fix: ensure Agent is able to load a list of Tools dynamically

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

@@ -1,46 +0,0 @@
-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

@@ -1,102 +0,0 @@
-# For most projects, this workflow file will not need changing; you simply need
-# to commit it to your repository.
-#
-# You may wish to alter this file to override the set of languages analyzed,
-# or to provide custom queries or build logic.
-#
-# ******** NOTE ********
-# We have attempted to detect the languages in your repository. Please check
-# the `language` matrix defined below to confirm you have the correct set of
-# supported CodeQL languages.
-#
-name: "CodeQL Advanced"
-
-on:
-  push:
-    branches: [ "main" ]
-    paths-ignore:
-      - "lib/crewai/src/crewai/cli/templates/**"
-  pull_request:
-    branches: [ "main" ]
-    paths-ignore:
-      - "lib/crewai/src/crewai/cli/templates/**"
-
-jobs:
-  analyze:
-    name: Analyze (${{ matrix.language }})
-    # Runner size impacts CodeQL analysis time. To learn more, please see:
-    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
-    #   - https://gh.io/supported-runners-and-hardware-resources
-    #   - https://gh.io/using-larger-runners (GitHub.com only)
-    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
-    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
-    permissions:
-      # required for all workflows
-      security-events: write
-
-      # required to fetch internal or private CodeQL packs
-      packages: read
-
-      # only required for workflows in private repositories
-      actions: read
-      contents: read
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-        - language: actions
-          build-mode: none
-        - language: python
-          build-mode: none
-        # CodeQL supports the following values keywords for 'language': 'actions', 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'rust', 'swift'
-        # Use `c-cpp` to analyze code written in C, C++ or both
-        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
-        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
-        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
-        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
-        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
-        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
-    steps:
-    - name: Checkout repository
-      uses: actions/checkout@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

@@ -2,9 +2,6 @@ name: Lint
 
 on: [pull_request]
 
-permissions:
-  contents: read
-
 jobs:
   lint:
     runs-on: ubuntu-latest
@@ -18,27 +15,8 @@ jobs:
       - name: Fetch Target Branch
         run: git fetch origin $TARGET_BRANCH --depth=1
 
-      - 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: Install Ruff
+        run: pip install ruff
 
       - name: Get Changed Python Files
         id: changed-files
@@ -52,17 +30,7 @@ jobs:
       - name: Run Ruff on Changed Files
         if: ${{ steps.changed-files.outputs.files != '' }}
         run: |
-         echo "${{ steps.changed-files.outputs.files }}" \
-           | tr ' ' '\n' \
-           | grep -v 'src/crewai/cli/templates/' \
-           | xargs -I{} 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-${{ hashFiles('uv.lock') }}
+          echo "${{ steps.changed-files.outputs.files }}" \
+            | tr ' ' '\n' \
+            | grep -v 'src/crewai/cli/templates/' \
+            | xargs -I{} ruff check "{}"

.github/workflows/publish.yml

@@ -1,71 +0,0 @@
-name: Publish to PyPI
-
-on:
-  release:
-    types: [ published ]
-  workflow_dispatch:
-
-jobs:
-  build:
-    if: github.event.release.prerelease == true
-    name: Build packages
-    runs-on: ubuntu-latest
-    permissions:
-        contents: read
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-
-      - name: Build packages
-        run: |
-          uv build --all-packages
-          rm dist/.gitignore
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: dist
-          path: dist/
-
-  publish:
-    if: github.event.release.prerelease == true
-    name: Publish to PyPI
-    needs: build
-    runs-on: ubuntu-latest
-    environment:
-      name: pypi
-      url: https://pypi.org/p/crewai
-    permissions:
-      id-token: write
-      contents: read
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v6
-        with:
-          version: "0.8.4"
-          python-version: "3.12"
-          enable-cache: false
-
-      - name: Download artifacts
-        uses: actions/download-artifact@v4
-        with:
-          name: dist
-          path: dist
-
-      - name: Publish to PyPI
-        env:
-          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
-        run: |
-          for package in dist/*; do
-            echo "Publishing $package"
-            uv publish "$package"
-          done

.github/workflows/security-checker.yml

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

.github/workflows/tests.yml

@@ -3,116 +3,32 @@ name: Run Tests
 on: [pull_request]
 
 permissions:
-  contents: read
+  contents: write
 
 env:
   OPENAI_API_KEY: fake-api-key
-  PYTHONUNBUFFERED: 1
-  BRAVE_API_KEY: fake-brave-key
-  SNOWFLAKE_USER: fake-snowflake-user
-  SNOWFLAKE_PASSWORD: fake-snowflake-password
-  SNOWFLAKE_ACCOUNT: fake-snowflake-account
-  SNOWFLAKE_WAREHOUSE: fake-snowflake-warehouse
-  SNOWFLAKE_DATABASE: fake-snowflake-database
-  SNOWFLAKE_SCHEMA: fake-snowflake-schema
-  EMBEDCHAIN_DB_URI: sqlite:///test.db
 
 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@v6
+        uses: astral-sh/setup-uv@v3
         with:
-          version: "0.8.4"
-          python-version: ${{ matrix.python-version }}
-          enable-cache: false
+          enable-cache: true
+
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
 
       - name: Install the project
-        run: uv sync --all-groups --all-extras
+        run: uv sync --dev --all-extras
 
-      - name: Restore test durations
-        uses: actions/cache/restore@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
-
-          cd lib/crewai && uv run pytest \
-            --block-network \
-            --timeout=30 \
-            -vv \
-            --splits 8 \
-            --group ${{ matrix.group }} \
-            $DURATIONS_ARG \
-            --durations=10 \
-            -n auto \
-            --maxfail=3
-
-      - name: Run tool tests (group ${{ matrix.group }} of 8)
-        run: |
-          cd lib/crewai-tools && uv run pytest \
-            --block-network \
-            --timeout=30 \
-            -vv \
-            --splits 8 \
-            --group ${{ matrix.group }} \
-            --durations=10 \
-            -n auto \
-            --maxfail=3
-
-
-      - name: Save uv caches
-        if: steps.cache-restore.outputs.cache-hit != 'true'
-        uses: actions/cache/save@v4
-        with:
-          path: |
-            ~/.cache/uv
-            ~/.local/share/uv
-            .venv
-          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+      - name: Run tests
+        run: uv run pytest --block-network --timeout=60 -vv

.github/workflows/type-checker.yml

@@ -3,99 +3,24 @@ name: Run Type Checks
 on: [pull_request]
 
 permissions:
-  contents: read
+  contents: write
 
 jobs:
-  type-checker-matrix:
-    name: type-checker (${{ matrix.python-version }})
+  type-checker:
     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:
-          fetch-depth: 0 # Fetch all history for proper diff
+          python-version: "3.11.9"
 
-      - 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: ${{ matrix.python-version }}
-          enable-cache: false
-
-      - name: Install dependencies
-        run: uv sync --all-groups --all-extras
-
-      - name: Get changed Python files
-        id: changed-files
+      - name: Install Requirements
         run: |
-          # Get the list of changed Python files compared to the base branch
-          echo "Fetching changed files..."
-          git diff --name-only --diff-filter=ACMRT origin/${{ github.base_ref }}...HEAD -- '*.py' > changed_files.txt
+          pip install mypy
 
-          # 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
+      - name: Run type checks
+        run: mypy src

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

@@ -1,71 +0,0 @@
-name: Update Test Durations
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - 'tests/**/*.py'
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  update-durations:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ['3.10', '3.11', '3.12', '3.13']
-    env:
-      OPENAI_API_KEY: fake-api-key
-      PYTHONUNBUFFERED: 1
-    
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@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

@@ -2,6 +2,7 @@
 .pytest_cache
 __pycache__
 dist/
+lib/
 .env
 assets/*
 .idea
@@ -20,9 +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
-chromadb-*.lock
+build_image

.pre-commit-config.yaml

@@ -1,21 +1,7 @@
 repos:
-  - repo: local
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.8.2
     hooks:
       - id: ruff
-        name: ruff
-        entry: uv run ruff check
-        language: system
-        types: [python]
-        exclude: ^lib/crewai/
+        args: ["--fix"]
       - id: ruff-format
-        name: ruff-format
-        entry: uv run ruff format
-        language: system
-        types: [python]
-        exclude: ^lib/crewai/
-      - id: mypy
-        name: mypy
-        entry: uv run mypy
-        language: system
-        types: [python]
-        exclude: ^lib/crewai/

.ruff.toml

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

README.md

@@ -62,9 +62,9 @@
 With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
 standard for enterprise-ready AI automation.
 
-# CrewAI AMP Suite
+# CrewAI Enterprise Suite
 
-CrewAI AMP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
+CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
 
 You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
 
@@ -76,9 +76,9 @@ You can try one part of the suite the [Crew Control Plane for free](https://app.
 - **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
 - **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
 - **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
-- **On-premise and Cloud Deployment Options**: Deploy CrewAI AMP on-premise or in the cloud, depending on your security and compliance requirements.
+- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
 
-CrewAI AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
+CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
 intelligent automations.
 
 ## Table of contents
@@ -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/crews/landing_page_generator)
+- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
 - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
-- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner)
-- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
 
 ### Quick Tutorial
 
@@ -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/crews/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
 
 [![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
 
 ### Stock Analysis
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
 
 [![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 
@@ -674,9 +674,9 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
 
 ### Enterprise Features
 
-- [What additional features does CrewAI AMP offer?](#q-what-additional-features-does-crewai-amp-offer)
-- [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
-- [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-amp-for-free)
+- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
+- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
+- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
 
 ### Q: What exactly is CrewAI?
 
@@ -732,17 +732,17 @@ A: Check out practical examples in the [CrewAI-examples repository](https://gith
 
 A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
 
-### Q: What additional features does CrewAI AMP offer?
+### Q: What additional features does CrewAI Enterprise offer?
 
-A: CrewAI AMP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
+A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
 
-### Q: Is CrewAI AMP available for cloud and on-premise deployments?
+### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
 
-A: Yes, CrewAI AMP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
+A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
 
-### Q: Can I try CrewAI AMP for free?
+### Q: Can I try CrewAI Enterprise for free?
 
-A: Yes, you can explore part of the CrewAI AMP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
+A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
 
 ### Q: Does CrewAI support fine-tuning or training custom models?
 
@@ -762,7 +762,7 @@ A: CrewAI is highly scalable, supporting simple automations and large-scale ente
 
 ### Q: Does CrewAI offer debugging and monitoring tools?
 
-A: Yes, CrewAI AMP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
+A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
 
 ### Q: What programming languages does CrewAI support?
 

docs/api-reference/introduction.mdx

@@ -1,29 +1,28 @@
 ---
 title: "Introduction"
-description: "Complete reference for the CrewAI AMP REST API"
+description: "Complete reference for the CrewAI Enterprise REST API"
 icon: "code"
-mode: "wide"
 ---
 
-# CrewAI AMP API
+# CrewAI Enterprise API
 
-Welcome to the CrewAI AMP API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
+Welcome to the CrewAI Enterprise API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
 
 ## Quick Start
 
 <Steps>
   <Step title="Get Your API Credentials">
-    Navigate to your crew's detail page in the CrewAI AMP dashboard and copy your Bearer Token from the Status tab.
+    Navigate to your crew's detail page in the CrewAI Enterprise dashboard and copy your Bearer Token from the Status tab.
   </Step>
-
+  
   <Step title="Discover Required Inputs">
     Use the `GET /inputs` endpoint to see what parameters your crew expects.
   </Step>
-
+  
   <Step title="Start a Crew Execution">
     Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`.
   </Step>
-
+  
   <Step title="Monitor Progress">
     Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
   </Step>
@@ -46,7 +45,7 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
 | **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
 
 <Tip>
-You can find both token types in the Status tab of your crew's detail page in the CrewAI AMP dashboard.
+You can find both token types in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
 </Tip>
 
 ## Base URL
@@ -62,7 +61,7 @@ Replace `your-crew-name` with your actual crew's URL from the dashboard.
 ## Typical Workflow
 
 1. **Discovery**: Call `GET /inputs` to understand what your crew needs
-2. **Execution**: Submit inputs via `POST /kickoff` to start processing
+2. **Execution**: Submit inputs via `POST /kickoff` to start processing  
 3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
 4. **Results**: Extract the final output from the completed response
 
@@ -82,12 +81,12 @@ The API uses standard HTTP status codes:
 ## Interactive Testing
 
 <Info>
-**Why no "Send" button?** Since each CrewAI AMP user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
+**Why no "Send" button?** Since each CrewAI Enterprise user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
 </Info>
 
 Each endpoint page shows you:
 - ✅ **Exact request format** with all parameters
-- ✅ **Response examples** for success and error cases
+- ✅ **Response examples** for success and error cases  
 - ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.)
 - ✅ **Authentication examples** with proper Bearer token format
 
@@ -104,7 +103,7 @@ Each endpoint page shows you:
 
 **Example workflow:**
 1. **Copy this cURL example** from any endpoint page
-2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL
+2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL  
 3. **Replace the Bearer token** with your real token from the dashboard
 4. **Run the request** in your terminal or API client
 

docs/changelog.mdx

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

docs/concepts/agents.mdx

@@ -2,7 +2,6 @@
 title: Agents
 description: Detailed guide on creating and managing agents within the CrewAI framework.
 icon: robot
-mode: "wide"
 ---
 
 ## Overview of an Agent
@@ -20,7 +19,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 </Tip>
 
 <Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
-CrewAI AMP includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
+CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
 
 ![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
 
@@ -72,7 +71,7 @@ There are two ways to create agents in CrewAI: using **YAML configuration (recom
 
 Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
+After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
 
 <Note>
 Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
@@ -313,7 +312,7 @@ multimodal_agent = Agent(
 
 <Note>
 When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting.
-</Note>
+</Note>  
 
 <Note>
 When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
@@ -426,7 +425,7 @@ strict_agent = Agent(
 ```python Code
 # Perfect for document processing
 document_processor = Agent(
-    role="Document Analyst",
+    role="Document Analyst", 
     goal="Extract insights from large research papers",
     backstory="Expert at analyzing extensive documentation",
     respect_context_window=True,  # Handle large documents gracefully
@@ -527,103 +526,6 @@ 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/concepts/cli.mdx

@@ -2,11 +2,8 @@
 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 AMP 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.
@@ -89,7 +86,7 @@ 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
 ```
 
@@ -135,7 +132,7 @@ 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
 ```
 
@@ -152,7 +149,7 @@ 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 +183,13 @@ def crew(self) -> Crew:
 
 ### 10. Deploy
 
-Deploy the crew or flow to [CrewAI AMP](https://app.crewai.com).
+Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
 
-- **Authentication**: You need to be authenticated to deploy to CrewAI AMP.
-    You can login or create an account with:
+- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
+    ```shell Terminal
+    crewai signup
+    ```
+    If you already have an account, you can login with:
     ```shell Terminal
     crewai login
     ```
@@ -203,7 +203,7 @@ Deploy the crew or flow to [CrewAI AMP](https://app.crewai.com).
 
 ### 11. Organization Management
 
-Manage your CrewAI AMP organizations.
+Manage your CrewAI Enterprise organizations.
 
 ```shell Terminal
 crewai org [COMMAND] [OPTIONS]
@@ -227,17 +227,17 @@ crewai org switch <organization_id>
 ```
 
 <Note>
-You must be authenticated to CrewAI AMP to use these organization management commands.
+You must be authenticated to CrewAI Enterprise to use these organization management commands.
 </Note>
 
 - **Create a deployment** (continued):
     - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
 
-- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI AMP.
+- **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 AMP platform.
+    ``` 
+    - Initiates the deployment process on the CrewAI Enterprise platform.
     - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
 
 - **Deployment Status**: You can check the status of your deployment with:
@@ -262,7 +262,7 @@ You must be authenticated to CrewAI AMP to use these organization management com
     ```shell Terminal
     crewai deploy remove
     ```
-    This deletes the deployment from the CrewAI AMP platform.
+    This deletes the deployment from the CrewAI Enterprise platform.
 
 - **Help Command**: You can get help with the CLI with:
     ```shell Terminal
@@ -270,44 +270,28 @@ You must be authenticated to CrewAI AMP to use these organization management com
     ```
     This shows the help message for the CrewAI Deploy CLI.
 
-Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AMP](http://app.crewai.com) using the CLI.
+Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
 
 <iframe
-  className="w-full aspect-video rounded-xl"
+  width="100%"
+  height="400"
   src="https://www.youtube.com/embed/3EqSV-CYDZA"
   title="CrewAI Deployment Guide"
-  frameBorder="0"
+  frameborder="0"
+  style={{ borderRadius: '10px' }}
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
+  allowfullscreen
 ></iframe>
 
-### 11. Login
+### 11. API Keys
 
-Authenticate with CrewAI AMP using a secure device code flow (no email entry required).
+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.
 
-```shell Terminal
-crewai login
-```
+Once you've selected an LLM provider, you will be prompted for API keys.
 
-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
+#### Initial API key providers
 
-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:
+The CLI will initially prompt for API keys for the following services:
 
 * OpenAI
 * Groq
@@ -315,11 +299,11 @@ Here's a list of the most popular LLM providers suggested by the CLI:
 * Google Gemini
 * SambaNova
 
-When you select a provider, the CLI will then show you available models for that provider and prompt you to enter your API key.
+When you select a provider, the CLI will prompt you to enter your API key.
 
 #### Other Options
 
-If you select "other", you will be able to select from a list of LiteLLM supported providers.
+If you select option 6, you will be able to select from a list of LiteLLM supported providers.
 
 When you select a provider, the CLI will prompt you to enter the Key name and the API key.
 
@@ -327,85 +311,5 @@ 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 AMP 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 AMP 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/concepts/collaboration.mdx

@@ -2,7 +2,6 @@
 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/concepts/crews.mdx

@@ -2,7 +2,6 @@
 title: Crews
 description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
 icon: people-group
-mode: "wide"
 ---
 
 ## Overview
@@ -21,7 +20,8 @@ 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** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
+| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
 | **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
 | **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
@@ -32,7 +32,6 @@ 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.
@@ -46,7 +45,7 @@ There are two ways to create crews in CrewAI: using **YAML configuration (recomm
 
 Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/en/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
+After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
 
 #### Example Crew Class with Decorators
 
@@ -67,8 +66,8 @@ class YourCrewName:
     # To see an example agent and task defined in YAML, checkout the following:
     # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
     # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    agents_config = 'config/agents.yaml'
-    tasks_config = 'config/tasks.yaml'
+    agents_config = 'config/agents.yaml' 
+    tasks_config = 'config/tasks.yaml' 
 
     @before_kickoff
     def prepare_inputs(self, inputs):
@@ -112,7 +111,7 @@ class YourCrewName:
     def crew(self) -> Crew:
         return Crew(
             agents=self.agents,  # Automatically collected by the @agent decorator
-            tasks=self.tasks,    # Automatically collected by the @task decorator.
+            tasks=self.tasks,    # Automatically collected by the @task decorator. 
             process=Process.sequential,
             verbose=True,
         )

docs/concepts/event-listener.mdx

@@ -2,7 +2,6 @@
 title: 'Event Listeners'
 description: 'Tap into CrewAI events to build custom integrations and monitoring'
 icon: spinner
-mode: "wide"
 ---
 
 ## Overview
@@ -20,7 +19,7 @@ CrewAI uses an event bus architecture to emit events throughout the execution li
 When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
 
 <Note type="info" title="Enterprise Enhancement: Prompt Tracing">
-CrewAI AMP provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
+CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
 
 ![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
 
@@ -45,12 +44,12 @@ To create a custom event listener, you need to:
 Here's a simple example of a custom event listener class:
 
 ```python
-from crewai.events import (
+from crewai.utilities.events import (
     CrewKickoffStartedEvent,
     CrewKickoffCompletedEvent,
     AgentExecutionCompletedEvent,
 )
-from crewai.events import BaseEventListener
+from crewai.utilities.events.base_event_listener import BaseEventListener
 
 class MyCustomListener(BaseEventListener):
     def __init__(self):
@@ -147,7 +146,7 @@ my_project/
 
 ```python
 # my_custom_listener.py
-from crewai.events import BaseEventListener
+from crewai.utilities.events.base_event_listener import BaseEventListener
 # ... import events ...
 
 class MyCustomListener(BaseEventListener):
@@ -178,7 +177,14 @@ class MyCustomCrew:
     # Your crew implementation...
 ```
 
-This is how third-party event listeners are registered in the CrewAI codebase.
+This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
+
+```python
+# src/crewai/utilities/events/third_party/__init__.py
+from .agentops_listener import agentops_listener
+```
+
+This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
 
 ## Available Event Types
 
@@ -249,17 +255,6 @@ 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:
@@ -274,13 +269,84 @@ 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.events import crewai_event_bus, CrewKickoffStartedEvent
+from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
 
 with crewai_event_bus.scoped_handlers():
     @crewai_event_bus.on(CrewKickoffStartedEvent)

docs/concepts/flows.mdx

@@ -2,7 +2,6 @@
 title: Flows
 description: Learn how to create and manage AI workflows using CrewAI Flows.
 icon: arrow-progress
-mode: "wide"
 ---
 
 ## Overview
@@ -98,13 +97,7 @@ The state's unique ID and stored data can be useful for tracking flow executions
 
 ### @start()
 
-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.
+The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
 
 ### @listen()
 
@@ -875,13 +868,14 @@ By exploring these examples, you can gain insights into how to leverage CrewAI F
 Also, check out our YouTube video on how to use flows in CrewAI below!
 
 <iframe
-  className="w-full aspect-video rounded-xl"
+  width="560"
+  height="315"
   src="https://www.youtube.com/embed/MTb5my6VOT8"
-  title="CrewAI Flows overview"
-  frameBorder="0"
+  title="YouTube video player"
+  frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerPolicy="strict-origin-when-cross-origin"
-  allowFullScreen
+  referrerpolicy="strict-origin-when-cross-origin"
+  allowfullscreen
 ></iframe>
 
 ## Running Flows

docs/concepts/knowledge.mdx

@@ -2,7 +2,6 @@
 title: Knowledge
 description: What is knowledge in CrewAI and how to use it.
 icon: book
-mode: "wide"
 ---
 
 ## Overview
@@ -25,41 +24,6 @@ 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
@@ -717,11 +681,11 @@ CrewAI emits events during the knowledge retrieval process that you can listen f
 #### Example: Monitoring Knowledge Retrieval
 
 ```python
-from crewai.events import (
+from crewai.utilities.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/concepts/llms.mdx

@@ -2,7 +2,6 @@
 title: 'LLMs'
 description: 'A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects'
 icon: 'microchip-ai'
-mode: "wide"
 ---
 
 ## Overview
@@ -271,7 +270,7 @@ In this section, you'll find detailed examples that help you select, configure,
     from crewai import LLM
 
     llm = LLM(
-        model="gemini-1.5-pro-latest", # or vertex_ai/gemini-1.5-pro-latest
+        model="gemini/gemini-1.5-pro-latest",
         temperature=0.7,
         vertex_credentials=vertex_credentials_json
     )
@@ -685,28 +684,6 @@ 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
@@ -734,10 +711,10 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     CrewAI emits events for each chunk received during streaming:
 
     ```python
-    from crewai.events import (
+    from crewai.utilities.events import (
       LLMStreamChunkEvent
     )
-    from crewai.events import BaseEventListener
+    from crewai.utilities.events.base_event_listener import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):
@@ -750,58 +727,9 @@ 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
@@ -897,7 +825,7 @@ 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/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 **two 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 **three distinct memory approaches** that serve different use cases:
 
 1. **Basic Memory System** - Built-in short-term, long-term, and entity memory
-2. **External Memory** - Standalone external memory providers
+2. **User Memory** - User-specific memory with Mem0 integration (legacy approach)  
+3. **External Memory** - Standalone external memory providers (new approach)
 
 ## 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`, `ExternalMemory` 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`, 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
 ```
@@ -540,71 +540,16 @@ 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
 
-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 |
-
+| Provider | Best For | Pros | Cons |
+|:---------|:----------|:------|:------|
+| **OpenAI** | General use, reliability | High quality, well-tested | Cost, requires API key |
+| **Ollama** | Privacy, cost savings | Free, local, private | Requires local setup |
+| **Google AI** | Google ecosystem | Good performance | Requires Google account |
+| **Azure OpenAI** | Enterprise, compliance | Enterprise features | Complex setup |
+| **Cohere** | Multilingual content | Great language support | Specialized use case |
+| **VoyageAI** | Retrieval tasks | Optimized for search | Newer provider |
 
 ### Environment Variable Configuration
 
@@ -651,7 +596,7 @@ providers_to_test = [
     {
         "name": "Ollama",
         "config": {
-            "provider": "ollama",
+            "provider": "ollama", 
             "config": {"model": "mxbai-embed-large"}
         }
     }
@@ -659,7 +604,7 @@ providers_to_test = [
 
 for provider in providers_to_test:
     print(f"\nTesting {provider['name']} embeddings...")
-
+    
     # Create crew with specific embedder
     crew = Crew(
         agents=[...],
@@ -667,7 +612,7 @@ 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")
@@ -678,7 +623,7 @@ for provider in providers_to_test:
 **Model not found errors:**
 ```python
 # Verify model availability
-from crewai.rag.embeddings.configurator import EmbeddingConfigurator
+from crewai.utilities.embedding_configurator import EmbeddingConfigurator
 
 configurator = EmbeddingConfigurator()
 try:
@@ -710,17 +655,17 @@ 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
 
@@ -731,7 +676,7 @@ openai_time = test_embedding_performance({
 })
 
 ollama_time = test_embedding_performance({
-    "provider": "ollama",
+    "provider": "ollama", 
     "config": {"model": "mxbai-embed-large"}
 })
 
@@ -739,29 +684,67 @@ print(f"OpenAI: {openai_time:.2f}s")
 print(f"Ollama: {ollama_time:.2f}s")
 ```
 
-### Entity Memory batching behavior
+## 2. User Memory with Mem0 (Legacy)
 
-Entity Memory supports batching when saving multiple entities at once. When you pass a list of `EntityMemoryItem`, the system:
+<Warning>
+**Legacy Approach**: While fully functional, this approach is considered legacy. For new projects requiring user-specific memory, consider using External Memory instead.
+</Warning>
 
-- 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)
+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.
 
-This improves performance and observability when writing many entities in one operation.
+### Prerequisites
+```bash
+pip install mem0ai
+```
 
-## 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
+### Mem0 Cloud Configuration
 ```python
 import os
-from crewai import Agent, Crew, Process, Task
-from crewai.memory.external.external_memory import ExternalMemory
+from crewai import Crew, Process
 
-# Create external memory instance with local Mem0 Configuration
-external_memory = ExternalMemory(
-    embedder_config={
+# Set your Mem0 API key
+os.environ["MEM0_API_KEY"] = "m0-your-api-key"
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    memory=True,  # Required for contextual memory integration
+    memory_config={
+        "provider": "mem0",
+        "config": {"user_id": "john"},
+        "user_memory": {}  # Required - triggers user memory initialization
+    },
+    process=Process.sequential,
+    verbose=True
+)
+```
+
+### Advanced Mem0 Configuration
+```python
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    memory=True,
+    memory_config={
+        "provider": "mem0",
+        "config": {
+            "user_id": "john",
+            "org_id": "my_org_id",        # Optional
+            "project_id": "my_project_id", # Optional
+            "api_key": "custom-api-key"    # Optional - overrides env var
+        },
+        "user_memory": {}
+    }
+)
+```
+
+### Local Mem0 Configuration
+```python
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    memory=True,
+    memory_config={
         "provider": "mem0",
         "config": {
             "user_id": "john",
@@ -778,60 +761,37 @@ external_memory = ExternalMemory(
                     "provider": "openai",
                     "config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}
                 }
-            },
-            "infer": True # Optional defaults to True
+            }
         },
+        "user_memory": {}
     }
 )
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    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/).
+## 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
 
-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
+# Create external memory instance
 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
-        },
+        "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
 )
@@ -848,8 +808,8 @@ class CustomStorage(Storage):
 
     def save(self, value, metadata=None, agent=None):
         self.memories.append({
-            "value": value,
-            "metadata": metadata,
+            "value": value, 
+            "metadata": metadata, 
             "agent": agent
         })
 
@@ -870,18 +830,17 @@ crew = Crew(
 )
 ```
 
-## 🧠 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   |
+## Memory System Comparison
 
+| Feature | Basic Memory | User Memory (Legacy) | External Memory |
+|---------|-------------|---------------------|----------------|
+| **Setup Complexity** | Simple | Medium | Medium |
+| **Integration** | Built-in contextual | Contextual + User-specific | Standalone |
+| **Storage** | Local files | Mem0 Cloud/Local | Custom/Mem0 |
+| **Cross-session** | ✅ | ✅ | ✅ |
+| **User-specific** | ❌ | ✅ | ✅ |
+| **Custom providers** | Limited | Mem0 only | Any provider |
+| **Recommended for** | Most use cases | Legacy projects | Specialized needs |
 
 ## Supported Embedding Providers
 
@@ -1027,201 +986,7 @@ 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/concepts/planning.mdx

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

docs/concepts/processes.mdx

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

docs/concepts/reasoning.mdx

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

docs/concepts/tasks.mdx

@@ -2,7 +2,6 @@
 title: Tasks
 description: Detailed guide on managing and creating tasks within the CrewAI framework.
 icon: list-check
-mode: "wide"
 ---
 
 ## Overview
@@ -14,7 +13,7 @@ Tasks provide all necessary details for execution, such as a description, the ag
 Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
 
 <Note type="info" title="Enterprise Enhancement: Visual Task Builder">
-CrewAI AMP includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
+CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
 
 ![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png)
 
@@ -55,17 +54,9 @@ 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
 
@@ -75,7 +66,7 @@ There are two ways to create tasks in CrewAI: using **YAML configuration (recomm
 
 Using YAML configuration provides a cleaner, more maintainable way to define tasks. We strongly recommend using this approach to define tasks in your CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
+After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
 
 <Note>
 Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
@@ -286,7 +277,7 @@ formatted_task = Task(
 
 When `markdown=True`, the agent will receive additional instructions to format the output using:
 - `#` for headers
-- `**text**` for bold text
+- `**text**` for bold text  
 - `*text*` for italic text
 - `-` or `*` for bullet points
 - `` `code` `` for inline code
@@ -341,11 +332,9 @@ 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.
 
-Guardrails are implemented as Python functions that contain custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
+### Using Task Guardrails
 
-### Function-Based Guardrails
-
-To add a function-based guardrail to a task, provide a validation function through the `guardrail` parameter:
+To add a guardrail to a task, provide a validation function through the `guardrail` parameter:
 
