrations/asana.mdx

@@ -2,6 +2,7 @@
 title: Asana Integration
 description: "Team task and project coordination with Asana integration for CrewAI."
 icon: "circle"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/box.mdx

@@ -2,6 +2,7 @@
 title: Box Integration
 description: "File storage and document management with Box integration for CrewAI."
 icon: "box"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/clickup.mdx

@@ -2,6 +2,7 @@
 title: ClickUp Integration
 description: "Task and productivity management with ClickUp integration for CrewAI."
 icon: "list-check"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/github.mdx

@@ -2,6 +2,7 @@
 title: GitHub Integration
 description: "Repository and issue management with GitHub integration for CrewAI."
 icon: "github"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/gmail.mdx

@@ -2,6 +2,7 @@
 title: Gmail Integration
 description: "Email and contact management with Gmail integration for CrewAI."
 icon: "envelope"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/google_calendar.mdx

@@ -2,6 +2,7 @@
 title: Google Calendar Integration
 description: "Event and schedule management with Google Calendar integration for CrewAI."
 icon: "calendar"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/google_sheets.mdx

@@ -2,6 +2,7 @@
 title: Google Sheets Integration
 description: "Spreadsheet data synchronization with Google Sheets integration for CrewAI."
 icon: "google"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/hubspot.mdx

@@ -2,6 +2,7 @@
 title: "HubSpot Integration"
 description: "Manage companies and contacts in HubSpot with CrewAI."
 icon: "briefcase"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/jira.mdx

@@ -2,6 +2,7 @@
 title: Jira Integration
 description: "Issue tracking and project management with Jira integration for CrewAI."
 icon: "bug"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/linear.mdx

@@ -2,6 +2,7 @@
 title: Linear Integration
 description: "Software project and bug tracking with Linear integration for CrewAI."
 icon: "list-check"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/notion.mdx

@@ -2,6 +2,7 @@
 title: Notion Integration
 description: "Page and database management with Notion integration for CrewAI."
 icon: "book"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/salesforce.mdx

@@ -2,6 +2,7 @@
 title: Salesforce Integration
 description: "CRM and sales automation with Salesforce integration for CrewAI."
 icon: "salesforce"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/shopify.mdx

@@ -2,6 +2,7 @@
 title: Shopify Integration
 description: "E-commerce and online store management with Shopify integration for CrewAI."
 icon: "shopify"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/slack.mdx

@@ -2,6 +2,7 @@
 title: Slack Integration
 description: "Team communication and collaboration with Slack integration for CrewAI."
 icon: "slack"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/stripe.mdx

@@ -2,6 +2,7 @@
 title: Stripe Integration
 description: "Payment processing and subscription management with Stripe integration for CrewAI."
 icon: "stripe"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/integrations/zendesk.mdx

@@ -2,6 +2,7 @@
 title: Zendesk Integration
 description: "Customer support and helpdesk management with Zendesk integration for CrewAI."
 icon: "headset"
+mode: "wide"
 ---
 
 ## Overview

docs/en/enterprise/introduction.mdx

@@ -2,6 +2,7 @@
 title: "CrewAI Enterprise"
 description: "Deploy, monitor, and scale your AI agent workflows"
 icon: "globe"
+mode: "wide"
 ---
 
 ## Introduction

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

@@ -2,6 +2,7 @@
 title: FAQs
 description: "Frequently asked questions about CrewAI Enterprise"
 icon: "circle-question"
+mode: "wide"
 ---
 
 <AccordionGroup>

docs/en/examples/cookbooks.mdx

@@ -0,0 +1,23 @@
+---
+title: CrewAI Cookbooks
+description: Feature-focused quickstarts and notebooks for learning patterns fast.
+icon: book
+mode: "wide"
+---
+
+## Quickstarts & Demos
+
+<CardGroup cols={2}>
+  <Card title="Task Guardrails" icon="shield-check" href="https://github.com/crewAIInc/crewAI-quickstarts/tree/main/Task%20Guardrails"> 
+    Interactive notebooks for hands-on exploration.
+  </Card>
+  <Card title="Browse Quickstarts" icon="bolt" href="https://github.com/crewAIInc/crewAI-quickstarts">
+    Feature demos and small projects showcasing specific CrewAI capabilities.
+  </Card>
+</CardGroup>
+
+<Tip>
+Use Cookbooks to learn a pattern quickly, then jump to Full Examples for production‑grade implementations.
+</Tip>
+
+

