fix: convert local file paths to base64 in AddImageTool for Anthropic compatibility

Fixes #3984

AddImageTool now converts local file paths to base64 data URLs before
returning them to the LLM. This enables multimodal functionality with
Anthropic models (Claude 3.5 Sonnet, etc.) which require images in
base64 format.

Changes:
- Add _normalize_image_url method to detect and convert local files
- Support absolute paths, relative paths, file:// URLs, and ~ expansion
- Preserve HTTP/HTTPS URLs and existing data URLs unchanged
- Add comprehensive tests for all conversion scenarios

Co-Authored-By: João <joao@crewai.com>

.github/codeql/codeql-config.yml

@@ -0,0 +1,28 @@
+name: "CodeQL Config"
+
+paths-ignore:
+  # Ignore template files - these are boilerplate code that shouldn't be analyzed
+  - "lib/crewai/src/crewai/cli/templates/**"
+  # Ignore test cassettes - these are test fixtures/recordings
+  - "lib/crewai/tests/cassettes/**"
+  - "lib/crewai-tools/tests/cassettes/**"
+  # Ignore cache and build artifacts
+  - ".cache/**"
+  # Ignore documentation build artifacts
+  - "docs/.cache/**"
+  # Ignore experimental code
+  - "lib/crewai/src/crewai/experimental/a2a/**"
+
+paths:
+  # Include all Python source code from workspace packages
+  - "lib/crewai/src/**"
+  - "lib/crewai-tools/src/**"
+  - "lib/devtools/src/**"
+  # Include tests (but exclude cassettes via paths-ignore)
+  - "lib/crewai/tests/**"
+  - "lib/crewai-tools/tests/**"
+  - "lib/devtools/tests/**"
+
+# Configure specific queries or packs if needed
+# queries:
+#   - uses: security-and-quality

.github/dependabot.yml

@@ -0,0 +1,11 @@
+# To get started with Dependabot version updates, you'll need to specify which 
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+version: 2
+updates:
+  - package-ecosystem: uv # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"

.github/security.md

@@ -1,27 +1,50 @@
-## CrewAI Security Vulnerability Reporting Policy
+## CrewAI Security Policy
 
-CrewAI prioritizes the security of our software products, services, and GitHub repositories. To promptly address vulnerabilities, follow these steps for reporting security issues:
+We are committed to protecting the confidentiality, integrity, and availability of the CrewAI ecosystem. This policy explains how to report potential vulnerabilities and what you can expect from us when you do.
 
-### Reporting Process
-Do **not** report vulnerabilities via public GitHub issues.
+### Scope
 
-Email all vulnerability reports directly to:  
-**security@crewai.com**
+We welcome reports for vulnerabilities that could impact:
 
-### Required Information
-To help us quickly validate and remediate the issue, your report must include:
+- CrewAI-maintained source code and repositories
+- CrewAI-operated infrastructure and services
+- Official CrewAI releases, packages, and distributions
 
-- **Vulnerability Type:** Clearly state the vulnerability type (e.g., SQL injection, XSS, privilege escalation).
-- **Affected Source Code:** Provide full file paths and direct URLs (branch, tag, or commit).
-- **Reproduction Steps:** Include detailed, step-by-step instructions. Screenshots are recommended.
-- **Special Configuration:** Document any special settings or configurations required to reproduce.
-- **Proof-of-Concept (PoC):** Provide exploit or PoC code (if available).
-- **Impact Assessment:** Clearly explain the severity and potential exploitation scenarios.
+Issues affecting clearly unaffiliated third-party services or user-generated content are out of scope, unless you can demonstrate a direct impact on CrewAI systems or customers.
 
-### Our Response
-- We will acknowledge receipt of your report promptly via your provided email.
-- Confirmed vulnerabilities will receive priority remediation based on severity.
-- Patches will be released as swiftly as possible following verification.
+### How to Report
 
-### Reward Notice
-Currently, we do not offer a bug bounty program. Rewards, if issued, are discretionary.
+- **Please do not** disclose vulnerabilities via public GitHub issues, pull requests, or social media.
+- Email detailed reports to **security@crewai.com** with the subject line `Security Report`.
+- If you need to share large files or sensitive artifacts, mention it in your email and we will coordinate a secure transfer method.
+
+### What to Include
+
+Providing comprehensive information enables us to validate the issue quickly:
+
+- **Vulnerability overview** — a concise description and classification (e.g., RCE, privilege escalation)
+- **Affected components** — repository, branch, tag, or deployed service along with relevant file paths or endpoints
+- **Reproduction steps** — detailed, step-by-step instructions; include logs, screenshots, or screen recordings when helpful
+- **Proof-of-concept** — exploit details or code that demonstrates the impact (if available)
+- **Impact analysis** — severity assessment, potential exploitation scenarios, and any prerequisites or special configurations
+
+### Our Commitment
+
+- **Acknowledgement:** We aim to acknowledge your report within two business days.
+- **Communication:** We will keep you informed about triage results, remediation progress, and planned release timelines.
+- **Resolution:** Confirmed vulnerabilities will be prioritized based on severity and fixed as quickly as possible.
+- **Recognition:** We currently do not run a bug bounty program; any rewards or recognition are issued at CrewAI's discretion.
+
+### Coordinated Disclosure
+
+We ask that you allow us a reasonable window to investigate and remediate confirmed issues before any public disclosure. We will coordinate publication timelines with you whenever possible.
+
+### Safe Harbor
+
+We will not pursue or support legal action against individuals who, in good faith:
+
+- Follow this policy and refrain from violating any applicable laws
+- Avoid privacy violations, data destruction, or service disruption
+- Limit testing to systems in scope and respect rate limits and terms of service
+
+If you are unsure whether your testing is covered, please contact us at **security@crewai.com** before proceeding.

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

@@ -0,0 +1,48 @@
+name: Build uv cache
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - "uv.lock"
+      - "pyproject.toml"
+  schedule:
+    - cron: "0 0 */5 * *"  # Run every 5 days at midnight UTC to prevent cache expiration
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  build-cache:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
+
+      - name: Install dependencies and populate cache
+        run: |
+          echo "Building global UV cache for Python ${{ matrix.python-version }}..."
+          uv sync --all-groups --all-extras --no-install-project
+          echo "Cache populated successfully"
+
+      - name: Save uv caches
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.github/workflows/codeql.yml

@@ -0,0 +1,103 @@
+# 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: ${{ matrix.build-mode }}
+        config-file: ./.github/codeql/codeql-config.yml
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+
+        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+        # queries: security-extended,security-and-quality
+
+    # If the analyze step fails for one of the languages you are analyzing with
+    # "We were unable to automatically build your code", modify the matrix above
+    # to set the build mode to "manual" for that language. Then modify this step
+    # to build your code.
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+    - if: matrix.build-mode == 'manual'
+      shell: bash
+      run: |
+        echo 'If you are using a "manual" build mode for one or more of the' \
+          'languages you are analyzing, replace this with the commands to build' \
+          'your code, for example:'
+        echo '  make bootstrap'
+        echo '  make release'
+        exit 1
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v3
+      with:
+        category: "/language:${{matrix.language}}"

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

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

.github/workflows/linter.yml

@@ -2,6 +2,9 @@ name: Lint
 
 on: [pull_request]
 
+permissions:
+  contents: read
+
 jobs:
   lint:
     runs-on: ubuntu-latest
@@ -15,8 +18,27 @@ jobs:
       - name: Fetch Target Branch
         run: git fetch origin $TARGET_BRANCH --depth=1
 
-      - name: Install Ruff
-        run: pip install ruff
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py3.11-
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: "3.11"
+          enable-cache: false
+
+      - name: Install dependencies
+        run: uv sync --all-groups --all-extras --no-install-project
 
       - name: Get Changed Python Files
         id: changed-files
@@ -30,7 +52,18 @@ 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{} ruff check "{}"
+         echo "${{ steps.changed-files.outputs.files }}" \
+           | tr ' ' '\n' \
+           | grep -v 'src/crewai/cli/templates/' \
+           | grep -v '/tests/' \
+           | xargs -I{} uv run ruff check "{}"
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}

.github/workflows/publish.yml

@@ -0,0 +1,81 @@
+name: Publish to PyPI
+
+on:
+  release:
+    types: [ published ]
+  workflow_dispatch:
+
+jobs:
+  build:
+    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:
+    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: ${{ secrets.PYPI_API_TOKEN }}
+        run: |
+          failed=0
+          for package in dist/*; do
+            if [[ "$package" == *"crewai_devtools"* ]]; then
+              echo "Skipping private package: $package"
+              continue
+            fi
+            echo "Publishing $package"
+            if ! uv publish "$package"; then
+              echo "Failed to publish $package"
+              failed=1
+            fi
+          done
+          if [ $failed -eq 1 ]; then
+            echo "Some packages failed to publish"
+            exit 1
+          fi

.github/workflows/security-checker.yml

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

.github/workflows/tests.yml

@@ -3,11 +3,19 @@ name: Run Tests
 on: [pull_request]
 
 permissions:
-  contents: write
+  contents: read
 
 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:
@@ -22,24 +30,73 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0  # Fetch all history for proper diff
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py${{ matrix.python-version }}-
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v3
+        uses: astral-sh/setup-uv@v6
         with:
-          enable-cache: true
-          cache-dependency-glob: |
-            **/pyproject.toml
-            **/uv.lock
-
-      - name: Set up Python ${{ matrix.python-version }}
-        run: uv python install ${{ matrix.python-version }}
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
 
       - name: Install the project
-        run: uv sync --dev --all-extras
+        run: uv sync --all-groups --all-extras
+
+      - name: Restore test durations
+        uses: actions/cache/restore@v4
+        with:
+          path: .test_durations_py*
+          key: test-durations-py${{ matrix.python-version }}
 
       - name: Run tests (group ${{ matrix.group }} of 8)
         run: |
-          uv run pytest \
+          PYTHON_VERSION_SAFE=$(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 ${{ matrix.group }} of 8)
+        run: |
+          cd lib/crewai-tools && uv run pytest \
             --block-network \
             --timeout=30 \
             -vv \
@@ -48,3 +105,14 @@ jobs:
             --durations=10 \
             -n auto \
             --maxfail=3
+
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.github/workflows/type-checker.yml

@@ -3,24 +3,99 @@ name: Run Type Checks
 on: [pull_request]
 
 permissions:
-  contents: write
+  contents: read
 
 jobs:
-  type-checker:
+  type-checker-matrix:
+    name: type-checker (${{ matrix.python-version }})
     runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
         with:
-          python-version: "3.11.9"
+          fetch-depth: 0 # Fetch all history for proper diff
 
-      - name: Install Requirements
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py${{ matrix.python-version }}-
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
+
+      - name: Install dependencies
+        run: uv sync --all-groups --all-extras
+
+      - name: Get changed Python files
+        id: changed-files
         run: |
-          pip install mypy
+          # Get the list of changed Python files compared to the base branch
+          echo "Fetching changed files..."
+          git diff --name-only --diff-filter=ACMRT origin/${{ github.base_ref }}...HEAD -- '*.py' > changed_files.txt
 
-      - name: Run type checks
-        run: mypy src
+          # Filter for files in src/ directory only (excluding tests/)
+          grep -E "^src/" changed_files.txt > filtered_changed_files.txt || true
+
+          # Check if there are any changed files
+          if [ -s filtered_changed_files.txt ]; then
+            echo "Changed Python files in src/:"
+            cat filtered_changed_files.txt
+            echo "has_changes=true" >> $GITHUB_OUTPUT
+            # Convert newlines to spaces for mypy command
+            echo "files=$(cat filtered_changed_files.txt | tr '\n' ' ')" >> $GITHUB_OUTPUT
+          else
+            echo "No Python files changed in src/"
+            echo "has_changes=false" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Run type checks on changed files
+        if: steps.changed-files.outputs.has_changes == 'true'
+        run: |
+          echo "Running mypy on changed files with Python ${{ matrix.python-version }}..."
+          uv run mypy ${{ steps.changed-files.outputs.files }}
+
+      - name: No files to check
+        if: steps.changed-files.outputs.has_changes == 'false'
+        run: echo "No Python files in src/ were modified - skipping type checks"
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+
+  # Summary job to provide single status for branch protection
+  type-checker:
+    name: type-checker
+    runs-on: ubuntu-latest
+    needs: type-checker-matrix
+    if: always()
+    steps:
+      - name: Check matrix results
+        run: |
+          if [ "${{ needs.type-checker-matrix.result }}" == "success" ] || [ "${{ needs.type-checker-matrix.result }}" == "skipped" ]; then
+            echo "✅ All type checks passed"
+          else
+            echo "❌ Type checks failed"
+            exit 1
+          fi