 ```python Code
 from typing import Tuple, Union, Dict, Any
@@ -383,7 +372,9 @@ 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
 
@@ -438,7 +429,7 @@ 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 (`guardrail_max_retries`)
+   - Maximum retries are reached
 
 Example with retry handling:
 ```python Code
@@ -459,7 +450,7 @@ task = Task(
     expected_output="A valid JSON object",
     agent=analyst,
     guardrail=validate_json_output,
-    guardrail_max_retries=3  # Limit retry attempts
+    max_retries=3  # Limit retry attempts
 )
 ```
 
@@ -807,103 +798,197 @@ 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
 
-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:
+You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
 
 ```python Code
-# 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
+# ...
+
+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
 )
-```
 
-### 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:
 
 <iframe
-  className="w-full aspect-video rounded-xl"
+  width="560"
+  height="315"
   src="https://www.youtube.com/embed/dNpKQk5uxHw"
-  title="Structured outputs in CrewAI"
-  frameBorder="0"
+  title="YouTube video player"
+  frameborder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerPolicy="strict-origin-when-cross-origin"
-  allowFullScreen
+  referrerpolicy="strict-origin-when-cross-origin"
+  allowfullscreen
 ></iframe>
 
 ## Conclusion

docs/concepts/testing.mdx

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

docs/concepts/tools.mdx

@@ -2,7 +2,6 @@
 title: Tools
 description: Understanding and leveraging tools within the CrewAI framework for agent collaboration and task execution.
 icon: screwdriver-wrench
-mode: "wide"
 ---
 
 ## Overview
@@ -17,7 +16,7 @@ This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/cre
 enabling everything from simple searches to complex interactions and effective teamwork among agents.
 
 <Note type="info" title="Enterprise Enhancement: Tools Repository">
-CrewAI AMP provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
+CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
 
 The Enterprise Tools Repository includes:
 - Pre-built connectors for popular enterprise systems
@@ -208,7 +207,7 @@ from crewai.tools import BaseTool
 class AsyncCustomTool(BaseTool):
     name: str = "async_custom_tool"
     description: str = "An asynchronous custom tool"
-
+    
     async def _run(self, query: str = "") -> str:
         """Asynchronously run the tool"""
         # Your async implementation here

docs/concepts/training.mdx

@@ -0,0 +1,67 @@
+---
+title: Training
+description: Learn how to train your CrewAI agents by giving them feedback early on and get consistent results.
+icon: dumbbell
+---
+
+## Overview
+
+The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
+By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
+
+During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. 
+This helps the agents improve their understanding, decision-making, and problem-solving abilities.
+
+### Training Your Crew Using the CLI
+
+To use the training feature, follow these steps:
+
+1. Open your terminal or command prompt.
+2. Navigate to the directory where your CrewAI project is located.
+3. Run the following command:
+
+```shell
+crewai train -n <n_iterations> <filename> (optional)
+```
+<Tip>
+  Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
+</Tip>
+
+### Training Your Crew Programmatically
+
+To train your crew programmatically, use the following steps:
+
+1. Define the number of iterations for training.
+2. Specify the input parameters for the training process.
+3. Execute the training command within a try-except block to handle potential errors.
+
+```python Code
+n_iterations = 2
+inputs = {"topic": "CrewAI Training"}
+filename = "your_model.pkl"
+
+try:
+    YourCrewName_Crew().crew().train(
+      n_iterations=n_iterations, 
+      inputs=inputs, 
+      filename=filename
+    )
+
+except Exception as e:
+    raise Exception(f"An error occurred while training the crew: {e}")
+```
+
+### 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.
+
+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/api-reference/inputs.mdx

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

docs/en/api-reference/kickoff.mdx

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

docs/en/api-reference/status.mdx

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

docs/en/concepts/training.mdx

@@ -1,197 +0,0 @@
----
-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).
-By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
-
-During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback.
-This helps the agents improve their understanding, decision-making, and problem-solving abilities.
-
-### Training Your Crew Using the CLI
-
-To use the training feature, follow these steps:
-
-1. Open your terminal or command prompt.
-2. Navigate to the directory where your CrewAI project is located.
-3. Run the following command:
-
-```shell
-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>
-
-<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:
-
-1. Define the number of iterations for training.
-2. Specify the input parameters for the training process.
-3. Execute the training command within a try-except block to handle potential errors.
-
-```python Code
-n_iterations = 2
-inputs = {"topic": "CrewAI Training"}
-filename = "your_model.pkl"
-
-try:
-    YourCrewName_Crew().crew().train(
-      n_iterations=n_iterations,
-      inputs=inputs,
-      filename=filename
-    )
-
-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.

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

@@ -1,155 +0,0 @@
----
-title: 'Agent Repositories'
-description: 'Learn how to use Agent Repositories to share and reuse your agents across teams and projects'
-icon: 'people-group'
-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.
-
-<Frame>
-  ![Agent Repositories](/images/enterprise/agent-repositories.png)
-</Frame>
-
-## 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
-
-## Creating and Use Agent Repositories
-
-1. You must have an account at CrewAI, try the [free plan](https://app.crewai.com).
-2. Create agents with specific roles and goals for your workflows.
-3. Configure tools and capabilities for each specialized assistant.
-4. Deploy agents across projects via visual interface or API integration.
-
-<Frame>
-  ![Agent Repositories](/images/enterprise/create-agent-repository.png)
-</Frame>
-
-
-### Loading Agents from Repositories
-
-You can load agents from repositories in your code using the `from_repository` parameter to run locally:
-
-```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/automations.mdx

@@ -1,104 +0,0 @@
----
-title: Automations
-description: "Manage, deploy, and monitor your live crews (automations) in one place."
-icon: "rocket"
-mode: "wide"
----
-
-## Overview
-
-Automations is the live operations hub for your deployed crews. Use it to deploy from GitHub or a ZIP file, manage environment variables, re‑deploy when needed, and monitor the status of each automation.
-
-<Frame>
-  ![Automations Overview](/images/enterprise/automations-overview.png)
-
-</Frame>
-
-## Deployment Methods
-
-### Deploy from GitHub
-
-Use this for version‑controlled projects and continuous deployment.
-
-<Steps>
-  <Step title="Connect GitHub">
-    Click <b>Configure GitHub</b> and authorize access.
-  </Step>
-  <Step title="Select Repository & Branch">
-    Choose the <b>Repository</b> and <b>Branch</b> you want to deploy from.
-  </Step>
-  <Step title="Enable Auto‑deploy (optional)">
-    Turn on <b>Automatically deploy new commits</b> to ship updates on every push.
-  </Step>
-  <Step title="Add Environment Variables">
-    Add secrets individually or use <b>Bulk View</b> for multiple variables.
-  </Step>
-  <Step title="Deploy">
-    Click <b>Deploy</b> to create your live automation.
-  </Step>
-</Steps>
-
-<Frame>
-  ![GitHub Deployment](/images/enterprise/deploy-from-github.png)
-</Frame>
-
-### Deploy from ZIP
-
-Ship quickly without Git—upload a compressed package of your project.
-
-<Steps>
-  <Step title="Choose File">
-    Select the ZIP archive from your computer.
-  </Step>
-  <Step title="Add Environment Variables">
-    Provide any required variables or keys.
-  </Step>
-  <Step title="Deploy">
-    Click <b>Deploy</b> to create your live automation.
-  </Step>
-</Steps>
-
-<Frame>
-  ![ZIP Deployment](/images/enterprise/deploy-from-zip.png)
-</Frame>
-
-## Automations Dashboard
-
-The table lists all live automations with key details:
-
-- **CREW**: Automation name
-- **STATUS**: Online / Failed / In Progress
-- **URL**: Endpoint for kickoff/status
-- **TOKEN**: Automation token
-- **ACTIONS**: Re‑deploy, delete, and more
-
-Use the top‑right controls to filter and search:
-
-- Search by name
-- Filter by <b>Status</b>
-- Filter by <b>Source</b> (GitHub / Studio / ZIP)
-
-Once deployed, you can view the automation details and have the **Options** dropdown menu to `chat with this crew`, `Export React Component` and `Export as MCP`.
-
-<Frame>
-  ![Automations Table](/images/enterprise/automations-table.png)
-</Frame>
-
-## Best Practices
-
-- Prefer GitHub deployments for version control and CI/CD
-- Use re‑deploy to roll forward after code or config updates or set it to auto-deploy on every push
-
-## Related
-
-<CardGroup cols={3}>
-  <Card title="Deploy a Crew" href="/en/enterprise/guides/deploy-crew" icon="rocket">
-    Deploy a Crew from GitHub or ZIP file.
-  </Card>
-  <Card title="Automation Triggers" href="/en/enterprise/guides/automation-triggers" icon="trigger">
-    Trigger automations via webhooks or API.
-  </Card>
-  <Card title="Webhook Automation" href="/en/enterprise/guides/webhook-automation" icon="webhook">
-    Stream real-time events and updates to your systems.
-  </Card>
-</CardGroup>

docs/en/enterprise/features/crew-studio.mdx

@@ -1,88 +0,0 @@
----
-title: Crew Studio
-description: "Build new automations with AI assistance, a visual editor, and integrated testing."
-icon: "pencil"
-mode: "wide"
----
-
-## Overview
-
-Crew Studio is an interactive, AI‑assisted workspace for creating new automations from scratch using natural language and a visual workflow editor.
-
-<Frame>
-  ![Crew Studio Overview](/images/enterprise/crew-studio-overview.png)
-</Frame>
-
-## Prompt‑based Creation
-
-- Describe the automation you want; the AI generates agents, tasks, and tools.
-- Use voice input via the microphone icon if preferred.
-- Start from built‑in prompts for common use cases.
-
-<Frame>
-  ![Prompt Builder](/images/enterprise/crew-studio-prompt.png)
-</Frame>
-
-## Visual Editor
-
-The canvas reflects the workflow as nodes and edges with three supporting panels that allow you to configure the workflow easily without writing code; a.k.a. "**vibe coding AI Agents**".
-
-You can use the drag-and-drop functionality to add agents, tasks, and tools to the canvas or you can use the chat section to build the agents. Both approaches share state and can be used interchangeably.
-
-- **AI Thoughts (left)**: streaming reasoning as the workflow is designed
-- **Canvas (center)**: agents and tasks as connected nodes
-- **Resources (right)**: drag‑and‑drop components (agents, tasks, tools)
-
-<Frame>
-  ![Visual Canvas](/images/enterprise/crew-studio-canvas.png)
-</Frame>
-
-## Execution & Debugging
-
-Switch to the <b>Execution</b> view to run and observe the workflow:
-
-- Event timeline
-- Detailed logs (Details, Messages, Raw Data)
-- Local test runs before publishing
-
-<Frame>
-  ![Execution View](/images/enterprise/crew-studio-execution.png)
-</Frame>
-
-## Publish & Export
-
-- <b>Publish</b> to deploy a live automation
-- <b>Download</b> source as a ZIP for local development or customization
-
-<Frame>
-  ![Publish & Download](/images/enterprise/crew-studio-publish.png)
-</Frame>
-
-Once published, you can view the automation details and have the **Options** dropdown menu to `chat with this crew`, `Export React Component` and `Export as MCP`.
-
-<Frame>
-  ![Published Automation](/images/enterprise/crew-studio-published.png)
-</Frame>
-
-## Best Practices
-
-- Iterate quickly in Studio; publish only when stable
-- Keep tools constrained to minimum permissions needed
-- Use Traces to validate behavior and performance
-
-## Related
-
-<CardGroup cols={4}>
-  <Card title="Enable Crew Studio" href="/en/enterprise/guides/enable-crew-studio" icon="palette">
-    Enable Crew Studio.
-  </Card>
-  <Card title="Build a Crew" href="/en/enterprise/guides/build-crew" icon="paintbrush">
-    Build a Crew.
-  </Card>
-  <Card title="Deploy a Crew" href="/en/enterprise/guides/deploy-crew" icon="rocket">
-    Deploy a Crew from GitHub or ZIP file.
-  </Card>
-  <Card title="Export a React Component" href="/en/enterprise/guides/react-component-export" icon="download">
-    Export a React Component.
-  </Card>
-</CardGroup>

docs/en/enterprise/features/marketplace.mdx

@@ -1,46 +0,0 @@
----
-title: Marketplace
-description: "Discover, install, and govern reusable assets for your enterprise crews."
-icon: "store"
-mode: "wide"
----
-
-## Overview
-
-The Marketplace provides a curated surface for discovering integrations, internal tools, and reusable assets that accelerate crew development.
-
-<Frame>
-  ![Marketplace Overview](/images/enterprise/marketplace-overview.png)
-</Frame>
-
-## Discoverability
-
-- Browse by category and capability
-- Search for assets by name or keyword
-
-## Install & Enable
-
-- One‑click install for approved assets
-- Enable or disable per crew as needed
-- Configure required environment variables and scopes
-
-<Frame>
-  ![Install & Configure](/images/enterprise/marketplace-install.png)
-</Frame>
-
-You can also download the templates directly from the marketplace by clicking on the `Download` button so
-you can use them locally or refine them to your needs.
-
-## Related
-
-<CardGroup cols={3}>
-  <Card title="Tools & Integrations" href="/en/enterprise/features/tools-and-integrations" icon="wrench">
-    Connect external apps and manage internal tools your agents can use.
-  </Card>
-  <Card title="Tool Repository" href="/en/enterprise/features/tool-repository" icon="toolbox">
-    Publish and install tools to enhance your crews' capabilities.
-  </Card>
-  <Card title="Agents Repository" href="/en/enterprise/features/agent-repositories" icon="people-group">
-    Store, share, and reuse agent definitions across teams and projects.
-  </Card>
-</CardGroup>

docs/en/enterprise/features/rbac.mdx

@@ -1,102 +0,0 @@
----
-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 AMP 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 AMP" />
-
-</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 AMP.
-  </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 AMP" />
-
-</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/tools-and-integrations.mdx

@@ -1,249 +0,0 @@
----
-title: Tools & Integrations
-description: "Connect external apps and manage internal tools your agents can use."
-icon: "wrench"
-mode: "wide"
----
-
-## Overview
-
-Tools & Integrations is the central hub for connecting third‑party apps and managing internal tools that your agents can use at runtime.
-
-<Frame>
-  ![Tools & Integrations Overview](/images/enterprise/crew_connectors.png)
-</Frame>
-
-## Explore
-
-<Tabs>
-  <Tab title="Integrations" icon="plug">
-
-  ## Agent Apps (Integrations)
-
-  Connect enterprise‑grade applications (e.g., Gmail, Google Drive, HubSpot, Slack) via OAuth to enable agent actions.
-
-  <Steps>
-    <Step title="Connect">
-      Click <b>Connect</b> on an app and complete OAuth.
-    </Step>
-    <Step title="Configure">
-      Optionally adjust scopes, triggers, and action availability.
-    </Step>
-    <Step title="Use in Agents">
-      Connected services become available as tools for your agents.
-    </Step>
-  </Steps>
-
-  <Frame>
-    ![Integrations Grid](/images/enterprise/agent-apps.png)
-  </Frame>
-
-  ### Connect your Account
-
-  1. Go to <Link href="https://app.crewai.com/crewai_plus/connectors">Integrations</Link>
-  2. Click <b>Connect</b> on the desired service
-  3. Complete the OAuth flow and grant scopes
-  4. Copy your Enterprise Token from the <b>Integration</b> tab
-
-  <Frame>
-    ![Enterprise Token](/images/enterprise/enterprise_action_auth_token.png)
-  </Frame>
-
-  ### Install Integration Tools
-
-  To use the integrations locally, you need to install the latest `crewai-tools` package.
-
-  ```bash
-  uv add crewai-tools
-  ```
-
-  ### Usage Example
-
-  <Tip>
-    All services you have authenticated will be available as tools. Add `CrewaiEnterpriseTools` to your agent and you’re set.
-  </Tip>
-
-  ```python
-  from crewai import Agent, Task, Crew
-  from crewai_tools import CrewaiEnterpriseTools
-
-  # Get enterprise tools (Gmail tool will be included)
-  enterprise_tools = CrewaiEnterpriseTools(
-      enterprise_token="your_enterprise_token"
-  )
-  # print the tools
-  print(enterprise_tools)
-
-  # Create an agent with Gmail capabilities
-  email_agent = Agent(
-      role="Email Manager",
-      goal="Manage and organize email communications",
-      backstory="An AI assistant specialized in email management and communication.",
-      tools=enterprise_tools
-  )
-
-  # Task to send an email
-  email_task = Task(
-      description="Draft and send a follow-up email to john@example.com about the project update",
-      agent=email_agent,
-      expected_output="Confirmation that email was sent successfully"
-  )
-
-  # Run the task
-  crew = Crew(
-      agents=[email_agent],
-      tasks=[email_task]
-  )
-
-  # Run the crew
-  crew.kickoff()
-  ```
-
-  ### Filtering Tools
-
-  ```python
-  from crewai_tools import CrewaiEnterpriseTools
-
-  enterprise_tools = CrewaiEnterpriseTools(
-      actions_list=["gmail_find_email"]  # only gmail_find_email tool will be available
-  )
-
-
-  gmail_tool = enterprise_tools["gmail_find_email"]
-
-
-  gmail_agent = Agent(
-      role="Gmail Manager",
-      goal="Manage gmail communications and notifications",
-      backstory="An AI assistant that helps coordinate gmail communications.",
-      tools=[gmail_tool]
-  )
-
-  notification_task = Task(
-      description="Find the email from john@example.com",
-      agent=gmail_agent,
-      expected_output="Email found from john@example.com"
-  )
-
-  crew = Crew(
-      agents=[gmail_agent],
-      tasks=[notification_task]
-  )
-  ```
-
-  On a deployed crew, you can specify which actions are available for each integration from the service settings page.
-
-  <Frame>
-    ![Filter Actions](/images/enterprise/filtering_enterprise_action_tools.png)
-  </Frame>
-
-  ### Scoped Deployments (multi‑user orgs)
-
-  You can scope each integration to a specific user. For example, a crew that connects to Google can use a specific user’s Gmail account.
-
-  <Tip>
-    Useful when different teams/users must keep data access separated.
-  </Tip>
-
-  Use the `user_bearer_token` to scope authentication to the requesting user. If the user isn’t logged in, the crew won’t use connected integrations. Otherwise it falls back to the default bearer token configured for the deployment.
-
-  <Frame>
-    ![User Bearer Token](/images/enterprise/user_bearer_token.png)
-  </Frame>
-
-  <div id="catalog"></div>
-  ### Catalog
-
-  #### Communication & Collaboration
-  - Gmail — Manage emails and drafts
-  - Slack — Workspace notifications and alerts
-  - Microsoft — Office 365 and Teams integration
-
-  #### Project Management
-  - Jira — Issue tracking and project management
-  - ClickUp — Task and productivity management
-  - Asana — Team task and project coordination
-  - Notion — Page and database management
-  - Linear — Software project and bug tracking
-  - GitHub — Repository and issue management
-
-  #### Customer Relationship Management
-  - Salesforce — CRM account and opportunity management
-  - HubSpot — Sales pipeline and contact management
-  - Zendesk — Customer support ticket management
-
-  #### Business & Finance
-  - Stripe — Payment processing and customer management
-  - Shopify — E‑commerce store and product management
-
-  #### Productivity & Storage
-  - Google Sheets — Spreadsheet data synchronization
-  - Google Calendar — Event and schedule management
-  - Box — File storage and document management
-
-  …and more to come!
-
-  </Tab>
-  <Tab title="Internal Tools" icon="toolbox">
-
-  ## Internal Tools
-
-  Create custom tools locally, publish them on CrewAI AMP Tool Repository and use them in your agents.
-
-  <Tip>
-  Before running the commands below, make sure you log in to your CrewAI AMP account by running this command:
-    ```bash
-    crewai login
-    ```
-  </Tip>
-
-  <Frame>
-    ![Internal Tool Detail](/images/enterprise/tools-integrations-internal.png)
-  </Frame>
-
-  <Steps>
-    <Step title="Create">
-      Create a new tool locally.
-      ```bash
-      crewai tool create your-tool
-      ```
-    </Step>
-    <Step title="Publish">
-      Publish the tool to the CrewAI AMP Tool Repository.
-      ```bash
-      crewai tool publish
-      ```
-    </Step>
-    <Step title="Install">
-      Install the tool from the CrewAI AMP Tool Repository.
-      ```bash
-      crewai tool install your-tool
-      ```
-    </Step>
-  </Steps>
-
-  Manage:
-
-  - Name and description
-  - Visibility (Private / Public)
-  - Required environment variables
-  - Version history and downloads
-  - Team and role access
-
-  <Frame>
-    ![Internal Tool Detail](/images/enterprise/tool-configs.png)
-  </Frame>
-
-  </Tab>
-</Tabs>
-
-## Related
-
-<CardGroup cols={2}>
-  <Card title="Tool Repository" href="/en/enterprise/features/tool-repository" icon="toolbox">
-    Create, publish, and version custom tools for your organization.
-  </Card>
-  <Card title="Webhook Automation" href="/en/enterprise/guides/webhook-automation" icon="bolt">
-    Automate workflows and integrate with external platforms and services.
-  </Card>
-</CardGroup>

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

@@ -1,168 +0,0 @@
----
-title: Webhook Streaming
-description: "Using Webhook Streaming to stream events to your webhook"
-icon: "webhook"
-mode: "wide"
----
-
-## Overview
-
-Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to
-CrewAI AMP, such as model calls, tool usage, and flow steps.
-
-## Usage
-
-When using the Kickoff API, include a `webhooks` object to your request, for example:
-
-```json
-{
-  "inputs": {"foo": "bar"},
-  "webhooks": {
-    "events": ["crew_kickoff_started", "llm_call_started"],
-    "url": "https://your.endpoint/webhook",
-    "realtime": false,
-    "authentication": {
-      "strategy": "bearer",
-      "token": "my-secret-token"
-    }
-  }
-}
-```
-
-If `realtime` is set to `true`, each event is delivered individually and immediately, at the cost of crew/flow performance.
-
-## Webhook Format
-
-Each webhook sends a list of events:
-
-```json
-{
-  "events": [
-    {
-      "id": "event-id",
-      "execution_id": "crew-run-id",
-      "timestamp": "2025-02-16T10:58:44.965Z",
-      "type": "llm_call_started",
-      "data": {
-        "model": "gpt-4",
-        "messages": [
-          {"role": "system", "content": "You are an assistant."},
-          {"role": "user", "content": "Summarize this article."}
-        ]
-      }
-    }
-  ]
-}
-```
-
-The `data` object structure varies by event type. Refer to the [event list](https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events) on GitHub.
-
-As requests are sent over HTTP, the order of events can't be guaranteed. If you need ordering, use the `timestamp` field.
-
-## Supported Events
-
-CrewAI supports both system events and custom events in Enterprise Event Streaming. These events are sent to your configured webhook endpoint during crew and flow execution.
-
-### 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 for the full list of events.
-
-You can emit your own custom events, and they will be delivered through the webhook stream alongside system events.
-
-<CardGroup>
- <Card title="GitHub" icon="github" href="https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events">
-  Full list of events
- </Card>
-<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
-  Contact our support team for assistance with webhook integration or troubleshooting.
-</Card>
-</CardGroup>

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

@@ -1,255 +0,0 @@
----
-title: "Triggers Overview"
-description: "Understand how CrewAI AMP triggers work, how to manage them, and where to find integration-specific playbooks"
-icon: "face-smile"
-mode: "wide"
----
-
-CrewAI AMP triggers connect your automations to real-time events across the tools your teams already use. Instead of polling systems or relying on manual kickoffs, triggers listen for changes—new emails, calendar updates, CRM status changes—and immediately launch the crew or flow you specify.
-
-<Frame>
-  ![Automation Triggers Overview](/images/enterprise/crew_connectors.png)
-</Frame>
-
-### Integration Playbooks
-
-Deep-dive guides walk through setup and sample workflows for each integration:
-
-<CardGroup cols={2}>
-  <Card title="Gmail Trigger" icon="envelope">
-    <a href="/en/enterprise/guides/gmail-trigger">Enable crews when emails arrive or threads update.</a>
-  </Card>
-
-  <Card title="Google Calendar Trigger" icon="calendar-days">
-    <a href="/en/enterprise/guides/google-calendar-trigger">React to calendar events as they are created, updated, or cancelled.</a>
-  </Card>
-
-  <Card title="Google Drive Trigger" icon="folder-open">
-    <a href="/en/enterprise/guides/google-drive-trigger">Handle Drive file uploads, edits, and deletions.</a>
-  </Card>
-
-  <Card title="Outlook Trigger" icon="envelope-open">
-    <a href="/en/enterprise/guides/outlook-trigger">Automate responses to new Outlook messages and calendar updates.</a>
-  </Card>
-
-  <Card title="OneDrive Trigger" icon="cloud">
-    <a href="/en/enterprise/guides/onedrive-trigger">Audit file activity and sharing changes in OneDrive.</a>
-  </Card>
-
-  <Card title="Microsoft Teams Trigger" icon="comments">
-    <a href="/en/enterprise/guides/microsoft-teams-trigger">Kick off workflows when new Teams chats start.</a>
-  </Card>
-
-  <Card title="HubSpot Trigger" icon="hubspot">
-    <a href="/en/enterprise/guides/hubspot-trigger">Launch automations from HubSpot workflows and lifecycle events.</a>
-  </Card>
-
-  <Card title="Salesforce Trigger" icon="salesforce">
-    <a href="/en/enterprise/guides/salesforce-trigger">Connect Salesforce processes to CrewAI for CRM automation.</a>
-  </Card>
-
-  <Card title="Slack Trigger" icon="slack">
-    <a href="/en/enterprise/guides/slack-trigger">Start crews directly from Slack slash commands.</a>
-  </Card>
-
-  <Card title="Zapier Trigger" icon="bolt">
-    <a href="/en/enterprise/guides/zapier-trigger">Bridge CrewAI with thousands of Zapier-supported apps.</a>
-  </Card>
-</CardGroup>
-
-## Trigger Capabilities
-
-With 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 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 caption="Example of available automation triggers for a Gmail deployment">
-  <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 caption="Enable or disable triggers with toggle">
-  <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 caption="List of executions triggered by automation">
-  <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
-</Frame>
-
-## Building Trigger-Driven Automations
-
-Before building your automation, it's helpful to understand the structure of trigger payloads that your crews and flows will receive.
-
-### Trigger Setup Checklist
-
-Before wiring a trigger into production, make sure you:
-
-- Connect the integration under **Tools & Integrations** and complete any OAuth or API key steps
-- Enable the trigger toggle on the deployment that should respond to events
-- Provide any required environment variables (API tokens, tenant IDs, shared secrets)
-- Create or update tasks that can parse the incoming payload within the first crew task or flow step
-- Decide whether to pass trigger context automatically using `allow_crewai_trigger_context`
-- Set up monitoring—webhook logs, CrewAI execution history, and optional external alerting
-
-### Payload & Crew Examples Repository
-
-We maintain a comprehensive repository with end-to-end trigger examples to help you build and test your automations:
-
-This repository contains:
-
-- **Realistic payload samples** for every supported trigger integration
-- **Ready-to-run crew implementations** that parse each payload and turn it into a business workflow
-- **Multiple scenarios per integration** (e.g., new events, updates, deletions) so you can match the shape of your data
-
-| Integration | When it fires | Payload Samples | Crew Examples |
-| :-- | :-- | :-- | :-- |
-| Gmail | New messages, thread updates | [New alerts, thread updates](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | [`new-email-crew.py`, `gmail-alert-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) |
-| Google Calendar | Event created / updated / started / ended / cancelled | [Event lifecycle payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | [`calendar-event-crew.py`, `calendar-meeting-crew.py`, `calendar-working-location-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) |
-| Google Drive | File created / updated / deleted | [File lifecycle payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | [`drive-file-crew.py`, `drive-file-deletion-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) |
-| Outlook | New email, calendar event removed | [Outlook payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | [`outlook-message-crew.py`, `outlook-event-removal-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) |
-| OneDrive | File operations (create, update, share, delete) | [OneDrive payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | [`onedrive-file-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) |
-| HubSpot | Record created / updated (contacts, companies, deals) | [HubSpot payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | [`hubspot-company-crew.py`, `hubspot-contact-crew.py`, `hubspot-record-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) |
-| Microsoft Teams | Chat thread created | [Teams chat payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | [`teams-chat-created-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) |
-
-Use these samples to understand payload shape, copy the matching crew, and then replace the test payload with your live trigger data.
-
-### 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.
-
-<Card title="CrewAI AMP Trigger Examples" href="https://github.com/crewAIInc/crewai-enterprise-trigger-examples" icon="github">
-    Check them out on GitHub!
-</Card>

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

@@ -1,85 +0,0 @@
----
-title: "Gmail Trigger"
-description: "Trigger automations when Gmail events occur (e.g., new emails, labels)."
-icon: "envelope"
-mode: "wide"
----
-
-## Overview
-
-Use the Gmail Trigger to kick off your deployed crews when Gmail events happen in connected accounts, such as receiving a new email or messages matching a label/filter.
-
-<Tip>
-  Make sure Gmail is connected in Tools & Integrations and the trigger is enabled for your deployment.
-</Tip>
-
-## Enabling the Gmail Trigger
-
-1. Open your deployment in CrewAI AMP
-2. Go to the **Triggers** tab
-3. Locate **Gmail** and switch the toggle to enable
-
-<Frame>
-  <img src="/images/enterprise/trigger-selected.png" alt="Enable or disable triggers with toggle" />
-</Frame>
-
-## Example: Process new emails
-
-When a new email arrives, the Gmail Trigger will send the payload to your Crew or Flow. Below is a Crew example that parses and processes the trigger payload.
-
-```python
-@CrewBase
-class GmailProcessingCrew:
-    @agent
-    def parser(self) -> Agent:
-        return Agent(
-            config=self.agents_config['parser'],
-        )
-
-    @task
-    def parse_gmail_payload(self) -> Task:
-        return Task(
-            config=self.tasks_config['parse_gmail_payload'],
-            agent=self.parser(),
-        )
-
-    @task
-    def act_on_email(self) -> Task:
-        return Task(
-            config=self.tasks_config['act_on_email'],
-            agent=self.parser(),
-        )
-```
-
-The Gmail payload will be available via the standard context mechanisms. See the payload samples repository for structure and fields.
-
-### Sample payloads & crews
-
-The [CrewAI AMP Trigger Examples repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) includes:
-
-- `new-email-payload-1.json` / `new-email-payload-2.json` — production-style new message alerts with matching crews in `new-email-crew.py`
-- `thread-updated-sample-1.json` — follow-up messages on an existing thread, processed by `gmail-alert-crew.py`
-
-Use these samples to validate your parsing logic locally before wiring the trigger to your live Gmail accounts.
-
-## Monitoring Executions
-
-Track history and performance of triggered runs:
-
-<Frame>
-  <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
-</Frame>
-
-## Payload Reference
-
-See the sample payloads and field descriptions:
-
-<Card title="Gmail samples in Trigger Examples Repo" href="https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail" icon="envelopes-bulk">
-    Gmail samples in Trigger Examples Repo
-</Card>
-
-## Troubleshooting
-
-- Ensure Gmail is connected in Tools & Integrations
-- Verify the Gmail Trigger is enabled on the Triggers tab
-- Check the execution logs and confirm the payload is passed as `crewai_trigger_payload`

docs/en/enterprise/guides/google-calendar-trigger.mdx

@@ -1,65 +0,0 @@
----
-title: "Google Calendar Trigger"
-description: "Kick off crews when Google Calendar events are created, updated, or cancelled"
-icon: "calendar"
-mode: "wide"
----
-
-## Overview
-
-Use the Google Calendar trigger to launch automations whenever calendar events change. Common use cases include briefing a team before a meeting, notifying stakeholders when a critical event is cancelled, or summarizing daily schedules.
-
-<Tip>
-  Make sure Google Calendar is connected in **Tools & Integrations** and enabled for the deployment you want to automate.
-</Tip>
-
-## Enabling the Google Calendar Trigger
-
-1. Open your deployment in CrewAI AMP
-2. Go to the **Triggers** tab
-3. Locate **Google Calendar** and switch the toggle to enable