docs/en/examples/example.mdx

@@ -1,62 +1,86 @@
 ---
 title: CrewAI Examples
-description: A collection of examples that show how to use CrewAI framework to automate workflows.
+description: Explore curated examples organized by Crews, Flows, Integrations, and Notebooks.
 icon: rocket-launch
+mode: "wide"
 ---
 
+## Crews
+
 <CardGroup cols={3}>
-  <Card
-  title="Marketing Strategy"
-  color="#F3A78B"
-  href="https://github.com/crewAIInc/crewAI-examples/tree/main/marketing_strategy"
-  icon="bullhorn"
-    iconType="solid"
-  >
-    Automate marketing strategy creation with CrewAI. 
+  <Card title="Marketing Strategy" icon="bullhorn" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/marketing_strategy">
+    Multi‑agent marketing campaign planning.
+  </Card>
+  <Card title="Surprise Trip" icon="plane" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/surprise_trip">
+    Personalized surprise travel planning.
+  </Card>
+  <Card title="Match Profile to Positions" icon="id-card" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/match_profile_to_positions">
+    CV‑to‑job matching with vector search.
+  </Card>
+  <Card title="Job Posting" icon="newspaper" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting">
+    Automated job description creation.
+  </Card>
+  <Card title="Game Builder Crew" icon="gamepad" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/game-builder-crew">
+    Multi‑agent team that designs and builds Python games.
+  </Card>
+  <Card title="Recruitment" icon="user-group" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/recruitment">
+    Candidate sourcing and evaluation.
+  </Card>
+  <Card title="Browse all Crews" icon="users" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews">
+    See the full list of crew examples.
+  </Card>
+</CardGroup>
+
+## Flows
+
+<CardGroup cols={3}>
+  <Card title="Content Creator Flow" icon="pen" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/content_creator_flow">
+    Multi‑crew content generation with routing.
+  </Card>
+  <Card title="Email Auto Responder" icon="envelope" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/email_auto_responder_flow">
+    Automated email monitoring and replies.
+  </Card>
+  <Card title="Lead Score Flow" icon="chart-line" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/lead_score_flow">
+    Lead qualification with human‑in‑the‑loop.
+  </Card>
+  <Card title="Meeting Assistant Flow" icon="calendar" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/meeting_assistant_flow">
+    Notes processing with integrations.
+  </Card>
+  <Card title="Self Evaluation Loop" icon="rotate" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/self_evaluation_loop_flow">
+    Iterative self‑improvement workflows.
+  </Card>
+  <Card title="Write a Book (Flows)" icon="book" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/write_a_book_with_flows">
+    Parallel chapter generation.
+  </Card>
+  <Card title="Browse all Flows" icon="diagram-project" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows">
+    See the full list of flow examples.
+  </Card>
+</CardGroup>
+
+## Integrations
+
+<CardGroup cols={3}>
+  <Card title="CrewAI ↔ LangGraph" icon="link" href="https://github.com/crewAIInc/crewAI-examples/tree/main/integrations/crewai-langgraph">
+    Integration with LangGraph framework.
+  </Card>
+  <Card title="Azure OpenAI" icon="cloud" href="https://github.com/crewAIInc/crewAI-examples/tree/main/integrations/azure_model">
+    Using CrewAI with Azure OpenAI.
+  </Card>
+  <Card title="NVIDIA Models" icon="microchip" href="https://github.com/crewAIInc/crewAI-examples/tree/main/integrations/nvidia_models">
+    NVIDIA ecosystem integrations.
+  </Card>
+  <Card title="Browse Integrations" icon="puzzle-piece" href="https://github.com/crewAIInc/crewAI-examples/tree/main/integrations">
+    See all integration examples.
+  </Card>
+</CardGroup>
+
+## Notebooks
+
+<CardGroup cols={2}>
+  <Card title="Simple QA Crew + Flow" icon="book" href="https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/Simple%20QA%20Crew%20%2B%20Flow">
+    Simple QA Crew + Flow.
+  </Card>
+  <Card title="All Notebooks" icon="book" href="https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks">
+    Interactive examples for learning and experimentation.
   </Card>
-  <Card
-    title="Surprise Trip"
-    color="#F3A78B"
-    href="https://github.com/crewAIInc/crewAI-examples/tree/main/surprise_trip"
-    icon="plane"
-      iconType="duotone"
-    >
-      Create a surprise trip itinerary with CrewAI.
-    </Card>
-    <Card
-    title="Match Profile to Positions"
-    color="#F3A78B"
-    href="https://github.com/crewAIInc/crewAI-examples/tree/main/match_profile_to_positions"
-    icon="linkedin"
-      iconType="duotone"
-    >
-      Match a profile to jobpositions with CrewAI.
-    </Card>
-    <Card
-    title="Create Job Posting"
-    color="#F3A78B"
-    href="https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting"
-    icon="newspaper"
-      iconType="duotone"
-    >
-      Create a job posting with CrewAI.
-    </Card>
-    <Card
-    title="Game Generator"
-    color="#F3A78B"
-    href="https://github.com/crewAIInc/crewAI-examples/tree/main/game-builder-crew"
-    icon="gamepad"
-      iconType="duotone"
-    >
-      Create a game with CrewAI.
-    </Card>
-    <Card
-    title="Find Job Candidates"
-    color="#F3A78B"
-    href="https://github.com/crewAIInc/crewAI-examples/tree/main/recruitment"
-    icon="user-group"
-      iconType="duotone"
-    >
-      Find job candidates with CrewAI.
-    </Card>
 </CardGroup>

