Fix lint errors: remove unused variable and add raw string prefixes

- Remove unused 'crew' variable in test_hierarchical_crew_does_not_propagate_agent_tools_to_manager (F841)
- Add raw string prefixes to regex patterns to comply with RUF043 linting rule
- Pre-existing RUF043 errors in changed files were blocking PR, so fixed them

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

.github/codeql/codeql-config.yml

@@ -0,0 +1,21 @@
+name: "CodeQL Config"
+
+paths-ignore:
+  # Ignore template files - these are boilerplate code that shouldn't be analyzed
+  - "src/crewai/cli/templates/**"
+  # Ignore test cassettes - these are test fixtures/recordings
+  - "tests/cassettes/**"
+  # Ignore cache and build artifacts
+  - ".cache/**"
+  # Ignore documentation build artifacts
+  - "docs/.cache/**"
+  
+paths:
+  # Include all Python source code
+  - "src/**"
+  # Include tests (but exclude cassettes)
+  - "tests/**"
+
+# Configure specific queries or packs if needed
+# queries:
+#   - uses: security-and-quality

.github/security.md

@@ -1,19 +1,50 @@
-CrewAI takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organization.
-If you believe you have found a security vulnerability in any CrewAI product or service, please report it to us as described below.
+## CrewAI Security Policy
 
- ## Reporting a Vulnerability
- Please do not report security vulnerabilities through public GitHub issues.
- To report a vulnerability, please email us at security@crewai.com.
- Please include the requested information listed below so that we can triage your report more quickly
+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.
 
- - Type of issue (e.g. SQL injection, cross-site scripting, etc.)
- - Full paths of source file(s) related to the manifestation of the issue
- - The location of the affected source code (tag/branch/commit or direct URL)
- - Any special configuration required to reproduce the issue
- - Step-by-step instructions to reproduce the issue (please include screenshots if needed)
- - Proof-of-concept or exploit code (if possible)
- - Impact of the issue, including how an attacker might exploit the issue
+### Scope
 
- Once we have received your report, we will respond to you at the email address you provide. If the issue is confirmed, we will release a patch as soon as possible depending on the complexity of the issue.
+We welcome reports for vulnerabilities that could impact:
 
- At this time, we are not offering a bug bounty program. Any rewards will be at our discretion.
+- CrewAI-maintained source code and repositories
+- CrewAI-operated infrastructure and services
+- Official CrewAI releases, packages, and distributions
+
+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.
+
+### How to Report
+
+- **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:
+      - "src/crewai/cli/templates/**"
+  pull_request:
+    branches: [ "main" ]
+    paths-ignore:
+      - "src/crewai/cli/templates/**"
+
+jobs:
+  analyze:
+    name: Analyze (${{ matrix.language }})
+    # Runner size impacts CodeQL analysis time. To learn more, please see:
+    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
+    #   - https://gh.io/supported-runners-and-hardware-resources
+    #   - https://gh.io/using-larger-runners (GitHub.com only)
+    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
+    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
+    permissions:
+      # required for all workflows
+      security-events: write
+
+      # required to fetch internal or private CodeQL packs
+      packages: read
+
+      # only required for workflows in private repositories
+      actions: read
+      contents: read
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+        - language: actions
+          build-mode: none
+        - language: python
+          build-mode: none
+        # CodeQL supports the following values keywords for 'language': 'actions', 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'rust', 'swift'
+        # Use `c-cpp` to analyze code written in C, C++ or both
+        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
+        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
+        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
+        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
+        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
+        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    # Add any setup steps before running the `github/codeql-action/init` action.
+    # This includes steps like installing compilers or runtimes (`actions/setup-node`
+    # or others). This is typically only required for manual builds.
+    # - name: Setup runtime (example)
+    #   uses: actions/setup-example@v1
+
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v3
+      with:
+        languages: ${{ matrix.language }}
+        build-mode: ${{ matrix.build-mode }}
+        config-file: ./.github/codeql/codeql-config.yml
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+
+        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+        # queries: security-extended,security-and-quality
+
+    # If the analyze step fails for one of the languages you are analyzing with
+    # "We were unable to automatically build your code", modify the matrix above
+    # to set the build mode to "manual" for that language. Then modify this step
+    # to build your code.
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+    - if: matrix.build-mode == 'manual'
+      shell: bash
+      run: |
+        echo 'If you are using a "manual" build mode for one or more of the' \
+          'languages you are analyzing, replace this with the commands to build' \
+          'your code, for example:'
+        echo '  make bootstrap'
+        echo '  make release'
+        exit 1
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v3
+      with:
+        category: "/language:${{matrix.language}}"