-
-<Frame>
-  <img src="/images/enterprise/calendar-trigger.png" alt="Enable or disable triggers with toggle" />
-</Frame>
-
-## Example: Summarize meeting details
-
-The snippet below mirrors the `calendar-event-crew.py` example in the trigger repository. It parses the payload, analyses the attendees and timing, and produces a meeting brief for downstream tools.
-
-```python
-from calendar_event_crew import GoogleCalendarEventTrigger
-
-crew = GoogleCalendarEventTrigger().crew()
-result = crew.kickoff({
-    "crewai_trigger_payload": calendar_payload,
-})
-print(result.raw)
-```
-
-Use `crewai_trigger_payload` exactly as it is delivered by the trigger so the crew can extract the proper fields.
-
-## Sample payloads & crews
-
-The [Google Calendar examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) show how to handle multiple event types:
-
-- `new-event.json` → standard event creation handled by `calendar-event-crew.py`
-- `event-updated.json` / `event-started.json` / `event-ended.json` → in-flight updates processed by `calendar-meeting-crew.py`
-- `event-canceled.json` → cancellation workflow that alerts attendees via `calendar-meeting-crew.py`
-- Working location events use `calendar-working-location-crew.py` to extract on-site schedules
-
-Each crew transforms raw event metadata (attendees, rooms, working locations) into the summaries your teams need.
-
-## Monitoring Executions
-
-The **Executions** list in the deployment dashboard tracks every triggered run and surfaces payload metadata, output summaries, and errors.
-
-<Frame>
-  <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
-</Frame>
-
-## Troubleshooting
-
-- Ensure the correct Google account is connected and the trigger is enabled
-- Confirm your workflow handles all-day events (payloads use `start.date` and `end.date` instead of timestamps)
-- Check execution logs if reminders or attendee arrays are missing—calendar permissions can limit fields in the payload

docs/en/enterprise/guides/google-drive-trigger.mdx

@@ -1,61 +0,0 @@
----
-title: "Google Drive Trigger"
-description: "Respond to Google Drive file events with automated crews"
-icon: "folder"
-mode: "wide"
----
-
-## Overview
-
-Trigger your automations when files are created, updated, or removed in Google Drive. Typical workflows include summarizing newly uploaded content, enforcing sharing policies, or notifying owners when critical files change.
-
-<Tip>
-  Connect Google Drive in **Tools & Integrations** and confirm the trigger is enabled for the automation you want to monitor.
-</Tip>
-
-## Enabling the Google Drive Trigger
-
-1. Open your deployment in CrewAI AMP
-2. Go to the **Triggers** tab
-3. Locate **Google Drive** and switch the toggle to enable
-
-<Frame>
-  <img src="/images/enterprise/gdrive-trigger.png" alt="Enable or disable triggers with toggle" />
-</Frame>
-
-## Example: Summarize file activity
-
-The drive example crews parse the payload to extract file metadata, evaluate permissions, and publish a summary.
-
-```python
-from drive_file_crew import GoogleDriveFileTrigger
-
-crew = GoogleDriveFileTrigger().crew()
-crew.kickoff({
-    "crewai_trigger_payload": drive_payload,
-})
-```
-
-## Sample payloads & crews
-
-Explore the [Google Drive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) to cover different operations:
-
-- `new-file.json` → new uploads processed by `drive-file-crew.py`
-- `updated-file.json` → file edits and metadata changes handled by `drive-file-crew.py`
-- `deleted-file.json` → deletion events routed through `drive-file-deletion-crew.py`
-
-Each crew highlights the file name, operation type, owner, permissions, and security considerations so downstream systems can respond appropriately.
-
-## Monitoring Executions
-
-Track history and performance of triggered runs with the **Executions** list in the deployment dashboard.
-
-<Frame>
-  <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
-</Frame>
-
-## Troubleshooting
-
-- Verify Google Drive is connected and the trigger toggle is enabled
-- If a payload is missing permission data, ensure the connected account has access to the file or folder
-- The trigger sends file IDs only; use the Drive API if you need to fetch binary content during the crew run

docs/en/enterprise/guides/microsoft-teams-trigger.mdx

@@ -1,52 +0,0 @@
----
-title: "Microsoft Teams Trigger"
-description: "Kick off crews from Microsoft Teams chat activity"
-icon: "microsoft"
-mode: "wide"
----
-
-## Overview
-
-Use the Microsoft Teams trigger to start automations whenever a new chat is created. Common patterns include summarizing inbound requests, routing urgent messages to support teams, or creating follow-up tasks in other systems.
-
-<Tip>
-  Confirm Microsoft Teams is connected under **Tools & Integrations** and enabled in the **Triggers** tab for your deployment.
-</Tip>
-
-## Enabling the Microsoft Teams Trigger
-
-1. Open your deployment in CrewAI AMP
-2. Go to the **Triggers** tab
-3. Locate **Microsoft Teams** and switch the toggle to enable
-
-<Frame caption="Microsoft Teams trigger connection">
-  <img src="/images/enterprise/msteams-trigger.png" alt="Enable or disable triggers with toggle" />
-</Frame>
-
-## Example: Summarize a new chat thread
-
-```python
-from teams_chat_created_crew import MicrosoftTeamsChatTrigger
-
-crew = MicrosoftTeamsChatTrigger().crew()
-result = crew.kickoff({
-    "crewai_trigger_payload": teams_payload,
-})
-print(result.raw)
-```
-
-The crew parses thread metadata (subject, created time, roster) and generates an action plan for the receiving team.
-
-## Sample payloads & crews
-
-The [Microsoft Teams examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) include:
-
-- `chat-created.json` → chat creation payload processed by `teams-chat-created-crew.py`
-
-The crew demonstrates how to extract participants, initial messages, tenant information, and compliance metadata from the Microsoft Graph webhook payload.
-
-## Troubleshooting
-
-- Ensure the Teams connection is active; it must be refreshed if the tenant revokes permissions
-- Confirm the webhook subscription in Microsoft 365 is still valid if payloads stop arriving
-- Review execution logs for payload shape mismatches—Graph notifications may omit fields when a chat is private or restricted

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

@@ -1,53 +0,0 @@
----
-title: "OneDrive Trigger"
-description: "Automate responses to OneDrive file activity"
-icon: "cloud"
-mode: "wide"
----
-
-## Overview
-
-Start automations when files change inside OneDrive. You can generate audit summaries, notify security teams about external sharing, or update downstream line-of-business systems with new document metadata.
-
-<Tip>
-  Connect OneDrive in **Tools & Integrations** and toggle the trigger on for your deployment.
-</Tip>
-
-## Enabling the OneDrive Trigger
-
-1. Open your deployment in CrewAI AMP
-2. Go to the **Triggers** tab
-3. Locate **OneDrive** and switch the toggle to enable
-
-<Frame caption="Microsoft OneDrive trigger connection">
-  <img src="/images/enterprise/onedrive-trigger.png" alt="Enable or disable triggers with toggle" />
-</Frame>
-
-## Example: Audit file permissions
-
-```python
-from onedrive_file_crew import OneDriveFileTrigger
-
-crew = OneDriveFileTrigger().crew()
-crew.kickoff({
-    "crewai_trigger_payload": onedrive_payload,
-})
-```
-
-The crew inspects file metadata, user activity, and permission changes to produce a compliance-friendly summary.
-
-## Sample payloads & crews
-
-The [OneDrive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) showcase how to:
-
-- Parse file metadata, size, and folder paths
-- Track who created and last modified the file
-- Highlight permission and external sharing changes
-
-`onedrive-file-crew.py` bundles the analysis and summarization tasks so you can add remediation steps as needed.
-
-## Troubleshooting
-
-- Ensure the connected account has permission to read the file metadata included in the webhook
-- If the trigger fires but the payload is missing `permissions`, confirm the site-level sharing settings allow Graph to return this field
-- For large tenants, filter notifications upstream so the crew only runs on relevant directories

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

@@ -1,52 +0,0 @@
----
-title: "Outlook Trigger"
-description: "Launch automations from Outlook emails and calendar updates"
-icon: "microsoft"
-mode: "wide"
----
-
-## Overview
-
-Automate responses when Outlook delivers a new message or when an event is removed from the calendar. Teams commonly route escalations, file tickets, or alert attendees of cancellations.
-
-<Tip>
-  Connect Outlook in **Tools & Integrations** and ensure the trigger is enabled for your deployment.
-</Tip>
-
-## Enabling the Outlook Trigger
-
-1. Open your deployment in CrewAI AMP
-2. Go to the **Triggers** tab
-3. Locate **Outlook** and switch the toggle to enable
-
-<Frame caption="Microsoft Outlook trigger connection">
-  <img src="/images/enterprise/outlook-trigger.png" alt="Enable or disable triggers with toggle" />
-</Frame>
-
-## Example: Summarize a new email
-
-```python
-from outlook_message_crew import OutlookMessageTrigger
-
-crew = OutlookMessageTrigger().crew()
-crew.kickoff({
-    "crewai_trigger_payload": outlook_payload,
-})
-```
-
-The crew extracts sender details, subject, body preview, and attachments before generating a structured response.
-
-## Sample payloads & crews
-
-Review the [Outlook examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) for two common scenarios:
-
-- `new-message.json` → new mail notifications parsed by `outlook-message-crew.py`
-- `event-removed.json` → calendar cleanup handled by `outlook-event-removal-crew.py`
-
-Each crew demonstrates how to handle Microsoft Graph payloads, normalize headers, and keep humans in-the-loop with concise summaries.
-
-## Troubleshooting
-
-- Verify the Outlook connector is still authorized; the subscription must be renewed periodically
-- If attachments are missing, confirm the webhook subscription includes the `includeResourceData` flag
-- Review execution logs when events fail to match—cancellation payloads lack attendee lists by design and the crew should account for that

docs/en/examples/cookbooks.mdx

@@ -1,49 +0,0 @@
----
-title: CrewAI Cookbooks
-description: Feature-focused quickstarts and notebooks for learning patterns fast.
-icon: book
-mode: "wide"
----
-
-## Quickstarts & Demos
-
-<CardGroup cols={3}>
-  <Card title="Collaboration" icon="people-arrows" href="https://github.com/crewAIInc/crewAI-quickstarts/blob/main/Collaboration/crewai_collaboration.ipynb">
-    Coordinate multiple agents on shared tasks. Includes notebook with end-to-end collaboration pattern.
-  </Card>
-
-  <Card title="Planning" icon="timeline" href="https://github.com/crewAIInc/crewAI-quickstarts/blob/main/Planning/crewai_planning.ipynb">
-    Teach agents to reason about multi-step plans before execution using the planning toolkit.
-  </Card>
-
-  <Card title="Reasoning" icon="lightbulb" href="https://github.com/crewAIInc/crewAI-quickstarts/blob/main/Reasoning/crewai_reasoning.ipynb">
-    Explore self-reflection loops, critique prompts, and structured thinking patterns.
-  </Card>
-</CardGroup>
-
-<CardGroup cols={3}>
-  <Card title="Structured Guardrails" icon="shield-check" href="https://github.com/crewAIInc/crewAI-quickstarts/blob/main/Guardrails/task_guardrails.ipynb">
-    Apply task-level guardrails with retries, validation functions, and safe fallbacks.
-  </Card>
-
-  <Card title="Gemini Search & Grounding" icon="magnifying-glass" href="https://github.com/crewAIInc/crewAI-quickstarts/blob/main/Custom%20LLM/gemini_search_grounding_crewai.ipynb">
-    Connect CrewAI to Gemini with search grounding for factual, citation-rich outputs.
-  </Card>
-
-  <Card title="Gemini Video Summaries" icon="video" href="https://github.com/crewAIInc/crewAI-quickstarts/blob/main/Custom%20LLM/summarize_video_gemini_crewai.ipynb">
-    Generate video recaps using Gemini multimodal LLM and CrewAI orchestration.
-  </Card>
-</CardGroup>
-
-<CardGroup cols={2}>
-  <Card title="Browse Quickstarts" icon="bolt" href="https://github.com/crewAIInc/crewAI-quickstarts">
-    View all notebooks and feature demos showcasing specific CrewAI capabilities.
-  </Card>
-  <Card title="Request a cookbook" icon="message-plus" href="https://community.crewai.com">
-    Missing a pattern? Drop a request in the community forum and we’ll expand the library.
-  </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

@@ -1,86 +0,0 @@
----
-title: CrewAI Examples
-description: Explore curated examples organized by Crews, Flows, Integrations, and Notebooks.
-icon: rocket-launch
-mode: "wide"
----
-
-## Crews
-
-<CardGroup cols={3}>
-  <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>
-</CardGroup>

docs/en/index.mdx

@@ -1,105 +0,0 @@
----
-title: "CrewAI Documentation"
-description: "Build collaborative AI agents, crews, and flows — production ready from day one."
-icon: "house"
-mode: "wide"
----
-
-<div
-  style={{
-    display: 'flex',
-    flexDirection: 'column',
-    alignItems: 'center',
-    gap: 20,
-    textAlign: 'center',
-    padding: '48px 24px',
-    borderRadius: 16,
-    background: 'linear-gradient(180deg, rgba(235,102,88,0.12) 0%, rgba(201,76,60,0.08) 100%)',
-    border: '1px solid rgba(235,102,88,0.18)'
-  }}
->
-  <img src="/images/crew_only_logo.png" alt="CrewAI" width="250" height="100" />
-  <div style={{ maxWidth: 720 }}>
-    <h1 style={{ marginBottom: 12 }}>Ship multi‑agent systems with confidence</h1>
-    <p style={{ color: 'var(--mint-text-2)' }}>
-      Design agents, orchestrate crews, and automate flows with guardrails, memory, knowledge, and observability baked in.
-    </p>
-  </div>
-
-  <div style={{ display: 'flex', flexWrap: 'wrap', gap: 12, justifyContent: 'center' }}>
-    <a className="button button-primary" href="/en/quickstart">Get started</a>
-    <a className="button" href="/en/changelog">View changelog</a>
-    <a className="button" href="/en/api-reference/introduction">API Reference</a>
-  </div>
-
-</div>
-
-<div style={{ marginTop: 32 }} />
-
-## Get started
-
-<CardGroup cols={3}>
-  <Card title="Introduction" href="/en/introduction" icon="sparkles">
-    Overview of CrewAI concepts, architecture, and what you can build with agents, crews, and flows.
-  </Card>
-  <Card title="Installation" href="/en/installation" icon="wrench">
-    Install via `uv`, configure API keys, and set up the CLI for local development.
-  </Card>
-  <Card title="Quickstart" href="/en/quickstart" icon="rocket">
-    Spin up your first crew in minutes. Learn the core runtime, project layout, and dev loop.
-  </Card>
-</CardGroup>
-
-## Build the basics
-
-<CardGroup cols={3}>
-  <Card title="Agents" href="/en/concepts/agents" icon="users">
-    Compose agents with tools, memory, knowledge, and structured outputs using Pydantic. Includes templates and best practices.
-  </Card>
-  <Card title="Flows" href="/en/concepts/flows" icon="arrow-progress">
-    Orchestrate start/listen/router steps, manage state, persist execution, and resume long-running workflows.
-  </Card>
-  <Card title="Tasks & Processes" href="/en/concepts/tasks" icon="check">
-    Define sequential, hierarchical, or hybrid processes with guardrails, callbacks, and human-in-the-loop triggers.
-  </Card>
-</CardGroup>
-
-## Enterprise journey
-
-<CardGroup cols={3}>
-  <Card title="Deploy automations" href="/en/enterprise/features/automations" icon="server">
-    Manage environments, redeploy safely, and monitor live runs directly from the Enterprise console.
-  </Card>
-  <Card title="Triggers & Flows" href="/en/enterprise/guides/automation-triggers" icon="bolt">
-    Connect Gmail, Slack, Salesforce, and more. Pass trigger payloads into crews and flows automatically.
-  </Card>
-  <Card title="Team management" href="/en/enterprise/guides/team-management" icon="users-gear">
-    Invite teammates, configure RBAC, and control access to production automations.
-  </Card>
-</CardGroup>
-
-## What’s new
-
-<CardGroup cols={2}>
-  <Card title="Triggers overview" href="/en/enterprise/guides/automation-triggers" icon="sparkles">
-    Unified overview for Gmail, Drive, Outlook, Teams, OneDrive, HubSpot, and more — now with sample payloads and crews.
-  </Card>
-  <Card title="Integration tools" href="/en/tools/integration/overview" icon="plug">
-    Call existing CrewAI automations or Amazon Bedrock Agents directly from your crews using the updated integration toolkit.
-  </Card>
-</CardGroup>
-
-<Callout title="Explore real-world patterns" icon="github">
-  Browse the <a href="/en/examples/cookbooks">examples and cookbooks</a> for end-to-end reference implementations across agents, flows, and enterprise automations.
-</Callout>
-
-## Stay connected
-
-<CardGroup cols={2}>
-  <Card title="Star us on GitHub" href="https://github.com/crewAIInc/crewAI" icon="star">
-    If CrewAI helps you ship faster, give us a star and share your builds with the community.
-  </Card>
-  <Card title="Join the community" href="https://community.crewai.com" icon="comments">
-    Ask questions, showcase workflows, and request features alongside other builders.
-  </Card>
-</CardGroup>

docs/en/mcp/overview.mdx

@@ -1,317 +0,0 @@
----
-title: 'MCP Servers as Tools in CrewAI'
-description: 'Learn how to integrate MCP servers as tools in your CrewAI agents using the `crewai-tools` library.'
-icon: plug
-mode: "wide"
----
-
-## Overview
-
-The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) provides a standardized way for AI agents to provide context to LLMs by communicating with external services, known as MCP Servers.
-The `crewai-tools` library extends CrewAI's capabilities by allowing you to seamlessly integrate tools from these MCP servers into your agents.
-This gives your crews access to a vast ecosystem of functionalities.
-
-We currently support the following transport mechanisms:
-
-- **Stdio**: for local servers (communication via standard input/output between processes on the same machine)
-- **Server-Sent Events (SSE)**: for remote servers (unidirectional, real-time data streaming from server to client over HTTP)
-- **Streamable HTTP**: for remote servers (flexible, potentially bi-directional communication over HTTP, often utilizing SSE for server-to-client streams)
-
-## Video Tutorial
-Watch this video tutorial for a comprehensive guide on MCP integration with CrewAI:
-
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.youtube.com/embed/TpQ45lAZh48"
-  title="CrewAI MCP Integration Guide"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
-></iframe>
-
-## Installation
-
-Before you start using MCP with `crewai-tools`, you need to install the `mcp` extra `crewai-tools` dependency with the following command:
-
-```shell
-uv pip install 'crewai-tools[mcp]'
-```
-
-## Key Concepts & Getting Started
-
-The `MCPServerAdapter` class from `crewai-tools` is the primary way to connect to an MCP server and make its tools available to your CrewAI agents. It supports different transport mechanisms and simplifies connection management.
-
-Using a Python context manager (`with` statement) is the **recommended approach** for `MCPServerAdapter`. It automatically handles starting and stopping the connection to the MCP server.
-
-## Connection Configuration
-
-The `MCPServerAdapter` supports several configuration options to customize the connection behavior:
-
-- **`connect_timeout`** (optional): Maximum time in seconds to wait for establishing a connection to the MCP server. Defaults to 30 seconds if not specified. This is particularly useful for remote servers that may have variable response times.
-
-```python
-# Example with custom connection timeout
-with MCPServerAdapter(server_params, connect_timeout=60) as tools:
-    # Connection will timeout after 60 seconds if not established
-    pass
-```
-
-```python
-from crewai import Agent
-from crewai_tools import MCPServerAdapter
-from mcp import StdioServerParameters # For Stdio Server
-
-# Example server_params (choose one based on your server type):
-# 1. Stdio Server:
-server_params=StdioServerParameters(
-    command="python3",
-    args=["servers/your_server.py"],
-    env={"UV_PYTHON": "3.12", **os.environ},
-)
-
-# 2. SSE Server:
-server_params = {
-    "url": "http://localhost:8000/sse",
-    "transport": "sse"
-}
-
-# 3. Streamable HTTP Server:
-server_params = {
-    "url": "http://localhost:8001/mcp",
-    "transport": "streamable-http"
-}
-
-# Example usage (uncomment and adapt once server_params is set):
-with MCPServerAdapter(server_params, connect_timeout=60) as mcp_tools:
-    print(f"Available tools: {[tool.name for tool in mcp_tools]}")
-
-    my_agent = Agent(
-        role="MCP Tool User",
-        goal="Utilize tools from an MCP server.",
-        backstory="I can connect to MCP servers and use their tools.",
-        tools=mcp_tools, # Pass the loaded tools to your agent
-        reasoning=True,
-        verbose=True
-    )
-    # ... rest of your crew setup ...
-```
-This general pattern shows how to integrate tools. For specific examples tailored to each transport, refer to the detailed guides below.
-
-## Filtering Tools
-
-There are two ways to filter tools:
-
-1. Accessing a specific tool using dictionary-style indexing.
-2. Pass a list of tool names to the `MCPServerAdapter` constructor.
-
-### Accessing a specific tool using dictionary-style indexing.
-
-```python
-with MCPServerAdapter(server_params, connect_timeout=60) as mcp_tools:
-    print(f"Available tools: {[tool.name for tool in mcp_tools]}")
-
-    my_agent = Agent(
-        role="MCP Tool User",
-        goal="Utilize tools from an MCP server.",
-        backstory="I can connect to MCP servers and use their tools.",
-        tools=[mcp_tools["tool_name"]], # Pass the loaded tools to your agent
-        reasoning=True,
-        verbose=True
-    )
-    # ... rest of your crew setup ...
-```
-
-### Pass a list of tool names to the `MCPServerAdapter` constructor.
-
-```python
-with MCPServerAdapter(server_params, "tool_name", connect_timeout=60) as mcp_tools:
-    print(f"Available tools: {[tool.name for tool in mcp_tools]}")
-
-    my_agent = Agent(
-        role="MCP Tool User",
-        goal="Utilize tools from an MCP server.",
-        backstory="I can connect to MCP servers and use their tools.",
-        tools=mcp_tools, # Pass the loaded tools to your agent
-        reasoning=True,
-        verbose=True
-    )
-    # ... rest of your crew setup ...
-```
-
-## Using with CrewBase
-
-To use MCPServer tools within a CrewBase class, use the `get_mcp_tools` method. Server configurations should be provided via the `mcp_server_params` attribute. You can pass either a single configuration or a list of multiple server configurations.
-
-```python
-@CrewBase
-class CrewWithMCP:
-  # ... define your agents and tasks config file ...
-
-  mcp_server_params = [
-    # Streamable HTTP Server
-    {
-        "url": "http://localhost:8001/mcp",
-        "transport": "streamable-http"
-    },
-    # SSE Server
-    {
-        "url": "http://localhost:8000/sse",
-        "transport": "sse"
-    },
-    # StdIO Server
-    StdioServerParameters(
-        command="python3",
-        args=["servers/your_stdio_server.py"],
-        env={"UV_PYTHON": "3.12", **os.environ},
-    )
-  ]
-
-  @agent
-  def your_agent(self):
-      return Agent(config=self.agents_config["your_agent"], tools=self.get_mcp_tools()) # get all available tools
-
-    # ... rest of your crew setup ...
-```
-
-<Tip>
-When a crew class is decorated with `@CrewBase`, the adapter lifecycle is managed for you:
-
-- The first call to `get_mcp_tools()` lazily creates a shared `MCPServerAdapter` that is reused by every agent in the crew.
-- The adapter automatically shuts down after `.kickoff()` completes thanks to an implicit after-kickoff hook injected by `@CrewBase`, so no manual cleanup is required.
-- If `mcp_server_params` is not defined, `get_mcp_tools()` simply returns an empty list, allowing the same code paths to run with or without MCP configured.
-
-This makes it safe to call `get_mcp_tools()` from multiple agent methods or selectively enable MCP per environment.
-</Tip>
-
-### Connection Timeout Configuration
-
-You can configure the connection timeout for MCP servers by setting the `mcp_connect_timeout` class attribute. If no timeout is specified, it defaults to 30 seconds.
-
-```python
-@CrewBase
-class CrewWithMCP:
-  mcp_server_params = [...]
-  mcp_connect_timeout = 60  # 60 seconds timeout for all MCP connections
-
-  @agent
-  def your_agent(self):
-      return Agent(config=self.agents_config["your_agent"], tools=self.get_mcp_tools())
-```
-
-```python
-@CrewBase
-class CrewWithDefaultTimeout:
-  mcp_server_params = [...]
-  # No mcp_connect_timeout specified - uses default 30 seconds
-
-  @agent
-  def your_agent(self):
-      return Agent(config=self.agents_config["your_agent"], tools=self.get_mcp_tools())
-```
-
-### Filtering Tools
-
-You can filter which tools are available to your agent by passing a list of tool names to the `get_mcp_tools` method.
-
-```python
-@agent
-def another_agent(self):
-    return Agent(
-      config=self.agents_config["your_agent"],
-      tools=self.get_mcp_tools("tool_1", "tool_2") # get specific tools
-    )
-```
-
-The timeout configuration applies to all MCP tool calls within the crew:
-
-```python
-@CrewBase
-class CrewWithCustomTimeout:
-  mcp_server_params = [...]
-  mcp_connect_timeout = 90  # 90 seconds timeout for all MCP connections
-
-  @agent
-  def filtered_agent(self):
-      return Agent(
-        config=self.agents_config["your_agent"],
-        tools=self.get_mcp_tools("tool_1", "tool_2") # specific tools with custom timeout
-      )
-```
-
-## Explore MCP Integrations
-
-<CardGroup cols={2}>
-  <Card
-    title="Stdio Transport"
-    icon="server"
-    href="/en/mcp/stdio"
-    color="#3B82F6"
-  >
-    Connect to local MCP servers via standard input/output. Ideal for scripts and local executables.
-  </Card>
-  <Card
-    title="SSE Transport"
-    icon="wifi"
-    href="/en/mcp/sse"
-    color="#10B981"
-  >
-    Integrate with remote MCP servers using Server-Sent Events for real-time data streaming.
-  </Card>
-  <Card
-    title="Streamable HTTP Transport"
-    icon="globe"
-    href="/en/mcp/streamable-http"
-    color="#F59E0B"
-  >
-    Utilize flexible Streamable HTTP for robust communication with remote MCP servers.
-  </Card>
-  <Card
-    title="Connecting to Multiple Servers"
-    icon="layer-group"
-    href="/en/mcp/multiple-servers"
-    color="#8B5CF6"
-  >
-    Aggregate tools from several MCP servers simultaneously using a single adapter.
-  </Card>
-  <Card
-    title="Security Considerations"
-    icon="lock"
-    href="/en/mcp/security"
-    color="#EF4444"
-  >
-    Review important security best practices for MCP integration to keep your agents safe.
-  </Card>
-</CardGroup>
-
-Checkout this repository for full demos and examples of MCP integration with CrewAI! 👇
-
-<Card
-title="GitHub Repository"
-icon="github"
-href="https://github.com/tonykipkemboi/crewai-mcp-demo"
-target="_blank"
->
-CrewAI MCP Demo
-</Card>
-
-## Staying Safe with MCP
-<Warning>
-Always ensure that you trust an MCP Server before using it.
-</Warning>
-
-#### Security Warning: DNS Rebinding Attacks
-SSE transports can be vulnerable to DNS rebinding attacks if not properly secured.
-To prevent this:
-
-1. **Always validate Origin headers** on incoming SSE connections to ensure they come from expected sources
-2. **Avoid binding servers to all network interfaces** (0.0.0.0) when running locally - bind only to localhost (127.0.0.1) instead
-3. **Implement proper authentication** for all SSE connections
-
-Without these protections, attackers could use DNS rebinding to interact with local MCP servers from remote websites.
-
-For more details, see the [Anthropic's MCP Transport Security docs](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations).
-
-### Limitations
-*   **Supported Primitives**: Currently, `MCPServerAdapter` primarily supports adapting MCP `tools`.
-Other MCP primitives like `prompts` or `resources` are not directly integrated as CrewAI components through this adapter at this time.
-*   **Output Handling**: The adapter typically processes the primary text output from an MCP tool (e.g., `.content[0].text`). Complex or multi-modal outputs might require custom handling if not fitting this pattern.

docs/en/observability/braintrust.mdx

@@ -1,237 +0,0 @@
----
-title: Braintrust
-description: Braintrust integration for CrewAI with OpenTelemetry tracing and evaluation
-icon: magnifying-glass-chart
-mode: "wide"
----
-
-# Braintrust Integration
-
-This guide demonstrates how to integrate **Braintrust** with **CrewAI** using OpenTelemetry for comprehensive tracing and evaluation. By the end of this guide, you will be able to trace your CrewAI agents, monitor their performance, and evaluate their outputs using Braintrust's powerful observability platform.
-
-> **What is Braintrust?** [Braintrust](https://www.braintrust.dev) is an AI evaluation and observability platform that provides comprehensive tracing, evaluation, and monitoring for AI applications with built-in experiment tracking and performance analytics.
-
-## Get Started
-
-We'll walk through a simple example of using CrewAI and integrating it with Braintrust via OpenTelemetry for comprehensive observability and evaluation.
-
-### Step 1: Install Dependencies
-
-```bash
-uv add braintrust[otel] crewai crewai-tools opentelemetry-instrumentation-openai opentelemetry-instrumentation-crewai python-dotenv
-```
-
-### Step 2: Set Up Environment Variables
-
-Setup Braintrust API keys and configure OpenTelemetry to send traces to Braintrust. You'll need a Braintrust API key and your OpenAI API key.
-
-```python
-import os
-from getpass import getpass
-
-# Get your Braintrust credentials
-BRAINTRUST_API_KEY = getpass("🔑 Enter your Braintrust API Key: ")
-
-# Get API keys for services
-OPENAI_API_KEY = getpass("🔑 Enter your OpenAI API key: ")
-
-# Set environment variables
-os.environ["BRAINTRUST_API_KEY"] = BRAINTRUST_API_KEY
-os.environ["BRAINTRUST_PARENT"] = "project_name:crewai-demo"
-os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
-```
-
-### Step 3: Initialize OpenTelemetry with Braintrust
-
-Initialize the Braintrust OpenTelemetry instrumentation to start capturing traces and send them to Braintrust.
-
-```python
-import os
-from typing import Any, Dict
-
-from braintrust.otel import BraintrustSpanProcessor
-from crewai import Agent, Crew, Task
-from crewai.llm import LLM
-from opentelemetry import trace
-from opentelemetry.instrumentation.crewai import CrewAIInstrumentor
-from opentelemetry.instrumentation.openai import OpenAIInstrumentor
-from opentelemetry.sdk.trace import TracerProvider
-
-def setup_tracing() -> None:
-    """Setup OpenTelemetry tracing with Braintrust."""
-    current_provider = trace.get_tracer_provider()
-    if isinstance(current_provider, TracerProvider):
-        provider = current_provider
-    else:
-        provider = TracerProvider()
-        trace.set_tracer_provider(provider)
-
-    provider.add_span_processor(BraintrustSpanProcessor())
-    CrewAIInstrumentor().instrument(tracer_provider=provider)
-    OpenAIInstrumentor().instrument(tracer_provider=provider)
-
-
-setup_tracing()
-```
-
-### Step 4: Create a CrewAI Application
-
-We'll create a CrewAI application where two agents collaborate to research and write a blog post about AI advancements, with comprehensive tracing enabled.
-
-```python
-from crewai import Agent, Crew, Process, Task
-from crewai_tools import SerperDevTool
-
-def create_crew() -> Crew:
-    """Create a crew with multiple agents for comprehensive tracing."""
-    llm = LLM(model="gpt-4o-mini")
-    search_tool = SerperDevTool()
-
-    # Define agents with specific roles
-    researcher = Agent(
-        role="Senior Research Analyst",
-        goal="Uncover cutting-edge developments in AI and data science",
-        backstory="""You work at a leading tech think tank.
-        Your expertise lies in identifying emerging trends.
-        You have a knack for dissecting complex data and presenting actionable insights.""",
-        verbose=True,
-        allow_delegation=False,
-        llm=llm,
-        tools=[search_tool],
-    )
-
-    writer = Agent(
-        role="Tech Content Strategist",
-        goal="Craft compelling content on tech advancements",
-        backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
-        You transform complex concepts into compelling narratives.""",
-        verbose=True,
-        allow_delegation=True,
-        llm=llm,
-    )
-
-    # Create tasks for your agents
-    research_task = Task(
-        description="""Conduct a comprehensive analysis of the latest advancements in {topic}.
-        Identify key trends, breakthrough technologies, and potential industry impacts.""",
-        expected_output="Full analysis report in bullet points",
-        agent=researcher,
-    )
-
-    writing_task = Task(
-        description="""Using the insights provided, develop an engaging blog
-        post that highlights the most significant {topic} advancements.
-        Your post should be informative yet accessible, catering to a tech-savvy audience.
-        Make it sound cool, avoid complex words so it doesn't sound like AI.""",
-        expected_output="Full blog post of at least 4 paragraphs",
-        agent=writer,
-        context=[research_task],
-    )
-
-    # Instantiate your crew with a sequential process
-    crew = Crew(
-        agents=[researcher, writer], 
-        tasks=[research_task, writing_task], 
-        verbose=True, 
-        process=Process.sequential
-    )
-
-    return crew
-
-def run_crew():
-    """Run the crew and return results."""
-    crew = create_crew()
-    result = crew.kickoff(inputs={"topic": "AI developments"})
-    return result
-
-# Run your crew
-if __name__ == "__main__":
-    # Instrumentation is already initialized above in this module
-    result = run_crew()
-    print(result)
-```
-
-### Step 5: View Traces in Braintrust
-
-After running your crew, you can view comprehensive traces in Braintrust through different perspectives:
-
-<Tabs>
-  <Tab title="Trace">
-    <Frame>
-      <img src="/images/braintrust-trace-view.png" alt="Braintrust Trace View"/>
-    </Frame>
-  </Tab>
-  
-  <Tab title="Timeline">
-    <Frame>