docs/en/guides/advanced/customizing-prompts.mdx

@@ -2,6 +2,7 @@
 title: Customizing Prompts
 description: Dive deeper into low-level prompt customization for CrewAI, enabling super custom and complex use cases for different models and languages.
 icon: message-pen
+mode: "wide"
 ---
 
 ## Why Customize Prompts?

docs/en/guides/advanced/fingerprinting.mdx

@@ -2,6 +2,7 @@
 title: Fingerprinting
 description: Learn how to use CrewAI's fingerprinting system to uniquely identify and track components throughout their lifecycle.
 icon: fingerprint
+mode: "wide"
 ---
 
 ## Overview

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

@@ -2,6 +2,7 @@
 title: Crafting Effective Agents
 description: Learn best practices for designing powerful, specialized AI agents that collaborate effectively to solve complex problems.
 icon: robot
+mode: "wide"
 ---
 
 ## The Art and Science of Agent Design

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

@@ -2,6 +2,7 @@
 title: Evaluating Use Cases for CrewAI
 description: Learn how to assess your AI application needs and choose the right approach between Crews and Flows based on complexity and precision requirements.
 icon: scale-balanced
+mode: "wide"
 ---
 
 ## Understanding the Decision Framework

docs/en/guides/crews/first-crew.mdx

@@ -2,6 +2,7 @@
 title: Build Your First Crew
 description: Step-by-step tutorial to create a collaborative AI team that works together to solve complex problems.
 icon: users-gear
+mode: "wide"
 ---
 
 ## Unleashing the Power of Collaborative AI

docs/en/guides/flows/first-flow.mdx

@@ -2,6 +2,7 @@
 title: Build Your First Flow
 description: Learn how to create structured, event-driven workflows with precise control over execution.
 icon: diagram-project
+mode: "wide"
 ---
 
 ## Taking Control of AI Workflows with Flows

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

@@ -2,6 +2,7 @@
 title: Mastering Flow State Management
 description: A comprehensive guide to managing, persisting, and leveraging state in CrewAI Flows for building robust AI applications.
 icon: diagram-project
+mode: "wide"
 ---
 