.github/workflows/linter.yml

@@ -2,15 +2,67 @@ name: Lint
 
 on: [pull_request]
 
+permissions:
+  contents: read
+
 jobs:
   lint:
     runs-on: ubuntu-latest
+    env:
+      TARGET_BRANCH: ${{ github.event.pull_request.base.ref }}
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
-      - name: Install Requirements
+      - name: Fetch Target Branch
+        run: git fetch origin $TARGET_BRANCH --depth=1
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py3.11-
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: "3.11"
+          enable-cache: false
+
+      - name: Install dependencies
+        run: uv sync --all-groups --all-extras --no-install-project
+
+      - name: Get Changed Python Files
+        id: changed-files
         run: |
-          pip install ruff
+          merge_base=$(git merge-base origin/"$TARGET_BRANCH" HEAD)
+          changed_files=$(git diff --name-only --diff-filter=ACMRTUB "$merge_base" | grep '\.py$' || true)
+          echo "files<<EOF" >> $GITHUB_OUTPUT
+          echo "$changed_files" >> $GITHUB_OUTPUT
+          echo "EOF" >> $GITHUB_OUTPUT
 
-      - name: Run Ruff Linter
-        run: ruff check
+      - name: Run Ruff on Changed Files
+        if: ${{ steps.changed-files.outputs.files != '' }}
+        run: |
+          echo "${{ steps.changed-files.outputs.files }}" \
+            | tr ' ' '\n' \
+            | grep -v 'src/crewai/cli/templates/' \
+            | xargs -I{} uv run ruff check "{}"
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}

.github/workflows/mkdocs.yml

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

.github/workflows/notify-downstream.yml

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

.github/workflows/security-checker.yml

@@ -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

@@ -3,30 +3,95 @@ name: Run Tests
 on: [pull_request]
 
 permissions:
-  contents: write
+  contents: read
 
 env:
   OPENAI_API_KEY: fake-api-key
+  PYTHONUNBUFFERED: 1
 
 jobs:
   tests:
+    name: tests (${{ matrix.python-version }})
     runs-on: ubuntu-latest
     timeout-minutes: 15
+    strategy:
+      fail-fast: true
+      matrix:
+        python-version: ['3.10', '3.11', '3.12', '3.13']
+        group: [1, 2, 3, 4, 5, 6, 7, 8]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0  # Fetch all history for proper diff
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py${{ matrix.python-version }}-
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v3
+        uses: astral-sh/setup-uv@v6
         with:
-          enable-cache: true
-
-
-      - name: Set up Python
-        run: uv python install 3.12.8
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
 
       - name: Install the project
-        run: uv sync --dev --all-extras
+        run: uv sync --all-groups --all-extras
 