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

@@ -0,0 +1,71 @@
+name: Update Test Durations
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'tests/**/*.py'
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  update-durations:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.10', '3.11', '3.12', '3.13']
+    env:
+      OPENAI_API_KEY: fake-api-key
+      PYTHONUNBUFFERED: 1
+    
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py${{ matrix.python-version }}-
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
+
+      - name: Install the project
+        run: uv sync --all-groups --all-extras
+
+      - name: Run all tests and store durations
+        run: |
+          PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
+          uv run pytest --store-durations --durations-path=.test_durations_py${PYTHON_VERSION_SAFE} -n auto
+        continue-on-error: true
+
+      - name: Save durations to cache
+        if: always()
+        uses: actions/cache/save@v4
+        with:
+          path: .test_durations_py*
+          key: test-durations-py${{ matrix.python-version }}
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.gitignore

@@ -2,7 +2,6 @@
 .pytest_cache
 __pycache__
 dist/
-lib/
 .env
 assets/*
 .idea

.pre-commit-config.yaml

@@ -1,7 +1,27 @@
 repos:
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.2
+  - repo: local
     hooks:
       - id: ruff
-        args: ["--fix"]
+        name: ruff
+        entry: bash -c 'source .venv/bin/activate && uv run ruff check --config pyproject.toml "$@"' --
+        language: system
+        pass_filenames: true
+        types: [python]
       - id: ruff-format
+        name: ruff-format
+        entry: bash -c 'source .venv/bin/activate && uv run ruff format --config pyproject.toml "$@"' --
+        language: system
+        pass_filenames: true
+        types: [python]
+      - id: mypy
+        name: mypy
+        entry: bash -c 'source .venv/bin/activate && uv run mypy --config-file pyproject.toml "$@"' --
+        language: system
+        pass_filenames: true
+        types: [python]
+        exclude: ^(lib/crewai/src/crewai/cli/templates/|lib/crewai/tests/|lib/crewai-tools/tests/)
+  - repo: https://github.com/astral-sh/uv-pre-commit
+    rev: 0.9.3
+    hooks:
+      - id: uv-lock
+

.ruff.toml

@@ -1,4 +0,0 @@
-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 Enterprise Suite
+# CrewAI AOP Suite
 
-CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
+CrewAI AOP 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 Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
+- **On-premise and Cloud Deployment Options**: Deploy CrewAI AOP on-premise or in the cloud, depending on your security and compliance requirements.
 
-CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
+CrewAI AOP 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/landing_page_generator)
+- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator)
 - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
-- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis)
 
 ### Quick Tutorial
 
@@ -429,19 +429,19 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 ### Write Job Descriptions
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below:
 
 [![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
 
 ### Stock Analysis
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below:
 
 [![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 
@@ -674,9 +674,9 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
 
 ### Enterprise Features
 
-- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
-- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
-- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
+- [What additional features does CrewAI AOP offer?](#q-what-additional-features-does-crewai-amp-offer)
+- [Is CrewAI AOP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
+- [Can I try CrewAI AOP for free?](#q-can-i-try-crewai-amp-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 Enterprise offer?
+### Q: What additional features does CrewAI AOP offer?
 
-A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
+A: CrewAI AOP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
 
-### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
+### Q: Is CrewAI AOP available for cloud and on-premise deployments?
 
-A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
+A: Yes, CrewAI AOP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
 
-### Q: Can I try CrewAI Enterprise for free?
+### Q: Can I try CrewAI AOP 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.
+A: Yes, you can explore part of the CrewAI AOP 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 Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
+A: Yes, CrewAI AOP 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/docs.json