-      <img src="/images/braintrust-timeline-view.png" alt="Braintrust Timeline View"/>
-    </Frame>
-  </Tab>
-  
-  <Tab title="Thread">
-    <Frame>
-      <img src="/images/braintrust-thread-view.png" alt="Braintrust Thread View"/>
-    </Frame>
-  </Tab>
-</Tabs>
-
-### Step 6: Evaluate via SDK (Experiments)
-
-You can also run evaluations using Braintrust's Eval SDK. This is useful for comparing versions or scoring outputs offline. Below is a Python example using the `Eval` class with the crew we created above:
-
-```python
-# eval_crew.py
-from braintrust import Eval
-from autoevals import Levenshtein
-
-def evaluate_crew_task(input_data):
-    """Task function that wraps our crew for evaluation."""
-    crew = create_crew()
-    result = crew.kickoff(inputs={"topic": input_data["topic"]})
-    return str(result)
-
-Eval(
-    "AI Research Crew",  # Project name
-    {
-        "data": lambda: [
-            {"topic": "artificial intelligence trends 2024"},
-            {"topic": "machine learning breakthroughs"},
-            {"topic": "AI ethics and governance"},
-        ],
-        "task": evaluate_crew_task,
-        "scores": [Levenshtein],
-    },
-)
-```
-
-Setup your API key and run:
-
-```bash
-export BRAINTRUST_API_KEY="YOUR_API_KEY"
-braintrust eval eval_crew.py
-```
-
-See the [Braintrust Eval SDK guide](https://www.braintrust.dev/docs/start/eval-sdk) for more details.
-
-### Key Features of Braintrust Integration
-
-- **Comprehensive Tracing**: Track all agent interactions, tool usage, and LLM calls
-- **Performance Monitoring**: Monitor execution times, token usage, and success rates
-- **Experiment Tracking**: Compare different crew configurations and models
-- **Automated Evaluation**: Set up custom evaluation metrics for crew outputs
-- **Error Tracking**: Monitor and debug failures across your crew executions
-- **Cost Analysis**: Track token usage and associated costs
-
-### Version Compatibility Information
-- Python 3.8+
-- CrewAI >= 0.86.0
-- Braintrust >= 0.1.0
-- OpenTelemetry SDK >= 1.31.0
-
-### References
-- [Braintrust Documentation](https://www.braintrust.dev/docs) - Overview of the Braintrust platform
-- [Braintrust CrewAI Integration](https://www.braintrust.dev/docs/integrations/crew-ai) - Official CrewAI integration guide
-- [Braintrust Eval SDK](https://www.braintrust.dev/docs/start/eval-sdk) - Run experiments via the SDK
-- [CrewAI Documentation](https://docs.crewai.com/) - Overview of the CrewAI framework
-- [OpenTelemetry Docs](https://opentelemetry.io/docs/) - OpenTelemetry guide
-- [Braintrust GitHub](https://github.com/braintrustdata/braintrust) - Source code for Braintrust SDK

docs/en/observability/langdb.mdx

@@ -1,287 +0,0 @@
----
-title: LangDB Integration
-description: Govern, secure, and optimize your CrewAI workflows with LangDB AI Gateway—access 350+ models, automatic routing, cost optimization, and full observability.
-icon: database
-mode: "wide"
----
-
-# Introduction
-
-[LangDB AI Gateway](https://langdb.ai) provides OpenAI-compatible APIs to connect with multiple Large Language Models and serves as an observability platform that makes it effortless to trace CrewAI workflows end-to-end while providing access to 350+ language models. With a single `init()` call, all agent interactions, task executions, and LLM calls are captured, providing comprehensive observability and production-ready AI infrastructure for your applications.
-
-<Frame caption="LangDB CrewAI Trace Example">
-  <img src="/images/langdb-1.png" alt="LangDB CrewAI trace example" />
-</Frame>
-
-**Checkout:** [View the live trace example](https://app.langdb.ai/sharing/threads/3becbfed-a1be-ae84-ea3c-4942867a3e22)
-
-## Features
-
-### AI Gateway Capabilities
-- **Access to 350+ LLMs**: Connect to all major language models through a single integration
-- **Virtual Models**: Create custom model configurations with specific parameters and routing rules
-- **Virtual MCP**: Enable compatibility and integration with MCP (Model Context Protocol) systems for enhanced agent communication
-- **Guardrails**: Implement safety measures and compliance controls for agent behavior
-
-### Observability & Tracing
-- **Automatic Tracing**: Single `init()` call captures all CrewAI interactions
-- **End-to-End Visibility**: Monitor agent workflows from start to finish
-- **Tool Usage Tracking**: Track which tools agents use and their outcomes
-- **Model Call Monitoring**: Detailed insights into LLM interactions
-- **Performance Analytics**: Monitor latency, token usage, and costs
-- **Debugging Support**: Step-through execution for troubleshooting
-- **Real-time Monitoring**: Live traces and metrics dashboard
-
-## Setup Instructions
-
-<Steps>
-  <Step title="Install LangDB">
-    Install the LangDB client with CrewAI feature flag:
-    ```bash
-    pip install 'pylangdb[crewai]'
-    ```
-  </Step>
-  <Step title="Set Environment Variables">
-    Configure your LangDB credentials:
-    ```bash
-    export LANGDB_API_KEY="<your_langdb_api_key>"
-    export LANGDB_PROJECT_ID="<your_langdb_project_id>"
-    export LANGDB_API_BASE_URL='https://api.us-east-1.langdb.ai'
-    ```
-  </Step>
-  <Step title="Initialize Tracing">
-    Import and initialize LangDB before configuring your CrewAI code:
-    ```python
-    from pylangdb.crewai import init
-    # Initialize LangDB
-    init()
-    ```
-  </Step>
-  <Step title="Configure CrewAI with LangDB">
-    Set up your LLM with LangDB headers:
-    ```python
-    from crewai import Agent, Task, Crew, LLM
-    import os
-
-    # Configure LLM with LangDB headers
-    llm = LLM(
-        model="openai/gpt-4o", # Replace with the model you want to use
-        api_key=os.getenv("LANGDB_API_KEY"),
-        base_url=os.getenv("LANGDB_API_BASE_URL"),
-        extra_headers={"x-project-id": os.getenv("LANGDB_PROJECT_ID")}
-    )
-    ```
-  </Step>
-</Steps>
-
-## Quick Start Example
-
-Here's a simple example to get you started with LangDB and CrewAI:
-
-```python
-import os
-from pylangdb.crewai import init
-from crewai import Agent, Task, Crew, LLM
-
-# Initialize LangDB before any CrewAI imports
-init()
-
-def create_llm(model):
-    return LLM(
-        model=model,
-        api_key=os.environ.get("LANGDB_API_KEY"),
-        base_url=os.environ.get("LANGDB_API_BASE_URL"),
-        extra_headers={"x-project-id": os.environ.get("LANGDB_PROJECT_ID")}
-    )
-
-# Define your agent
-researcher = Agent(
-    role="Research Specialist",
-    goal="Research topics thoroughly",
-    backstory="Expert researcher with skills in finding information",
-    llm=create_llm("openai/gpt-4o"), # Replace with the model you want to use
-    verbose=True
-)
-
-# Create a task
-task = Task(
-    description="Research the given topic and provide a comprehensive summary",
-    agent=researcher,
-    expected_output="Detailed research summary with key findings"
-)
-
-# Create and run the crew
-crew = Crew(agents=[researcher], tasks=[task])
-result = crew.kickoff()
-print(result)
-```
-
-## Complete Example: Research and Planning Agent
-
-This comprehensive example demonstrates a multi-agent workflow with research and planning capabilities.
-
-### Prerequisites
-
-```bash
-pip install crewai 'pylangdb[crewai]' crewai_tools setuptools python-dotenv
-```
-
-### Environment Setup
-
-```bash
-# LangDB credentials
-export LANGDB_API_KEY="<your_langdb_api_key>"
-export LANGDB_PROJECT_ID="<your_langdb_project_id>"
-export LANGDB_API_BASE_URL='https://api.us-east-1.langdb.ai'
-
-# Additional API keys (optional)
-export SERPER_API_KEY="<your_serper_api_key>"  # For web search capabilities
-```
-
-### Complete Implementation
-
-```python
-#!/usr/bin/env python3
-
-import os
-import sys
-from pylangdb.crewai import init
-init()  # Initialize LangDB before any CrewAI imports
-from dotenv import load_dotenv
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai_tools import SerperDevTool
-
-load_dotenv()
-
-def create_llm(model):
-    return LLM(
-        model=model,
-        api_key=os.environ.get("LANGDB_API_KEY"),
-        base_url=os.environ.get("LANGDB_API_BASE_URL"),
-        extra_headers={"x-project-id": os.environ.get("LANGDB_PROJECT_ID")}
-    )
-
-class ResearchPlanningCrew:
-    def researcher(self) -> Agent:
-        return Agent(
-            role="Research Specialist",
-            goal="Research topics thoroughly and compile comprehensive information",
-            backstory="Expert researcher with skills in finding and analyzing information from various sources",
-            tools=[SerperDevTool()],
-            llm=create_llm("openai/gpt-4o"),
-            verbose=True
-        )
-    
-    def planner(self) -> Agent:
-        return Agent(
-            role="Strategic Planner",
-            goal="Create actionable plans based on research findings",
-            backstory="Strategic planner who breaks down complex challenges into executable plans",
-            reasoning=True,
-            max_reasoning_attempts=3,
-            llm=create_llm("openai/anthropic/claude-3.7-sonnet"),
-            verbose=True
-        )
-    
-    def research_task(self) -> Task:
-        return Task(
-            description="Research the topic thoroughly and compile comprehensive information",
-            agent=self.researcher(),
-            expected_output="Comprehensive research report with key findings and insights"
-        )
-    
-    def planning_task(self) -> Task:
-        return Task(
-            description="Create a strategic plan based on the research findings",
-            agent=self.planner(),
-            expected_output="Strategic execution plan with phases, goals, and actionable steps",
-            context=[self.research_task()]
-        )
-    
-    def crew(self) -> Crew:
-        return Crew(
-            agents=[self.researcher(), self.planner()],
-            tasks=[self.research_task(), self.planning_task()],
-            verbose=True,
-            process=Process.sequential
-        )
-
-def main():
-        topic = sys.argv[1] if len(sys.argv) > 1 else "Artificial Intelligence in Healthcare"
-        
-        crew_instance = ResearchPlanningCrew()
-        
-        # Update task descriptions with the specific topic
-        crew_instance.research_task().description = f"Research {topic} thoroughly and compile comprehensive information"
-    crew_instance.planning_task().description = f"Create a strategic plan for {topic} based on the research findings"
-    
-    result = crew_instance.crew().kickoff()
-    print(result)
-
-if __name__ == "__main__":
-    main()
-```
-
-### Running the Example
-
-```bash
-python main.py "Sustainable Energy Solutions"
-```
-
-## Viewing Traces in LangDB
-
-After running your CrewAI application, you can view detailed traces in the LangDB dashboard:
-
-<Frame caption="LangDB Trace Dashboard">
-  <img src="/images/langdb-2.png" alt="LangDB trace dashboard showing CrewAI workflow" />
-</Frame>
-
-### What You'll See
-
-- **Agent Interactions**: Complete flow of agent conversations and task handoffs
-- **Tool Usage**: Which tools were called, their inputs, and outputs
-- **Model Calls**: Detailed LLM interactions with prompts image.pngand responses
-- **Performance Metrics**: Latency, token usage, and cost tracking
-- **Execution Timeline**: Step-by-step view of the entire workflow
-
-
-## Troubleshooting
-
-### Common Issues
-
-- **No traces appearing**: Ensure `init()` is called before any CrewAI imports
-- **Authentication errors**: Verify your LangDB API key and project ID
-
-
-## Resources
-
-<CardGroup cols={3}>
-  <Card title="LangDB Documentation" icon="book" href="https://docs.langdb.ai">
-    Official LangDB documentation and guides
-  </Card>
-  <Card title="LangDB Guides" icon="graduation-cap" href="https://docs.langdb.ai/guides">
-    Step-by-step tutorials for building AI agents
-  </Card>
-  <Card title="GitHub Examples" icon="github" href="https://github.com/langdb/langdb-samples/tree/main/examples/crewai" >
-    Complete CrewAI integration examples
-  </Card>
-  <Card title="LangDB Dashboard" icon="chart-line" href="https://app.langdb.ai">
-    Access your traces and analytics
-  </Card>
-  <Card title="Model Catalog" icon="list" href="https://app.langdb.ai/models">
-    Browse 350+ available language models
-  </Card>
-  <Card title="Enterprise Features" icon="building" href="https://docs.langdb.ai/enterprise">
-    Self-hosted options and enterprise capabilities
-  </Card>
-</CardGroup>
-
-## Next Steps
-
-This guide covered the basics of integrating LangDB AI Gateway with CrewAI. To further enhance your AI workflows, explore:
-
-- **Virtual Models**: Create custom model configurations with routing strategies
-- **Guardrails & Safety**: Implement content filtering and compliance controls
-- **Production Deployment**: Configure fallbacks, retries, and load balancing
-
-For more advanced features and use cases, visit the [LangDB Documentation](https://docs.langdb.ai) or explore the [Model Catalog](https://app.langdb.ai/models) to discover all available models.

docs/en/observability/maxim.mdx

@@ -1,232 +0,0 @@
----
-title: "Maxim Integration"
-description: "Start Agent monitoring, evaluation, and observability"
-icon: "infinity"
-mode: "wide"
----
-
-# Maxim Overview
-
-Maxim AI provides comprehensive agent monitoring, evaluation, and observability for your CrewAI applications. With Maxim's one-line integration, you can easily trace and analyse agent interactions, performance metrics, and more.
-
-## Features
-
-### Prompt Management
-
-Maxim's Prompt Management capabilities enable you to create, organize, and optimize prompts for your CrewAI agents. Rather than hardcoding instructions, leverage Maxim’s SDK to dynamically retrieve and apply version-controlled prompts.
-
-<Tabs>
-  <Tab title="Prompt Playground">
-    Create, refine, experiment and deploy your prompts via the playground. Organize of your prompts using folders and versions, experimenting with the real world cases by linking tools and context, and deploying based on custom logic.
-
-    Easily experiment across models by [**configuring models**](https://www.getmaxim.ai/docs/introduction/quickstart/setting-up-workspace#add-model-api-keys) and selecting the relevant model from the dropdown at the top of the prompt playground.
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_playground.png'> </img>
-  </Tab>
-  <Tab title="Prompt Versions">
-    As teams build their AI applications, a big part of experimentation is iterating on the prompt structure. In order to collaborate effectively and organize your changes clearly, Maxim allows prompt versioning and comparison runs across versions.
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_versions.png'> </img>
-  </Tab>
-  <Tab title="Prompt Comparisons">
-    Iterating on Prompts as you evolve your AI application would need experiments across models, prompt structures, etc. In order to compare versions and make informed decisions about changes, the comparison playground allows a side by side view of results.
-
-    ## **Why use Prompt comparison?**
-
-    Prompt comparison combines multiple single Prompts into one view, enabling a streamlined approach for various workflows:
-
-    1. **Model comparison**: Evaluate the performance of different models on the same Prompt.
-    2. **Prompt optimization**: Compare different versions of a Prompt to identify the most effective formulation.
-    3. **Cross-Model consistency**: Ensure consistent outputs across various models for the same Prompt.
-    4. **Performance benchmarking**: Analyze metrics like latency, cost, and token count across different models and Prompts.
-  </Tab>
-</Tabs>
-
-### Observability & Evals
-
-Maxim AI provides comprehensive observability & evaluation for your CrewAI agents, helping you understand exactly what's happening during each execution.
-
-<Tabs>
-  <Tab title="Agent Tracing">
-    Track your agent’s complete lifecycle, including tool calls, agent trajectories, and decision flows effortlessly.
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_agent_tracking.png'> </img>
-  </Tab>
-  <Tab title="Analytics + Evals">
-    Run detailed evaluations on full traces or individual nodes with support for:
-
-    - Multi-step interactions and granular trace analysis
-    - Session Level Evaluations
-    - Simulations for real-world testing
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_trace_eval.png'> </img>
-
-    <CardGroup cols={3}>
-      <Card title="Auto Evals on Logs" icon="e" href="https://www.getmaxim.ai/docs/observe/how-to/evaluate-logs/auto-evaluation">
-        <p>
-        Evaluate captured logs automatically from the UI based on filters and sampling
-
-        </p>
-      </Card>
-      <Card title="Human Evals on Logs" icon="hand" href="https://www.getmaxim.ai/docs/observe/how-to/evaluate-logs/human-evaluation">
-        <p>
-        Use human evaluation or rating to assess the quality of your logs and evaluate them.
-
-        </p>
-      </Card>
-      <Card title="Node Level Evals" icon="road" href="https://www.getmaxim.ai/docs/observe/how-to/evaluate-logs/node-level-evaluation">
-        <p>
-        Evaluate any component of your trace or log to gain insights into your agent’s behavior.
-
-        </p>
-      </Card>
-    </CardGroup>
-    ---
-  </Tab>
-  <Tab title="Alerting">
-    Set thresholds on **error**, **cost, token usage, user feedback, latency** and get real-time alerts via Slack or PagerDuty.
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_alerts_1.png'> </img>
-  </Tab>
-  <Tab title="Dashboards">
-    Visualize Traces over time, usage metrics, latency & error rates with ease.
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_dashboard_1.png'> </img>
-  </Tab>
-</Tabs>
-
-## Getting Started
-
-### Prerequisites
-
-
-- Python version \>=3.10
-- A Maxim account ([sign up here](https://getmaxim.ai/))
-- Generate Maxim API Key
-- A CrewAI project
-
-### Installation
-
-Install the Maxim SDK via pip:
-
-```python
-pip install maxim-py
-```
-
-Or add it to your `requirements.txt`:
-
-```
-maxim-py
-```
-### Basic Setup
-
-### 1. Set up environment variables
-
-```python
-### Environment Variables Setup
-
-# Create a `.env` file in your project root:
-
-# Maxim API Configuration
-MAXIM_API_KEY=your_api_key_here
-MAXIM_LOG_REPO_ID=your_repo_id_here
-```
-
-### 2. Import the required packages
-
-```python
-from crewai import Agent, Task, Crew, Process
-from maxim import Maxim
-from maxim.logger.crewai import instrument_crewai
-```
-
-### 3. Initialise Maxim with your API key
-
-
-```python {8}
-# Instrument CrewAI with just one line
-instrument_crewai(Maxim().logger())
-```
-
-### 4. Create and run your CrewAI application as usual
-
-```python
-# Create your agent
-researcher = Agent(
-    role='Senior Research Analyst',
-    goal='Uncover cutting-edge developments in AI',
-    backstory="You are an expert researcher at a tech think tank...",
-    verbose=True,
-    llm=llm
-)
-
-# Define the task
-research_task = Task(
-    description="Research the latest AI advancements...",
-    expected_output="",
-    agent=researcher
-)
-
-# Configure and run the crew
-crew = Crew(
-    agents=[researcher],
-    tasks=[research_task],
-    verbose=True
-)
-
-try:
-    result = crew.kickoff()
-finally:
-    maxim.cleanup()  # Ensure cleanup happens even if errors occur
-```
-
-
-That's it\! All your CrewAI agent interactions will now be logged and available in your Maxim dashboard.
-
-Check this Google Colab Notebook for a quick reference - [Notebook](https://colab.research.google.com/drive/1ZKIZWsmgQQ46n8TH9zLsT1negKkJA6K8?usp=sharing)
-
-## Viewing Your Traces
-
-After running your CrewAI application:
-
-1. Log in to your [Maxim Dashboard](https://app.getmaxim.ai/login)
-2. Navigate to your repository
-3. View detailed agent traces, including:
-   - Agent conversations
-   - Tool usage patterns
-   - Performance metrics
-   - Cost analytics
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/crewai_traces.gif'> </img>
-
-## Troubleshooting
-
-### Common Issues
-
-- **No traces appearing**: Ensure your API key and repository ID are correct
-- Ensure you've **`called instrument_crewai()`** **_before_** running your crew. This initializes logging hooks correctly.
-- Set `debug=True` in your `instrument_crewai()` call to surface any internal errors:
-
-  ```python
-  instrument_crewai(logger, debug=True)
-  ```
-- Configure your agents with `verbose=True` to capture detailed logs:
-
-  ```python
-  agent = CrewAgent(..., verbose=True)
-  ```
-- Double-check that `instrument_crewai()` is called **before** creating or executing agents. This might be obvious, but it's a common oversight.
-
-## Resources
-
-<CardGroup cols="3">
-  <Card title="CrewAI Docs" icon="book" href="https://docs.crewai.com/">
-    Official CrewAI documentation
-  </Card>
-  <Card title="Maxim Docs" icon="book" href="https://getmaxim.ai/docs">
-    Official Maxim documentation
-  </Card>
-  <Card title="Maxim Github" icon="github" href="https://github.com/maximhq">
-    Maxim Github
-  </Card>
-</CardGroup>

docs/en/observability/neatlogs.mdx

@@ -1,134 +0,0 @@
----
-title: Neatlogs Integration
-description: Understand, debug, and share your CrewAI agent runs
-icon: magnifying-glass-chart
-mode: "wide"
----
-
-# Introduction
-
-Neatlogs helps you **see what your agent did**, **why**, and **share it**.
-
-It captures every step: thoughts, tool calls, responses, evaluations. No raw logs. Just clear, structured traces. Great for debugging and collaboration.
-
-## Why use Neatlogs?
-
-CrewAI agents use multiple tools and reasoning steps. When something goes wrong, you need context — not just errors.
-
-Neatlogs lets you:
-
-- Follow the full decision path
-- Add feedback directly on steps
-- Chat with the trace using AI assistant
-- Share runs publicly for feedback
-- Turn insights into tasks
-
-All in one place.
-
-Manage your traces effortlessly
-
-![Traces](/images/neatlogs-1.png)
-![Trace Response](/images/neatlogs-2.png)
-
-The best UX to view a CrewAI trace. Post comments anywhere you want. Use AI to debug.
-
-![Trace Details](/images/neatlogs-3.png)
-![Ai Chat Bot With A Trace](/images/neatlogs-4.png)
-![Comments Drawer](/images/neatlogs-5.png)
-
-## Core Features
-
-- **Trace Viewer**: Track thoughts, tools, and decisions in sequence
-- **Inline Comments**: Tag teammates on any trace step
-- **Feedback & Evaluation**: Mark outputs as correct or incorrect
-- **Error Highlighting**: Automatic flagging of API/tool failures
-- **Task Conversion**: Convert comments into assigned tasks
-- **Ask the Trace (AI)**: Chat with your trace using Neatlogs AI bot
-- **Public Sharing**: Publish trace links to your community
-
-## Quick Setup with CrewAI
-
-<Steps>
-  <Step title="Sign Up & Get API Key">
-    Visit [neatlogs.com](https://neatlogs.com/?utm_source=crewAI-docs), create a project, copy the API key.
-  </Step>
-  <Step title="Install SDK">
-    ```bash
-    pip install neatlogs
-    ```
-    (Latest version 0.8.0, Python 3.8+; MIT license)
-  </Step>
-  <Step title="Initialize Neatlogs">
-    Before starting Crew agents, add:
-
-    ```python
-    import neatlogs
-    neatlogs.init("YOUR_PROJECT_API_KEY")
-    ```
-
-    Agents run as usual. Neatlogs captures everything automatically.
-
-  </Step>
-</Steps>
-
-
-
-## Under the Hood
-
-According to GitHub, Neatlogs:
-
-- Captures thoughts, tool calls, responses, errors, and token stats
-- Supports AI-powered task generation and robust evaluation workflows
-
-All with just two lines of code.
-
-
-
-## Watch It Work
-
-### 🔍 Full Demo (4 min)
-
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.youtube.com/embed/8KDme9T2I7Q?si=b8oHteaBwFNs_Duk"
-  title="NeatLogs overview"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
-></iframe>
-
-### ⚙️ CrewAI Integration (30 s)
-
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.loom.com/embed/9c78b552af43452bb3e4783cb8d91230?sid=e9d7d370-a91a-49b0-809e-2f375d9e801d"
-  title="Loom video player"
-  frameBorder="0"
-  allowFullScreen
-></iframe>
-
-
-
-## Links & Support
-
-- 📘 [Neatlogs Docs](https://docs.neatlogs.com/)
-- 🔐 [Dashboard & API Key](https://app.neatlogs.com/)
-- 🐦 [Follow on Twitter](https://twitter.com/neatlogs)
-- 📧 Contact: hello@neatlogs.com
-- 🛠 [GitHub SDK](https://github.com/NeatLogs/neatlogs)
-
-
-
-## TL;DR
-
-With just:
-
-```bash
-pip install neatlogs
-
-import neatlogs
-neatlogs.init("YOUR_API_KEY")
-
-You can now capture, understand, share, and act on your CrewAI agent runs in seconds.
-No setup overhead. Full trace transparency. Full team collaboration.
-```

docs/en/observability/tracing.mdx

@@ -1,213 +0,0 @@
----
-title: CrewAI Tracing
-description: Built-in tracing for CrewAI Crews and Flows with the CrewAI AMP platform
-icon: magnifying-glass-chart
-mode: "wide"
----
-
-# CrewAI Built-in Tracing
-
-CrewAI provides built-in tracing capabilities that allow you to monitor and debug your Crews and Flows in real-time. This guide demonstrates how to enable tracing for both **Crews** and **Flows** using CrewAI's integrated observability platform.
-
-> **What is CrewAI Tracing?** CrewAI's built-in tracing provides comprehensive observability for your AI agents, including agent decisions, task execution timelines, tool usage, and LLM calls - all accessible through the [CrewAI AMP platform](https://app.crewai.com).
-
-![CrewAI Tracing Interface](/images/crewai-tracing.png)
-
-## Prerequisites
-
-Before you can use CrewAI tracing, you need:
-
-1. **CrewAI AMP Account**: Sign up for a free account at [app.crewai.com](https://app.crewai.com)
-2. **CLI Authentication**: Use the CrewAI CLI to authenticate your local environment
-
-```bash
-crewai login
-```
-
-## Setup Instructions
-
-### Step 1: Create Your CrewAI AMP Account
-
-Visit [app.crewai.com](https://app.crewai.com) and create your free account. This will give you access to the CrewAI AMP platform where you can view traces, metrics, and manage your crews.
-
-### Step 2: Install CrewAI CLI and Authenticate
-
-If you haven't already, install CrewAI with the CLI tools:
-
-```bash
-uv add crewai[tools]
-```
-
-Then authenticate your CLI with your CrewAI AMP account:
-
-```bash
-crewai login
-```
-
-This command will:
-1. Open your browser to the authentication page
-2. Prompt you to enter a device code
-3. Authenticate your local environment with your CrewAI AMP account
-4. Enable tracing capabilities for your local development
-
-### Step 3: Enable Tracing in Your Crew
-
-You can enable tracing for your Crew by setting the `tracing` parameter to `True`:
-
-```python
-from crewai import Agent, Crew, Process, Task
-from crewai_tools import SerperDevTool
-
-# Define your agents
-researcher = Agent(
-    role="Senior Research Analyst",
-    goal="Uncover cutting-edge developments in AI and data science",
-    backstory="""You work at a leading tech think tank.
-    Your expertise lies in identifying emerging trends.
-    You have a knack for dissecting complex data and presenting actionable insights.""",
-    verbose=True,
-    tools=[SerperDevTool()],
-)
-
-writer = Agent(
-    role="Tech Content Strategist",
-    goal="Craft compelling content on tech advancements",
-    backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
-    You transform complex concepts into compelling narratives.""",
-    verbose=True,
-)
-
-# Create tasks for your agents
-research_task = Task(
-    description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-    Identify key trends, breakthrough technologies, and potential industry impacts.""",
-    expected_output="Full analysis report in bullet points",
-    agent=researcher,
-)
-
-writing_task = Task(
-    description="""Using the insights provided, develop an engaging blog
-    post that highlights the most significant AI advancements.
-    Your post should be informative yet accessible, catering to a tech-savvy audience.""",
-    expected_output="Full blog post of at least 4 paragraphs",
-    agent=writer,
-)
-
-# Enable tracing in your crew
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research_task, writing_task],
-    process=Process.sequential,
-    tracing=True,  # Enable built-in tracing
-    verbose=True
-)
-
-# Execute your crew
-result = crew.kickoff()
-```
-
-### Step 4: Enable Tracing in Your Flow
-
-Similarly, you can enable tracing for CrewAI Flows:
-
-```python
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-class ExampleState(BaseModel):
-    counter: int = 0
-    message: str = ""
-
-class ExampleFlow(Flow[ExampleState]):
-    def __init__(self):
-        super().__init__(tracing=True)  # Enable tracing for the flow
-
-    @start()
-    def first_method(self):
-        print("Starting the flow")
-        self.state.counter = 1
-        self.state.message = "Flow started"
-        return "continue"
-
-    @listen("continue")
-    def second_method(self):
-        print("Continuing the flow")
-        self.state.counter += 1
-        self.state.message = "Flow continued"
-        return "finish"
-
-    @listen("finish")
-    def final_method(self):
-        print("Finishing the flow")
-        self.state.counter += 1
-        self.state.message = "Flow completed"
-
-# Create and run the flow with tracing enabled
-flow = ExampleFlow(tracing=True)
-result = flow.kickoff()
-```
-
-### Step 5: View Traces in the CrewAI AMP Dashboard
-
-After running the crew or flow, you can view the traces generated by your CrewAI application in the CrewAI AMP dashboard. You should see detailed steps of the agent interactions, tool usages, and LLM calls.
-Just click on the link below to view the traces or head over to the traces tab in the dashboard [here](https://app.crewai.com/crewai_plus/trace_batches)
-![CrewAI Tracing Interface](/images/view-traces.png)
-
-
-### Alternative: Environment Variable Configuration
-
-You can also enable tracing globally by setting an environment variable:
-
-```bash
-export CREWAI_TRACING_ENABLED=true
-```
-
-Or add it to your `.env` file:
-
-```env
-CREWAI_TRACING_ENABLED=true
-```
-
-When this environment variable is set, all Crews and Flows will automatically have tracing enabled, even without explicitly setting `tracing=True`.
-
-## Viewing Your Traces
-
-### Access the CrewAI AMP Dashboard
-
-1. Visit [app.crewai.com](https://app.crewai.com) and log in to your account
-2. Navigate to your project dashboard
-3. Click on the **Traces** tab to view execution details
-
-### What You'll See in Traces
-
-CrewAI tracing provides comprehensive visibility into:
-
-- **Agent Decisions**: See how agents reason through tasks and make decisions
-- **Task Execution Timeline**: Visual representation of task sequences and dependencies
-- **Tool Usage**: Monitor which tools are called and their results
-- **LLM Calls**: Track all language model interactions, including prompts and responses
-- **Performance Metrics**: Execution times, token usage, and costs
-- **Error Tracking**: Detailed error information and stack traces
-
-### Trace Features
-- **Execution Timeline**: Click through different stages of execution
-- **Detailed Logs**: Access comprehensive logs for debugging
-- **Performance Analytics**: Analyze execution patterns and optimize performance
-- **Export Capabilities**: Download traces for further analysis
-
-### Authentication Issues
-
-If you encounter authentication problems:
-
-1. Ensure you're logged in: `crewai login`
-2. Check your internet connection
-3. Verify your account at [app.crewai.com](https://app.crewai.com)
-
-### Traces Not Appearing
-
-If traces aren't showing up in the dashboard:
-
-1. Confirm `tracing=True` is set in your Crew/Flow
-2. Check that `CREWAI_TRACING_ENABLED=true` if using environment variables
-3. Ensure you're authenticated with `crewai login`
-4. Verify your crew/flow is actually executing

docs/en/observability/truefoundry.mdx

@@ -1,147 +0,0 @@
----
-title: TrueFoundry Integration
-icon: chart-line
-mode: "wide"
----
-
-TrueFoundry provides an enterprise-ready [AI Gateway](https://www.truefoundry.com/ai-gateway) which can integrate with agentic frameworks like CrewAI and provides governance and observability for your AI Applications. TrueFoundry AI Gateway serves as a unified interface for LLM access, providing:
-
-- **Unified API Access**: Connect to 250+ LLMs (OpenAI, Claude, Gemini, Groq, Mistral) through one API
-- **Low Latency**: Sub-3ms internal latency with intelligent routing and load balancing  
-- **Enterprise Security**: SOC 2, HIPAA, GDPR compliance with RBAC and audit logging
-- **Quota and cost management**: Token-based quotas, rate limiting, and comprehensive usage tracking
-- **Observability**: Full request/response logging, metrics, and traces with customizable retention
-
-## How TrueFoundry Integrates with CrewAI
-
-
-### Installation & Setup
-
-<Steps>
-<Step title="Install CrewAI">
-```bash
-pip install crewai
-```
-</Step>
-
-<Step title="Get TrueFoundry Access Token">
-1. Sign up for a [TrueFoundry account](https://www.truefoundry.com/register)
-2. Follow the steps here in [Quick start](https://docs.truefoundry.com/gateway/quick-start)
-</Step>
-
-<Step title="Configure CrewAI with TrueFoundry">
-![TrueFoundry Code Configuration](/images/new-code-snippet.png)
-
-```python
-from crewai import LLM
-
-# Create an LLM instance with TrueFoundry AI Gateway
-truefoundry_llm = LLM(
-    model="openai-main/gpt-4o",  # Similarly, you can call any model from any provider
-    base_url="your_truefoundry_gateway_base_url",
-    api_key="your_truefoundry_api_key"
-)
-
-# Use in your CrewAI agents
-from crewai import Agent
-
-@agent
-def researcher(self) -> Agent:
-    return Agent(
-        config=self.agents_config['researcher'],
-        llm=truefoundry_llm,
-        verbose=True
-    )
-```
-</Step>
-</Steps>
-
-### Complete CrewAI Example
-
-```python
-from crewai import Agent, Task, Crew, LLM
-
-# Configure LLM with TrueFoundry
-llm = LLM(
-    model="openai-main/gpt-4o",
-    base_url="your_truefoundry_gateway_base_url", 
-    api_key="your_truefoundry_api_key"
-)
-
-# Create agents
-researcher = Agent(
-    role='Research Analyst',
-    goal='Conduct detailed market research',
-    backstory='Expert market analyst with attention to detail',
-    llm=llm,
-    verbose=True
-)
-
-writer = Agent(
-    role='Content Writer', 
-    goal='Create comprehensive reports',
-    backstory='Experienced technical writer',
-    llm=llm,
-    verbose=True
-)
-
-# Create tasks
-research_task = Task(
-    description='Research AI market trends for 2024',
-    agent=researcher,