-      - name: Run tests
-        run: uv run pytest tests -vv
+      - name: Restore test durations
+        uses: actions/cache/restore@v4
+        with:
+          path: .test_durations_py*
+          key: test-durations-py${{ matrix.python-version }}
+
+      - name: Run tests (group ${{ matrix.group }} of 8)
+        run: |
+          PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
+          DURATION_FILE=".test_durations_py${PYTHON_VERSION_SAFE}"
+          
+          # Temporarily always skip cached durations to fix test splitting
+          # When durations don't match, pytest-split runs duplicate tests instead of splitting
+          echo "Using even test splitting (duration cache disabled until fix merged)"
+          DURATIONS_ARG=""
+          
+          # Original logic (disabled temporarily):
+          # if [ ! -f "$DURATION_FILE" ]; then
+          #   echo "No cached durations found, tests will be split evenly"
+          #   DURATIONS_ARG=""
+          # elif git diff origin/${{ github.base_ref }}...HEAD --name-only 2>/dev/null | grep -q "^tests/.*\.py$"; then
+          #   echo "Test files have changed, skipping cached durations to avoid mismatches"
+          #   DURATIONS_ARG=""
+          # else
+          #   echo "No test changes detected, using cached test durations for optimal splitting"
+          #   DURATIONS_ARG="--durations-path=${DURATION_FILE}"
+          # fi
+          
+          uv run pytest \
+            --block-network \
+            --timeout=30 \
+            -vv \
+            --splits 8 \
+            --group ${{ matrix.group }} \
+            $DURATIONS_ARG \
+            --durations=10 \
+            -n auto \
+            --maxfail=3
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.github/workflows/type-checker.yml

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

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

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

.gitignore

@@ -21,8 +21,9 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log
 test_flow.html
 crewairules.mdc
 plan.md
-conceptual_plan.md
+conceptual_plan.md
+build_image
+chromadb-*.lock

.pre-commit-config.yaml

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

.ruff.toml

@@ -1,9 +0,0 @@
-exclude = [
-    "templates",
-    "__init__.py",
-]
-
-[lint]
-select = [
-    "I", # isort rules
-]

README.md

@@ -1,55 +1,86 @@
-<div align="center">
+<p align="center">
+  <a href="https://github.com/crewAIInc/crewAI">
+    <img src="docs/images/crewai_logo.png" width="600px" alt="Open source Multi-AI Agent orchestration framework">
+  </a>
+</p>
+<p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;">
+  <a href="https://trendshift.io/repositories/11239" target="_blank">
+    <img src="https://trendshift.io/api/badge/repositories/11239" alt="crewAIInc%2FcrewAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
+  </a>
+</p>
 
-![Logo of CrewAI](./docs/crewai_logo.png)
+<p align="center">
+  <a href="https://crewai.com">Homepage</a>
+  ·
+  <a href="https://docs.crewai.com">Docs</a>
+  ·
+  <a href="https://app.crewai.com">Start Cloud Trial</a>
+  ·
+  <a href="https://blog.crewai.com">Blog</a>
+  ·
+  <a href="https://community.crewai.com">Forum</a>
+</p>
 
+<p align="center">
+  <a href="https://github.com/crewAIInc/crewAI">
+    <img src="https://img.shields.io/github/stars/crewAIInc/crewAI" alt="GitHub Repo stars">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/network/members">
+    <img src="https://img.shields.io/github/forks/crewAIInc/crewAI" alt="GitHub forks">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/issues">
+    <img src="https://img.shields.io/github/issues/crewAIInc/crewAI" alt="GitHub issues">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/pulls">
+    <img src="https://img.shields.io/github/issues-pr/crewAIInc/crewAI" alt="GitHub pull requests">
+  </a>
+  <a href="https://opensource.org/licenses/MIT">
+    <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT">
+  </a>
+</p>
 
-</div>
+<p align="center">
+  <a href="https://pypi.org/project/crewai/">
+    <img src="https://img.shields.io/pypi/v/crewai" alt="PyPI version">
+  </a>
+  <a href="https://pypi.org/project/crewai/">
+    <img src="https://img.shields.io/pypi/dm/crewai" alt="PyPI downloads">
+  </a>
+  <a href="https://twitter.com/crewAIInc">
+    <img src="https://img.shields.io/twitter/follow/crewAIInc?style=social" alt="Twitter Follow">
+  </a>
+</p>
 
 ### Fast and Flexible Multi-Agent Automation Framework
 
-CrewAI is a lean, lightning-fast Python framework built entirely from
-scratch—completely **independent of LangChain or other agent frameworks**.
-It empowers developers with both high-level simplicity and precise low-level
-control, ideal for creating autonomous AI agents tailored to any scenario.
+> CrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely **independent of LangChain or other agent frameworks**.
+> It empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario.
 