@@ -1,6 +1,6 @@
 {
   "$schema": "https://mintlify.com/docs.json",
-  "theme": "mint",
+  "theme": "aspen",
   "name": "CrewAI",
   "colors": {
     "primary": "#EB6658",
@@ -9,7 +9,22 @@
   },
   "favicon": "/images/favicon.svg",
   "contextual": {
-    "options": ["copy", "view", "chatgpt", "claude"]
+    "options": [
+      "copy",
+      "view",
+      "chatgpt",
+      "claude",
+      "perplexity",
+      "mcp",
+      "cursor",
+      "vscode",
+      {
+        "title": "Request a feature",
+        "description": "Join the discussion on GitHub to request a new feature",
+        "icon": "plus",
+        "href": "https://github.com/crewAIInc/crewAI/issues/new/choose"
+      }
+    ]
   },
   "navigation": {
     "languages": [
@@ -28,20 +43,31 @@
               "icon": "discourse"
             },
             {
-              "anchor": "Crew GPT",
-              "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
-              "icon": "robot"
+              "anchor": "Blog",
+              "href": "https://blog.crewai.com",
+              "icon": "newspaper"
             },
             {
-              "anchor": "Releases",
-              "href": "https://github.com/crewAIInc/crewAI/releases",
-              "icon": "tag"
+              "anchor": "CrewGPT",
+              "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
+              "icon": "robot"
             }
           ]
         },
         "tabs": [
+          {
+            "tab": "Home",
+            "icon": "house",
+            "groups": [
+              {
+                "group": "Welcome",
+                "pages": ["index"]
+              }
+            ]
+          },
           {
             "tab": "Documentation",
+            "icon": "book-open",
             "groups": [
               {
                 "group": "Get Started",
@@ -52,18 +78,22 @@
                 "pages": [
                   {
                     "group": "Strategy",
+                    "icon": "compass",
                     "pages": ["en/guides/concepts/evaluating-use-cases"]
                   },
                   {
                     "group": "Agents",
+                    "icon": "user",
                     "pages": ["en/guides/agents/crafting-effective-agents"]
                   },
                   {
                     "group": "Crews",
+                    "icon": "users",
                     "pages": ["en/guides/crews/first-crew"]
                   },
                   {
                     "group": "Flows",
+                    "icon": "code-branch",
                     "pages": [
                       "en/guides/flows/first-flow",
                       "en/guides/flows/mastering-flow-state"
@@ -71,6 +101,7 @@
                   },
                   {
                     "group": "Advanced",
+                    "icon": "gear",
                     "pages": [
                       "en/guides/advanced/customizing-prompts",
                       "en/guides/advanced/fingerprinting"
@@ -103,6 +134,7 @@
                 "group": "MCP Integration",
                 "pages": [
                   "en/mcp/overview",
+                  "en/mcp/dsl-integration",
                   "en/mcp/stdio",
                   "en/mcp/sse",
                   "en/mcp/streamable-http",
@@ -116,6 +148,7 @@
                   "en/tools/overview",
                   {
                     "group": "File & Document",
+                    "icon": "folder-open",
                     "pages": [
                       "en/tools/file-document/overview",
                       "en/tools/file-document/filereadtool",
@@ -135,6 +168,7 @@
                   },
                   {
                     "group": "Web Scraping & Browsing",
+                    "icon": "globe",
                     "pages": [
                       "en/tools/web-scraping/overview",
                       "en/tools/web-scraping/scrapewebsitetool",
@@ -154,6 +188,7 @@
                   },
                   {
                     "group": "Search & Research",
+                    "icon": "magnifying-glass",
                     "pages": [
                       "en/tools/search-research/overview",
                       "en/tools/search-research/serperdevtool",
@@ -175,6 +210,7 @@
                   },
                   {
                     "group": "Database & Data",
+                    "icon": "database",
                     "pages": [
                       "en/tools/database-data/overview",
                       "en/tools/database-data/mysqltool",
@@ -189,6 +225,7 @@
                   },
                   {
                     "group": "AI & Machine Learning",
+                    "icon": "brain",
                     "pages": [
                       "en/tools/ai-ml/overview",
                       "en/tools/ai-ml/dalletool",
@@ -202,16 +239,26 @@
                   },
                   {
                     "group": "Cloud & Storage",
+                    "icon": "cloud",
                     "pages": [
                       "en/tools/cloud-storage/overview",
                       "en/tools/cloud-storage/s3readertool",
                       "en/tools/cloud-storage/s3writertool",
-                      "en/tools/cloud-storage/bedrockinvokeagenttool",
                       "en/tools/cloud-storage/bedrockkbretriever"
                     ]
                   },
                   {
-                    "group": "Automation & Integration",
+                    "group": "Integrations",
+                    "icon": "plug",
+                    "pages": [
+                      "en/tools/integration/overview",
+                      "en/tools/integration/bedrockinvokeagenttool",
+                      "en/tools/integration/crewaiautomationtool"
+                    ]
+                  },
+                  {
+                    "group": "Automation",
+                    "icon": "bolt",
                     "pages": [
                       "en/tools/automation/overview",
                       "en/tools/automation/apifyactorstool",
@@ -225,8 +272,11 @@
               {
                 "group": "Observability",
                 "pages": [
+                  "en/observability/tracing",
                   "en/observability/overview",
                   "en/observability/arize-phoenix",
+                  "en/observability/braintrust",
+                  "en/observability/datadog",
                   "en/observability/langdb",
                   "en/observability/langfuse",
                   "en/observability/langtrace",
@@ -256,13 +306,17 @@
                   "en/learn/force-tool-output-as-result",
                   "en/learn/hierarchical-process",
                   "en/learn/human-input-on-execution",
+                  "en/learn/human-in-the-loop",
                   "en/learn/kickoff-async",
                   "en/learn/kickoff-for-each",
                   "en/learn/llm-connections",
                   "en/learn/multimodal-agents",
                   "en/learn/replay-tasks-from-latest-crew-kickoff",
                   "en/learn/sequential-process",
-                  "en/learn/using-annotations"
+                  "en/learn/using-annotations",
+                  "en/learn/execution-hooks",
+                  "en/learn/llm-hooks",
+                  "en/learn/tool-hooks"
                 ]
               },
               {
@@ -272,22 +326,35 @@
             ]
           },
           {
-            "tab": "Enterprise",
+            "tab": "AOP",
+            "icon": "briefcase",
             "groups": [
               {
                 "group": "Getting Started",
                 "pages": ["en/enterprise/introduction"]
               },
               {
-                "group": "Features",
+                "group": "Build",
+                "pages": [
+                  "en/enterprise/features/automations",
+                  "en/enterprise/features/crew-studio",
+                  "en/enterprise/features/marketplace",
+                  "en/enterprise/features/agent-repositories",
+                  "en/enterprise/features/tools-and-integrations"
+                ]
+              },
+              {
+                "group": "Operate",
                 "pages": [
-                  "en/enterprise/features/rbac",
-                  "en/enterprise/features/tool-repository",
-                  "en/enterprise/features/webhook-streaming",
                   "en/enterprise/features/traces",
-                  "en/enterprise/features/hallucination-guardrail",
-                  "en/enterprise/features/integrations",
-                  "en/enterprise/features/agent-repositories"
+                  "en/enterprise/features/webhook-streaming",
+                  "en/enterprise/features/hallucination-guardrail"
+                ]
+              },
+              {
+                "group": "Manage",
+                "pages": [
+                  "en/enterprise/features/rbac"
                 ]
               },
               {
@@ -299,10 +366,20 @@
                   "en/enterprise/integrations/github",
                   "en/enterprise/integrations/gmail",
                   "en/enterprise/integrations/google_calendar",
+                  "en/enterprise/integrations/google_contacts",
+                  "en/enterprise/integrations/google_docs",
+                  "en/enterprise/integrations/google_drive",
                   "en/enterprise/integrations/google_sheets",
+                  "en/enterprise/integrations/google_slides",
                   "en/enterprise/integrations/hubspot",
                   "en/enterprise/integrations/jira",
                   "en/enterprise/integrations/linear",
+                  "en/enterprise/integrations/microsoft_excel",
+                  "en/enterprise/integrations/microsoft_onedrive",
+                  "en/enterprise/integrations/microsoft_outlook",
+                  "en/enterprise/integrations/microsoft_sharepoint",
+                  "en/enterprise/integrations/microsoft_teams",
+                  "en/enterprise/integrations/microsoft_word",
                   "en/enterprise/integrations/notion",
                   "en/enterprise/integrations/salesforce",
                   "en/enterprise/integrations/shopify",
@@ -311,6 +388,22 @@
                   "en/enterprise/integrations/zendesk"
                 ]
               },
+              {
+                "group": "Triggers",
+                "pages": [
+                  "en/enterprise/guides/automation-triggers",
+                  "en/enterprise/guides/gmail-trigger",
+                  "en/enterprise/guides/google-calendar-trigger",
+                  "en/enterprise/guides/google-drive-trigger",
+                  "en/enterprise/guides/outlook-trigger",
+                  "en/enterprise/guides/onedrive-trigger",
+                  "en/enterprise/guides/microsoft-teams-trigger",
+                  "en/enterprise/guides/slack-trigger",
+                  "en/enterprise/guides/hubspot-trigger",
+                  "en/enterprise/guides/salesforce-trigger",
+                  "en/enterprise/guides/zapier-trigger"
+                ]
+              },
               {
                 "group": "How-To Guides",
                 "pages": [
@@ -319,16 +412,13 @@
                   "en/enterprise/guides/kickoff-crew",
                   "en/enterprise/guides/update-crew",
                   "en/enterprise/guides/enable-crew-studio",
+                  "en/enterprise/guides/capture_telemetry_logs",
                   "en/enterprise/guides/azure-openai-setup",
-                  "en/enterprise/guides/automation-triggers",
-                  "en/enterprise/guides/hubspot-trigger",
+                  "en/enterprise/guides/tool-repository",
                   "en/enterprise/guides/react-component-export",
-                  "en/enterprise/guides/salesforce-trigger",
-                  "en/enterprise/guides/slack-trigger",
                   "en/enterprise/guides/team-management",
-                  "en/enterprise/guides/webhook-automation",
                   "en/enterprise/guides/human-in-the-loop",
-                  "en/enterprise/guides/zapier-trigger"
+                  "en/enterprise/guides/webhook-automation"
                 ]
               },
               {
@@ -339,6 +429,7 @@
           },
           {
             "tab": "API Reference",
+            "icon": "magnifying-glass",
             "groups": [
               {
                 "group": "Getting Started",
@@ -346,6 +437,7 @@
                   "en/api-reference/introduction",
                   "en/api-reference/inputs",
                   "en/api-reference/kickoff",
+                  "en/api-reference/resume",
                   "en/api-reference/status"
                 ]
               }
@@ -353,12 +445,23 @@
           },
           {
             "tab": "Examples",
+            "icon": "code",
             "groups": [
               {
                 "group": "Examples",
                 "pages": ["en/examples/example", "en/examples/cookbooks"]
               }
             ]
+          },
+          {
+            "tab": "Changelog",
+            "icon": "clock",
+            "groups": [
+              {
+                "group": "Release Notes",
+                "pages": ["en/changelog"]
+              }
+            ]
           }
         ]
       },
@@ -377,20 +480,31 @@
               "icon": "discourse"
             },
             {
-              "anchor": "Crew GPT",
-              "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
-              "icon": "robot"
+              "anchor": "Blog",
+              "href": "https://blog.crewai.com",
+              "icon": "newspaper"
             },
             {
-              "anchor": "Lançamentos",
-              "href": "https://github.com/crewAIInc/crewAI/releases",
-              "icon": "tag"
+              "anchor": "CrewGPT",
+              "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
+              "icon": "robot"
             }
           ]
         },
         "tabs": [
+          {
+            "tab": "Início",
+            "icon": "house",
+            "groups": [
+              {
+                "group": "Bem-vindo",
+                "pages": ["pt-BR/index"]
+              }
+            ]
+          },
           {
             "tab": "Documentação",
+            "icon": "book-open",
             "groups": [
               {
                 "group": "Começando",
@@ -405,18 +519,22 @@
                 "pages": [
                   {
                     "group": "Estratégia",
+                    "icon": "compass",
                     "pages": ["pt-BR/guides/concepts/evaluating-use-cases"]
                   },
                   {
                     "group": "Agentes",
+                    "icon": "user",
                     "pages": ["pt-BR/guides/agents/crafting-effective-agents"]
                   },
                   {
                     "group": "Crews",
+                    "icon": "users",
                     "pages": ["pt-BR/guides/crews/first-crew"]
                   },
                   {
                     "group": "Flows",
+                    "icon": "code-branch",
                     "pages": [
                       "pt-BR/guides/flows/first-flow",
                       "pt-BR/guides/flows/mastering-flow-state"
@@ -424,6 +542,7 @@
                   },
                   {
                     "group": "Avançado",
+                    "icon": "gear",
                     "pages": [
                       "pt-BR/guides/advanced/customizing-prompts",
                       "pt-BR/guides/advanced/fingerprinting"
@@ -456,6 +575,7 @@
                 "group": "Integração MCP",
                 "pages": [
                   "pt-BR/mcp/overview",
+                  "pt-BR/mcp/dsl-integration",
                   "pt-BR/mcp/stdio",
                   "pt-BR/mcp/sse",
                   "pt-BR/mcp/streamable-http",
@@ -469,6 +589,7 @@
                   "pt-BR/tools/overview",
                   {
                     "group": "Arquivo & Documento",
+                    "icon": "folder-open",
                     "pages": [
                       "pt-BR/tools/file-document/overview",
                       "pt-BR/tools/file-document/filereadtool",
@@ -486,6 +607,7 @@
                   },
                   {
                     "group": "Web Scraping & Navegação",
+                    "icon": "globe",
                     "pages": [
                       "pt-BR/tools/web-scraping/overview",
                       "pt-BR/tools/web-scraping/scrapewebsitetool",
@@ -504,6 +626,7 @@
                   },
                   {
                     "group": "Pesquisa",
+                    "icon": "magnifying-glass",
                     "pages": [
                       "pt-BR/tools/search-research/overview",
                       "pt-BR/tools/search-research/serperdevtool",
@@ -519,6 +642,7 @@
                   },
                   {
                     "group": "Dados",
+                    "icon": "database",
                     "pages": [
                       "pt-BR/tools/database-data/overview",
                       "pt-BR/tools/database-data/mysqltool",
@@ -531,6 +655,7 @@
                   },
                   {
                     "group": "IA & Machine Learning",
+                    "icon": "brain",
                     "pages": [
                       "pt-BR/tools/ai-ml/overview",
                       "pt-BR/tools/ai-ml/dalletool",
@@ -544,16 +669,26 @@
                   },
                   {
                     "group": "Cloud & Armazenamento",
+                    "icon": "cloud",
                     "pages": [
                       "pt-BR/tools/cloud-storage/overview",
                       "pt-BR/tools/cloud-storage/s3readertool",
                       "pt-BR/tools/cloud-storage/s3writertool",
-                      "pt-BR/tools/cloud-storage/bedrockinvokeagenttool",
                       "pt-BR/tools/cloud-storage/bedrockkbretriever"
                     ]
                   },
                   {
-                    "group": "Automação & Integração",
+                    "group": "Integrations",
+                    "icon": "plug",
+                    "pages": [
+                      "pt-BR/tools/integration/overview",
+                      "pt-BR/tools/integration/bedrockinvokeagenttool",
+                      "pt-BR/tools/integration/crewaiautomationtool"
+                    ]
+                  },
+                  {
+                    "group": "Automação",
+                    "icon": "bolt",
                     "pages": [
                       "pt-BR/tools/automation/overview",
                       "pt-BR/tools/automation/apifyactorstool",
@@ -568,6 +703,8 @@
                 "pages": [
                   "pt-BR/observability/overview",
                   "pt-BR/observability/arize-phoenix",
+                  "pt-BR/observability/braintrust",
+                  "pt-BR/observability/datadog",
                   "pt-BR/observability/langdb",
                   "pt-BR/observability/langfuse",
                   "pt-BR/observability/langtrace",
@@ -596,13 +733,17 @@
                   "pt-BR/learn/force-tool-output-as-result",
                   "pt-BR/learn/hierarchical-process",
                   "pt-BR/learn/human-input-on-execution",
+                  "pt-BR/learn/human-in-the-loop",
                   "pt-BR/learn/kickoff-async",
                   "pt-BR/learn/kickoff-for-each",
                   "pt-BR/learn/llm-connections",