-    expected_output='Comprehensive research summary'
-)
-
-writing_task = Task(
-    description='Create a market research report',
-    agent=writer,
-    expected_output='Well-structured report with insights',
-    context=[research_task]
-)
-
-# Create and execute crew
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research_task, writing_task],
-    verbose=True
-)
-
-result = crew.kickoff()
-```
-
-### Observability and Governance
-
-Monitor your CrewAI agents through TrueFoundry's metrics tab:
-![TrueFoundry metrics](/images/gateway-metrics.png)
-
-With Truefoundry's AI gateway, you can monitor and analyze:
-
-- **Performance Metrics**: Track key latency metrics like Request Latency, Time to First Token (TTFS), and Inter-Token Latency (ITL) with P99, P90, and P50 percentiles
-- **Cost and Token Usage**: Gain visibility into your application's costs with detailed breakdowns of input/output tokens and the associated expenses for each model
-- **Usage Patterns**: Understand how your application is being used with detailed analytics on user activity, model distribution, and team-based usage
-- **Rate limit and Load balancing**: You can set up rate limiting, load balancing and fallback for your models
-
-## Tracing
-
-For a more detailed understanding on tracing, please see [getting-started-tracing](https://docs.truefoundry.com/docs/tracing/tracing-getting-started).For tracing, you can add the Traceloop SDK: 
-For tracing, you can add the Traceloop SDK: 
-
-```bash
-pip install traceloop-sdk
-```
-
-```python
-from traceloop.sdk import Traceloop
-
-# Initialize enhanced tracing
-Traceloop.init(
-    api_endpoint="https://your-truefoundry-endpoint/api/tracing",
-    headers={
-        "Authorization": f"Bearer {your_truefoundry_pat_token}",
-        "TFY-Tracing-Project": "your_project_name",
-    },
-)
-```
-
-This provides additional trace correlation across your entire CrewAI workflow.
-![TrueFoundry CrewAI Tracing](/images/tracing_crewai.png)

docs/en/tools/automation/zapieractionstool.mdx

@@ -1,59 +0,0 @@
----
-title: Zapier Actions Tool
-description: The `ZapierActionsAdapter` exposes Zapier actions as CrewAI tools for automation.
-icon: bolt
-mode: "wide"
----
-
-# `ZapierActionsAdapter`
-
-## Description
-
-Use the Zapier adapter to list and call Zapier actions as CrewAI tools. This enables agents to trigger automations across thousands of apps.
-
-## Installation
-
-This adapter is included with `crewai-tools`. No extra install required.
-
-## Environment Variables
-
-- `ZAPIER_API_KEY` (required): Zapier API key. Get one from the Zapier Actions dashboard at https://actions.zapier.com/ (create an account, then generate an API key). You can also pass `zapier_api_key` directly when constructing the adapter.
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools.adapters.zapier_adapter import ZapierActionsAdapter
-
-adapter = ZapierActionsAdapter(api_key="your_zapier_api_key")
-tools = adapter.tools()
-
-agent = Agent(
-    role="Automator",
-    goal="Execute Zapier actions",
-    backstory="Automation specialist",
-    tools=tools,
-    verbose=True,
-)
-
-task = Task(
-    description="Create a new Google Sheet and add a row using Zapier actions",
-    expected_output="Confirmation with created resource IDs",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Notes & limits
-
-- The adapter lists available actions for your key and creates `BaseTool` wrappers dynamically.
-- Handle action‑specific required fields in your task instructions or tool call.
-- Rate limits depend on your Zapier plan; see the Zapier Actions docs.
-
-## Notes
-
-- The adapter fetches available actions and generates `BaseTool` wrappers dynamically.
-
-

docs/en/tools/database-data/mongodbvectorsearchtool.mdx

@@ -1,169 +0,0 @@
----
-title: MongoDB Vector Search Tool
-description: The `MongoDBVectorSearchTool` performs vector search on MongoDB Atlas with optional indexing helpers.
-icon: "leaf"
-mode: "wide"
----
-
-# `MongoDBVectorSearchTool`
-
-## Description
-
-Perform vector similarity queries on MongoDB Atlas collections. Supports index creation helpers and bulk insert of embedded texts.
-
-MongoDB Atlas supports native vector search. Learn more:
-https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/
-
-## Installation
-
-Install with the MongoDB extra:
-
-```shell
-pip install crewai-tools[mongodb]
-```
-
-or
-
-```shell
-uv add crewai-tools --extra mongodb
-```
-
-## Parameters
-
-### Initialization
-
-- `connection_string` (str, required)
-- `database_name` (str, required)
-- `collection_name` (str, required)
-- `vector_index_name` (str, default `vector_index`)
-- `text_key` (str, default `text`)
-- `embedding_key` (str, default `embedding`)
-- `dimensions` (int, default `1536`)
-
-### Run Parameters
-
-- `query` (str, required): Natural language query to embed and search.
-
-## Quick start
-
-```python Code
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-  connection_string="mongodb+srv://...",
-  database_name="mydb",
-  collection_name="docs",
-)
-
-print(tool.run(query="how to create vector index"))
-```
-
-## Index creation helpers
-
-Use `create_vector_search_index(...)` to provision an Atlas Vector Search index with the correct dimensions and similarity.
-
-## Common issues
-
-- Authentication failures: ensure your Atlas IP Access List allows your runner and the connection string includes credentials.
-- Index not found: create the vector index first; name must match `vector_index_name`.
-- Dimensions mismatch: align embedding model dimensions with `dimensions`.
-
-## More examples
-
-### Basic initialization
-
-```python Code
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-    database_name="example_database",
-    collection_name="example_collection",
-    connection_string="<your_mongodb_connection_string>",
-)
-```
-
-### Custom query configuration
-
-```python Code
-from crewai_tools import MongoDBVectorSearchConfig, MongoDBVectorSearchTool
-
-query_config = MongoDBVectorSearchConfig(limit=10, oversampling_factor=2)
-tool = MongoDBVectorSearchTool(
-    database_name="example_database",
-    collection_name="example_collection",
-    connection_string="<your_mongodb_connection_string>",
-    query_config=query_config,
-    vector_index_name="my_vector_index",
-)
-
-rag_agent = Agent(
-    name="rag_agent",
-    role="You are a helpful assistant that can answer questions with the help of the MongoDBVectorSearchTool.",
-    goal="...",
-    backstory="...",
-    tools=[tool],
-)
-```
-
-### Preloading the database and creating the index
-
-```python Code
-import os
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-    database_name="example_database",
-    collection_name="example_collection",
-    connection_string="<your_mongodb_connection_string>",
-)
-
-# Load text content from a local folder and add to MongoDB
-texts = []
-for fname in os.listdir("knowledge"):
-    path = os.path.join("knowledge", fname)
-    if os.path.isfile(path):
-        with open(path, "r", encoding="utf-8") as f:
-            texts.append(f.read())
-
-tool.add_texts(texts)
-
-# Create the Atlas Vector Search index (e.g., 3072 dims for text-embedding-3-large)
-tool.create_vector_search_index(dimensions=3072)
-```
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-    connection_string="mongodb+srv://...",
-    database_name="mydb",
-    collection_name="docs",
-)
-
-agent = Agent(
-    role="RAG Agent",
-    goal="Answer using MongoDB vector search",
-    backstory="Knowledge retrieval specialist",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Find relevant content for 'indexing guidance'",
-    expected_output="A concise answer citing the most relevant matches",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-
-result = crew.kickoff()
-```
-
-

docs/en/tools/database-data/singlestoresearchtool.mdx

@@ -1,62 +0,0 @@
----
-title: SingleStore Search Tool
-description: The `SingleStoreSearchTool` safely executes SELECT/SHOW queries on SingleStore with pooling.
-icon: circle
-mode: "wide"
----
-
-# `SingleStoreSearchTool`
-
-## Description
-
-Execute read‑only queries (`SELECT`/`SHOW`) against SingleStore with connection pooling and input validation.
-
-## Installation
-
-```shell
-uv add crewai-tools[singlestore]
-```
-
-## Environment Variables
-
-Variables like `SINGLESTOREDB_HOST`, `SINGLESTOREDB_USER`, `SINGLESTOREDB_PASSWORD`, etc., can be used, or `SINGLESTOREDB_URL` as a single DSN.
-
-Generate the API key from the SingleStore dashboard, [docs here](https://docs.singlestore.com/cloud/reference/management-api/#generate-an-api-key).
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import SingleStoreSearchTool
-
-tool = SingleStoreSearchTool(
-    tables=["products"], 
-    host="host", 
-    user="user", 
-    password="pass", 
-    database="db",
-)
-
-agent = Agent(
-    role="Analyst", 
-    goal="Query SingleStore", 
-    tools=[tool], 
-    verbose=True,
-)
-
-task = Task(
-    description="List 5 products", 
-    expected_output="5 rows as JSON/text", 
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-
-result = crew.kickoff()
-```
-
-

docs/en/tools/file-document/ocrtool.mdx

@@ -1,90 +0,0 @@
----
-title: OCR Tool
-description: The `OCRTool` extracts text from local images or image URLs using an LLM with vision.
-icon: image
-mode: "wide"
----
-
-# `OCRTool`
-
-## Description
-
-Extract text from images (local path or URL). Uses a vision‑capable LLM via CrewAI’s LLM interface.
-
-## Installation
-
-No extra install beyond `crewai-tools`. Ensure your selected LLM supports vision.
-
-## Parameters
-
-### Run Parameters
-
-- `image_path_url` (str, required): Local image path or HTTP(S) URL.
-
-## Examples
-
-### Direct usage
-
-```python Code
-from crewai_tools import OCRTool
-
-print(OCRTool().run(image_path_url="/tmp/receipt.png"))
-```
-
-### With an agent
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import OCRTool
-
-ocr = OCRTool()
-
-agent = Agent(
-    role="OCR", 
-    goal="Extract text", 
-    tools=[ocr],
-)
-
-task = Task(
-    description="Extract text from https://example.com/invoice.jpg", 
-    expected_output="All detected text in plain text",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Notes
-
-- Ensure the selected LLM supports image inputs.
-- For large images, consider downscaling to reduce token usage.
- - You can pass a specific LLM instance to the tool (e.g., `LLM(model="gpt-4o")`) if needed, matching the README guidance.
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import OCRTool
-
-tool = OCRTool()
-
-agent = Agent(
-    role="OCR Specialist",
-    goal="Extract text from images",
-    backstory="Vision‑enabled analyst",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Extract text from https://example.com/receipt.png",
-    expected_output="All detected text in plain text",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-

docs/en/tools/file-document/pdf-text-writing-tool.mdx

@@ -1,77 +0,0 @@
----
-title: PDF Text Writing Tool
-description: The `PDFTextWritingTool` writes text to specific positions in a PDF, supporting custom fonts.
-icon: file-pdf
-mode: "wide"
----
-
-# `PDFTextWritingTool`
-
-## Description
-
-Write text at precise coordinates on a PDF page, optionally embedding a custom TrueType font.
-
-## Parameters
-
-### Run Parameters
-
-- `pdf_path` (str, required): Path to the input PDF.
-- `text` (str, required): Text to add.
-- `position` (tuple[int, int], required): `(x, y)` coordinates.
-- `font_size` (int, default `12`)
-- `font_color` (str, default `"0 0 0 rg"`)
-- `font_name` (str, default `"F1"`)
-- `font_file` (str, optional): Path to `.ttf` file.
-- `page_number` (int, default `0`)
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import PDFTextWritingTool
-
-tool = PDFTextWritingTool()
-
-agent = Agent(
-    role="PDF Editor",
-    goal="Annotate PDFs",
-    backstory="Documentation specialist",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Write 'CONFIDENTIAL' at (72, 720) on page 1 of ./sample.pdf",
-    expected_output="Confirmation message",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-
-result = crew.kickoff()
-```
-
-### Direct usage
-
-```python Code
-from crewai_tools import PDFTextWritingTool
-
-PDFTextWritingTool().run(
-  pdf_path="./input.pdf",
-  text="CONFIDENTIAL",
-  position=(72, 720),
-  font_size=18,
-  page_number=0,
-)
-```
-
-## Tips
-
-- Coordinate origin is the bottom‑left corner.
-- If using a custom font (`font_file`), ensure it is a valid `.ttf`.
-
-

docs/en/tools/integration/crewaiautomationtool.mdx

@@ -1,276 +0,0 @@
----
-title: CrewAI Run Automation Tool
-description: Enables CrewAI agents to invoke CrewAI Platform automations and leverage external crew services within your workflows.
-icon: robot
----
-
-# `InvokeCrewAIAutomationTool`
-
-The `InvokeCrewAIAutomationTool` provides CrewAI Platform API integration with external crew services. This tool allows you to invoke and interact with CrewAI Platform automations from within your CrewAI agents, enabling seamless integration between different crew workflows.
-
-## Installation
-
-```bash
-uv pip install 'crewai[tools]'
-```
-
-## Requirements
-
-- CrewAI Platform API access
-- Valid bearer token for authentication
-- Network access to CrewAI Platform automation endpoints
-
-## Usage
-
-Here's how to use the tool with a CrewAI agent:
-
-```python {2, 4-9}
-from crewai import Agent, Task, Crew
-from crewai_tools import InvokeCrewAIAutomationTool
-
-# Initialize the tool
-automation_tool = InvokeCrewAIAutomationTool(
-    crew_api_url="https://data-analysis-crew-[...].crewai.com",
-    crew_bearer_token="your_bearer_token_here",
-    crew_name="Data Analysis Crew",
-    crew_description="Analyzes data and generates insights"
-)
-
-# Create a CrewAI agent that uses the tool
-automation_coordinator = Agent(
-    role='Automation Coordinator',
-    goal='Coordinate and execute automated crew tasks',
-    backstory='I am an expert at leveraging automation tools to execute complex workflows.',
-    tools=[automation_tool],
-    verbose=True
-)
-
-# Create a task for the agent
-analysis_task = Task(
-    description="Execute data analysis automation and provide insights",
-    agent=automation_coordinator,
-    expected_output="Comprehensive data analysis report"
-)
-
-# Create a crew with the agent
-crew = Crew(
-    agents=[automation_coordinator],
-    tasks=[analysis_task],
-    verbose=2
-)
-
-# Run the crew
-result = crew.kickoff()
-print(result)
-```
-
-## Tool Arguments
-
-| Argument | Type | Required | Default | Description |
-|:---------|:-----|:---------|:--------|:------------|
-| **crew_api_url** | `str` | Yes | None | Base URL of the CrewAI Platform automation API |
-| **crew_bearer_token** | `str` | Yes | None | Bearer token for API authentication |
-| **crew_name** | `str` | Yes | None | Name of the crew automation |
-| **crew_description** | `str` | Yes | None | Description of what the crew automation does |
-| **max_polling_time** | `int` | No | 600 | Maximum time in seconds to wait for task completion |
-| **crew_inputs** | `dict` | No | None | Dictionary defining custom input schema fields |
-
-## Environment Variables
-
-```bash
-CREWAI_API_URL=https://your-crew-automation.crewai.com  # Alternative to passing crew_api_url
-CREWAI_BEARER_TOKEN=your_bearer_token_here              # Alternative to passing crew_bearer_token
-```
-
-## Advanced Usage
-
-### Custom Input Schema with Dynamic Parameters
-
-```python {2, 4-15}
-from crewai import Agent, Task, Crew
-from crewai_tools import InvokeCrewAIAutomationTool
-from pydantic import Field
-
-# Define custom input schema
-custom_inputs = {
-    "year": Field(..., description="Year to retrieve the report for (integer)"),
-    "region": Field(default="global", description="Geographic region for analysis"),
-    "format": Field(default="summary", description="Report format (summary, detailed, raw)")
-}
-
-# Create tool with custom inputs
-market_research_tool = InvokeCrewAIAutomationTool(
-    crew_api_url="https://state-of-ai-report-crew-[...].crewai.com",
-    crew_bearer_token="your_bearer_token_here",
-    crew_name="State of AI Report",
-    crew_description="Retrieves a comprehensive report on state of AI for a given year and region",
-    crew_inputs=custom_inputs,
-    max_polling_time=15 * 60  # 15 minutes timeout
-)
-
-# Create an agent with the tool
-research_agent = Agent(
-    role="Research Coordinator",
-    goal="Coordinate and execute market research tasks",
-    backstory="You are an expert at coordinating research tasks and leveraging automation tools.",
-    tools=[market_research_tool],
-    verbose=True
-)
-
-# Create and execute a task with custom parameters
-research_task = Task(
-    description="Conduct market research on AI tools market for 2024 in North America with detailed format",
-    agent=research_agent,
-    expected_output="Comprehensive market research report"
-)
-
-crew = Crew(
-    agents=[research_agent],
-    tasks=[research_task]
-)
-
-result = crew.kickoff()
-```
-
-### Multi-Stage Automation Workflow
-
-```python {2, 4-35}
-from crewai import Agent, Task, Crew, Process
-from crewai_tools import InvokeCrewAIAutomationTool
-
-# Initialize different automation tools
-data_collection_tool = InvokeCrewAIAutomationTool(
-    crew_api_url="https://data-collection-crew-[...].crewai.com",
-    crew_bearer_token="your_bearer_token_here",
-    crew_name="Data Collection Automation",
-    crew_description="Collects and preprocesses raw data"
-)
-
-analysis_tool = InvokeCrewAIAutomationTool(
-    crew_api_url="https://analysis-crew-[...].crewai.com",
-    crew_bearer_token="your_bearer_token_here",
-    crew_name="Analysis Automation",
-    crew_description="Performs advanced data analysis and modeling"
-)
-
-reporting_tool = InvokeCrewAIAutomationTool(
-    crew_api_url="https://reporting-crew-[...].crewai.com",
-    crew_bearer_token="your_bearer_token_here",
-    crew_name="Reporting Automation",
-    crew_description="Generates comprehensive reports and visualizations"
-)
-
-# Create specialized agents
-data_collector = Agent(
-    role='Data Collection Specialist',
-    goal='Gather and preprocess data from various sources',
-    backstory='I specialize in collecting and cleaning data from multiple sources.',
-    tools=[data_collection_tool]
-)
-
-data_analyst = Agent(
-    role='Data Analysis Expert',
-    goal='Perform advanced analysis on collected data',
-    backstory='I am an expert in statistical analysis and machine learning.',
-    tools=[analysis_tool]
-)
-
-report_generator = Agent(
-    role='Report Generation Specialist',
-    goal='Create comprehensive reports and visualizations',
-    backstory='I excel at creating clear, actionable reports from complex data.',
-    tools=[reporting_tool]
-)
-
-# Create sequential tasks
-collection_task = Task(
-    description="Collect market data for Q4 2024 analysis",
-    agent=data_collector
-)
-
-analysis_task = Task(
-    description="Analyze collected data to identify trends and patterns",
-    agent=data_analyst
-)
-
-reporting_task = Task(
-    description="Generate executive summary report with key insights and recommendations",
-    agent=report_generator
-)
-
-# Create a crew with sequential processing
-crew = Crew(
-    agents=[data_collector, data_analyst, report_generator],
-    tasks=[collection_task, analysis_task, reporting_task],
-    process=Process.sequential,
-    verbose=2
-)
-
-result = crew.kickoff()
-```
-
-## Use Cases
-
-### Distributed Crew Orchestration
-- Coordinate multiple specialized crew automations to handle complex, multi-stage workflows
-- Enable seamless handoffs between different automation services for comprehensive task execution
-- Scale processing by distributing workloads across multiple CrewAI Platform automations
-
-### Cross-Platform Integration
-- Bridge CrewAI agents with CrewAI Platform automations for hybrid local-cloud workflows
-- Leverage specialized automations while maintaining local control and orchestration
-- Enable secure collaboration between local agents and cloud-based automation services
-
-### Enterprise Automation Pipelines
-- Create enterprise-grade automation pipelines that combine local intelligence with cloud processing power
-- Implement complex business workflows that span multiple automation services
-- Enable scalable, repeatable processes for data analysis, reporting, and decision-making
-
-### Dynamic Workflow Composition
-- Dynamically compose workflows by chaining different automation services based on task requirements
-- Enable adaptive processing where the choice of automation depends on data characteristics or business rules
-- Create flexible, reusable automation components that can be combined in various ways
-
-### Specialized Domain Processing
-- Access domain-specific automations (financial analysis, legal research, technical documentation) from general-purpose agents
-- Leverage pre-built, specialized crew automations without rebuilding complex domain logic
-- Enable agents to access expert-level capabilities through targeted automation services
-
-## Custom Input Schema
-
-When defining `crew_inputs`, use Pydantic Field objects to specify the input parameters:
-
-```python
-from pydantic import Field
-
-crew_inputs = {
-    "required_param": Field(..., description="This parameter is required"),
-    "optional_param": Field(default="default_value", description="This parameter is optional"),
-    "typed_param": Field(..., description="Integer parameter", ge=1, le=100)  # With validation
-}
-```
-
-## Error Handling
-
-The tool provides comprehensive error handling for common scenarios:
-
-- **API Connection Errors**: Network connectivity issues with CrewAI Platform
-- **Authentication Errors**: Invalid or expired bearer tokens
-- **Timeout Errors**: Tasks that exceed the maximum polling time
-- **Task Failures**: Crew automations that fail during execution
-- **Input Validation Errors**: Invalid parameters passed to automation endpoints
-
-## API Endpoints
-
-The tool interacts with two main API endpoints:
-
-- `POST {crew_api_url}/kickoff`: Starts a new crew automation task
-- `GET {crew_api_url}/status/{crew_id}`: Checks the status of a running task
-
-## Notes
-
-- The tool automatically polls the status endpoint every second until completion or timeout
-- Successful tasks return the result directly, while failed tasks return error information
-- Bearer tokens should be kept secure and not hardcoded in production environments
-- Consider using environment variables for sensitive configuration like bearer tokens
-- Custom input schemas must be compatible with the target crew automation's expected parameters

docs/en/tools/integration/overview.mdx

@@ -1,72 +0,0 @@
----
-title: "Overview"
-description: "Connect CrewAI agents with external automations and managed AI services"
-icon: "face-smile"
-mode: "wide"
----
-
-Integration tools let your agents hand off work to other automation platforms and managed AI services. Use them when a workflow needs to invoke an existing CrewAI deployment or delegate specialised tasks to providers such as Amazon Bedrock.
-
-## **Available Tools**
-
-<CardGroup cols={2}>
-  <Card title="CrewAI Run Automation Tool" icon="robot" href="/en/tools/integration/crewaiautomationtool">
-    Invoke live CrewAI Platform automations, pass custom inputs, and poll for results directly from your agent.
-  </Card>
-
-  <Card title="Bedrock Invoke Agent Tool" icon="aws" href="/en/tools/integration/bedrockinvokeagenttool">
-    Call Amazon Bedrock Agents from your crews, reuse AWS guardrails, and stream responses back into the workflow.
-  </Card>
-</CardGroup>
-
-## **Common Use Cases**
-
-- **Chain automations**: Kick off an existing CrewAI deployment from within another crew or flow
-- **Enterprise hand-off**: Route tasks to Bedrock Agents that already encapsulate company logic and guardrails
-- **Hybrid workflows**: Combine CrewAI reasoning with downstream systems that expose their own agent APIs
-- **Long-running jobs**: Poll external automations and merge the final results back into the current run
-
-## **Quick Start Example**
-
-```python
-from crewai import Agent, Task, Crew
-from crewai_tools import InvokeCrewAIAutomationTool
-from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
-
-# External automation
-analysis_automation = InvokeCrewAIAutomationTool(
-    crew_api_url="https://analysis-crew.acme.crewai.com",
-    crew_bearer_token="YOUR_BEARER_TOKEN",
-    crew_name="Analysis Automation",
-    crew_description="Runs the production-grade analysis pipeline",
-)
-
-# Managed agent on Bedrock
-knowledge_router = BedrockInvokeAgentTool(
-    agent_id="bedrock-agent-id",
-    agent_alias_id="prod",
-)
-
-automation_strategist = Agent(
-    role="Automation Strategist",
-    goal="Orchestrate external automations and summarise their output",
-    backstory="You coordinate enterprise workflows and know when to delegate tasks to specialised services.",
-    tools=[analysis_automation, knowledge_router],
-    verbose=True,
-)
-
-execute_playbook = Task(
-    description="Run the analysis automation and ask the Bedrock agent for executive talking points.",
-    agent=automation_strategist,
-)
-
-Crew(agents=[automation_strategist], tasks=[execute_playbook]).kickoff()
-```
-
-## **Best Practices**
-
-- **Secure credentials**: Store API keys and bearer tokens in environment variables or a secrets manager
-- **Plan for latency**: External automations may take longer—set appropriate polling intervals and timeouts
-- **Reuse sessions**: Bedrock Agents support session IDs so you can maintain context across multiple tool calls
-- **Validate responses**: Normalise remote output (JSON, text, status codes) before forwarding it to downstream tasks
-- **Monitor usage**: Track audit logs in CrewAI Platform or AWS CloudWatch to stay ahead of quota limits and failures

docs/en/tools/search-research/arxivpapertool.mdx

@@ -1,113 +0,0 @@
----
-title: Arxiv Paper Tool
-description: The `ArxivPaperTool` searches arXiv for papers matching a query and optionally downloads PDFs.
-icon: box-archive
-mode: "wide"
----
-
-# `ArxivPaperTool`
-
-## Description
-
-The `ArxivPaperTool` queries the arXiv API for academic papers and returns compact, readable results. It can also optionally download PDFs to disk.
-
-## Installation
-
-This tool has no special installation beyond `crewai-tools`.
-
-```shell
-uv add crewai-tools
-```
-
-No API key is required. This tool uses the public arXiv Atom API.
-
-## Steps to Get Started
-
-1. Initialize the tool.
-2. Provide a `search_query` (e.g., "transformer neural network").
-3. Optionally set `max_results` (1–100) and enable PDF downloads in the constructor.
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import ArxivPaperTool
-
-tool = ArxivPaperTool(
-    download_pdfs=False,
-    save_dir="./arxiv_pdfs",
-    use_title_as_filename=True,
-)
-
-agent = Agent(
-    role="Researcher",
-    goal="Find relevant arXiv papers",
-    backstory="Expert at literature discovery",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Search arXiv for 'transformer neural network' and list top 5 results.",
-    expected_output="A concise list of 5 relevant papers with titles, links, and summaries.",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-### Direct usage (without Agent)
-
-```python Code
-from crewai_tools import ArxivPaperTool
-
-tool = ArxivPaperTool(
-    download_pdfs=True, 
-    save_dir="./arxiv_pdfs",
-)
-print(tool.run(search_query="mixture of experts", max_results=3))
-```
-
-## Parameters
-
-### Initialization Parameters
-
-- `download_pdfs` (bool, default `False`): Whether to download PDFs.
-- `save_dir` (str, default `./arxiv_pdfs`): Directory to save PDFs.
-- `use_title_as_filename` (bool, default `False`): Use paper titles for filenames.
-
-### Run Parameters
-
-- `search_query` (str, required): The arXiv search query.
-- `max_results` (int, default `5`, range 1–100): Number of results.
-
-## Output format
-
-The tool returns a human‑readable list of papers with:
-- Title
-- Link (abs page)
-- Snippet/summary (truncated)
-
-When `download_pdfs=True`, PDFs are saved to disk and the summary mentions saved files.
-
-## Usage Notes
-
-- The tool returns formatted text with key metadata and links.
-- When `download_pdfs=True`, PDFs will be stored in `save_dir`.
-
-## Troubleshooting
-
-- If you receive a network timeout, re‑try or reduce `max_results`.
-- Invalid XML errors indicate an arXiv response parse issue; try a simpler query.
-- File system errors (e.g., permission denied) may occur when saving PDFs; ensure `save_dir` is writable.
-
-## Related links
-
-- arXiv API docs: https://info.arxiv.org/help/api/index.html
-
-## Error Handling
-
-- Network issues, invalid XML, and OS errors are handled with informative messages.
-
-

docs/en/tools/search-research/databricks-query-tool.mdx

@@ -1,81 +0,0 @@
----
-title: Databricks SQL Query Tool
-description: The `DatabricksQueryTool` executes SQL queries against Databricks workspace tables.
-icon: trowel-bricks
-mode: "wide"
----
-
-# `DatabricksQueryTool`
-
-## Description
-
-Run SQL against Databricks workspace tables with either CLI profile or direct host/token authentication.
-
-## Installation
-
-```shell
-uv add crewai-tools[databricks-sdk]
-```
-
-## Environment Variables
-
-- `DATABRICKS_CONFIG_PROFILE` or (`DATABRICKS_HOST` + `DATABRICKS_TOKEN`)
-
-Create a personal access token and find host details in the Databricks workspace under User Settings → Developer. 
-Docs: https://docs.databricks.com/en/dev-tools/auth/pat.html
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import DatabricksQueryTool
-
-tool = DatabricksQueryTool(
-    default_catalog="main", 
-    default_schema="default",
-)
-
-agent = Agent(
-    role="Data Analyst",
-    goal="Query Databricks",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="SELECT * FROM my_table LIMIT 10",
-    expected_output="10 rows", 
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-result = crew.kickoff()
-
-print(result)
-```
-
-## Parameters
-
-- `query` (required): SQL query to execute
-- `catalog` (optional): Override default catalog
-- `db_schema` (optional): Override default schema
-- `warehouse_id` (optional): Override default SQL warehouse
-- `row_limit` (optional): Maximum rows to return (default: 1000)
-
-## Defaults on initialization
-
-- `default_catalog`
-- `default_schema`
-- `default_warehouse_id`
-
-### Error handling & tips
-
-- Authentication errors: verify `DATABRICKS_HOST` begins with `https://` and token is valid.
-- Permissions: ensure your SQL warehouse and schema are accessible by your token.
-- Limits: long‑running queries should be avoided in agent loops; add filters/limits.
-
-

docs/en/tools/search-research/overview.mdx

@@ -1,94 +0,0 @@
----
-title: "Overview"
-description: "Perform web searches, find repositories, and research information across the internet"
-icon: "face-smile"
-mode: "wide"
----
-
-These tools enable your agents to search the web, research topics, and find information across various platforms including search engines, GitHub, and YouTube.
-
-## **Available Tools**
-
-<CardGroup cols={2}>
-  <Card title="Serper Dev Tool" icon="google" href="/en/tools/search-research/serperdevtool">
-    Google search API integration for comprehensive web search capabilities.
-  </Card>
-
-  <Card title="Brave Search Tool" icon="shield" href="/en/tools/search-research/bravesearchtool">
-    Privacy-focused search with Brave's independent search index.
-  </Card>
-
-  <Card title="Exa Search Tool" icon="magnifying-glass" href="/en/tools/search-research/exasearchtool">
-    AI-powered search for finding specific and relevant content.
-  </Card>
-
-  <Card title="LinkUp Search Tool" icon="link" href="/en/tools/search-research/linkupsearchtool">
-    Real-time web search with fresh content indexing.
-  </Card>
-
-  <Card title="GitHub Search Tool" icon="github" href="/en/tools/search-research/githubsearchtool">
-    Search GitHub repositories, code, issues, and documentation.
-  </Card>
-
-  <Card title="Website Search Tool" icon="globe" href="/en/tools/search-research/websitesearchtool">
-    Search within specific websites and domains.
-  </Card>
-
-  <Card title="Code Docs Search Tool" icon="code" href="/en/tools/search-research/codedocssearchtool">
-    Search through code documentation and technical resources.
-  </Card>
-
-  <Card title="YouTube Channel Search" icon="youtube" href="/en/tools/search-research/youtubechannelsearchtool">
-    Search YouTube channels for specific content and creators.
-  </Card>
-
-  <Card title="YouTube Video Search" icon="play" href="/en/tools/search-research/youtubevideosearchtool">
-    Find and analyze YouTube videos by topic, keyword, or criteria.
-  </Card>
-
-  <Card title="Tavily Search Tool" icon="magnifying-glass" href="/en/tools/search-research/tavilysearchtool">
-    Comprehensive web search using Tavily's AI-powered search API.
-  </Card>
-
-  <Card title="Tavily Extractor Tool" icon="file-text" href="/en/tools/search-research/tavilyextractortool">
-    Extract structured content from web pages using the Tavily API.
-  </Card>
-
-  <Card title="Arxiv Paper Tool" icon="box-archive" href="/en/tools/search-research/arxivpapertool">
-    Search arXiv and optionally download PDFs.
-  </Card>
-
-  <Card title="SerpApi Google Search" icon="search" href="/en/tools/search-research/serpapi-googlesearchtool">
-    Google search via SerpApi with structured results.
-  </Card>
-
-  <Card title="SerpApi Google Shopping" icon="cart-shopping" href="/en/tools/search-research/serpapi-googleshoppingtool">
-    Google Shopping queries via SerpApi.
-  </Card>
-</CardGroup>
-
-## **Common Use Cases**
-
-- **Market Research**: Search for industry trends and competitor analysis
-- **Content Discovery**: Find relevant articles, videos, and resources
-- **Code Research**: Search repositories and documentation for solutions
-- **Lead Generation**: Research companies and individuals
-- **Academic Research**: Find scholarly articles and technical papers
-
-```python
-from crewai_tools import SerperDevTool, GitHubSearchTool, YoutubeVideoSearchTool, TavilySearchTool, TavilyExtractorTool
-
-# Create research tools
-web_search = SerperDevTool()
-code_search = GitHubSearchTool()
-video_research = YoutubeVideoSearchTool()
-tavily_search = TavilySearchTool()
-content_extractor = TavilyExtractorTool()
-
-# Add to your agent
-agent = Agent(
-    role="Research Analyst",