 - **CrewAI Crews**: Optimize for autonomy and collaborative intelligence.
 - **CrewAI Flows**: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively
 
-With over 100,000 developers certified through our community courses at
-[learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
+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 AMP Suite
 
-CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations
-that require secure, scalable, and easy-to-manage agent-driven automation.
+CrewAI AMP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
 
 You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
 
 ## Crew Control Plane Key Features:
+
 - **Tracing & Observability**: Monitor and track your AI agents and workflows in real-time, including metrics, logs, and traces.
 - **Unified Control Plane**: A centralized platform for managing, monitoring, and scaling your AI agents and workflows.
 - **Seamless Integrations**: Easily connect with existing enterprise systems, data sources, and cloud infrastructure.
 - **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
 - **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
 - **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
-- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
+- **On-premise and Cloud Deployment Options**: Deploy CrewAI AMP 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 AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
 intelligent automations.
 
-<h3>
-
-[Homepage](https://www.crewai.com/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Discourse](https://community.crewai.com)
-
-</h3>
-
-[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/crewAIInc/crewAI)
-[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
-
-</div>
-
 ## Table of contents
 
 - [Why CrewAI?](#why-crewai)
@@ -73,7 +104,7 @@ intelligent automations.
 ## Why CrewAI?
 
 <div align="center" style="margin-bottom: 30px;">
-  <img src="docs/asset.png" alt="CrewAI Logo" width="100%">
+  <img src="docs/images/asset.png" alt="CrewAI Logo" width="100%">
 </div>
 
 CrewAI unlocks the true potential of multi-agent automation, delivering the best-in-class combination of speed, flexibility, and control with either Crews of AI Agents or Flows of Events:
@@ -88,9 +119,15 @@ CrewAI empowers developers and enterprises to confidently build intelligent auto
 
 ## Getting Started
 
-### Learning Resources
+Setup and run your first CrewAI agents by following this tutorial.
+
+[![CrewAI Getting Started Tutorial](https://img.youtube.com/vi/-kSOTtYzgEw/hqdefault.jpg)](https://www.youtube.com/watch?v=-kSOTtYzgEw "CrewAI Getting Started Tutorial")
+
+###
+ Learning Resources
 
 Learn CrewAI through our comprehensive courses:
+
 - [Multi AI Agent Systems with CrewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/) - Master the fundamentals of multi-agent systems
 - [Practical Multi AI Agents and Advanced Use Cases](https://www.deeplearning.ai/short-courses/practical-multi-ai-agents-and-advanced-use-cases-with-crewai/) - Deep dive into advanced implementations
 
@@ -99,18 +136,20 @@ Learn CrewAI through our comprehensive courses:
 CrewAI offers two powerful, complementary approaches that work seamlessly together to build sophisticated AI applications:
 
 1. **Crews**: Teams of AI agents with true autonomy and agency, working together to accomplish complex tasks through role-based collaboration. Crews enable:
+
    - Natural, autonomous decision-making between agents
    - Dynamic task delegation and collaboration
    - Specialized roles with defined goals and expertise
    - Flexible problem-solving approaches
-
 2. **Flows**: Production-ready, event-driven workflows that deliver precise control over complex automations. Flows provide:
+
    - Fine-grained control over execution paths for real-world scenarios
    - Secure, consistent state management between tasks
    - Clean integration of AI agents with production Python code
    - Conditional branching for complex business logic
 
 The true power of CrewAI emerges when combining Crews and Flows. This synergy allows you to:
+
 - Build complex, production-grade applications
 - Balance autonomy with precise control
 - Handle sophisticated real-world scenarios
@@ -122,18 +161,20 @@ To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
 
-Ensure you have Python >=3.10 <3.13 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <3.14 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, install CrewAI:
 
 ```shell
 pip install crewai
 ```
+
 If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command:
 
 ```shell
 pip install 'crewai[tools]'
 ```
+
 The command above installs the basic package and also adds extra components which require more dependencies to function.
 