 ## Understanding the Power of State in Flows
@@ -348,6 +349,31 @@ class SelectivePersistFlow(Flow):
 
 ## Advanced State Patterns
 
+### Conditional starts and resumable execution
+
+Flows support conditional `@start()` and resumable execution for HITL/cyclic scenarios:
+
+```python
+from crewai.flow.flow import Flow, start, listen, and_, or_
+
+class ResumableFlow(Flow):
+    @start()  # unconditional start
+    def init(self):
+        ...
+
+    # Conditional start: run after "init" or external trigger name
+    @start("init")
+    def maybe_begin(self):
+        ...
+
+    @listen(and_(init, maybe_begin))
+    def proceed(self):
+        ...
+```
+
+- Conditional `@start()` accepts a method name, a router label, or a callable condition.
+- During resume, listeners continue from prior checkpoints; cycle/router branches honor resumption flags.
+
 ### State-Based Conditional Logic
 
 You can use state to implement complex conditional logic in your flows:

docs/en/installation.mdx

@@ -2,6 +2,7 @@
 title: Installation
 description: Get started with CrewAI - Install, configure, and build your first AI crew
 icon: wrench
+mode: "wide"
 ---
 
 ## Video Tutorial
@@ -30,6 +31,12 @@ Watch this video tutorial for a step-by-step demonstration of the installation p
   If you need to update Python, visit [python.org/downloads](https://python.org/downloads)
 </Note>
 
+<Note>
+  **OpenAI SDK Requirement**
+
+  CrewAI 0.175.0 requires `openai >= 1.13.3`. If you manage dependencies yourself, ensure your environment satisfies this constraint to avoid import/runtime issues.
+</Note>
+
 CrewAI uses the `uv` as its dependency management and package handling tool. It simplifies project setup and execution, offering a seamless experience.
 
 If you haven't installed `uv` yet, follow **step 1** to quickly get it set up on your system, else you can skip to **step 2**.
@@ -72,7 +79,7 @@ If you haven't installed `uv` yet, follow **step 1** to quickly get it set up on
           </Warning>
 
           <Warning>
-            If you encounter the `chroma-hnswlib==0.7.6` build error (`fatal error C1083: Cannot open include file: 'float.h'`) on Windows, install (Visual Studio Build Tools)[https://visualstudio.microsoft.com/downloads/] with *Desktop development with C++*.
+            If you encounter the `chroma-hnswlib==0.7.6` build error (`fatal error C1083: Cannot open include file: 'float.h'`) on Windows, install [Visual Studio Build Tools](https://visualstudio.microsoft.com/downloads/) with *Desktop development with C++*.
           </Warning>
 
       - To verify that `crewai` is installed, run:
@@ -104,7 +111,6 @@ We recommend using the `YAML` template scaffolding for a structured approach to
       ```
 
     - This creates a new project with the following structure:
-      <Frame>
       ```
       my_project/
       ├── .gitignore
@@ -124,7 +130,6 @@ We recommend using the `YAML` template scaffolding for a structured approach to
                   ├── agents.yaml
                   └── tasks.yaml
       ```
-      </Frame>
   </Step>
 
   <Step title="Customize Your Project">
@@ -172,7 +177,7 @@ For teams and organizations, CrewAI offers enterprise deployment options that el
 
 ### CrewAI Factory (Self-hosted)
 - Containerized deployment for your infrastructure
-- Supports any hyperscaler including on prem depployments
+- Supports any hyperscaler including on prem deployments
 - Integration with your existing security systems
 
 <Card title="Explore Enterprise Options" icon="building" href="https://crewai.com/enterprise">

docs/en/introduction.mdx

@@ -2,6 +2,7 @@
 title: Introduction
 description: Build AI agent teams that work together to tackle complex tasks
 icon: handshake
+mode: "wide"
 ---
 
 # What is CrewAI?

docs/en/learn/before-and-after-kickoff-hooks.mdx

@@ -1,6 +1,7 @@
 ---
 title: Before and After Kickoff Hooks
 description: Learn how to use before and after kickoff hooks in CrewAI
+mode: "wide"
 ---
 
 CrewAI provides hooks that allow you to execute code before and after a crew's kickoff. These hooks are useful for preprocessing inputs or post-processing results.

docs/en/learn/bring-your-own-agent.mdx

@@ -2,6 +2,7 @@
 title: Bring your own agent
 description: Learn how to bring your own agents that work within a Crew.
 icon: robots
+mode: "wide"
 ---
 