-    tools=[web_search, code_search, video_research, tavily_search, content_extractor],
-    goal="Gather comprehensive information on any topic"
-)
-```

docs/en/tools/search-research/serpapi-googlesearchtool.mdx

@@ -1,66 +0,0 @@
----
-title: SerpApi Google Search Tool
-description: The `SerpApiGoogleSearchTool` performs Google searches using the SerpApi service.
-icon: google
-mode: "wide"
----
-
-# `SerpApiGoogleSearchTool`
-
-## Description
-
-Use the `SerpApiGoogleSearchTool` to run Google searches with SerpApi and retrieve structured results. Requires a SerpApi API key.
-
-## Installation
-
-```shell
-uv add crewai-tools[serpapi]
-```
-
-## Environment Variables
-
-- `SERPAPI_API_KEY` (required): API key for SerpApi. Create one at https://serpapi.com/ (free tier available).
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import SerpApiGoogleSearchTool
-
-tool = SerpApiGoogleSearchTool()
-
-agent = Agent(
-    role="Researcher",
-    goal="Answer questions using Google search",
-    backstory="Search specialist",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Search for the latest CrewAI releases",
-    expected_output="A concise list of relevant results with titles and links",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Notes
-
-- Set `SERPAPI_API_KEY` in the environment. Create a key at https://serpapi.com/
-- See also Google Shopping via SerpApi: `/en/tools/search-research/serpapi-googleshoppingtool`
-
-## Parameters
-
-### Run Parameters
-
-- `search_query` (str, required): The Google query.
-- `location` (str, optional): Geographic location parameter.
-
-## Notes
-
-- This tool wraps SerpApi and returns structured search results.
-
-

docs/en/tools/search-research/serpapi-googleshoppingtool.mdx

@@ -1,62 +0,0 @@
----
-title: SerpApi Google Shopping Tool
-description: The `SerpApiGoogleShoppingTool` searches Google Shopping results using SerpApi.
-icon: cart-shopping
-mode: "wide"
----
-
-# `SerpApiGoogleShoppingTool`
-
-## Description
-
-Leverage `SerpApiGoogleShoppingTool` to query Google Shopping via SerpApi and retrieve product-oriented results.
-
-## Installation
-
-```shell
-uv add crewai-tools[serpapi]
-```
-
-## Environment Variables
-
-- `SERPAPI_API_KEY` (required): API key for SerpApi. Create one at https://serpapi.com/ (free tier available).
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import SerpApiGoogleShoppingTool
-
-tool = SerpApiGoogleShoppingTool()
-
-agent = Agent(
-    role="Shopping Researcher",
-    goal="Find relevant products",
-    backstory="Expert in product search",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Search Google Shopping for 'wireless noise-canceling headphones'",
-    expected_output="Top relevant products with titles and links",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Notes
-
-- Set `SERPAPI_API_KEY` in the environment. Create a key at https://serpapi.com/
-- See also Google Web Search via SerpApi: `/en/tools/search-research/serpapi-googlesearchtool`
-
-## Parameters
-
-### Run Parameters
-
-- `search_query` (str, required): Product search query.
-- `location` (str, optional): Geographic location parameter.
-
-

docs/en/tools/search-research/tavilyextractortool.mdx

@@ -1,140 +0,0 @@
----
-title: "Tavily Extractor Tool"
-description: "Extract structured content from web pages using the Tavily API"
-icon: square-poll-horizontal
-mode: "wide"
----
-
-The `TavilyExtractorTool` allows CrewAI agents to extract structured content from web pages using the Tavily API. It can process single URLs or lists of URLs and provides options for controlling the extraction depth and including images.
-
-## Installation
-
-To use the `TavilyExtractorTool`, you need to install the `tavily-python` library:
-
-```shell
-pip install 'crewai[tools]' tavily-python
-```
-
-You also need to set your Tavily API key as an environment variable:
-
-```bash
-export TAVILY_API_KEY='your-tavily-api-key'
-```
-
-## Example Usage
-
-Here's how to initialize and use the `TavilyExtractorTool` within a CrewAI agent:
-
-```python
-import os
-from crewai import Agent, Task, Crew
-from crewai_tools import TavilyExtractorTool
-
-# Ensure TAVILY_API_KEY is set in your environment
-# os.environ["TAVILY_API_KEY"] = "YOUR_API_KEY"
-
-# Initialize the tool
-tavily_tool = TavilyExtractorTool()
-
-# Create an agent that uses the tool
-extractor_agent = Agent(
-    role='Web Content Extractor',
-    goal='Extract key information from specified web pages',
-    backstory='You are an expert at extracting relevant content from websites using the Tavily API.',
-    tools=[tavily_tool],
-    verbose=True
-)
-
-# Define a task for the agent
-extract_task = Task(
-    description='Extract the main content from the URL https://example.com using basic extraction depth.',
-    expected_output='A JSON string containing the extracted content from the URL.',
-    agent=extractor_agent
-)
-
-# Create and run the crew
-crew = Crew(
-    agents=[extractor_agent],
-    tasks=[extract_task],
-    verbose=2
-)
-
-result = crew.kickoff()
-print(result)
-```
-
-## Configuration Options
-
-The `TavilyExtractorTool` accepts the following arguments:
-
-- `urls` (Union[List[str], str]): **Required**. A single URL string or a list of URL strings to extract data from.
-- `include_images` (Optional[bool]): Whether to include images in the extraction results. Defaults to `False`.
-- `extract_depth` (Literal["basic", "advanced"]): The depth of extraction. Use `"basic"` for faster, surface-level extraction or `"advanced"` for more comprehensive extraction. Defaults to `"basic"`.
-- `timeout` (int): The maximum time in seconds to wait for the extraction request to complete. Defaults to `60`.
-
-## Advanced Usage
-
-### Multiple URLs with Advanced Extraction
-
-```python
-# Example with multiple URLs and advanced extraction
-multi_extract_task = Task(
-    description='Extract content from https://example.com and https://anotherexample.org using advanced extraction.',
-    expected_output='A JSON string containing the extracted content from both URLs.',
-    agent=extractor_agent
-)
-
-# Configure the tool with custom parameters
-custom_extractor = TavilyExtractorTool(
-    extract_depth='advanced',
-    include_images=True,
-    timeout=120
-)
-
-agent_with_custom_tool = Agent(
-    role="Advanced Content Extractor",
-    goal="Extract comprehensive content with images",
-    tools=[custom_extractor]
-)
-```
-
-### Tool Parameters
-
-You can customize the tool's behavior by setting parameters during initialization:
-
-```python
-# Initialize with custom configuration
-extractor_tool = TavilyExtractorTool(
-    extract_depth='advanced',  # More comprehensive extraction
-    include_images=True,       # Include image results
-    timeout=90                 # Custom timeout
-)
-```
-
-## Features
-
-- **Single or Multiple URLs**: Extract content from one URL or process multiple URLs in a single request
-- **Configurable Depth**: Choose between basic (fast) and advanced (comprehensive) extraction modes
-- **Image Support**: Optionally include images in the extraction results
-- **Structured Output**: Returns well-formatted JSON containing the extracted content
-- **Error Handling**: Robust handling of network timeouts and extraction errors
-
-## Response Format
-
-The tool returns a JSON string representing the structured data extracted from the provided URL(s). The exact structure depends on the content of the pages and the `extract_depth` used.
-
-Common response elements include:
-- **Title**: The page title
-- **Content**: Main text content of the page
-- **Images**: Image URLs and metadata (when `include_images=True`)
-- **Metadata**: Additional page information like author, description, etc.
-
-## Use Cases
-
-- **Content Analysis**: Extract and analyze content from competitor websites
-- **Research**: Gather structured data from multiple sources for analysis
-- **Content Migration**: Extract content from existing websites for migration
-- **Monitoring**: Regular extraction of content for change detection
-- **Data Collection**: Systematic extraction of information from web sources
-
-Refer to the [Tavily API documentation](https://docs.tavily.com/docs/tavily-api/python-sdk#extract) for detailed information about the response structure and available options.

docs/en/tools/search-research/tavilysearchtool.mdx

@@ -1,125 +0,0 @@
----
-title: "Tavily Search Tool"
-description: "Perform comprehensive web searches using the Tavily Search API"
-icon: "magnifying-glass"
-mode: "wide"
----
-
-The `TavilySearchTool` provides an interface to the Tavily Search API, enabling CrewAI agents to perform comprehensive web searches. It allows for specifying search depth, topics, time ranges, included/excluded domains, and whether to include direct answers, raw content, or images in the results.
-
-## Installation
-
-To use the `TavilySearchTool`, you need to install the `tavily-python` library:
-
-```shell
-pip install 'crewai[tools]' tavily-python
-```
-
-## Environment Variables
-
-Ensure your Tavily API key is set as an environment variable:
-
-```bash
-export TAVILY_API_KEY='your_tavily_api_key'
-```
-
-Get an API key at https://app.tavily.com/ (sign up, then create a key).
-
-## Example Usage
-
-Here's how to initialize and use the `TavilySearchTool` within a CrewAI agent:
-
-```python
-import os
-from crewai import Agent, Task, Crew
-from crewai_tools import TavilySearchTool
-
-# Ensure the TAVILY_API_KEY environment variable is set
-# os.environ["TAVILY_API_KEY"] = "YOUR_TAVILY_API_KEY"
-
-# Initialize the tool
-tavily_tool = TavilySearchTool()
-
-# Create an agent that uses the tool
-researcher = Agent(
-    role='Market Researcher',
-    goal='Find information about the latest AI trends',
-    backstory='An expert market researcher specializing in technology.',
-    tools=[tavily_tool],
-    verbose=True
-)
-
-# Create a task for the agent
-research_task = Task(
-    description='Search for the top 3 AI trends in 2024.',
-    expected_output='A JSON report summarizing the top 3 AI trends found.',
-    agent=researcher
-)
-
-# Form the crew and kick it off
-crew = Crew(
-    agents=[researcher],
-    tasks=[research_task],
-    verbose=2
-)
-
-result = crew.kickoff()
-print(result)
-```
-
-## Configuration Options
-
-The `TavilySearchTool` accepts the following arguments during initialization or when calling the `run` method:
-
-- `query` (str): **Required**. The search query string.
-- `search_depth` (Literal["basic", "advanced"], optional): The depth of the search. Defaults to `"basic"`.
-- `topic` (Literal["general", "news", "finance"], optional): The topic to focus the search on. Defaults to `"general"`.
-- `time_range` (Literal["day", "week", "month", "year"], optional): The time range for the search. Defaults to `None`.
-- `days` (int, optional): The number of days to search back. Relevant if `time_range` is not set. Defaults to `7`.
-- `max_results` (int, optional): The maximum number of search results to return. Defaults to `5`.
-- `include_domains` (Sequence[str], optional): A list of domains to prioritize in the search. Defaults to `None`.
-- `exclude_domains` (Sequence[str], optional): A list of domains to exclude from the search. Defaults to `None`.
-- `include_answer` (Union[bool, Literal["basic", "advanced"]], optional): Whether to include a direct answer synthesized from the search results. Defaults to `False`.
-- `include_raw_content` (bool, optional): Whether to include the raw HTML content of the searched pages. Defaults to `False`.
-- `include_images` (bool, optional): Whether to include image results. Defaults to `False`.
-- `timeout` (int, optional): The request timeout in seconds. Defaults to `60`.
-
-## Advanced Usage
-
-You can configure the tool with custom parameters:
-
-```python
-# Example: Initialize with specific parameters
-custom_tavily_tool = TavilySearchTool(
-    search_depth='advanced',
-    max_results=10,
-    include_answer=True
-)
-
-# The agent will use these defaults
-agent_with_custom_tool = Agent(
-    role="Advanced Researcher",
-    goal="Conduct detailed research with comprehensive results",
-    tools=[custom_tavily_tool]
-)
-```
-
-## Features
-
-- **Comprehensive Search**: Access to Tavily's powerful search index
-- **Configurable Depth**: Choose between basic and advanced search modes
-- **Topic Filtering**: Focus searches on general, news, or finance topics
-- **Time Range Control**: Limit results to specific time periods
-- **Domain Control**: Include or exclude specific domains
-- **Direct Answers**: Get synthesized answers from search results
-- **Content Filtering**: Prevent context window issues with automatic content truncation
-
-## Response Format
-
-The tool returns search results as a JSON string containing:
-- Search results with titles, URLs, and content snippets
-- Optional direct answers to queries
-- Optional image results
-- Optional raw HTML content (when enabled)
-
-Content for each result is automatically truncated to prevent context window issues while maintaining the most relevant information.

docs/en/tools/tool-integrations/overview.mdx

@@ -1,31 +0,0 @@
----
-title: Overview
-description: Integrations for deploying and automating crews with external platforms
-icon: face-smile
-mode: "wide"
----
-
-## Available Integrations
-
-<CardGroup cols={2}>
-  <Card
-    title="Bedrock Invoke Agent Tool"
-    icon="cloud"
-    href="/en/tools/tool-integrations/bedrockinvokeagenttool"
-    color="#0891B2"
-  >
-    Invoke Amazon Bedrock Agents from CrewAI to orchestrate actions across AWS services.
-  </Card>
-
-  <Card
-    title="CrewAI Automation Tool"
-    icon="bolt"
-    href="/en/tools/tool-integrations/crewaiautomationtool"
-    color="#7C3AED"
-  >
-    Automate deployment and operations by integrating CrewAI with external platforms and workflows.
-  </Card>
-</CardGroup>
-
-Use these integrations to connect CrewAI with your infrastructure and workflows.
-

docs/en/tools/web-scraping/brightdata-tools.mdx

@@ -1,112 +0,0 @@
----
-title: Bright Data Tools
-description: Bright Data integrations for SERP search, Web Unlocker scraping, and Dataset API.
-icon: spider
-mode: "wide"
----
-
-# Bright Data Tools
-
-This set of tools integrates Bright Data services for web extraction.
-
-## Installation
-
-```shell
-uv add crewai-tools requests aiohttp
-```
-
-## Environment Variables
-
-- `BRIGHT_DATA_API_KEY` (required)
-- `BRIGHT_DATA_ZONE` (for SERP/Web Unlocker)
-
-Create credentials at https://brightdata.com/ (sign up, then create an API token and zone). 
-See their docs: https://developers.brightdata.com/
-
-## Included Tools
-
-- `BrightDataSearchTool`: SERP search (Google/Bing/Yandex) with geo/language/device options.
-- `BrightDataWebUnlockerTool`: Scrape pages with anti-bot bypass and rendering.
-- `BrightDataDatasetTool`: Run Dataset API jobs and fetch results.
-
-## Examples
-
-### SERP Search
-
-```python Code
-from crewai_tools import BrightDataSearchTool
-
-tool = BrightDataSearchTool(
-    query="CrewAI", 
-    country="us",
-)
-
-print(tool.run())
-```
-
-### Web Unlocker
-
-```python Code
-from crewai_tools import BrightDataWebUnlockerTool
-
-tool = BrightDataWebUnlockerTool(
-    url="https://example.com", 
-    format="markdown",
-)
-
-print(tool.run(url="https://example.com"))
-```
-
-### Dataset API
-
-```python Code
-from crewai_tools import BrightDataDatasetTool
-
-tool = BrightDataDatasetTool(
-    dataset_type="ecommerce", 
-    url="https://example.com/product",
-)
-
-print(tool.run())
-```
-
-## Troubleshooting
-
-- 401/403: verify `BRIGHT_DATA_API_KEY` and `BRIGHT_DATA_ZONE`.
-- Empty/blocked content: enable rendering or try a different zone.
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import BrightDataSearchTool
-
-tool = BrightDataSearchTool(
-    query="CrewAI", 
-    country="us",
-)
-
-agent = Agent(
-    role="Web Researcher",
-    goal="Search with Bright Data",
-    backstory="Finds reliable results",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Search for CrewAI and summarize top results",
-    expected_output="Short summary with links",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-
-result = crew.kickoff()
-```
-
-

docs/en/tools/web-scraping/oxylabsscraperstool.mdx

@@ -1,237 +0,0 @@
----
-title: Oxylabs Scrapers
-description: >
-  Oxylabs Scrapers allow to easily access the information from the respective sources. Please see the list of available sources below:
-    - `Amazon Product`
-    - `Amazon Search`
-    - `Google Seach`
-    - `Universal`
-icon: globe
-mode: "wide"
----
-
-## Installation
-
-Get the credentials by creating an Oxylabs Account [here](https://oxylabs.io).
-```shell
-pip install 'crewai[tools]' oxylabs
-```
-Check [Oxylabs Documentation](https://developers.oxylabs.io/scraping-solutions/web-scraper-api/targets) to get more information about API parameters.
-
-# `OxylabsAmazonProductScraperTool`
-
-### Example
-
-```python
-from crewai_tools import OxylabsAmazonProductScraperTool
-
-# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set
-tool = OxylabsAmazonProductScraperTool()
-
-result = tool.run(query="AAAAABBBBCC")
-
-print(result)
-```
-
-### Parameters
-
-- `query` - 10-symbol ASIN code.
-- `domain` - domain localization for Amazon.
-- `geo_location` - the _Deliver to_ location.
-- `user_agent_type` - device type and browser.
-- `render` - enables JavaScript rendering when set to `html`.
-- `callback_url` - URL to your callback endpoint.
-- `context` - Additional advanced settings and controls for specialized requirements.
-- `parse` - returns parsed data when set to true.
-- `parsing_instructions` - define your own parsing and data transformation logic that will be executed on an HTML scraping result.
-
-### Advanced example
-
-```python
-from crewai_tools import OxylabsAmazonProductScraperTool
-
-# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set
-tool = OxylabsAmazonProductScraperTool(
-    config={
-        "domain": "com",
-        "parse": True,
-        "context": [
-            {
-                "key": "autoselect_variant",
-                "value": True
-            }
-        ]
-    }
-)
-
-result = tool.run(query="AAAAABBBBCC")
-
-print(result)
-```
-
-# `OxylabsAmazonSearchScraperTool`
-
-### Example
-
-```python
-from crewai_tools import OxylabsAmazonSearchScraperTool
-
-# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set
-tool = OxylabsAmazonSearchScraperTool()
-
-result = tool.run(query="headsets")
-
-print(result)
-```
-
-### Parameters
-
-- `query` - Amazon search term.
-- `domain` - Domain localization for Bestbuy.
-- `start_page` - starting page number.
-- `pages` - number of pages to retrieve.
-- `geo_location` - the _Deliver to_ location.
-- `user_agent_type` - device type and browser.
-- `render` - enables JavaScript rendering when set to `html`.
-- `callback_url` - URL to your callback endpoint.
-- `context` - Additional advanced settings and controls for specialized requirements.
-- `parse` - returns parsed data when set to true.
-- `parsing_instructions` - define your own parsing and data transformation logic that will be executed on an HTML scraping result.
-
-### Advanced example
-
-```python
-from crewai_tools import OxylabsAmazonSearchScraperTool
-
-# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set
-tool = OxylabsAmazonSearchScraperTool(
-    config={
-        "domain": 'nl',
-        "start_page": 2,
-        "pages": 2,
-        "parse": True,
-        "context": [
-            {'key': 'category_id', 'value': 16391693031}
-        ],
-    }
-)
-
-result = tool.run(query='nirvana tshirt')
-
-print(result)
-```
-
-# `OxylabsGoogleSearchScraperTool`
-
-### Example
-
-```python
-from crewai_tools import OxylabsGoogleSearchScraperTool
-
-# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set
-tool = OxylabsGoogleSearchScraperTool()
-
-result = tool.run(query="iPhone 16")
-
-print(result)
-```
-
-### Parameters
-
-- `query` - search keyword.
-- `domain` - domain localization for Google.
-- `start_page` - starting page number.
-- `pages` - number of pages to retrieve.
-- `limit` - number of results to retrieve in each page.
-- `locale` - `Accept-Language` header value which changes your Google search page web interface language.
-- `geo_location` - the geographical location that the result should be adapted for. Using this parameter correctly is extremely important to get the right data.
-- `user_agent_type` - device type and browser.
-- `render` - enables JavaScript rendering when set to `html`.
-- `callback_url` - URL to your callback endpoint.
-- `context` - Additional advanced settings and controls for specialized requirements.
-- `parse` - returns parsed data when set to true.
-- `parsing_instructions` - define your own parsing and data transformation logic that will be executed on an HTML scraping result.
-
-### Advanced example
-
-```python
-from crewai_tools import OxylabsGoogleSearchScraperTool
-
-# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set
-tool = OxylabsGoogleSearchScraperTool(
-    config={
-        "parse": True,
-        "geo_location": "Paris, France",
-        "user_agent_type": "tablet",
-    }
-)
-
-result = tool.run(query="iPhone 16")
-
-print(result)
-```
-
-# `OxylabsUniversalScraperTool`
-
-### Example
-
-```python
-from crewai_tools import OxylabsUniversalScraperTool
-
-# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set
-tool = OxylabsUniversalScraperTool()
-
-result = tool.run(url="https://ip.oxylabs.io")
-
-print(result)
-```
-
-### Parameters
-
-- `url` - website url to scrape.
-- `user_agent_type` - device type and browser.
-- `geo_location` - sets the proxy's geolocation to retrieve data.
-- `render` - enables JavaScript rendering when set to `html`.
-- `callback_url` - URL to your callback endpoint.
-- `context` - Additional advanced settings and controls for specialized requirements.
-- `parse` - returns parsed data when set to `true`, as long as a dedicated parser exists for the submitted URL's page type.
-- `parsing_instructions` - define your own parsing and data transformation logic that will be executed on an HTML scraping result.
-
-
-### Advanced example
-
-```python
-from crewai_tools import OxylabsUniversalScraperTool
-
-# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set
-tool = OxylabsUniversalScraperTool(
-    config={
-        "render": "html",
-        "user_agent_type": "mobile",
-        "context": [
-            {"key": "force_headers", "value": True},
-            {"key": "force_cookies", "value": True},
-            {
-                "key": "headers",
-                "value": {
-                    "Custom-Header-Name": "custom header content",
-                },
-            },
-            {
-                "key": "cookies",
-                "value": [
-                    {"key": "NID", "value": "1234567890"},
-                    {"key": "1P JAR", "value": "0987654321"},
-                ],
-            },
-            {"key": "http_method", "value": "get"},
-            {"key": "follow_redirects", "value": True},
-            {"key": "successful_status_codes", "value": [808, 909]},
-        ],
-    }
-)
-
-result = tool.run(url="https://ip.oxylabs.io")
-
-print(result)
-```

docs/en/tools/web-scraping/serperscrapewebsitetool.mdx

@@ -1,101 +0,0 @@
----
-title: Serper Scrape Website
-description: The `SerperScrapeWebsiteTool` is designed to scrape websites and extract clean, readable content using Serper's scraping API.
-icon: globe
-mode: "wide"
----
-
-# `SerperScrapeWebsiteTool`
-
-## Description
-
-This tool is designed to scrape website content and extract clean, readable text from any website URL. It utilizes the [serper.dev](https://serper.dev) scraping API to fetch and process web pages, optionally including markdown formatting for better structure and readability.
-
-## Installation
-
-To effectively use the `SerperScrapeWebsiteTool`, follow these steps:
-
-1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
-2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for an account at `serper.dev`.
-3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPER_API_KEY` to facilitate its use by the tool.
-
-To incorporate this tool into your project, follow the installation instructions below:
-
-```shell
-pip install 'crewai[tools]'
-```
-
-## Example
-
-The following example demonstrates how to initialize the tool and scrape a website:
-
-```python Code
-from crewai_tools import SerperScrapeWebsiteTool
-
-# Initialize the tool for website scraping capabilities
-tool = SerperScrapeWebsiteTool()
-
-# Scrape a website with markdown formatting
-result = tool.run(url="https://example.com", include_markdown=True)
-```
-
-## Arguments
-
-The `SerperScrapeWebsiteTool` accepts the following arguments:
-
-- **url**: Required. The URL of the website to scrape.
-- **include_markdown**: Optional. Whether to include markdown formatting in the scraped content. Defaults to `True`.
-
-## Example with Parameters
-
-Here is an example demonstrating how to use the tool with different parameters:
-
-```python Code
-from crewai_tools import SerperScrapeWebsiteTool
-
-tool = SerperScrapeWebsiteTool()
-
-# Scrape with markdown formatting (default)
-markdown_result = tool.run(
-    url="https://docs.crewai.com",
-    include_markdown=True
-)
-
-# Scrape without markdown formatting for plain text
-plain_result = tool.run(
-    url="https://docs.crewai.com",
-    include_markdown=False
-)
-
-print("Markdown formatted content:")
-print(markdown_result)
-
-print("\nPlain text content:")
-print(plain_result)
-```
-
-## Use Cases
-
-The `SerperScrapeWebsiteTool` is particularly useful for:
-
-- **Content Analysis**: Extract and analyze website content for research purposes
-- **Data Collection**: Gather structured information from web pages
-- **Documentation Processing**: Convert web-based documentation into readable formats
-- **Competitive Analysis**: Scrape competitor websites for market research
-- **Content Migration**: Extract content from existing websites for migration purposes
-
-## Error Handling
-
-The tool includes comprehensive error handling for:
-
-- **Network Issues**: Handles connection timeouts and network errors gracefully
-- **API Errors**: Provides detailed error messages for API-related issues
-- **Invalid URLs**: Validates and reports issues with malformed URLs
-- **Authentication**: Clear error messages for missing or invalid API keys
-
-## Security Considerations
-
-- Always store your `SERPER_API_KEY` in environment variables, never hardcode it in your source code
-- Be mindful of rate limits imposed by the Serper API
-- Respect robots.txt and website terms of service when scraping content
-- Consider implementing delays between requests for large-scale scraping operations

docs/enterprise-api.base.yaml

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

docs/enterprise-api.ko.yaml

@@ -1,230 +0,0 @@
-openapi: 3.0.3
-info:
-  title: CrewAI 엔터프라이즈 API
-  description: |
-    CrewAI AMP에 배포된 crew와 상호작용하기 위한 REST API입니다.
-
-    ## 시작하기
-    1. **Crew URL 확인**: 대시보드에서 고유한 crew URL을 확인하세요
-    2. **예제 복사**: 각 엔드포인트의 예제를 템플릿으로 사용하세요
-    3. **플레이스홀더 교체**: 실제 URL과 토큰으로 바꾸세요
-    4. **도구로 테스트**: cURL, Postman 등 선호하는 도구로 테스트하세요
-  version: 1.0.0
-  contact:
-    name: CrewAI 지원
-    email: support@crewai.com
-    url: https://crewai.com
-servers:
-  - url: https://your-actual-crew-name.crewai.com
-    description: 대시보드의 실제 crew URL로 교체하세요
-security:
-  - BearerAuth: []
-paths:
-  /inputs:
-    get:
-      summary: 필요 입력값 조회
-      description: |
-        **📋 참조 예제만 제공** - *요청 형식을 보여줍니다. 실제 호출은 cURL 예제를 복사해 URL과 토큰을 교체하세요.*
-
-        실행에 필요한 입력 파라미터 목록을 반환합니다.
-      operationId: getRequiredInputs
-      responses:
-        '200':
-          description: 입력값을 성공적으로 조회
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  inputs:
-                    type: array
-                    items:
-                      type: string
-        '401':
-          $ref: '#/components/responses/UnauthorizedError'
-        '404':
-          $ref: '#/components/responses/NotFoundError'
-        '500':
-          $ref: '#/components/responses/ServerError'
-
-  /kickoff:
-    post:
-      summary: Crew 실행 시작
-      description: |
-        **📋 참조 예제만 제공** - *요청 형식을 보여줍니다. 실제 호출은 cURL 예제를 복사해 URL과 토큰을 교체하세요.*
-
-        제공된 입력으로 새로운 실행을 시작하고 kickoff ID를 반환합니다.
-      operationId: startCrewExecution
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              required:
-                - inputs
-              properties:
-                inputs:
-                  type: object
-                  additionalProperties:
-                    type: string
-      responses:
-        '200':
-          description: 실행이 성공적으로 시작됨
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  kickoff_id:
-                    type: string
-                    format: uuid
-        '401':
-          $ref: '#/components/responses/UnauthorizedError'
-        '500':
-          $ref: '#/components/responses/ServerError'
-
-  /status/{kickoff_id}:
-    get:
-      summary: 실행 상태 조회
-      description: |
-        **📋 참조 예제만 제공** - *요청 형식을 보여줍니다. 실제 호출은 cURL 예제를 복사해 URL과 토큰을 교체하세요.*
-
-        kickoff ID로 실행 상태와 결과를 조회합니다.
-      operationId: getExecutionStatus
-      parameters:
-        - name: kickoff_id
-          in: path
-          required: true
-          schema:
-            type: string
-            format: uuid
-      responses:
-        '200':
-          description: 상태를 성공적으로 조회
-          content:
-            application/json:
-              schema:
-                oneOf:
-                  - $ref: '#/components/schemas/ExecutionRunning'
-                  - $ref: '#/components/schemas/ExecutionCompleted'
-                  - $ref: '#/components/schemas/ExecutionError'
-        '401':
-          $ref: '#/components/responses/UnauthorizedError'
-        '404':
-          description: Kickoff ID를 찾을 수 없음
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/Error'
-        '500':
-          $ref: '#/components/responses/ServerError'
-
-components:
-  securitySchemes:
-    BearerAuth:
-      type: http
-      scheme: bearer
-      description: |
-        **📋 참고** - *예시의 토큰은 자리 표시자입니다.* 실제 토큰을 사용하세요.
-
-  schemas:
-    ExecutionRunning:
-      type: object
-      properties:
-        status:
-          type: string
-          enum: ["running"]
-        current_task:
-          type: string
-        progress:
-          type: object
-          properties:
-            completed_tasks:
-              type: integer
-            total_tasks:
-              type: integer
-
-    ExecutionCompleted:
-      type: object
-      properties:
-        status:
-          type: string
-          enum: ["completed"]
-        result:
-          type: object
-          properties:
-            output:
-              type: string
-            tasks:
-              type: array
-              items:
-                $ref: '#/components/schemas/TaskResult'
-        execution_time:
-          type: number
-
-    ExecutionError:
-      type: object
-      properties:
-        status:
-          type: string
-          enum: ["error"]
-        error:
-          type: string
-        execution_time:
-          type: number
-
-    TaskResult:
-      type: object
-      properties:
-        task_id:
-          type: string
-        output:
-          type: string
-        agent:
-          type: string
-        execution_time:
-          type: number
-
-    Error:
-      type: object
-      properties:
-        error:
-          type: string
-        message:
-          type: string
-
-    ValidationError:
-      type: object
-      properties:
-        error:
-          type: string
-        message:
-          type: string
-        details:
-          type: object
-          properties:
-            missing_inputs:
-              type: array
-              items:
-                type: string
-
-  responses:
-    UnauthorizedError:
-      description: 인증 실패
-      content:
-        application/json:
-          schema:
-            $ref: '#/components/schemas/Error'
-    NotFoundError:
-      description: 리소스를 찾을 수 없음
-      content:
-        application/json:
-          schema:
-            $ref: '#/components/schemas/Error'
-    ServerError:
-      description: 서버 내부 오류
-      content:
-        application/json:
-          schema:
-            $ref: '#/components/schemas/Error'

docs/enterprise-api.pt-BR.yaml

@@ -1,267 +0,0 @@
-openapi: 3.0.3
-info:
-  title: CrewAI AMP API
-  description: |
-    REST API para interagir com suas crews implantadas no CrewAI AMP.
-
-    ## Introdução
-
-    1. **Encontre a URL da sua crew**: Obtenha sua URL única no painel do CrewAI AMP
-    2. **Copie os exemplos**: Use os exemplos de cada endpoint como modelo
-    3. **Substitua os placeholders**: Atualize URLs e tokens com seus valores reais
-    4. **Teste com suas ferramentas**: Use cURL, Postman ou seu cliente preferido
-
-    ## Autenticação
-
-    Todas as requisições exigem um token bearer. Existem dois tipos:
-
-    - **Bearer Token**: Token em nível de organização para operações completas
-    - **User Bearer Token**: Token com escopo de usuário com permissões limitadas
-
-    Você encontra os tokens na aba Status da sua crew no painel do CrewAI AMP.
-
-    ## Documentação de Referência
-
-    Este documento fornece exemplos completos para cada endpoint:
-
-    - **Formatos de requisição** com parâmetros obrigatórios e opcionais
-    - **Exemplos de resposta** para sucesso e erro
-    - **Amostras de código** em várias linguagens
-    - **Padrões de autenticação** com uso correto de Bearer token
-
-    Copie os exemplos e personalize com sua URL e tokens reais.
-
-    ## Fluxo
-
-    1. **Descubra os inputs** usando `GET /inputs`
-    2. **Inicie a execução** usando `POST /kickoff`
-    3. **Monitore o progresso** usando `GET /status/{kickoff_id}`
-  version: 1.0.0
-  contact:
-    name: CrewAI Suporte
-    email: support@crewai.com
-    url: https://crewai.com
-servers:
-  - url: https://your-actual-crew-name.crewai.com
-    description: Substitua pela URL real da sua crew no painel do CrewAI AMP
-security:
-  - BearerAuth: []
-paths:
-  /inputs:
-    get:
-      summary: Obter Inputs Requeridos
-      description: |
-        **📋 Exemplo de Referência** - *Mostra o formato da requisição. Para testar com sua crew real, copie o cURL e substitua URL + token.*
-
-        Retorna a lista de parâmetros de entrada que sua crew espera.
-      operationId: getRequiredInputs
-      responses:
-        '200':