 ### Troubleshooting Dependencies
@@ -143,10 +184,11 @@ If you encounter issues during installation or usage, here are some common solut
 #### Common Issues
 
 1. **ModuleNotFoundError: No module named 'tiktoken'**
+
    - Install tiktoken explicitly: `pip install 'crewai[embeddings]'`
    - If using embedchain or other tools: `pip install 'crewai[tools]'`
-
 2. **Failed building wheel for tiktoken**
+
    - Ensure Rust compiler is installed (see installation steps above)
    - For Windows: Verify Visual C++ Build Tools are installed
    - Try upgrading pip: `pip install --upgrade pip`
@@ -257,10 +299,14 @@ reporting_task:
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 from crewai_tools import SerperDevTool
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
 
 @CrewBase
 class LatestAiDevelopmentCrew():
 	"""LatestAiDevelopment crew"""
+	agents: List[BaseAgent]
+	tasks: List[Task]
 
 	@agent
 	def researcher(self) -> Agent:
@@ -357,7 +403,7 @@ In addition to the sequential process, you can use the hierarchical process, whi
 
 ## Key Features
 
-CrewAI stands apart as a lean, standalone, high-performance framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
+CrewAI stands apart as a lean, standalone, high-performance multi-AI Agent framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
 
 - **Standalone & Lean**: Completely independent from other frameworks like LangChain, offering faster execution and lighter resource demands.
 - **Flexible & Precise**: Easily orchestrate autonomous agents through intuitive [Crews](https://docs.crewai.com/concepts/crews) or precise [Flows](https://docs.crewai.com/concepts/flows), achieving perfect balance for your needs.
@@ -372,10 +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
 
@@ -383,29 +429,35 @@ 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")
 
 ### Using Crews and Flows Together
 
-CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines. Here's how you can orchestrate multiple Crews within a Flow:
+CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines.
+CrewAI flows support logical operators like `or_` and `and_` to combine multiple conditions. This can be used with `@start`, `@listen`, or `@router` decorators to create complex triggering conditions.
+
+- `or_`: Triggers when any of the specified conditions are met.
+- `and_`Triggers when all of the specified conditions are met.
+
+Here's how you can orchestrate multiple Crews within a Flow:
 
 ```python
-from crewai.flow.flow import Flow, listen, start, router
-from crewai import Crew, Agent, Task
+from crewai.flow.flow import Flow, listen, start, router, or_
+from crewai import Crew, Agent, Task, Process
 from pydantic import BaseModel
 
 # Define structured state for precise control
@@ -479,13 +531,14 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
         )
         return strategy_crew.kickoff()
 
-    @listen("medium_confidence", "low_confidence")
+    @listen(or_("medium_confidence", "low_confidence"))
     def request_additional_analysis(self):
         self.state.recommendations.append("Gather more data")
         return "Additional analysis required"
 ```
 
 This example demonstrates how to:
+
 1. Use Python code for basic data operations
 2. Create and execute Crews as steps in your workflow
 3. Use Flow decorators to manage the sequence of operations
@@ -495,7 +548,7 @@ This example demonstrates how to:
 
 CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
 
-Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models.
+Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models.
 
 ## How CrewAI Compares
 
@@ -506,7 +559,6 @@ Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-
 *P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example ([see comparison](https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/QA%20Agent)) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example ([detailed analysis](https://github.com/crewAIInc/crewAI-examples/blob/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/Coding%20Assistant/coding_assistant_eval.ipynb)).*
 
 - **Autogen**: While Autogen excels at creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
-
 - **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
 
 ## Contribution
@@ -597,10 +649,10 @@ Users can opt-in to Further Telemetry, sharing the complete telemetry data by se
 
 CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/blob/main/LICENSE).
 