                   "pt-BR/learn/multimodal-agents",
                   "pt-BR/learn/replay-tasks-from-latest-crew-kickoff",
                   "pt-BR/learn/sequential-process",
-                  "pt-BR/learn/using-annotations"
+                  "pt-BR/learn/using-annotations",
+                  "pt-BR/learn/execution-hooks",
+                  "pt-BR/learn/llm-hooks",
+                  "pt-BR/learn/tool-hooks"
                 ]
               },
               {
@@ -612,21 +753,35 @@
             ]
           },
           {
-            "tab": "Enterprise",
+            "tab": "AOP",
+            "icon": "briefcase",
             "groups": [
               {
                 "group": "Começando",
                 "pages": ["pt-BR/enterprise/introduction"]
               },
               {
-                "group": "Funcionalidades",
+                "group": "Construir",
+                "pages": [
+                  "pt-BR/enterprise/features/automations",
+                  "pt-BR/enterprise/features/crew-studio",
+                  "pt-BR/enterprise/features/marketplace",
+                  "pt-BR/enterprise/features/agent-repositories",
+                  "pt-BR/enterprise/features/tools-and-integrations"
+                ]
+              },
+              {
+                "group": "Operar",
                 "pages": [
-                  "pt-BR/enterprise/features/rbac",
-                  "pt-BR/enterprise/features/tool-repository",
-                  "pt-BR/enterprise/features/webhook-streaming",
                   "pt-BR/enterprise/features/traces",
-                  "pt-BR/enterprise/features/hallucination-guardrail",
-                  "pt-BR/enterprise/features/integrations"
+                  "pt-BR/enterprise/features/webhook-streaming",
+                  "pt-BR/enterprise/features/hallucination-guardrail"
+                ]
+              },
+              {
+                "group": "Gerenciar",
+                "pages": [
+                  "pt-BR/enterprise/features/rbac"
                 ]
               },
               {
@@ -638,10 +793,20 @@
                   "pt-BR/enterprise/integrations/github",
                   "pt-BR/enterprise/integrations/gmail",
                   "pt-BR/enterprise/integrations/google_calendar",
+                  "pt-BR/enterprise/integrations/google_contacts",
+                  "pt-BR/enterprise/integrations/google_docs",
+                  "pt-BR/enterprise/integrations/google_drive",
                   "pt-BR/enterprise/integrations/google_sheets",
+                  "pt-BR/enterprise/integrations/google_slides",
                   "pt-BR/enterprise/integrations/hubspot",
                   "pt-BR/enterprise/integrations/jira",
                   "pt-BR/enterprise/integrations/linear",
+                  "pt-BR/enterprise/integrations/microsoft_excel",
+                  "pt-BR/enterprise/integrations/microsoft_onedrive",
+                  "pt-BR/enterprise/integrations/microsoft_outlook",
+                  "pt-BR/enterprise/integrations/microsoft_sharepoint",
+                  "pt-BR/enterprise/integrations/microsoft_teams",
+                  "pt-BR/enterprise/integrations/microsoft_word",
                   "pt-BR/enterprise/integrations/notion",
                   "pt-BR/enterprise/integrations/salesforce",
                   "pt-BR/enterprise/integrations/shopify",
@@ -659,14 +824,26 @@
                   "pt-BR/enterprise/guides/update-crew",
                   "pt-BR/enterprise/guides/enable-crew-studio",
                   "pt-BR/enterprise/guides/azure-openai-setup",
-                  "pt-BR/enterprise/guides/automation-triggers",
-                  "pt-BR/enterprise/guides/hubspot-trigger",
+                  "pt-BR/enterprise/guides/tool-repository",
                   "pt-BR/enterprise/guides/react-component-export",
-                  "pt-BR/enterprise/guides/salesforce-trigger",
-                  "pt-BR/enterprise/guides/slack-trigger",
                   "pt-BR/enterprise/guides/team-management",
-                  "pt-BR/enterprise/guides/webhook-automation",
                   "pt-BR/enterprise/guides/human-in-the-loop",
+                  "pt-BR/enterprise/guides/webhook-automation"
+                ]
+              },
+              {
+                "group": "Triggers",
+                "pages": [
+                  "pt-BR/enterprise/guides/automation-triggers",
+                  "pt-BR/enterprise/guides/gmail-trigger",
+                  "pt-BR/enterprise/guides/google-calendar-trigger",
+                  "pt-BR/enterprise/guides/google-drive-trigger",
+                  "pt-BR/enterprise/guides/outlook-trigger",
+                  "pt-BR/enterprise/guides/onedrive-trigger",
+                  "pt-BR/enterprise/guides/microsoft-teams-trigger",
+                  "pt-BR/enterprise/guides/slack-trigger",
+                  "pt-BR/enterprise/guides/hubspot-trigger",
+                  "pt-BR/enterprise/guides/salesforce-trigger",
                   "pt-BR/enterprise/guides/zapier-trigger"
                 ]
               },
@@ -680,6 +857,7 @@
           },
           {
             "tab": "Referência da API",
+            "icon": "magnifying-glass",
             "groups": [
               {
                 "group": "Começando",
@@ -687,6 +865,7 @@
                   "pt-BR/api-reference/introduction",
                   "pt-BR/api-reference/inputs",
                   "pt-BR/api-reference/kickoff",
+                  "pt-BR/api-reference/resume",
                   "pt-BR/api-reference/status"
                 ]
               }
@@ -694,12 +873,23 @@
           },
           {
             "tab": "Exemplos",
+            "icon": "code",
             "groups": [
               {
                 "group": "Exemplos",
                 "pages": ["pt-BR/examples/example", "pt-BR/examples/cookbooks"]
               }
             ]
+          },
+          {
+            "tab": "Notas de Versão",
+            "icon": "clock",
+            "groups": [
+              {
+                "group": "Notas de Versão",
+                "pages": ["pt-BR/changelog"]
+              }
+            ]
           }
         ]
       },
@@ -718,20 +908,31 @@
               "icon": "discourse"
             },
             {
-              "anchor": "Crew GPT",
-              "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
-              "icon": "robot"
+              "anchor": "블로그",
+              "href": "https://blog.crewai.com",
+              "icon": "newspaper"
             },
             {
-              "anchor": "릴리스",
-              "href": "https://github.com/crewAIInc/crewAI/releases",
-              "icon": "tag"
+              "anchor": "CrewGPT",
+              "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
+              "icon": "robot"
             }
           ]
         },
         "tabs": [
+          {
+            "tab": "홈",
+            "icon": "house",
+            "groups": [
+              {
+                "group": "환영합니다",
+                "pages": ["ko/index"]
+              }
+            ]
+          },
           {
             "tab": "기술 문서",
+            "icon": "book-open",
             "groups": [
               {
                 "group": "시작 안내",
@@ -742,18 +943,22 @@
                 "pages": [
                   {
                     "group": "전략",
+                    "icon": "compass",
                     "pages": ["ko/guides/concepts/evaluating-use-cases"]
                   },
                   {
                     "group": "에이전트 (Agents)",
+                    "icon": "user",
                     "pages": ["ko/guides/agents/crafting-effective-agents"]
                   },
                   {
                     "group": "크루 (Crews)",
+                    "icon": "users",
                     "pages": ["ko/guides/crews/first-crew"]
                   },
                   {
                     "group": "플로우 (Flows)",
+                    "icon": "code-branch",
                     "pages": [
                       "ko/guides/flows/first-flow",
                       "ko/guides/flows/mastering-flow-state"
@@ -761,6 +966,7 @@
                   },
                   {
                     "group": "고급",
+                    "icon": "gear",
                     "pages": [
                       "ko/guides/advanced/customizing-prompts",
                       "ko/guides/advanced/fingerprinting"
@@ -793,6 +999,7 @@
                 "group": "MCP 통합",
                 "pages": [
                   "ko/mcp/overview",
+                  "ko/mcp/dsl-integration",
                   "ko/mcp/stdio",
                   "ko/mcp/sse",
                   "ko/mcp/streamable-http",
@@ -806,6 +1013,7 @@
                   "ko/tools/overview",
                   {
                     "group": "파일 & 문서",
+                    "icon": "folder-open",
                     "pages": [
                       "ko/tools/file-document/overview",
                       "ko/tools/file-document/filereadtool",
@@ -825,6 +1033,7 @@
                   },
                   {
                     "group": "웹 스크래핑 & 브라우징",
+                    "icon": "globe",
                     "pages": [
                       "ko/tools/web-scraping/overview",
                       "ko/tools/web-scraping/scrapewebsitetool",
@@ -844,6 +1053,7 @@
                   },
                   {
                     "group": "검색 및 연구",
+                    "icon": "magnifying-glass",
                     "pages": [
                       "ko/tools/search-research/overview",
                       "ko/tools/search-research/serperdevtool",
@@ -865,6 +1075,7 @@
                   },
                   {
                     "group": "데이터베이스 & 데이터",
+                    "icon": "database",
                     "pages": [
                       "ko/tools/database-data/overview",
                       "ko/tools/database-data/mysqltool",
@@ -879,6 +1090,7 @@
                   },
                   {
                     "group": "인공지능 & 머신러닝",
+                    "icon": "brain",
                     "pages": [
                       "ko/tools/ai-ml/overview",
                       "ko/tools/ai-ml/dalletool",
@@ -892,16 +1104,26 @@
                   },
                   {
                     "group": "클라우드 & 스토리지",
+                    "icon": "cloud",
                     "pages": [
                       "ko/tools/cloud-storage/overview",
                       "ko/tools/cloud-storage/s3readertool",
                       "ko/tools/cloud-storage/s3writertool",
-                      "ko/tools/cloud-storage/bedrockinvokeagenttool",
                       "ko/tools/cloud-storage/bedrockkbretriever"
                     ]
                   },
                   {
-                    "group": "자동화 & 통합",
+                    "group": "Integrations",
+                    "icon": "plug",
+                    "pages": [
+                      "ko/tools/integration/overview",
+                      "ko/tools/integration/bedrockinvokeagenttool",
+                      "ko/tools/integration/crewaiautomationtool"
+                    ]
+                  },
+                  {
+                    "group": "자동화",
+                    "icon": "bolt",
                     "pages": [
                       "ko/tools/automation/overview",
                       "ko/tools/automation/apifyactorstool",
@@ -917,6 +1139,8 @@
                 "pages": [
                   "ko/observability/overview",
                   "ko/observability/arize-phoenix",
+                  "ko/observability/braintrust",
+                  "ko/observability/datadog",
                   "ko/observability/langdb",
                   "ko/observability/langfuse",
                   "ko/observability/langtrace",
@@ -945,13 +1169,17 @@
                   "ko/learn/force-tool-output-as-result",
                   "ko/learn/hierarchical-process",
                   "ko/learn/human-input-on-execution",
+                  "ko/learn/human-in-the-loop",
                   "ko/learn/kickoff-async",
                   "ko/learn/kickoff-for-each",
                   "ko/learn/llm-connections",
                   "ko/learn/multimodal-agents",
                   "ko/learn/replay-tasks-from-latest-crew-kickoff",
                   "ko/learn/sequential-process",
-                  "ko/learn/using-annotations"
+                  "ko/learn/using-annotations",
+                  "ko/learn/execution-hooks",
+                  "ko/learn/llm-hooks",
+                  "ko/learn/tool-hooks"
                 ]
               },
               {
@@ -962,21 +1190,34 @@
           },
           {
             "tab": "엔터프라이즈",
+            "icon": "briefcase",
             "groups": [
               {
                 "group": "시작 안내",
                 "pages": ["ko/enterprise/introduction"]
               },
               {
-                "group": "특징",
+                "group": "빌드",
+                "pages": [
+                  "ko/enterprise/features/automations",
+                  "ko/enterprise/features/crew-studio",
+                  "ko/enterprise/features/marketplace",
+                  "ko/enterprise/features/agent-repositories",
+                  "ko/enterprise/features/tools-and-integrations"
+                ]
+              },
+              {
+                "group": "운영",
                 "pages": [
-                  "ko/enterprise/features/rbac",
-                  "ko/enterprise/features/tool-repository",
-                  "ko/enterprise/features/webhook-streaming",
                   "ko/enterprise/features/traces",
-                  "ko/enterprise/features/hallucination-guardrail",
-                  "ko/enterprise/features/integrations",
-                  "ko/enterprise/features/agent-repositories"
+                  "ko/enterprise/features/webhook-streaming",
+                  "ko/enterprise/features/hallucination-guardrail"
+                ]
+              },
+              {
+                "group": "관리",
+                "pages": [
+                  "ko/enterprise/features/rbac"
                 ]
               },
               {
@@ -988,10 +1229,20 @@
                   "ko/enterprise/integrations/github",
                   "ko/enterprise/integrations/gmail",
                   "ko/enterprise/integrations/google_calendar",
+                  "ko/enterprise/integrations/google_contacts",
+                  "ko/enterprise/integrations/google_docs",
+                  "ko/enterprise/integrations/google_drive",
                   "ko/enterprise/integrations/google_sheets",
+                  "ko/enterprise/integrations/google_slides",
                   "ko/enterprise/integrations/hubspot",
                   "ko/enterprise/integrations/jira",
                   "ko/enterprise/integrations/linear",
+                  "ko/enterprise/integrations/microsoft_excel",
+                  "ko/enterprise/integrations/microsoft_onedrive",
+                  "ko/enterprise/integrations/microsoft_outlook",
+                  "ko/enterprise/integrations/microsoft_sharepoint",
+                  "ko/enterprise/integrations/microsoft_teams",
+                  "ko/enterprise/integrations/microsoft_word",
                   "ko/enterprise/integrations/notion",
                   "ko/enterprise/integrations/salesforce",
                   "ko/enterprise/integrations/shopify",
@@ -1009,14 +1260,26 @@
                   "ko/enterprise/guides/update-crew",
                   "ko/enterprise/guides/enable-crew-studio",
                   "ko/enterprise/guides/azure-openai-setup",
-                  "ko/enterprise/guides/automation-triggers",
-                  "ko/enterprise/guides/hubspot-trigger",
+                  "ko/enterprise/guides/tool-repository",
                   "ko/enterprise/guides/react-component-export",
-                  "ko/enterprise/guides/salesforce-trigger",
-                  "ko/enterprise/guides/slack-trigger",
                   "ko/enterprise/guides/team-management",
-                  "ko/enterprise/guides/webhook-automation",
                   "ko/enterprise/guides/human-in-the-loop",
+                  "ko/enterprise/guides/webhook-automation"
+                ]
+              },
+              {
+                "group": "트리거",
+                "pages": [
+                  "ko/enterprise/guides/automation-triggers",
+                  "ko/enterprise/guides/gmail-trigger",
+                  "ko/enterprise/guides/google-calendar-trigger",
+                  "ko/enterprise/guides/google-drive-trigger",
+                  "ko/enterprise/guides/outlook-trigger",
+                  "ko/enterprise/guides/onedrive-trigger",
+                  "ko/enterprise/guides/microsoft-teams-trigger",
+                  "ko/enterprise/guides/slack-trigger",
+                  "ko/enterprise/guides/hubspot-trigger",
+                  "ko/enterprise/guides/salesforce-trigger",
                   "ko/enterprise/guides/zapier-trigger"
                 ]
               },
@@ -1028,6 +1291,7 @@
           },
           {
             "tab": "API 레퍼런스",
+            "icon": "magnifying-glass",
             "groups": [
               {
                 "group": "시작 안내",
@@ -1035,6 +1299,7 @@
                   "ko/api-reference/introduction",
                   "ko/api-reference/inputs",
                   "ko/api-reference/kickoff",
+                  "ko/api-reference/resume",
                   "ko/api-reference/status"
                 ]
               }
@@ -1042,12 +1307,23 @@
           },
           {
             "tab": "예시",
+            "icon": "code",
             "groups": [
               {
                 "group": "예시",
                 "pages": ["ko/examples/example", "ko/examples/cookbooks"]
               }
             ]
+          },
+          {
+            "tab": "변경 로그",
+            "icon": "clock",
+            "groups": [
+              {
+                "group": "릴리스 노트",
+                "pages": ["ko/changelog"]
+              }
+            ]
           }
         ]
       }
@@ -1057,15 +1333,23 @@
     "light": "/images/crew_only_logo.png",
     "dark": "/images/crew_only_logo.png"
   },
+  "fonts": {
+    "family": "Inter"
+  },
   "appearance": {
-    "default": "dark",
-    "strict": false
+    "default": "system",
+    "strict": false,
+    "layout": "sidenav"
+  },
+  "background": {
+    "decoration": "grid"
   },
   "navbar": {
     "links": [
       {
         "label": "Start Cloud Trial",
-        "href": "https://app.crewai.com"
+        "href": "https://app.crewai.com",
+        "icon": "arrow-up-right-from-square"
       }
     ],
     "primary": {
@@ -1084,7 +1368,20 @@
     }
   },
   "seo": {
-    "indexing": "all"
+    "indexing": "all",
+    "metatags": {
+      "og:type": "website",
+      "og:site_name": "CrewAI Documentation",
+      "og:image": "https://docs.crewai.com/images/crew_only_logo.png",
+      "twitter:card": "summary_large_image",
+      "twitter:site": "@crewAIInc",
+      "keywords": "AI agents, multi-agent systems, CrewAI, artificial intelligence, automation, Python framework, agent collaboration, AI workflows"
+    }
+  },
+  "feedback": {
+    "enabled": true,
+    "thumbsRating": true,
+    "suggestEdit": true
   },
   "redirects": [
     {
@@ -1105,7 +1402,7 @@
     },
     {
       "source": "/changelog",
-      "destination": "https://github.com/crewAIInc/crewAI/releases"
+      "destination": "/en/changelog"
     },
     {
       "source": "/telemetry",