-          description: Inputs requeridos obtidos com sucesso
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  inputs:
-                    type: array
-                    items:
-                      type: string
-                    description: Nomes dos parâmetros de entrada
-                    example: ["budget", "interests", "duration", "age"]
-        '401':
-          $ref: '#/components/responses/UnauthorizedError'
-        '404':
-          $ref: '#/components/responses/NotFoundError'
-        '500':
-          $ref: '#/components/responses/ServerError'
-
-  /kickoff:
-    post:
-      summary: Iniciar Execução da Crew
-      description: |
-        **📋 Exemplo de Referência** - *Mostra o formato da requisição. Para testar com sua crew real, copie o cURL e substitua URL + token.*
-
-        Inicia uma nova execução da crew com os inputs fornecidos e retorna um kickoff ID.
-      operationId: startCrewExecution
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              required:
-                - inputs
-              properties:
-                inputs:
-                  type: object
-                  additionalProperties:
-                    type: string
-                  example:
-                    budget: "1000 USD"
-                    interests: "games, tech, ai, relaxing hikes, amazing food"
-                    duration: "7 days"
-                    age: "35"
-
-      responses:
-        '200':
-          description: Execução iniciada com sucesso
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  kickoff_id:
-                    type: string
-                    format: uuid
-                    example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
-        '401':
-          $ref: '#/components/responses/UnauthorizedError'
-        '500':
-          $ref: '#/components/responses/ServerError'
-
-  /status/{kickoff_id}:
-    get:
-      summary: Obter Status da Execução
-      description: |
-        **📋 Exemplo de Referência** - *Mostra o formato da requisição. Para testar com sua crew real, copie o cURL e substitua URL + token.*
-
-        Retorna o status atual e os resultados de uma execução usando o kickoff ID.
-      operationId: getExecutionStatus
-      parameters:
-        - name: kickoff_id
-          in: path
-          required: true
-          schema:
-            type: string
-            format: uuid
-      responses:
-        '200':
-          description: Status recuperado com sucesso
-          content:
-            application/json:
-              schema:
-                oneOf:
-                  - $ref: '#/components/schemas/ExecutionRunning'
-                  - $ref: '#/components/schemas/ExecutionCompleted'
-                  - $ref: '#/components/schemas/ExecutionError'
-        '401':
-          $ref: '#/components/responses/UnauthorizedError'
-        '404':
-          description: Kickoff ID não encontrado
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/Error'
-        '500':
-          $ref: '#/components/responses/ServerError'
-
-components:
-  securitySchemes:
-    BearerAuth:
-      type: http
-      scheme: bearer
-      description: |
-        **📋 Referência** - *Os tokens mostrados são apenas exemplos.*
-        Use seus tokens reais do painel do CrewAI AMP.
-
-  schemas:
-    ExecutionRunning:
-      type: object
-      properties:
-        status:
-          type: string
-          enum: ["running"]
-        current_task:
-          type: string
-        progress:
-          type: object
-          properties:
-            completed_tasks:
-              type: integer
-            total_tasks:
-              type: integer
-
-    ExecutionCompleted:
-      type: object
-      properties:
-        status:
-          type: string
-          enum: ["completed"]
-        result:
-          type: object
-          properties:
-            output:
-              type: string
-            tasks:
-              type: array
-              items:
-                $ref: '#/components/schemas/TaskResult'
-        execution_time:
-          type: number
-
-    ExecutionError:
-      type: object
-      properties:
-        status:
-          type: string
-          enum: ["error"]
-        error:
-          type: string
-        execution_time:
-          type: number
-
-    TaskResult:
-      type: object
-      properties:
-        task_id:
-          type: string
-        output:
-          type: string
-        agent:
-          type: string
-        execution_time:
-          type: number
-
-    Error:
-      type: object
-      properties:
-        error:
-          type: string
-        message:
-          type: string
-
-    ValidationError:
-      type: object
-      properties:
-        error:
-          type: string
-        message:
-          type: string
-        details:
-          type: object
-          properties:
-            missing_inputs:
-              type: array
-              items:
-                type: string
-
-  responses:
-    UnauthorizedError:
-      description: Autenticação falhou
-      content:
-        application/json:
-          schema:
-            $ref: '#/components/schemas/Error'
-    NotFoundError:
-      description: Recurso não encontrado
-      content:
-        application/json:
-          schema:
-            $ref: '#/components/schemas/Error'
-    ServerError:
-      description: Erro interno do servidor
-      content:
-        application/json:
-          schema:
-            $ref: '#/components/schemas/Error'

docs/enterprise-api.yaml

@@ -1,40 +1,40 @@
 openapi: 3.0.3
 info:
-  title: CrewAI AMP API
+  title: CrewAI Enterprise API
   description: |
-    REST API for interacting with your deployed CrewAI crews on CrewAI AMP.
-
+    REST API for interacting with your deployed CrewAI crews on CrewAI Enterprise.
+    
     ## Getting Started
-
-    1. **Find your crew URL**: Get your unique crew URL from the CrewAI AMP dashboard
+    
+    1. **Find your crew URL**: Get your unique crew URL from the CrewAI Enterprise dashboard
     2. **Copy examples**: Use the code examples from each endpoint page as templates
     3. **Replace placeholders**: Update URLs and tokens with your actual values
     4. **Test with your tools**: Use cURL, Postman, or your preferred API client
-
+    
     ## Authentication
-
+    
     All API requests require a bearer token for authentication. There are two types of tokens:
-
+    
     - **Bearer Token**: Organization-level token for full crew operations
     - **User Bearer Token**: User-scoped token for individual access with limited permissions
-
-    You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI AMP dashboard.
-
+    
+    You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
+    
     ## Reference Documentation
-
+    
     This documentation provides comprehensive examples for each endpoint:
-
+    
     - **Request formats** with all required and optional parameters
     - **Response examples** for success and error scenarios
     - **Code samples** in multiple programming languages
     - **Authentication patterns** with proper Bearer token usage
-
+    
     Copy the examples and customize them with your actual crew URL and authentication tokens.
-
+    
     ## Workflow
-
+    
     1. **Discover inputs** using `GET /inputs`
-    2. **Start execution** using `POST /kickoff`
+    2. **Start execution** using `POST /kickoff`  
     3. **Monitor progress** using `GET /status/{kickoff_id}`
   version: 1.0.0
   contact:
@@ -43,7 +43,7 @@ info:
     url: https://crewai.com
 servers:
   - url: https://your-actual-crew-name.crewai.com
-    description: Replace with your actual deployed crew URL from the CrewAI AMP dashboard
+    description: Replace with your actual deployed crew URL from the CrewAI Enterprise dashboard
   - url: https://my-travel-crew.crewai.com
     description: Example travel planning crew (replace with your URL)
   - url: https://content-creation-crew.crewai.com
@@ -58,7 +58,7 @@ paths:
       summary: Get Required Inputs
       description: |
         **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
-
+        
         Retrieves the list of all required input parameters that your crew expects for execution.
         Use this endpoint to discover what inputs you need to provide when starting a crew execution.
       operationId: getRequiredInputs
@@ -82,7 +82,7 @@ paths:
                   value:
                     inputs: ["budget", "interests", "duration", "age"]
                 outreach_crew:
-                  summary: Outreach crew inputs
+                  summary: Outreach crew inputs  
                   value:
                     inputs: ["name", "title", "company", "industry", "our_product", "linkedin_url"]
         '401':
@@ -97,10 +97,10 @@ paths:
       summary: Start Crew Execution
       description: |
         **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
-
-        Initiates a new crew execution with the provided inputs. Returns a kickoff ID that can be used
+        
+        Initiates a new crew execution with the provided inputs. Returns a kickoff ID that can be used 
         to track the execution progress and retrieve results.
-
+        
         Crew executions can take anywhere from seconds to minutes depending on their complexity.
         Consider using webhooks for real-time notifications or implement polling with the status endpoint.
       operationId: startCrewExecution
@@ -152,7 +152,7 @@ paths:
                   inputs:
                     budget: "1000 USD"
                     interests: "games, tech, ai, relaxing hikes, amazing food"
-                    duration: "7 days"
+                    duration: "7 days" 
                     age: "35"
                   meta:
                     requestId: "travel-req-123"
@@ -204,9 +204,9 @@ paths:
       summary: Get Execution Status
       description: |
         **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
-
+        
         Retrieves the current status and results of a crew execution using its kickoff ID.
-
+        
         The response structure varies depending on the execution state:
         - **running**: Execution in progress with current task info
         - **completed**: Execution finished with full results
@@ -283,9 +283,9 @@ components:
       scheme: bearer
       description: |
         **📋 Reference Documentation** - *The tokens shown in examples are placeholders for reference only.*
-
-        Use your actual Bearer Token or User Bearer Token from the CrewAI AMP dashboard for real API calls.
-
+        
+        Use your actual Bearer Token or User Bearer Token from the CrewAI Enterprise dashboard for real API calls.
+        
         **Bearer Token**: Organization-level access for full crew operations
         **User Bearer Token**: User-scoped access with limited permissions
 
@@ -309,7 +309,7 @@ components:
               description: Number of completed tasks
               example: 1
             total_tasks:
-              type: integer
+              type: integer  
               description: Total number of tasks in the crew
               example: 3
 
@@ -430,5 +430,5 @@ components:
           schema:
             $ref: '#/components/schemas/Error'
           example:
-            error: "Internal Server Error"
-            message: "An unexpected error occurred"
+            error: "Internal Server Error" 
+            message: "An unexpected error occurred" 

docs/enterprise/features/hallucination-guardrail.mdx

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

docs/enterprise/features/integrations.mdx

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

docs/enterprise/features/tool-repository.mdx

@@ -2,7 +2,6 @@
 title: Tool Repository
 description: "Using the Tool Repository to manage your tools"
 icon: "toolbox"
-mode: "wide"
 ---
 
 ## Overview
@@ -20,11 +19,11 @@ The repository is not a version control system. Use Git to track code changes an
 
 Before using the Tool Repository, ensure you have:
 
-- A [CrewAI AMP](https://app.crewai.com) account
+- A [CrewAI Enterprise](https://app.crewai.com) account
 - [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) installed
 - uv>=0.5.0 installed. Check out [how to upgrade](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv)
 - [Git](https://git-scm.com) installed and configured
-- Access permissions to publish or install tools in your CrewAI AMP organization
+- Access permissions to publish or install tools in your CrewAI Enterprise organization
 
 ## Installing Tools
 
@@ -36,52 +35,6 @@ 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 AMP 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 AMP 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:
@@ -131,7 +84,7 @@ crewai tool publish
 
 To delete a tool:
 
-1. Go to [CrewAI AMP](https://app.crewai.com)
+1. Go to [CrewAI Enterprise](https://app.crewai.com)
 2. Navigate to **Tools**
 3. Select the tool
 4. Click **Delete**
@@ -142,14 +95,13 @@ Deletion is permanent. Deleted tools cannot be restored or re-installed.
 
 ## Security Checks
 
-Every published version undergoes automated security checks, and are only available to install after they pass.
+Every published version undergoes automated security checks, and are only available to install after they pass. 
 
 You can check the security check status of a tool at:
 
-`CrewAI AMP > Tools > Your Tool > Versions`
+`CrewAI Enterprise > Tools > Your Tool > Versions`
+
 
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
   Contact our support team for assistance with API integration or troubleshooting.
 </Card>
-
-

docs/enterprise/features/traces.mdx

@@ -2,7 +2,6 @@
 title: Traces
 description: "Using Traces to monitor your Crews"
 icon: "timeline"
-mode: "wide"
 ---
 
 ## Overview
@@ -11,7 +10,7 @@ Traces provide comprehensive visibility into your crew executions, helping you m
 
 ## What are Traces?
 
-Traces in CrewAI AMP are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record:
+Traces in CrewAI Enterprise are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record:
 
 - Agent thoughts and reasoning
 - Task execution details
@@ -28,9 +27,9 @@ Traces in CrewAI AMP are detailed execution records that capture every aspect of
 
 <Steps>
   <Step title="Navigate to the Traces Tab">
-    Once in your CrewAI AMP dashboard, click on the **Traces** to view all execution records.
+    Once in your CrewAI Enterprise dashboard, click on the **Traces** to view all execution records.
   </Step>
-
+  
   <Step title="Select an Execution">
     You'll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace.
   </Step>
@@ -112,7 +111,7 @@ Traces are invaluable for troubleshooting issues with your crews:
 <Steps>
   <Step title="Identify Failure Points">
     When a crew execution doesn't produce the expected results, examine the trace to find where things went wrong. Look for:
-
+    
     - Failed tasks
     - Unexpected agent decisions
     - Tool usage errors
@@ -122,19 +121,19 @@ Traces are invaluable for troubleshooting issues with your crews:
       ![Failure Points](/images/enterprise/failure.png)
     </Frame>
   </Step>
-
+  
   <Step title="Optimize Performance">
     Use execution metrics to identify performance bottlenecks:
-
+    
     - Tasks that took longer than expected
     - Excessive token usage
     - Redundant tool operations
     - Unnecessary API calls
   </Step>
-
+  
   <Step title="Improve Cost Efficiency">
     Analyze token usage and cost estimates to optimize your crew's efficiency:
-
+    
     - Consider using smaller models for simpler tasks
     - Refine prompts to be more concise
     - Cache frequently accessed information
@@ -142,16 +141,6 @@ 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 AMP features.
-</Card>
+  Contact our support team for assistance with trace analysis or any other CrewAI Enterprise features.
+</Card>

docs/enterprise/features/webhook-streaming.mdx

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

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

@@ -2,7 +2,6 @@
 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.
@@ -10,17 +9,17 @@ This guide walks you through connecting Azure OpenAI with Crew Studio for seamle
 ## Setup Process
 
 <Steps>
-    <Step title="Access Azure AI Foundry">
-        1. In Azure, go to [Azure AI Foundry](https://ai.azure.com/) > select your Azure OpenAI deployment.
+    <Step title="Access Azure OpenAI Studio">
+        1. In Azure, go to `Azure AI Services > select your deployment > open Azure OpenAI Studio`.
         2. On the left menu, click `Deployments`. If you don't have one, create a deployment with your desired model.
         3. Once created, select your deployment and locate the `Target URI` and `Key` on the right side of the page. Keep this page open, as you'll need this information.
             <Frame>
-                <img src="/images/enterprise/azure-openai-studio.png" alt="Azure AI Foundry" />
+                <img src="/images/enterprise/azure-openai-studio.png" alt="Azure OpenAI Studio" />
             </Frame>
     </Step>
 
-    <Step title="Configure CrewAI AMP Connection">
-        4. In another tab, open `CrewAI AMP > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure.
+    <Step title="Configure CrewAI Enterprise Connection">
+        4. In another tab, open `CrewAI Enterprise > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure.
         5. On the same page, add environment variables from step 3:
             - One named `AZURE_DEPLOYMENT_TARGET_URL` (using the Target URI). The URL should look like this: https://your-deployment.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview
             - Another named `AZURE_API_KEY` (using the Key).
@@ -28,7 +27,7 @@ This guide walks you through connecting Azure OpenAI with Crew Studio for seamle
     </Step>
 
     <Step title="Set Default Configuration">
-        7. In `CrewAI AMP > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults.
+        7. In `CrewAI Enterprise > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults.
     </Step>
 
     <Step title="Configure Network Access">
@@ -49,4 +48,4 @@ 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/enterprise/guides/build-crew.mdx

@@ -2,33 +2,34 @@
 title: "Build Crew"
 description: "A Crew is a group of agents that work together to complete a task."
 icon: "people-arrows"
-mode: "wide"
 ---
 
 ## Overview
 
-[CrewAI AMP](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments.
+[CrewAI Enterprise](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments.
 
 ## Getting Started
 
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.youtube.com/embed/-kSOTtYzgEw"
-  title="Building crews with the CrewAI CLI"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
+<iframe 
+    width="100%"
+    height="400"
+    src="https://www.youtube.com/embed/-kSOTtYzgEw"
+    title="Building Crews with CrewAI CLI" 
+    frameborder="0"
+    style={{ borderRadius: '10px' }}
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowfullscreen
 ></iframe>
 
 ### Installation and Setup
 
-<Card title="Follow Standard Installation" icon="wrench" href="/en/installation">
+<Card title="Follow Standard Installation" icon="wrench" href="/installation">
   Follow our standard installation guide to set up CrewAI CLI and create your first project.
 </Card>
 
 ### Building Your Crew
 
-<Card title="Quickstart Tutorial" icon="rocket" href="/en/quickstart">
+<Card title="Quickstart Tutorial" icon="rocket" href="/quickstart">
   Follow our quickstart guide to create your first agent crew using YAML configuration.
 </Card>
 
@@ -39,4 +40,4 @@ For Enterprise-specific support or questions, contact our dedicated support team
 
 <Card title="Schedule a Demo" icon="calendar" href="mailto:support@crewai.com">
   Book time with our team to learn more about Enterprise features and how they can benefit your organization.
-</Card>
+</Card>

docs/enterprise/guides/deploy-crew.mdx

@@ -1,12 +1,11 @@
 ---
 title: "Deploy Crew"
-description: "Deploying a Crew on CrewAI AMP"
+description: "Deploying a Crew on CrewAI Enterprise"
 icon: "rocket"
-mode: "wide"
 ---
 
 <Note>
-After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI AMP platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow.
+After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI Enterprise platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow.
 </Note>
 
 ## Prerequisites
@@ -39,11 +38,14 @@ The CLI provides the fastest way to deploy locally developed crews to the Enterp
   </Step>
 
   <Step title="Authenticate with the Enterprise Platform">
-    First, you need to authenticate your CLI with the CrewAI AMP platform:
+    First, you need to authenticate your CLI with the CrewAI Enterprise platform:
 
     ```bash
-    # If you already have a CrewAI AMP account, or want to create one:
+    # If you already have a CrewAI Enterprise account
     crewai login
+
+    # If you're creating a new account
+    crewai signup
     ```
 
     When you run either command, the CLI will:
@@ -120,24 +122,24 @@ The CrewAI CLI offers several commands to manage your deployments:
 
   # Remove a deployment
   crewai deploy remove <deployment_id>
-  ```
+  ``` 
 
 ## Option 2: Deploy Directly via Web Interface
 
-You can also deploy your crews directly through the CrewAI AMP web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine.
+You can also deploy your crews directly through the CrewAI Enterprise web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine.
 
 <Steps>
 
   <Step title="Pushing to GitHub">
 
-  You need to push your crew to a GitHub repository. If you haven't created a crew yet, you can [follow this tutorial](/en/quickstart).
+  You need to push your crew to a GitHub repository. If you haven't created a crew yet, you can [follow this tutorial](/quickstart).
 
   </Step>
 
-  <Step title="Connecting GitHub to CrewAI AMP">
+  <Step title="Connecting GitHub to CrewAI Enterprise">
 
-    1. Log in to [CrewAI AMP](https://app.crewai.com)
-    2. Click on the button "Connect GitHub"
+    1. Log in to [CrewAI Enterprise](https://app.crewai.com)
+    2. Click on the button "Connect GitHub" 
 
     <Frame>
       ![Connect GitHub Button](/images/enterprise/connect-github.png)
@@ -190,7 +192,7 @@ You can also deploy your crews directly through the CrewAI AMP web interface by
 ## ⚠️ Environment Variable Security Requirements
 
 <Warning>
-**Important**: CrewAI AMP has security restrictions on environment variable names that can cause deployment failures if not followed.
+**Important**: CrewAI Enterprise has security restrictions on environment variable names that can cause deployment failures if not followed.
 </Warning>
 
 ### Blocked Environment Variable Patterns
@@ -199,20 +201,20 @@ For security reasons, the following environment variable naming patterns are **a
 
 **Blocked Patterns:**
 - Variables ending with `_TOKEN` (e.g., `MY_API_TOKEN`)
-- Variables ending with `_PASSWORD` (e.g., `DB_PASSWORD`)
+- Variables ending with `_PASSWORD` (e.g., `DB_PASSWORD`) 
 - Variables ending with `_SECRET` (e.g., `API_SECRET`)
 - Variables ending with `_KEY` in certain contexts
 
 **Specific Blocked Variables:**
 - `GITHUB_USER`, `GITHUB_TOKEN`
-- `AWS_REGION`, `AWS_DEFAULT_REGION`
+- `AWS_REGION`, `AWS_DEFAULT_REGION` 
 - Various internal CrewAI system variables
 
 ### Allowed Exceptions
 
 Some variables are explicitly allowed despite matching blocked patterns:
 - `AZURE_AD_TOKEN`
-- `AZURE_OPENAI_AD_TOKEN`
+- `AZURE_OPENAI_AD_TOKEN` 
 - `ENTERPRISE_ACTION_TOKEN`
 - `CREWAI_ENTEPRISE_TOOLS_TOKEN`
 
@@ -226,7 +228,7 @@ OPENAI_TOKEN=sk-...
 DATABASE_PASSWORD=mypassword
 API_SECRET=secret123
 
-# ✅ Use these naming patterns instead
+# ✅ Use these naming patterns instead  
 OPENAI_API_KEY=sk-...
 DATABASE_CREDENTIALS=mypassword
 API_CONFIG=secret123

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

@@ -1,17 +1,16 @@
 ---
 title: "Enable Crew Studio"
-description: "Enabling Crew Studio on CrewAI AMP"
+description: "Enabling Crew Studio on CrewAI Enterprise"
 icon: "comments"
-mode: "wide"
 ---
 
 <Tip>
-Crew Studio is a powerful **no-code/low-code** tool that allows you to quickly scaffold or build Crews through a conversational interface.
+Crew Studio is a powerful **no-code/low-code** tool that allows you to quickly scaffold or build Crews through a conversational interface. 
 </Tip>
 
 ## What is Crew Studio?
 
-Crew Studio is an innovative way to create AI agent crews without writing code.
+Crew Studio is an innovative way to create AI agent crews without writing code. 
 
 <Frame>
   ![Crew Studio Interface](/images/enterprise/crew-studio-interface.png)
@@ -24,7 +23,7 @@ With Crew Studio, you can:
 - Select appropriate tools
 - Configure necessary inputs
 - Generate downloadable code for customization
-- Deploy directly to the CrewAI AMP platform
+- Deploy directly to the CrewAI Enterprise platform
 
 ## Configuration Steps
 
@@ -32,14 +31,14 @@ Before you can start using Crew Studio, you need to configure your LLM connectio
 
 <Steps>
   <Step title="Set Up LLM Connection">
-    Go to the **LLM Connections** tab in your CrewAI AMP dashboard and create a new LLM connection.
+    Go to the **LLM Connections** tab in your CrewAI Enterprise dashboard and create a new LLM connection.
 
     <Note>
       Feel free to use any LLM provider you want that is supported by CrewAI.
     </Note>
-
+    
     Configure your LLM connection:
-
+    
     - Enter a `Connection Name` (e.g., `OpenAI`)
     - Select your model provider: `openai` or `azure`
     - Select models you'd like to use in your Studio-generated Crews
@@ -48,28 +47,28 @@ Before you can start using Crew Studio, you need to configure your LLM connectio
       - For OpenAI: Add `OPENAI_API_KEY` with your API key
       - For Azure OpenAI: Refer to [this article](https://blog.crewai.com/configuring-azure-openai-with-crewai-a-comprehensive-guide/) for configuration details
     - Click `Add Connection` to save your configuration
-
+    
     <Frame>
       ![LLM Connection Configuration](/images/enterprise/llm-connection-config.png)
     </Frame>
   </Step>
-
+  
   <Step title="Verify Connection Added">
     Once you complete the setup, you'll see your new connection added to the list of available connections.
-
+    
     <Frame>
       ![Connection Added](/images/enterprise/connection-added.png)
     </Frame>
   </Step>
-
+  
   <Step title="Configure LLM Defaults">
     In the main menu, go to **Settings → Defaults** and configure the LLM Defaults settings:
-
+    
     - Select default models for agents and other components
     - Set default configurations for Crew Studio
-
+    
     Click `Save Settings` to apply your changes.
-
+    
     <Frame>
       ![LLM Defaults Configuration](/images/enterprise/llm-defaults.png)
     </Frame>
@@ -82,38 +81,38 @@ Now that you've configured your LLM connection and default settings, you're read
 
 <Steps>
   <Step title="Access Studio">
-    Navigate to the **Studio** section in your CrewAI AMP dashboard.
+    Navigate to the **Studio** section in your CrewAI Enterprise dashboard.
   </Step>
-
+  
   <Step title="Start a Conversation">
     Start a conversation with the Crew Assistant by describing the problem you want to solve:
-
+    
     ```md
     I need a crew that can research the latest AI developments and create a summary report.
     ```
-
+    
     The Crew Assistant will ask clarifying questions to better understand your requirements.
   </Step>
-
+  
   <Step title="Review Generated Crew">
     Review the generated crew configuration, including:
-
+    
     - Agents and their roles
     - Tasks to be performed
     - Required inputs
     - Tools to be used
-
+    
     This is your opportunity to refine the configuration before proceeding.
   </Step>
-
+  
   <Step title="Deploy or Download">
     Once you're satisfied with the configuration, you can:
-
+    
     - Download the generated code for local customization
-    - Deploy the crew directly to the CrewAI AMP platform
+    - Deploy the crew directly to the CrewAI Enterprise platform
     - Modify the configuration and regenerate the crew
   </Step>
-
+  
   <Step title="Test Your Crew">
     After deployment, test your crew with sample inputs to ensure it performs as expected.
   </Step>
@@ -130,37 +129,38 @@ Here's a typical workflow for creating a crew with Crew Studio:
 <Steps>
   <Step title="Describe Your Problem">
     Start by describing your problem:
-
+    
     ```md
     I need a crew that can analyze financial news and provide investment recommendations
     ```
   </Step>
-
+  
   <Step title="Answer Questions">
     Respond to clarifying questions from the Crew Assistant to refine your requirements.
   </Step>
-
+  
   <Step title="Review the Plan">
     Review the generated crew plan, which might include:
-
+    
     - A Research Agent to gather financial news
     - An Analysis Agent to interpret the data
     - A Recommendations Agent to provide investment advice
   </Step>
-
+  
   <Step title="Approve or Modify">
     Approve the plan or request changes if necessary.
   </Step>
-
+  
   <Step title="Download or Deploy">
     Download the code for customization or deploy directly to the platform.
   </Step>
-
+  
   <Step title="Test and Refine">
     Test your crew with sample inputs and refine as needed.
   </Step>
 </Steps>
 
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
-  Contact our support team for assistance with Crew Studio or any other CrewAI AMP features.
+  Contact our support team for assistance with Crew Studio or any other CrewAI Enterprise features.
 </Card>
+

docs/enterprise/guides/hubspot-trigger.mdx

@@ -2,25 +2,24 @@
 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 AMP, enabling you to initiate crews directly from HubSpot Workflows.
+This guide provides a step-by-step process to set up HubSpot triggers for CrewAI Enterprise, enabling you to initiate crews directly from HubSpot Workflows.
 
 ## Prerequisites
 
-- A CrewAI AMP account
+- A CrewAI Enterprise account
 - A HubSpot account with the [HubSpot Workflows](https://knowledge.hubspot.com/workflows/create-workflows) feature
 
 ## Setup Steps
 
 <Steps>
-    <Step title="Connect your HubSpot account with CrewAI AMP">
-        - Log in to your `CrewAI AMP account > Triggers`
+    <Step title="Connect your HubSpot account with CrewAI Enterprise">
+        - Log in to your `CrewAI Enterprise account > Triggers`
         - Select `HubSpot` from the list of available triggers
-        - Choose the HubSpot account you want to connect with CrewAI AMP
-        - Follow the on-screen prompts to authorize CrewAI AMP access to your HubSpot account
-        - A confirmation message will appear once HubSpot is successfully connected with CrewAI AMP
+        - Choose the HubSpot account you want to connect with CrewAI Enterprise
+        - Follow the on-screen prompts to authorize CrewAI Enterprise access to your HubSpot account
+        - A confirmation message will appear once HubSpot is successfully connected with CrewAI Enterprise
     </Step>
     <Step title="Create a HubSpot Workflow">
         - Log in to your `HubSpot account > Automations > Workflows > New workflow`
@@ -51,14 +50,4 @@ This guide provides a step-by-step process to set up HubSpot triggers for CrewAI
 
 ## Additional Resources
 
-### Sample payloads & crews
-
-You can jump-start development with the [HubSpot examples in the trigger repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot):
-
-- `record-created-contact.json`, `record-updated-contact.json` → contact lifecycle events handled by `hubspot-contact-crew.py`
-- `record-created-company.json`, `record-updated-company.json` → company enrichment flows in `hubspot-company-crew.py`
-- `record-created-deals.json`, `record-updated-deals.json` → deal pipeline automation in `hubspot-record-crew.py`
-
-Each crew demonstrates how to parse HubSpot record fields, enrich context, and return structured insights.
-
-For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows).
+For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows). 

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

@@ -2,7 +2,6 @@
 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/enterprise/guides/kickoff-crew.mdx

@@ -1,19 +1,18 @@
 ---
 title: "Kickoff Crew"
-description: "Kickoff a Crew on CrewAI AMP"
+description: "Kickoff a Crew on CrewAI Enterprise"
 icon: "flag-checkered"
-mode: "wide"
 ---
 
 ## Overview
 
-Once you've deployed your crew to the CrewAI AMP platform, you can kickoff executions through the web interface or the API. This guide covers both approaches.
+Once you've deployed your crew to the CrewAI Enterprise platform, you can kickoff executions through the web interface or the API. This guide covers both approaches.
 
 ## Method 1: Using the Web Interface
 
 ### Step 1: Navigate to Your Deployed Crew
 
-1. Log in to [CrewAI AMP](https://app.crewai.com)
+1. Log in to [CrewAI Enterprise](https://app.crewai.com)
 2. Click on the crew name from your projects list
 3. You'll be taken to the crew's detail page
 
@@ -83,7 +82,7 @@ Once execution is complete:
 
 ## Method 2: Using the API
 
-You can also kickoff crews programmatically using the CrewAI AMP REST API.
+You can also kickoff crews programmatically using the CrewAI Enterprise REST API.
 
 ### Authentication
 
@@ -184,3 +183,4 @@ If an execution fails:
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
   Contact our support team for assistance with execution issues or questions about the Enterprise platform.
 </Card>
+

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

@@ -1,11 +1,10 @@
 ---
 title: "React Component Export"
-description: "Learn how to export and integrate CrewAI AMP React components into your applications"
+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 AMP crews as React components and integrate them into your own applications.
+This guide explains how to export CrewAI Enterprise crews as React components and integrate them into your own applications.
 
 ## Exporting a React Component
 
@@ -38,7 +37,7 @@ To run this React component locally, you'll need to set up a React development e
             npx create-react-app my-crew-app
             ```
         - Change into the project directory:
-
+        
             ```bash
             cd my-crew-app
             ```
@@ -77,7 +76,7 @@ To run this React component locally, you'll need to set up a React development e
 