-
 ## Frequently Asked Questions (FAQ)
 
 ### General
+
 - [What exactly is CrewAI?](#q-what-exactly-is-crewai)
 - [How do I install CrewAI?](#q-how-do-i-install-crewai)
 - [Does CrewAI depend on LangChain?](#q-does-crewai-depend-on-langchain)
@@ -608,6 +660,7 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
 - [Does CrewAI collect data from users?](#q-does-crewai-collect-data-from-users)
 
 ### Features and Capabilities
+
 - [Can CrewAI handle complex use cases?](#q-can-crewai-handle-complex-use-cases)
 - [Can I use CrewAI with local AI models?](#q-can-i-use-crewai-with-local-ai-models)
 - [What makes Crews different from Flows?](#q-what-makes-crews-different-from-flows)
@@ -615,84 +668,110 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
 - [Does CrewAI support fine-tuning or training custom models?](#q-does-crewai-support-fine-tuning-or-training-custom-models)
 
 ### Resources and Community
+
 - [Where can I find real-world CrewAI examples?](#q-where-can-i-find-real-world-crewai-examples)
 - [How can I contribute to CrewAI?](#q-how-can-i-contribute-to-crewai)
 
 ### Enterprise Features
-- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
-- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
-- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
-
 
+- [What additional features does CrewAI AMP offer?](#q-what-additional-features-does-crewai-amp-offer)
+- [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
+- [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-amp-for-free)
 
 ### Q: What exactly is CrewAI?
+
 A: CrewAI is a standalone, lean, and fast Python framework built specifically for orchestrating autonomous AI agents. Unlike frameworks like LangChain, CrewAI does not rely on external dependencies, making it leaner, faster, and simpler.
 
 ### Q: How do I install CrewAI?
+
 A: Install CrewAI using pip:
+
 ```shell
 pip install crewai
 ```
+
 For additional tools, use:
+
 ```shell
 pip install 'crewai[tools]'
 ```
+
 ### Q: Does CrewAI depend on LangChain?
+
 A: No. CrewAI is built entirely from the ground up, with no dependencies on LangChain or other agent frameworks. This ensures a lean, fast, and flexible experience.
 
 ### Q: Can CrewAI handle complex use cases?
+
 A: Yes. CrewAI excels at both simple and highly complex real-world scenarios, offering deep customization options at both high and low levels, from internal prompts to sophisticated workflow orchestration.
 
 ### Q: Can I use CrewAI with local AI models?
+
 A: Absolutely! CrewAI supports various language models, including local ones. Tools like Ollama and LM Studio allow seamless integration. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
 
 ### Q: What makes Crews different from Flows?
+
 A: Crews provide autonomous agent collaboration, ideal for tasks requiring flexible decision-making and dynamic interaction. Flows offer precise, event-driven control, ideal for managing detailed execution paths and secure state management. You can seamlessly combine both for maximum effectiveness.
 
 ### Q: How is CrewAI better than LangChain?
+
 A: CrewAI provides simpler, more intuitive APIs, faster execution speeds, more reliable and consistent results, robust documentation, and an active community—addressing common criticisms and limitations associated with LangChain.
 
 ### Q: Is CrewAI open-source?
+
 A: Yes, CrewAI is open-source and actively encourages community contributions and collaboration.
 
 ### Q: Does CrewAI collect data from users?
+
 A: CrewAI collects anonymous telemetry data strictly for improvement purposes. Sensitive data such as prompts, tasks, or API responses are never collected unless explicitly enabled by the user.
 
 ### Q: Where can I find real-world CrewAI examples?
+
 A: Check out practical examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), covering use cases like trip planners, stock analysis, and job postings.
 
 ### Q: How can I contribute to CrewAI?
+
 A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
 
-### Q: What additional features does CrewAI Enterprise offer?
-A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
+### Q: What additional features does CrewAI AMP offer?
 
-### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
-A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
+A: CrewAI AMP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
 
-### Q: Can I try CrewAI Enterprise for free?
-A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
+### Q: Is CrewAI AMP available for cloud and on-premise deployments?
+
+A: Yes, CrewAI AMP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
+
+### Q: Can I try CrewAI AMP for free?
+
+A: Yes, you can explore part of the CrewAI AMP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
 
 ### Q: Does CrewAI support fine-tuning or training custom models?
+
 A: Yes, CrewAI can integrate with custom-trained or fine-tuned models, allowing you to enhance your agents with domain-specific knowledge and accuracy.
 