docs/en/api-reference/inputs.mdx

@@ -2,6 +2,7 @@
 title: "GET /inputs"
 description: "Get required inputs for your crew"
 openapi: "/enterprise-api.en.yaml GET /inputs"
+mode: "wide"
 ---
 
 

docs/en/api-reference/introduction.mdx

@@ -1,28 +1,29 @@
 ---
 title: "Introduction"
-description: "Complete reference for the CrewAI Enterprise REST API"
+description: "Complete reference for the CrewAI AOP REST API"
 icon: "code"
+mode: "wide"
 ---
 
-# CrewAI Enterprise API
+# CrewAI AOP API
 
-Welcome to the CrewAI Enterprise API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
+Welcome to the CrewAI AOP API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
 
 ## Quick Start
 
 <Steps>
   <Step title="Get Your API Credentials">
-    Navigate to your crew's detail page in the CrewAI Enterprise dashboard and copy your Bearer Token from the Status tab.
+    Navigate to your crew's detail page in the CrewAI AOP 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>
@@ -45,7 +46,7 @@ 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 Enterprise dashboard.
+You can find both token types in the Status tab of your crew's detail page in the CrewAI AOP dashboard.
 </Tip>
 
 ## Base URL
@@ -61,7 +62,7 @@ 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
 
@@ -81,12 +82,12 @@ The API uses standard HTTP status codes:
 ## Interactive Testing
 
 <Info>
-**Why no "Send" button?** Since each CrewAI Enterprise user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
+**Why no "Send" button?** Since each CrewAI AOP 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
 
@@ -103,7 +104,7 @@ 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/en/api-reference/kickoff.mdx

@@ -2,6 +2,7 @@
 title: "POST /kickoff"
 description: "Start a crew execution"
 openapi: "/enterprise-api.en.yaml POST /kickoff"
+mode: "wide"
 ---
 
 

docs/en/api-reference/resume.mdx

@@ -0,0 +1,6 @@
+---
+title: "POST /resume"
+description: "Resume crew execution with human feedback"
+openapi: "/enterprise-api.en.yaml POST /resume"
+mode: "wide"
+---

docs/en/api-reference/status.mdx

@@ -2,6 +2,7 @@
 title: "GET /status/{kickoff_id}"
 description: "Get execution status"
 openapi: "/enterprise-api.en.yaml GET /status/{kickoff_id}"
+mode: "wide"
 ---
 
 

docs/en/concepts/agents.mdx

@@ -2,6 +2,7 @@
 title: Agents
 description: Detailed guide on creating and managing agents within the CrewAI framework.
 icon: robot
+mode: "wide"
 ---
 
 ## Overview of an Agent
@@ -19,7 +20,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 </Tip>
 
 <Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
-CrewAI 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.
+CrewAI AOP 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)
 

docs/en/concepts/cli.mdx

@@ -2,9 +2,10 @@
 title: CLI
 description: Learn how to use the CrewAI CLI to interact with CrewAI.
 icon: terminal
+mode: "wide"
 ---
 
-<Warning>Since release 0.140.0, CrewAI Enterprise started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
+<Warning>Since release 0.140.0, CrewAI AOP 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
 
@@ -185,9 +186,9 @@ def crew(self) -> Crew:
 
 ### 10. Deploy
 
-Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
+Deploy the crew or flow to [CrewAI AOP](https://app.crewai.com).
 
-- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
+- **Authentication**: You need to be authenticated to deploy to CrewAI AOP.
     You can login or create an account with:
     ```shell Terminal
     crewai login
@@ -202,7 +203,7 @@ Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
 
 ### 11. Organization Management
 
-Manage your CrewAI Enterprise organizations.
+Manage your CrewAI AOP organizations.
 
 ```shell Terminal
 crewai org [COMMAND] [OPTIONS]
@@ -226,17 +227,17 @@ crewai org switch <organization_id>
 ```
 
 <Note>
-You must be authenticated to CrewAI Enterprise to use these organization management commands.
+You must be authenticated to CrewAI AOP to use these organization management commands.
 </Note>
 
 - **Create a deployment** (continued):
     - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
 
-- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
+- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI AOP.
     ```shell Terminal
     crewai deploy push
     ```
-    - Initiates the deployment process on the CrewAI Enterprise platform.
+    - Initiates the deployment process on the CrewAI AOP 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:
@@ -261,7 +262,7 @@ You must be authenticated to CrewAI Enterprise to use these organization managem
     ```shell Terminal
     crewai deploy remove
     ```
-    This deletes the deployment from the CrewAI Enterprise platform.
+    This deletes the deployment from the CrewAI AOP platform.
 
 - **Help Command**: You can get help with the CLI with:
     ```shell Terminal
@@ -269,20 +270,36 @@ You must be authenticated to CrewAI Enterprise to use these organization managem
     ```
     This shows the help message for the CrewAI Deploy CLI.
 
-Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
+Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AOP](http://app.crewai.com) using the CLI.
 
 <iframe
-  width="100%"
-  height="400"
+  className="w-full aspect-video rounded-xl"
   src="https://www.youtube.com/embed/3EqSV-CYDZA"
   title="CrewAI Deployment Guide"
-  frameborder="0"
-  style={{ borderRadius: '10px' }}
+  frameBorder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowfullscreen
+  allowFullScreen
 ></iframe>
 
-### 11. API Keys
+### 11. Login
+
+Authenticate with CrewAI AOP using a secure device code flow (no email entry required).
+
+```shell Terminal
+crewai login
+```
+
+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
+
+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.
 
@@ -310,7 +327,7 @@ See the following link for each provider's key name:
 
 * [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
 
-### 12. Configuration Management
+### 13. Configuration Management
 
 Manage CLI configuration settings for CrewAI.
 
@@ -337,7 +354,7 @@ crewai config reset
 
 #### Available Configuration Parameters
 
-- `enterprise_base_url`: Base URL of the CrewAI Enterprise instance
+- `enterprise_base_url`: Base URL of the CrewAI AOP 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
@@ -351,19 +368,15 @@ crewai config list
 ```
 
 Example output:
-```
-CrewAI CLI Configuration
-┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
-┃ Setting            ┃ Value                              ┃ Description                                                                                      ┃
-┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
-│ enterprise_base_url│ https://app.crewai.com             │ Base URL of the CrewAI Enterprise instance                                                       │
-│ org_name           │ Not set                            │ Name of the currently active organization                                                        │
-│ org_uuid           │ Not set                            │ UUID of the currently active organization                                                        │
-│ oauth2_provider    │ workos                             │ OAuth2 provider used for authentication (e.g., workos, okta, auth0).                             │
-│ oauth2_audience    │ client_01YYY                       │ OAuth2 audience value, typically used to identify the target API or resource.                    │
-│ oauth2_client_id   │ client_01XXX                       │ OAuth2 client ID issued by the provider, used during authentication requests.                    │
-│ oauth2_domain      │ login.crewai.com                   │ OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens.                     │
-```
+| Setting             | Value                    | Description                                                 |
+| :------------------ | :----------------------- | :---------------------------------------------------------- |
+| enterprise_base_url | https://app.crewai.com   | Base URL of the CrewAI AOP 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
@@ -385,6 +398,85 @@ Reset all configuration to defaults:
 crewai config reset
 ```
 
+<Tip>
+After resetting configuration, re-run `crewai login` to authenticate again.
+</Tip>
+
+### 14. Trace Management
+
+Manage trace collection preferences for your Crew and Flow executions.
+
+```shell Terminal
+crewai traces [COMMAND]
+```
+
+#### Commands:
+
+- `enable`: Enable trace collection for crew/flow executions
+```shell Terminal
+crewai traces enable
+```
+
+- `disable`: Disable trace collection for crew/flow executions
+```shell Terminal
+crewai traces disable
+```
+
+- `status`: Show current trace collection status
+```shell Terminal
+crewai traces status
+```
+
+#### How Tracing Works
+
+Trace collection is controlled by checking three settings in priority order:
+
+1. **Explicit flag in code** (highest priority - can enable OR disable):
+   ```python
+   crew = Crew(agents=[...], tasks=[...], tracing=True)   # Always enable
+   crew = Crew(agents=[...], tasks=[...], tracing=False)  # Always disable
+   crew = Crew(agents=[...], tasks=[...])                 # Check lower priorities (default)
+   ```
+   - `tracing=True` will **always enable** tracing (overrides everything)
+   - `tracing=False` will **always disable** tracing (overrides everything)
+   - `tracing=None` or omitted will check lower priority settings
+
+2. **Environment variable** (second priority):
+   ```env
+   CREWAI_TRACING_ENABLED=true
+   ```
+   - Checked only if `tracing` is not explicitly set to `True` or `False` in code
+   - Set to `true` or `1` to enable tracing
+
+3. **User preference** (lowest priority):
+   ```shell Terminal
+   crewai traces enable
+   ```
+   - Checked only if `tracing` is not set in code and `CREWAI_TRACING_ENABLED` is not set to `true`
+   - Running `crewai traces enable` is sufficient to enable tracing by itself
+
+<Note>
+**To enable tracing**, use any one of these methods:
+- Set `tracing=True` in your Crew/Flow code, OR
+- Add `CREWAI_TRACING_ENABLED=true` to your `.env` file, OR
+- Run `crewai traces enable`
+
+**To disable tracing**, use any ONE of these methods:
+- Set `tracing=False` in your Crew/Flow code (overrides everything), OR
+- Remove or set to `false` the `CREWAI_TRACING_ENABLED` env var, OR
+- Run `crewai traces disable`
+
+Higher priority settings override lower ones.
+</Note>
+
+<Tip>
+For more information about tracing, see the [Tracing documentation](/observability/tracing).
+</Tip>
+
+<Tip>
+CrewAI CLI handles authentication to the Tool Repository automatically when adding packages to your project. Just append `crewai` before any `uv` command to use it. E.g. `crewai uv add requests`. For more information, see [Tool Repository](https://docs.crewai.com/enterprise/features/tool-repository) docs.
+</Tip>
+
 <Note>
 Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
 </Note>

docs/en/concepts/collaboration.mdx

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

docs/en/concepts/crews.mdx

@@ -2,6 +2,7 @@
 title: Crews
 description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
 icon: people-group
+mode: "wide"
 ---
 
 ## Overview
@@ -32,6 +33,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **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.                                                                                                                                                                                    |
+| **Stream** _(optional)_               | `stream`               | Enable streaming output to receive real-time updates during crew execution. Returns a `CrewStreamingOutput` object that can be iterated for chunks. Defaults to `False`.                                                                                    |
 
 <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.
@@ -337,6 +339,29 @@ for async_result in async_results:
 
 These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs.
 
+### Streaming Crew Execution
+
+For real-time visibility into crew execution, you can enable streaming to receive output as it's generated:
+
+```python Code
+# Enable streaming
+crew = Crew(
+    agents=[researcher],
+    tasks=[task],
+    stream=True
+)
+
+# Iterate over streaming output
+streaming = crew.kickoff(inputs={"topic": "AI"})
+for chunk in streaming:
+    print(chunk.content, end="", flush=True)
+
+# Access final result
+result = streaming.result
+```
+
+Learn more about streaming in the [Streaming Crew Execution](/en/learn/streaming-crew-execution) guide.
+
 ### Replaying from a Specific Task
 
 You can now replay from a specific task using our CLI command `replay`.

docs/en/concepts/event-listener.mdx

@@ -2,6 +2,7 @@
 title: 'Event Listeners'
 description: 'Tap into CrewAI events to build custom integrations and monitoring'
 icon: spinner
+mode: "wide"
 ---
 
 ## Overview
@@ -19,7 +20,7 @@ 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 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.
+CrewAI AOP 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)
 
@@ -44,12 +45,12 @@ To create a custom event listener, you need to:
 Here's a simple example of a custom event listener class:
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     CrewKickoffStartedEvent,
     CrewKickoffCompletedEvent,
     AgentExecutionCompletedEvent,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 
 class MyCustomListener(BaseEventListener):
     def __init__(self):
@@ -146,7 +147,7 @@ my_project/
 
 ```python
 # my_custom_listener.py
-from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.events import BaseEventListener
 # ... import events ...
 
 class MyCustomListener(BaseEventListener):
@@ -279,7 +280,7 @@ Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` i
 For temporary event handling (useful for testing or specific operations), you can use the `scoped_handlers` context manager:
 
 ```python
-from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
+from crewai.events import crewai_event_bus, CrewKickoffStartedEvent
 
 with crewai_event_bus.scoped_handlers():
     @crewai_event_bus.on(CrewKickoffStartedEvent)

docs/en/concepts/flows.mdx

@@ -2,6 +2,7 @@
 title: Flows
 description: Learn how to create and manage AI workflows using CrewAI Flows.
 icon: arrow-progress
+mode: "wide"
 ---
 
 ## Overview
@@ -97,7 +98,13 @@ The state's unique ID and stored data can be useful for tracking flow executions
 
 ### @start()
 
-The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
+The `@start()` decorator marks entry points for a Flow. You can:
+
+- Declare multiple unconditional starts: `@start()`
+- Gate a start on a prior method or router label: `@start("method_or_label")`
+- Provide a callable condition to control when a start should fire
+
+All satisfied `@start()` methods will execute (often in parallel) when the Flow begins or resumes.
 