     <Step title="Start the development server">
         - In your project directory, run:
-
+        
             ```bash
             npm start
             ```
@@ -101,4 +100,4 @@ You can then customise the `CrewLead.jsx` to add color, title etc
 - Customize the component styling to match your application's design
 - Add additional props for configuration
 - Integrate with your application's state management
-- Add error handling and loading states
+- Add error handling and loading states 

docs/enterprise/guides/salesforce-trigger.mdx

@@ -2,10 +2,9 @@
 title: "Salesforce Trigger"
 description: "Trigger CrewAI crews from Salesforce workflows for CRM automation"
 icon: "salesforce"
-mode: "wide"
 ---
 
-CrewAI AMP can be triggered from Salesforce to automate customer relationship management workflows and enhance your sales operations.
+CrewAI Enterprise can be triggered from Salesforce to automate customer relationship management workflows and enhance your sales operations.
 
 ## Overview
 
@@ -18,20 +17,15 @@ Salesforce is a leading customer relationship management (CRM) platform that hel
 
 ## Demo
 
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.youtube.com/embed/oJunVqjjfu4"
-  title="CrewAI + Salesforce trigger demo"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
-></iframe>
+<Frame>
+    <iframe width="100%" height="400" src="https://www.youtube.com/embed/oJunVqjjfu4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+</Frame>
 
 ## Getting Started
 
 To set up Salesforce triggers:
 
-1. **Contact Support**: Reach out to CrewAI AMP support for assistance with Salesforce trigger setup
+1. **Contact Support**: Reach out to CrewAI Enterprise support for assistance with Salesforce trigger setup
 2. **Review Requirements**: Ensure you have the necessary Salesforce permissions and API access
 3. **Configure Connection**: Work with the support team to establish the connection between CrewAI and your Salesforce instance
 4. **Test Triggers**: Verify the triggers work correctly with your specific use cases
@@ -47,4 +41,4 @@ Common Salesforce + CrewAI trigger scenarios include:
 
 ## Next Steps
 
-For detailed setup instructions and advanced configuration options, please contact CrewAI AMP support who can provide tailored guidance for your specific Salesforce environment and business needs.
+For detailed setup instructions and advanced configuration options, please contact CrewAI Enterprise support who can provide tailored guidance for your specific Salesforce environment and business needs. 

docs/enterprise/guides/slack-trigger.mdx

@@ -2,7 +2,6 @@
 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/enterprise/guides/team-management.mdx

@@ -1,27 +1,26 @@
 ---
 title: "Team Management"
-description: "Learn how to invite and manage team members in your CrewAI AMP organization"
+description: "Learn how to invite and manage team members in your CrewAI Enterprise organization"
 icon: "users"
-mode: "wide"
 ---
 
-As an administrator of a CrewAI AMP account, you can easily invite new team members to join your organization. This guide will walk you through the process step-by-step.
+As an administrator of a CrewAI Enterprise account, you can easily invite new team members to join your organization. This guide will walk you through the process step-by-step.
 
 ## Inviting Team Members
 
 <Steps>
     <Step title="Access the Settings Page">
-        - Log in to your CrewAI AMP account
+        - Log in to your CrewAI Enterprise account
         - Look for the gear icon (⚙️) in the top right corner of the dashboard
         - Click on the gear icon to access the **Settings** page:
-            <Frame caption="Settings page">
+            <Frame>
                 <img src="/images/enterprise/settings-page.png" alt="Settings Page" />
             </Frame>
     </Step>
     <Step title="Navigate to the Members Section">
         - On the Settings page, you'll see a `Members` tab
         - Click on the `Members` tab to access the **Members** page:
-            <Frame caption="Members tab">
+            <Frame>
                 <img src="/images/enterprise/members-tab.png" alt="Members Tab" />
             </Frame>
     </Step>
@@ -43,7 +42,7 @@ You can add roles to your team members to control their access to different part
 
 <Steps>
     <Step title="Access the Settings Page">
-        - Log in to your CrewAI AMP account
+        - Log in to your CrewAI Enterprise account
         - Look for the gear icon (⚙️) in the top right corner of the dashboard
         - Click on the gear icon to access the **Settings** page:
             <Frame>
@@ -85,4 +84,4 @@ You can add roles to your team members to control their access to different part
 - **Invitation Acceptance**: Invited members will need to accept the invitation to join your organization
 - **Email Notifications**: You may want to inform your team members to check their email (including spam folders) for the invitation
 
-By following these steps, you can easily expand your team and collaborate more effectively within your CrewAI AMP organization.
+By following these steps, you can easily expand your team and collaborate more effectively within your CrewAI Enterprise organization. 

docs/enterprise/guides/update-crew.mdx

@@ -1,12 +1,11 @@
 ---
 title: "Update Crew"
-description: "Updating a Crew on CrewAI AMP"
+description: "Updating a Crew on CrewAI Enterprise"
 icon: "pencil"
-mode: "wide"
 ---
 
 <Note>
-After deploying your crew to CrewAI AMP, you may need to make updates to the code, security settings, or configuration.
+After deploying your crew to CrewAI Enterprise, you may need to make updates to the code, security settings, or configuration. 
 This guide explains how to perform these common update operations.
 </Note>
 
@@ -23,7 +22,7 @@ There are several reasons you might want to update your crew deployment:
 
 When you've pushed new commits to your GitHub repository and want to update your deployment:
 
-1. Navigate to your crew in the CrewAI AMP platform
+1. Navigate to your crew in the CrewAI Enterprise platform
 2. Click on the `Re-deploy` button on your crew details page
 
 <Frame>
@@ -36,7 +35,7 @@ This will trigger an update that you can track using the progress bar. The syste
 
 If you need to generate a new bearer token (for example, if you suspect the current token might have been compromised):
 
-1. Navigate to your crew in the CrewAI AMP platform
+1. Navigate to your crew in the CrewAI Enterprise platform
 2. Find the `Bearer Token` section
 3. Click the `Reset` button next to your current token
 
@@ -87,3 +86,4 @@ If you encounter any issues after updating, you can view deployment logs in the
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
   Contact our support team for assistance with updating your crew or troubleshooting deployment issues.
 </Card>
+