 Interoperability is a core concept in CrewAI. This guide will show you how to bring your own agents that work within a Crew.

docs/en/learn/coding-agents.mdx

@@ -2,6 +2,7 @@
 title: Coding Agents
 description: Learn how to enable your CrewAI Agents to write and execute code, and explore advanced features for enhanced functionality.
 icon: rectangle-code
+mode: "wide"
 ---
 
 ## Introduction

docs/en/learn/conditional-tasks.mdx

@@ -2,6 +2,7 @@
 title: Conditional Tasks
 description: Learn how to use conditional tasks in a crewAI kickoff
 icon: diagram-subtask
+mode: "wide"
 ---
 
 ## Introduction

docs/en/learn/create-custom-tools.mdx

@@ -2,6 +2,7 @@
 title: Create Custom Tools
 description: Comprehensive guide on crafting, using, and managing custom tools within the CrewAI framework, including new functionalities and error handling.
 icon: hammer
+mode: "wide"
 ---
 
 ## Creating and Utilizing Tools in CrewAI

docs/en/learn/custom-llm.mdx

@@ -2,6 +2,7 @@
 title: Custom LLM Implementation
 description: Learn how to create custom LLM implementations in CrewAI.
 icon: code
+mode: "wide"
 ---
 
 ## Overview

docs/en/learn/custom-manager-agent.mdx

@@ -2,6 +2,7 @@
 title: Custom Manager Agent
 description: Learn how to set a custom agent as the manager in CrewAI, providing more control over task management and coordination.
 icon: user-shield
+mode: "wide"
 ---
 
 # Setting a Specific Agent as Manager in CrewAI

docs/en/learn/customizing-agents.mdx

@@ -2,6 +2,7 @@
 title: Customize Agents
 description: A comprehensive guide to tailoring agents for specific roles, tasks, and advanced customizations within the CrewAI framework.
 icon: user-pen
+mode: "wide"
 ---
 
 ## Customizable Attributes

docs/en/learn/dalle-image-generation.mdx

@@ -2,6 +2,7 @@
 title: "Image Generation with DALL-E"
 description: "Learn how to use DALL-E for AI-powered image generation in your CrewAI projects"
 icon: "image"
+mode: "wide"
 ---
 
 CrewAI supports integration with OpenAI's DALL-E, allowing your AI agents to generate images as part of their tasks. This guide will walk you through how to set up and use the DALL-E tool in your CrewAI projects.

docs/en/learn/force-tool-output-as-result.mdx

@@ -2,6 +2,7 @@
 title: Force Tool Output as Result
 description: Learn how to force tool output as the result in an Agent's task in CrewAI.
 icon: wrench-simple
+mode: "wide"
 ---
 
 ## Introduction

docs/en/learn/hierarchical-process.mdx

@@ -2,6 +2,7 @@
 title: Hierarchical Process
 description: A comprehensive guide to understanding and applying the hierarchical process within your CrewAI projects, updated to reflect the latest coding practices and functionalities.
 icon: sitemap
+mode: "wide"
 ---
 
 ## Introduction

docs/en/learn/human-in-the-loop.mdx

@@ -2,6 +2,7 @@
 title: "Human-in-the-Loop (HITL) Workflows"
 description: "Learn how to implement Human-in-the-Loop workflows in CrewAI for enhanced decision-making"
 icon: "user-check"
+mode: "wide"
 ---
 
 Human-in-the-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. This guide shows you how to implement HITL within CrewAI.

docs/en/learn/human-input-on-execution.mdx

@@ -2,6 +2,7 @@
 title: Human Input on Execution
 description: Integrating CrewAI with human input during execution in complex decision-making processes and leveraging the full capabilities of the agent's attributes and tools.
 icon: user-check
+mode: "wide"
 ---
 
 ## Human input in agent execution

docs/en/learn/kickoff-async.mdx

@@ -2,6 +2,7 @@
 title: Kickoff Crew Asynchronously
 description: Kickoff a Crew Asynchronously
 icon: rocket-launch
+mode: "wide"
 ---
 
 ## Introduction