 ### @listen()
 
@@ -868,14 +875,13 @@ 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
-  width="560"
-  height="315"
+  className="w-full aspect-video rounded-xl"
   src="https://www.youtube.com/embed/MTb5my6VOT8"
-  title="YouTube video player"
-  frameborder="0"
+  title="CrewAI Flows overview"
+  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
@@ -891,6 +897,31 @@ flow = ExampleFlow()
 result = flow.kickoff()
 ```
 
+### Streaming Flow Execution
+
+For real-time visibility into flow execution, you can enable streaming to receive output as it's generated:
+
+```python
+class StreamingFlow(Flow):
+    stream = True  # Enable streaming
+
+    @start()
+    def research(self):
+        # Your flow implementation
+        pass
+
+# Iterate over streaming output
+flow = StreamingFlow()
+streaming = flow.kickoff()
+for chunk in streaming:
+    print(chunk.content, end="", flush=True)
+
+# Access final result
+result = streaming.result
+```
+
+Learn more about streaming in the [Streaming Flow Execution](/en/learn/streaming-flow-execution) guide.
+
 ### Using the CLI
 
 Starting from version 0.103.0, you can run flows using the `crewai run` command:

docs/en/concepts/knowledge.mdx

@@ -2,6 +2,7 @@
 title: Knowledge
 description: What is knowledge in CrewAI and how to use it.
 icon: book
+mode: "wide"
 ---
 
 ## Overview
@@ -24,6 +25,41 @@ For file-based Knowledge Sources, make sure to place your files in a `knowledge`
 Also, use relative paths from the `knowledge` directory when creating the source.
 </Tip>
 
+### Vector store (RAG) client configuration
+
+CrewAI exposes a provider-neutral RAG client abstraction for vector stores. The default provider is ChromaDB, and Qdrant is supported as well. You can switch providers using configuration utilities.
+
+Supported today:
+- ChromaDB (default)
+- Qdrant
+
+```python Code
+from crewai.rag.config.utils import set_rag_config, get_rag_client, clear_rag_config
+
+# ChromaDB (default)
+from crewai.rag.chromadb.config import ChromaDBConfig
+set_rag_config(ChromaDBConfig())
+chromadb_client = get_rag_client()
+
+# Qdrant
+from crewai.rag.qdrant.config import QdrantConfig
+set_rag_config(QdrantConfig())
+qdrant_client = get_rag_client()
+
+# Example operations (same API for any provider)
+client = qdrant_client  # or chromadb_client
+client.create_collection(collection_name="docs")
+client.add_documents(
+    collection_name="docs",
+    documents=[{"id": "1", "content": "CrewAI enables collaborative AI agents."}],
+)
+results = client.search(collection_name="docs", query="collaborative agents", limit=3)
+
+clear_rag_config()  # optional reset
+```
+
+This RAG client is separate from Knowledge’s built-in storage. Use it when you need direct vector-store control or custom retrieval pipelines.
+
 ### Basic String Knowledge Example
 
 ```python Code
@@ -352,8 +388,8 @@ crew = Crew(
     agents=[sales_agent, tech_agent, support_agent],
     tasks=[...],
     embedder={  # Fallback embedder for agents without their own
-        "provider": "google",
-        "config": {"model": "text-embedding-004"}
+        "provider": "google-generativeai",
+        "config": {"model_name": "gemini-embedding-001"}
     }
 )
 
@@ -593,9 +629,9 @@ agent = Agent(
     backstory="Expert researcher",
     knowledge_sources=[knowledge_source],
     embedder={
-        "provider": "google",
+        "provider": "google-generativeai",
         "config": {
-            "model": "models/text-embedding-004",
+            "model_name": "gemini-embedding-001",
             "api_key": "your-google-key"
         }
     }
@@ -681,11 +717,11 @@ CrewAI emits events during the knowledge retrieval process that you can listen f
 #### Example: Monitoring Knowledge Retrieval
 
 ```python
-from crewai.utilities.events import (
+from crewai.events import (
     KnowledgeRetrievalStartedEvent,
     KnowledgeRetrievalCompletedEvent,
+    BaseEventListener,
 )
-from crewai.utilities.events.base_event_listener import BaseEventListener
 
 class KnowledgeMonitorListener(BaseEventListener):
     def setup_listeners(self, crewai_event_bus):
@@ -703,7 +739,7 @@ class KnowledgeMonitorListener(BaseEventListener):
 knowledge_monitor = KnowledgeMonitorListener()
 ```
 
-For more information on using events, see the [Event Listeners](https://docs.crewai.com/concepts/event-listener) documentation.
+For more information on using events, see the [Event Listeners](/en/concepts/event-listener) documentation.
 
 ### Custom Knowledge Sources
 

docs/en/concepts/llms.mdx

@@ -2,11 +2,12 @@
 title: 'LLMs'
 description: 'A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects'
 icon: 'microchip-ai'
+mode: "wide"
 ---
 
 ## Overview
 
-CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
+CrewAI integrates with multiple LLM providers through providers native sdks, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
 
 
 ## What are LLMs?
@@ -112,44 +113,104 @@ In this section, you'll find detailed examples that help you select, configure,
 
 <AccordionGroup>
   <Accordion title="OpenAI">
-    Set the following environment variables in your `.env` file:
+    CrewAI provides native integration with OpenAI through the OpenAI Python SDK.
 
     ```toml Code
     # Required
     OPENAI_API_KEY=sk-...
 
     # Optional
-    OPENAI_API_BASE=<custom-base-url>
-    OPENAI_ORGANIZATION=<your-org-id>
+    OPENAI_BASE_URL=<custom-base-url>
     ```
 
-    Example usage in your CrewAI project:
+    **Basic Usage:**
     ```python Code
     from crewai import LLM
 
     llm = LLM(
-        model="openai/gpt-4", # call model by provider/model_name
-        temperature=0.8,
-        max_tokens=150,
+        model="openai/gpt-4o",
+        api_key="your-api-key",  # Or set OPENAI_API_KEY
+        temperature=0.7,
+        max_tokens=4000
+    )
+    ```
+
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="openai/gpt-4o",
+        api_key="your-api-key",
+        base_url="https://api.openai.com/v1",  # Optional custom endpoint
+        organization="org-...",  # Optional organization ID
+        project="proj_...",  # Optional project ID
+        temperature=0.7,
+        max_tokens=4000,
+        max_completion_tokens=4000,  # For newer models
         top_p=0.9,
         frequency_penalty=0.1,
         presence_penalty=0.1,
         stop=["END"],
-        seed=42
+        seed=42,  # For reproducible outputs
+        stream=True,  # Enable streaming
+        timeout=60.0,  # Request timeout in seconds
+        max_retries=3,  # Maximum retry attempts
+        logprobs=True,  # Return log probabilities
+        top_logprobs=5,  # Number of most likely tokens
+        reasoning_effort="medium"  # For o1 models: low, medium, high
     )
     ```
 
-    OpenAI is one of the leading providers of LLMs with a wide range of models and features.
+    **Structured Outputs:**
+    ```python Code
+    from pydantic import BaseModel
+    from crewai import LLM
+
+    class ResponseFormat(BaseModel):
+        name: str
+        age: int
+        summary: str
+
+    llm = LLM(
+        model="openai/gpt-4o",
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `OPENAI_API_KEY`: Your OpenAI API key (required)
+    - `OPENAI_BASE_URL`: Custom base URL for OpenAI API (optional)
+
+    **Features:**
+    - Native function calling support (except o1 models)
+    - Structured outputs with JSON schema
+    - Streaming support for real-time responses
+    - Token usage tracking
+    - Stop sequences support (except o1 models)
+    - Log probabilities for token-level insights
+    - Reasoning effort control for o1 models
+
+    **Supported Models:**
 
     | Model               | Context Window   | Best For                                      |
     |---------------------|------------------|-----------------------------------------------|
-    | GPT-4               | 8,192 tokens     | High-accuracy tasks, complex reasoning        |
-    | GPT-4 Turbo         | 128,000 tokens   | Long-form content, document analysis          |
-    | GPT-4o & GPT-4o-mini  | 128,000 tokens  | Cost-effective large context processing       |
-    | o3-mini             | 200,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1-mini             | 128,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1-preview          | 128,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1                  | 200,000 tokens   | Fast reasoning, complex reasoning             |
+    | gpt-4.1             | 1M tokens        | Latest model with enhanced capabilities       |
+    | gpt-4.1-mini        | 1M tokens        | Efficient version with large context          |
+    | gpt-4.1-nano        | 1M tokens        | Ultra-efficient variant                       |
+    | gpt-4o              | 128,000 tokens   | Optimized for speed and intelligence          |
+    | gpt-4o-mini         | 200,000 tokens   | Cost-effective with large context             |
+    | gpt-4-turbo         | 128,000 tokens   | Long-form content, document analysis          |
+    | gpt-4               | 8,192 tokens     | High-accuracy tasks, complex reasoning        |
+    | o1                  | 200,000 tokens   | Advanced reasoning, complex problem-solving   |
+    | o1-preview          | 128,000 tokens   | Preview of reasoning capabilities             |
+    | o1-mini             | 128,000 tokens   | Efficient reasoning model                     |
+    | o3-mini             | 200,000 tokens   | Lightweight reasoning model                   |
+    | o4-mini             | 200,000 tokens   | Next-gen efficient reasoning                  |
+
+    **Note:** To use OpenAI, install the required dependencies:
+    ```bash
+    uv add "crewai[openai]"
+    ```
   </Accordion>
 
   <Accordion title="Meta-Llama">
@@ -186,69 +247,186 @@ In this section, you'll find detailed examples that help you select, configure,
   </Accordion>
 
   <Accordion title="Anthropic">
+    CrewAI provides native integration with Anthropic through the Anthropic Python SDK.
+
     ```toml Code
     # Required
     ANTHROPIC_API_KEY=sk-ant-...
-
-    # Optional
-    ANTHROPIC_API_BASE=<custom-base-url>
     ```
 
-    Example usage in your CrewAI project:
+    **Basic Usage:**
     ```python Code
+    from crewai import LLM
+
     llm = LLM(
-        model="anthropic/claude-3-sonnet-20240229-v1:0",
-        temperature=0.7
+        model="anthropic/claude-3-5-sonnet-20241022",
+        api_key="your-api-key",  # Or set ANTHROPIC_API_KEY
+        max_tokens=4096  # Required for Anthropic
     )
     ```
+
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="anthropic/claude-3-5-sonnet-20241022",
+        api_key="your-api-key",
+        base_url="https://api.anthropic.com",  # Optional custom endpoint
+        temperature=0.7,
+        max_tokens=4096,  # Required parameter
+        top_p=0.9,
+        stop_sequences=["END", "STOP"],  # Anthropic uses stop_sequences
+        stream=True,  # Enable streaming
+        timeout=60.0,  # Request timeout in seconds
+        max_retries=3  # Maximum retry attempts
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `ANTHROPIC_API_KEY`: Your Anthropic API key (required)
+
+    **Features:**
+    - Native tool use support for Claude 3+ models
+    - Streaming support for real-time responses
+    - Automatic system message handling
+    - Stop sequences for controlled output
+    - Token usage tracking
+    - Multi-turn tool use conversations
+
+    **Important Notes:**
+    - `max_tokens` is a **required** parameter for all Anthropic models
+    - Claude uses `stop_sequences` instead of `stop`
+    - System messages are handled separately from conversation messages
+    - First message must be from the user (automatically handled)
+    - Messages must alternate between user and assistant
+
+    **Supported Models:**
+
+    | Model                        | Context Window | Best For                                      |
+    |------------------------------|----------------|-----------------------------------------------|
+    | claude-3-7-sonnet            | 200,000 tokens | Advanced reasoning and agentic tasks          |
+    | claude-3-5-sonnet-20241022   | 200,000 tokens | Latest Sonnet with best performance           |
+    | claude-3-5-haiku             | 200,000 tokens | Fast, compact model for quick responses       |
+    | claude-3-opus                | 200,000 tokens | Most capable for complex tasks                |
+    | claude-3-sonnet              | 200,000 tokens | Balanced intelligence and speed               |
+    | claude-3-haiku               | 200,000 tokens | Fastest for simple tasks                      |
+    | claude-2.1                   | 200,000 tokens | Extended context, reduced hallucinations      |
+    | claude-2                     | 100,000 tokens | Versatile model for various tasks             |
+    | claude-instant               | 100,000 tokens | Fast, cost-effective for everyday tasks       |
+
+    **Note:** To use Anthropic, install the required dependencies:
+    ```bash
+    uv add "crewai[anthropic]"
+    ```
   </Accordion>
 
   <Accordion title="Google (Gemini API)">
-    Set your API key in your `.env` file. If you need a key, or need to find an
-    existing key, check [AI Studio](https://aistudio.google.com/apikey).
+    CrewAI provides native integration with Google Gemini through the Google Gen AI Python SDK.
+
+    Set your API key in your `.env` file. If you need a key, check [AI Studio](https://aistudio.google.com/apikey).
 
     ```toml .env
-    # https://ai.google.dev/gemini-api/docs/api-key
+    # Required (one of the following)
+    GOOGLE_API_KEY=<your-api-key>
     GEMINI_API_KEY=<your-api-key>
+
+    # Optional - for Vertex AI
+    GOOGLE_CLOUD_PROJECT=<your-project-id>
+    GOOGLE_CLOUD_LOCATION=<location>  # Defaults to us-central1
+    GOOGLE_GENAI_USE_VERTEXAI=true  # Set to use Vertex AI
     ```
 
-    Example usage in your CrewAI project:
+    **Basic Usage:**
     ```python Code
     from crewai import LLM
 
     llm = LLM(
         model="gemini/gemini-2.0-flash",
-        temperature=0.7,
+        api_key="your-api-key",  # Or set GOOGLE_API_KEY/GEMINI_API_KEY
+        temperature=0.7
     )
     ```
 
-    ### Gemini models
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-2.5-flash",
+        api_key="your-api-key",
+        temperature=0.7,
+        top_p=0.9,
+        top_k=40,  # Top-k sampling parameter
+        max_output_tokens=8192,
+        stop_sequences=["END", "STOP"],
+        stream=True,  # Enable streaming
+        safety_settings={
+            "HARM_CATEGORY_HARASSMENT": "BLOCK_NONE",
+            "HARM_CATEGORY_HATE_SPEECH": "BLOCK_NONE"
+        }
+    )
+    ```
+
+    **Vertex AI Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-1.5-pro",
+        project="your-gcp-project-id",
+        location="us-central1"  # GCP region
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `GOOGLE_API_KEY` or `GEMINI_API_KEY`: Your Google API key (required for Gemini API)
+    - `GOOGLE_CLOUD_PROJECT`: Google Cloud project ID (for Vertex AI)
+    - `GOOGLE_CLOUD_LOCATION`: GCP location (defaults to `us-central1`)
+    - `GOOGLE_GENAI_USE_VERTEXAI`: Set to `true` to use Vertex AI
+
+    **Features:**
+    - Native function calling support for Gemini 1.5+ and 2.x models
+    - Streaming support for real-time responses
+    - Multimodal capabilities (text, images, video)
+    - Safety settings configuration
+    - Support for both Gemini API and Vertex AI
+    - Automatic system instruction handling
+    - Token usage tracking
+
+    **Gemini Models:**
 