 ### Q: Can CrewAI agents interact with external tools and APIs?
+
 A: Absolutely! CrewAI agents can easily integrate with external tools, APIs, and databases, empowering them to leverage real-world data and resources.
 
 ### Q: Is CrewAI suitable for production environments?
+
 A: Yes, CrewAI is explicitly designed with production-grade standards, ensuring reliability, stability, and scalability for enterprise deployments.
 
 ### Q: How scalable is CrewAI?
+
 A: CrewAI is highly scalable, supporting simple automations and large-scale enterprise workflows involving numerous agents and complex tasks simultaneously.
 
 ### Q: Does CrewAI offer debugging and monitoring tools?
-A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
+
+A: Yes, CrewAI AMP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
 
 ### Q: What programming languages does CrewAI support?
+
 A: CrewAI is primarily Python-based but easily integrates with services and APIs written in any programming language through its flexible API integration capabilities.
 
 ### Q: Does CrewAI offer educational resources for beginners?
+
 A: Yes, CrewAI provides extensive beginner-friendly tutorials, courses, and documentation through learn.crewai.com, supporting developers at all skill levels.
 
 ### Q: Can CrewAI automate human-in-the-loop workflows?
+
 A: Yes, CrewAI fully supports human-in-the-loop workflows, allowing seamless collaboration between human experts and AI agents for enhanced decision-making.