     Google offers a range of powerful models optimized for different use cases.
 
     | Model                          | Context Window | Best For                                                          |
     |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
+    | gemini-2.5-flash               | 1M tokens      | Adaptive thinking, cost efficiency                                |
+    | gemini-2.5-pro                 | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding         |
+    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking                         |
+    | gemini-2.0-flash-thinking      | 32,768 tokens  | Advanced reasoning with thinking process                          |
     | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
+    | gemini-1.5-pro                 | 2M tokens      | Best performing, logical reasoning, coding                        |
     | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+    | gemini-1.5-flash-8b            | 1M tokens      | Fastest, most cost-efficient                                      |
+    | gemini-1.0-pro                 | 32,768 tokens  | Earlier generation model                                          |
+
+    **Gemma Models:**
+
+    The Gemini API also supports [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
+
+    | Model          | Context Window | Best For                           |
+    |----------------|----------------|------------------------------------|
+    | gemma-3-1b     | 32,000 tokens  | Ultra-lightweight tasks            |
+    | gemma-3-4b     | 128,000 tokens | Efficient general-purpose tasks    |
+    | gemma-3-12b    | 128,000 tokens | Balanced performance and efficiency|
+    | gemma-3-27b    | 128,000 tokens | High-performance tasks             |
+
+    **Note:** To use Google Gemini, install the required dependencies:
+    ```bash
+    uv add "crewai[google-genai]"
+    ```
 
     The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
-
-    ### Gemma
-
-    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
-
-    | Model          | Context Window |
-    |----------------|----------------|
-    | gemma-3-1b-it  | 32k tokens     |
-    | gemma-3-4b-it  | 32k tokens     |
-    | gemma-3-12b-it | 32k tokens     |
-    | gemma-3-27b-it | 128k tokens    |
-
   </Accordion>
   <Accordion title="Google (Vertex AI)">
     Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
@@ -290,43 +468,146 @@ In this section, you'll find detailed examples that help you select, configure,
   </Accordion>
 
   <Accordion title="Azure">
+    CrewAI provides native integration with Azure AI Inference and Azure OpenAI through the Azure AI Inference Python SDK.
+
     ```toml Code
     # Required
     AZURE_API_KEY=<your-api-key>
-    AZURE_API_BASE=<your-resource-url>
-    AZURE_API_VERSION=<api-version>
+    AZURE_ENDPOINT=<your-endpoint-url>
 
     # Optional
-    AZURE_AD_TOKEN=<your-azure-ad-token>
-    AZURE_API_TYPE=<your-azure-api-type>
+    AZURE_API_VERSION=<api-version>  # Defaults to 2024-06-01
     ```
 
-    Example usage in your CrewAI project:
+    **Endpoint URL Formats:**
+
+    For Azure OpenAI deployments:
+    ```
+    https://<resource-name>.openai.azure.com/openai/deployments/<deployment-name>
+    ```
+
+    For Azure AI Inference endpoints:
+    ```
+    https://<resource-name>.inference.azure.com
+    ```
+
+    **Basic Usage:**
     ```python Code
     llm = LLM(
         model="azure/gpt-4",
-        api_version="2023-05-15"
+        api_key="<your-api-key>",  # Or set AZURE_API_KEY
+        endpoint="<your-endpoint-url>",
+        api_version="2024-06-01"
     )
     ```
+
+    **Advanced Configuration:**
+    ```python Code
+    llm = LLM(
+        model="azure/gpt-4o",
+        temperature=0.7,
+        max_tokens=4000,
+        top_p=0.9,
+        frequency_penalty=0.0,
+        presence_penalty=0.0,
+        stop=["END"],
+        stream=True,
+        timeout=60.0,
+        max_retries=3
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `AZURE_API_KEY`: Your Azure API key (required)
+    - `AZURE_ENDPOINT`: Your Azure endpoint URL (required, also checks `AZURE_OPENAI_ENDPOINT` and `AZURE_API_BASE`)
+    - `AZURE_API_VERSION`: API version (optional, defaults to `2024-06-01`)
+
+    **Features:**
+    - Native function calling support for Azure OpenAI models (gpt-4, gpt-4o, gpt-3.5-turbo, etc.)
+    - Streaming support for real-time responses
+    - Automatic endpoint URL validation and correction
+    - Comprehensive error handling with retry logic
+    - Token usage tracking
+
+    **Note:** To use Azure AI Inference, install the required dependencies:
+    ```bash
+    uv add "crewai[azure-ai-inference]"
+    ```
   </Accordion>
 
   <Accordion title="AWS Bedrock">
+    CrewAI provides native integration with AWS Bedrock through the boto3 SDK using the Converse API.
+
     ```toml Code
+    # Required
     AWS_ACCESS_KEY_ID=<your-access-key>
     AWS_SECRET_ACCESS_KEY=<your-secret-key>
-    AWS_DEFAULT_REGION=<your-region>
+
+    # Optional
+    AWS_SESSION_TOKEN=<your-session-token>  # For temporary credentials
+    AWS_DEFAULT_REGION=<your-region>  # Defaults to us-east-1
     ```
 
-    Example usage in your CrewAI project:
+    **Basic Usage:**
     ```python Code
+    from crewai import LLM
+
     llm = LLM(
-        model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
+        model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
+        region_name="us-east-1"
     )
     ```
 
-    Before using Amazon Bedrock, make sure you have boto3 installed in your environment
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
 
-    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API, enabling secure and responsible AI application development.
+    llm = LLM(
+        model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
+        aws_access_key_id="your-access-key",  # Or set AWS_ACCESS_KEY_ID
+        aws_secret_access_key="your-secret-key",  # Or set AWS_SECRET_ACCESS_KEY
+        aws_session_token="your-session-token",  # For temporary credentials
+        region_name="us-east-1",
+        temperature=0.7,
+        max_tokens=4096,
+        top_p=0.9,
+        top_k=250,  # For Claude models
+        stop_sequences=["END", "STOP"],
+        stream=True,  # Enable streaming
+        guardrail_config={  # Optional content filtering
+            "guardrailIdentifier": "your-guardrail-id",
+            "guardrailVersion": "1"
+        },
+        additional_model_request_fields={  # Model-specific parameters
+            "top_k": 250
+        }
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `AWS_ACCESS_KEY_ID`: AWS access key (required)
+    - `AWS_SECRET_ACCESS_KEY`: AWS secret key (required)
+    - `AWS_SESSION_TOKEN`: AWS session token for temporary credentials (optional)
+    - `AWS_DEFAULT_REGION`: AWS region (defaults to `us-east-1`)
+
+    **Features:**
+    - Native tool calling support via Converse API
+    - Streaming and non-streaming responses
+    - Comprehensive error handling with retry logic
+    - Guardrail configuration for content filtering
+    - Model-specific parameters via `additional_model_request_fields`
+    - Token usage tracking and stop reason logging
+    - Support for all Bedrock foundation models
+    - Automatic conversation format handling
+
+    **Important Notes:**
+    - Uses the modern Converse API for unified model access
+    - Automatic handling of model-specific conversation requirements
+    - System messages are handled separately from conversation
+    - First message must be from user (automatically handled)
+    - Some models (like Cohere) require conversation to end with user message
+
+    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API.
 
     | Model                   | Context Window       | Best For                                                          |
     |-------------------------|----------------------|-------------------------------------------------------------------|
@@ -356,7 +637,12 @@ In this section, you'll find detailed examples that help you select, configure,
     | Jamba-Instruct          | Up to 256k tokens    | Model with extended context window optimized for cost-effective text generation, summarization, and Q&A. |
     | Mistral 7B Instruct     | Up to 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
     | Mistral 8x7B Instruct   | Up to 32k tokens     | An MOE LLM that follows instructions, completes requests, and generates creative text. |
+    | DeepSeek R1             | 32,768 tokens        | Advanced reasoning model                                                       |
 
+    **Note:** To use AWS Bedrock, install the required dependencies:
+    ```bash
+    uv add "crewai[bedrock]"
+    ```
   </Accordion>
 
   <Accordion title="Amazon SageMaker">
@@ -733,10 +1019,10 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     CrewAI emits events for each chunk received during streaming:
 
     ```python
-    from crewai.utilities.events import (
+    from crewai.events import (
       LLMStreamChunkEvent
     )
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai.events import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):
@@ -749,7 +1035,7 @@ 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](/en/concepts/event-listener#event-listeners) for more details
     </Tip>
   </Tab>
 
@@ -758,8 +1044,8 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
 
     ```python
     from crewai import LLM, Agent, Task, Crew
-    from crewai.utilities.events import LLMStreamChunkEvent
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai.events import LLMStreamChunkEvent
+    from crewai.events import BaseEventListener
 
     class MyCustomListener(BaseEventListener):
         def setup_listeners(self, crewai_event_bus):
@@ -898,7 +1184,7 @@ Learn how to get the most out of your LLM configuration:
   </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.
+    CrewAI internally uses native sdks for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
     For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:
 
     ```python
@@ -914,6 +1200,52 @@ Learn how to get the most out of your LLM configuration:
     )
     ```
   </Accordion>
+
+  <Accordion title="Transport Interceptors">
+    CrewAI provides message interceptors for several providers, allowing you to hook into request/response cycles at the transport layer.
+
+    **Supported Providers:**
+    - ✅ OpenAI
+    - ✅ Anthropic
+
+    **Basic Usage:**
+    ```python
+import httpx
+from crewai import LLM
+from crewai.llms.hooks import BaseInterceptor
+
+class CustomInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Custom interceptor to modify requests and responses."""
+
+    def on_outbound(self, request: httpx.Request) -> httpx.Request:
+        """Print request before sending to the LLM provider."""
+        print(request)
+        return request
+
+    def on_inbound(self, response: httpx.Response) -> httpx.Response:
+        """Process response after receiving from the LLM provider."""
+        print(f"Status: {response.status_code}")
+        print(f"Response time: {response.elapsed}")
+        return response
+
+# Use the interceptor with an LLM
+llm = LLM(
+    model="openai/gpt-4o",
+    interceptor=CustomInterceptor()
+)
+    ```
+
+    **Important Notes:**
+    - Both methods must return the received object or type of object.
+    - Modifying received objects may result in unexpected behavior or application crashes.
+    - Not all providers support interceptors - check the supported providers list above
+
+    <Info>
+      Interceptors operate at the transport layer. This is particularly useful for:
+      - Message transformation and filtering
+      - Debugging API interactions
+    </Info>
+  </Accordion>
 </AccordionGroup>
 
 ## Common Issues and Solutions

docs/en/concepts/memory.mdx

@@ -2,11 +2,12 @@
 title: Memory
 description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
 icon: database
+mode: "wide"
 ---
 
 ## Overview
 
-The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **three distinct memory approaches** that serve different use cases:
+The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **two distinct memory approaches** that serve different use cases:
 
 1. **Basic Memory System** - Built-in short-term, long-term, and entity memory
 2. **External Memory** - Standalone external memory providers
@@ -340,7 +341,7 @@ crew = Crew(
     embedder={
         "provider": "openai",
         "config": {
-            "model": "text-embedding-3-small"  # or "text-embedding-3-large"
+            "model_name": "text-embedding-3-small"  # or "text-embedding-3-large"
         }
     }
 )
@@ -352,7 +353,7 @@ crew = Crew(
         "provider": "openai",
         "config": {
             "api_key": "your-openai-api-key",  # Optional: override env var
-            "model": "text-embedding-3-large",
+            "model_name": "text-embedding-3-large",
             "dimensions": 1536,  # Optional: reduce dimensions for smaller storage
             "organization_id": "your-org-id"  # Optional: for organization accounts
         }
@@ -374,7 +375,7 @@ crew = Crew(
             "api_base": "https://your-resource.openai.azure.com/",
             "api_type": "azure",
             "api_version": "2023-05-15",
-            "model": "text-embedding-3-small",
+            "model_name": "text-embedding-3-small",
             "deployment_id": "your-deployment-name"  # Azure deployment name
         }
     }
@@ -389,10 +390,10 @@ Use Google's text embedding models for integration with Google Cloud services.
 crew = Crew(
     memory=True,
     embedder={
-        "provider": "google",
+        "provider": "google-generativeai",
         "config": {
             "api_key": "your-google-api-key",
-            "model": "text-embedding-004"  # or "text-embedding-preview-0409"
+            "model_name": "gemini-embedding-001"  # or "text-embedding-005", "text-multilingual-embedding-002"
         }
     }
 )
@@ -460,7 +461,7 @@ crew = Crew(
         "provider": "cohere",
         "config": {
             "api_key": "your-cohere-api-key",
-            "model": "embed-english-v3.0"  # or "embed-multilingual-v3.0"
+            "model_name": "embed-english-v3.0"  # or "embed-multilingual-v3.0"
         }
     }
 )
@@ -477,7 +478,7 @@ crew = Crew(
         "provider": "voyageai",
         "config": {
             "api_key": "your-voyage-api-key",
-            "model": "voyage-large-2",  # or "voyage-code-2" for code
+            "model": "voyage-3",  # or "voyage-3-lite", "voyage-code-3"
             "input_type": "document"  # or "query"
         }
     }
@@ -738,6 +739,17 @@ print(f"OpenAI: {openai_time:.2f}s")
 print(f"Ollama: {ollama_time:.2f}s")
 ```
 
+### Entity Memory batching behavior
+
+Entity Memory supports batching when saving multiple entities at once. When you pass a list of `EntityMemoryItem`, the system:
+
+- Emits a single MemorySaveStartedEvent with `entity_count`
+- Saves each entity internally, collecting any partial errors
+- Emits MemorySaveCompletedEvent with aggregate metadata (saved count, errors)
+- Raises a partial-save exception if some entities failed (includes counts)
+
+This improves performance and observability when writing many entities in one operation.
+
 ## 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.
 
@@ -900,10 +912,10 @@ crew = Crew(
 crew = Crew(
     memory=True,
     embedder={
-        "provider": "google",
+        "provider": "google-generativeai",
         "config": {
             "api_key": "your-api-key",
-            "model": "text-embedding-004"
+            "model_name": "gemini-embedding-001"
         }
     }
 )
@@ -1041,8 +1053,8 @@ CrewAI emits the following memory-related events:
 Track memory operation timing to optimize your application:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemoryQueryCompletedEvent,
     MemorySaveCompletedEvent
 )
@@ -1076,8 +1088,8 @@ memory_monitor = MemoryPerformanceMonitor()
 Log memory operations for debugging and insights:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemorySaveStartedEvent,
     MemoryQueryStartedEvent,
     MemoryRetrievalCompletedEvent
@@ -1117,8 +1129,8 @@ memory_logger = MemoryLogger()
 Capture and respond to memory errors:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemorySaveFailedEvent,
     MemoryQueryFailedEvent
 )
@@ -1167,8 +1179,8 @@ error_tracker = MemoryErrorTracker(notify_email="admin@example.com")
 Memory events can be forwarded to analytics and monitoring platforms to track performance metrics, detect anomalies, and visualize memory usage patterns:
 
 ```python
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events import (
+from crewai.events import (
+    BaseEventListener,
     MemoryQueryCompletedEvent,
     MemorySaveCompletedEvent
 )

docs/en/concepts/planning.mdx

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

docs/en/concepts/processes.mdx

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

docs/en/concepts/reasoning.mdx

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

docs/en/concepts/tasks.mdx

@@ -2,6 +2,7 @@
 title: Tasks
 description: Detailed guide on managing and creating tasks within the CrewAI framework.
 icon: list-check
+mode: "wide"
 ---
 
 ## Overview
@@ -13,7 +14,7 @@ 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 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.
+CrewAI AOP 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)
 
@@ -59,8 +60,14 @@ crew = Crew(
 | **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.                                                  |
+| **Guardrails** _(optional)_       | `guardrails`       | `Optional[List[Callable] | List[str]]` | List of guardrails 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
 
 There are two ways to create tasks in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
@@ -217,6 +224,7 @@ By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput`
 | **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
 | **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
 | **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
+| **Messages**      | `messages`      | `list[LLMMessage]`         | The messages from the last task execution.                                                           |
 
 ### Task Methods and Properties
 
@@ -335,7 +343,11 @@ Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
 feedback to agents when their output doesn't meet specific criteria.
 
-Guardrails are implemented as Python functions that contain custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
+CrewAI supports two types of guardrails:
+
+1. **Function-based guardrails**: Python functions with custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
+
+2. **LLM-based guardrails**: String descriptions that use the agent's LLM to validate outputs based on natural language criteria. These are ideal for complex or subjective validation requirements.
 
 ### Function-Based Guardrails
 
@@ -349,12 +361,12 @@ def validate_blog_content(result: TaskOutput) -> Tuple[bool, Any]:
     """Validate blog content meets requirements."""
     try:
         # Check word count
-        word_count = len(result.split())
+        word_count = len(result.raw.split())
         if word_count > 200:
             return (False, "Blog content exceeds 200 words")
 
         # Additional validation logic here
-        return (True, result.strip())
+        return (True, result.raw.strip())
     except Exception as e:
         return (False, "Unexpected error during validation")
 
@@ -366,6 +378,147 @@ blog_task = Task(
 )
 ```
 
+### LLM-Based Guardrails (String Descriptions)
+
+Instead of writing custom validation functions, you can use string descriptions that leverage LLM-based validation. When you provide a string to the `guardrail` or `guardrails` parameter, CrewAI automatically creates an `LLMGuardrail` that uses the agent's LLM to validate the output based on your description.
+
+**Requirements**:
+- The task must have an `agent` assigned (the guardrail uses the agent's LLM)
+- Provide a clear, descriptive string explaining the validation criteria
+
+```python Code
+from crewai import Task
+
+# Single LLM-based guardrail
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A blog post under 200 words",
+    agent=blog_agent,
+    guardrail="The blog post must be under 200 words and contain no technical jargon"
+)
+```
+
+LLM-based guardrails are particularly useful for:
+- **Complex validation logic** that's difficult to express programmatically
+- **Subjective criteria** like tone, style, or quality assessments
+- **Natural language requirements** that are easier to describe than code
+
+The LLM guardrail will:
+1. Analyze the task output against your description
+2. Return `(True, output)` if the output complies with the criteria
+3. Return `(False, feedback)` with specific feedback if validation fails
+
+**Example with detailed validation criteria**:
+
+```python Code
+research_task = Task(
+    description="Research the latest developments in quantum computing",
+    expected_output="A comprehensive research report",
+    agent=researcher_agent,
+    guardrail="""
+    The research report must:
+    - Be at least 1000 words long
+    - Include at least 5 credible sources
+    - Cover both technical and practical applications
+    - Be written in a professional, academic tone
+    - Avoid speculation or unverified claims
+    """
+)
+```
+
+### Multiple Guardrails
+
+You can apply multiple guardrails to a task using the `guardrails` parameter. Multiple guardrails are executed sequentially, with each guardrail receiving the output from the previous one. This allows you to chain validation and transformation steps.
+
+The `guardrails` parameter accepts:
+- A list of guardrail functions or string descriptions
+- A single guardrail function or string (same as `guardrail`)
+
+**Note**: If `guardrails` is provided, it takes precedence over `guardrail`. The `guardrail` parameter will be ignored when `guardrails` is set.
+
+```python Code
+from typing import Tuple, Any
+from crewai import TaskOutput, Task
+
+def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
+    """Validate word count is within limits."""
+    word_count = len(result.raw.split())
+    if word_count < 100:
+        return (False, f"Content too short: {word_count} words. Need at least 100 words.")
+    if word_count > 500:
+        return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
+    return (True, result.raw)
+
+def validate_no_profanity(result: TaskOutput) -> Tuple[bool, Any]:
+    """Check for inappropriate language."""
+    profanity_words = ["badword1", "badword2"]  # Example list
+    content_lower = result.raw.lower()
+    for word in profanity_words:
+        if word in content_lower:
+            return (False, f"Inappropriate language detected: {word}")
+    return (True, result.raw)
+
+def format_output(result: TaskOutput) -> Tuple[bool, Any]:
+    """Format and clean the output."""
+    formatted = result.raw.strip()
+    # Capitalize first letter
+    formatted = formatted[0].upper() + formatted[1:] if formatted else formatted
+    return (True, formatted)
+
+# Apply multiple guardrails sequentially
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A well-formatted blog post between 100-500 words",
+    agent=blog_agent,
+    guardrails=[
+        validate_word_count,      # First: validate length
+        validate_no_profanity,    # Second: check content
+        format_output             # Third: format the result
+    ],
+    guardrail_max_retries=3
+)
+```
+
+In this example, the guardrails execute in order:
+1. `validate_word_count` checks the word count
+2. `validate_no_profanity` checks for inappropriate language (using the output from step 1)
+3. `format_output` formats the final result (using the output from step 2)
+
+If any guardrail fails, the error is sent back to the agent, and the task is retried up to `guardrail_max_retries` times.
+
+**Mixing function-based and LLM-based guardrails**:
+
+You can combine both function-based and string-based guardrails in the same list:
+
+```python Code
+from typing import Tuple, Any
+from crewai import TaskOutput, Task
+
+def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
+    """Validate word count is within limits."""
+    word_count = len(result.raw.split())
+    if word_count < 100:
+        return (False, f"Content too short: {word_count} words. Need at least 100 words.")
+    if word_count > 500:
+        return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
+    return (True, result.raw)
+
+# Mix function-based and LLM-based guardrails
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A well-formatted blog post between 100-500 words",
+    agent=blog_agent,
+    guardrails=[
+        validate_word_count,  # Function-based: precise word count check
+        "The content must be engaging and suitable for a general audience",  # LLM-based: subjective quality check
+        "The writing style should be clear, concise, and free of technical jargon"  # LLM-based: style validation
+    ],
+    guardrail_max_retries=3
+)
+```
+
+This approach combines the precision of programmatic validation with the flexibility of LLM-based assessment for subjective criteria.
+
 ### Guardrail Function Requirements
 
 1. **Function Signature**:
@@ -432,7 +585,7 @@ When a guardrail returns `(False, error)`:
 2. The agent attempts to fix the issue
 3. The process repeats until:
    - The guardrail returns `(True, result)`
-   - Maximum retries are reached
+   - Maximum retries are reached (`guardrail_max_retries`)
 
 Example with retry handling:
 ```python Code
@@ -891,14 +1044,13 @@ except RuntimeError as e:
 Check out the video below to see how to use structured outputs in CrewAI:
 
 <iframe
-  width="560"
-  height="315"
+  className="w-full aspect-video rounded-xl"
   src="https://www.youtube.com/embed/dNpKQk5uxHw"
-  title="YouTube video player"
-  frameborder="0"
+  title="Structured outputs in CrewAI"
+  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/en/concepts/testing.mdx

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