docs/changelog.mdx

@@ -1,187 +0,0 @@
----
-title: Changelog
-description: View the latest updates and changes to CrewAI
-icon: timeline
----
-
-<Update label="2025-03-17" description="v0.108.0">
-  **Features**
-  - Converted tabs to spaces in `crew.py` template
-  - Enhanced LLM Streaming Response Handling and Event System
-  - Included `model_name`
-  - Enhanced Event Listener with rich visualization and improved logging
-  - Added fingerprints
-
-  **Bug Fixes**
-  - Fixed Mistral issues
-  - Fixed a bug in documentation
-  - Fixed type check error in fingerprint property
-
-  **Documentation Updates**
-  - Improved tool documentation
-  - Updated installation guide for the `uv` tool package
-  - Added instructions for upgrading crewAI with the `uv` tool
-  - Added documentation for `ApifyActorsTool`
-</Update>
-
-<Update label="2025-03-10" description="v0.105.0">
-  **Core Improvements & Fixes**
-  - Fixed issues with missing template variables and user memory configuration
-  - Improved async flow support and addressed agent response formatting
-  - Enhanced memory reset functionality and fixed CLI memory commands
-  - Fixed type issues, tool calling properties, and telemetry decoupling
-
-  **New Features & Enhancements**
-  - Added Flow state export and improved state utilities
-  - Enhanced agent knowledge setup with optional crew embedder
-  - Introduced event emitter for better observability and LLM call tracking
-  - Added support for Python 3.10 and ChatOllama from langchain_ollama
-  - Integrated context window size support for the o3-mini model
-  - Added support for multiple router calls
-
-  **Documentation & Guides**
-  - Improved documentation layout and hierarchical structure
-  - Added QdrantVectorSearchTool guide and clarified event listener usage
-  - Fixed typos in prompts and updated Amazon Bedrock model listings
-</Update>
-
-<Update label="2025-02-12" description="v0.102.0">
-  **Core Improvements & Fixes**
-  - Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
-  - Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
-  - Memory & Storage Fixes: Fixed short-term memory handling with Bedrock, ensured correct embedder initialization, and added a reset memories function in the crew class
-  - Training & Execution Reliability: Fixed broken training and interpolation issues with dict and list input types
-
-  **New Features & Enhancements**
-  - Advanced Knowledge Management: Improved naming conventions and enhanced embedding configuration with custom embedder support
-  - Expanded Logging & Observability: Added JSON format support for logging and integrated MLflow tracing documentation
-  - Data Handling Improvements: Updated excel_knowledge_source.py to process multi-tab files
-  - General Performance & Codebase Clean-Up: Streamlined enterprise code alignment and resolved linting issues
-  - Adding new tool: `QdrantVectorSearchTool`
-
-  **Documentation & Guides**
-  - Updated AI & Memory Docs: Improved Bedrock, Google AI, and long-term memory documentation
-  - Task & Workflow Clarity: Added "Human Input" row to Task Attributes, Langfuse guide, and FileWriterTool documentation
-  - Fixed Various Typos & Formatting Issues
-</Update>
-
-<Update label="2025-01-28" description="v0.100.0">
-  **Features**
-  - Add Composio docs
-  - Add SageMaker as a LLM provider
-  
-  **Fixes**
-  - Overall LLM connection issues
-  - Using safe accessors on training
-  - Add version check to crew_chat.py
-  
-  **Documentation**
-  - New docs for crewai chat
-  - Improve formatting and clarity in CLI and Composio Tool docs
-</Update>
-
-<Update label="2025-01-20" description="v0.98.0">
-  **Features**
-  - Conversation crew v1
-  - Add unique ID to flow states
-  - Add @persist decorator with FlowPersistence interface
-  
-  **Integrations**
-  - Add SambaNova integration
-  - Add NVIDIA NIM provider in cli
-  - Introducing VoyageAI
-  
-  **Fixes**
-  - Fix API Key Behavior and Entity Handling in Mem0 Integration
-  - Fixed core invoke loop logic and relevant tests
-  - Make tool inputs actual objects and not strings
-  - Add important missing parts to creating tools
-  - Drop litellm version to prevent windows issue
-  - Before kickoff if inputs are none
-  - Fixed typos, nested pydantic model issue, and docling issues
-</Update>
-
-<Update label="2025-01-04" description="v0.95.0">
-  **New Features**
-  - Adding Multimodal Abilities to Crew
-  - Programatic Guardrails
-  - HITL multiple rounds
-  - Gemini 2.0 Support
-  - CrewAI Flows Improvements
-  - Add Workflow Permissions
-  - Add support for langfuse with litellm
-  - Portkey Integration with CrewAI
-  - Add interpolate_only method and improve error handling
-  - Docling Support
-  - Weviate Support
-  
-  **Fixes**
-  - output_file not respecting system path
-  - disk I/O error when resetting short-term memory
-  - CrewJSONEncoder now accepts enums
-  - Python max version
-  - Interpolation for output_file in Task
-  - Handle coworker role name case/whitespace properly
-  - Add tiktoken as explicit dependency and document Rust requirement
-  - Include agent knowledge in planning process
-  - Change storage initialization to None for KnowledgeStorage
-  - Fix optional storage checks
-  - include event emitter in flows
-  - Docstring, Error Handling, and Type Hints Improvements
-  - Suppressed userWarnings from litellm pydantic issues
-</Update>
-
-<Update label="2024-12-05" description="v0.86.0">
-  **Changes**
-  - Remove all references to pipeline and pipeline router
-  - Add Nvidia NIM as provider in Custom LLM
-  - Add knowledge demo + improve knowledge docs
-  - Add HITL multiple rounds of followup
-  - New docs about yaml crew with decorators
-  - Simplify template crew
-</Update>
-
-<Update label="2024-12-04" description="v0.85.0">
-  **Features**
-  - Added knowledge to agent level
-  - Feat/remove langchain
-  - Improve typed task outputs
-  - Log in to Tool Repository on crewai login
-  
-  **Fixes**
-  - Fixes issues with result as answer not properly exiting LLM loop
-  - Fix missing key name when running with ollama provider
-  - Fix spelling issue found
-  
-  **Documentation**
-  - Update readme for running mypy
-  - Add knowledge to mint.json
-  - Update Github actions
-  - Update Agents docs to include two approaches for creating an agent
-  - Improvements to LLM Configuration and Usage
-</Update>
-
-<Update label="2024-11-25" description="v0.83.0">
-  **New Features**
-  - New before_kickoff and after_kickoff crew callbacks
-  - Support to pre-seed agents with Knowledge
-  - Add support for retrieving user preferences and memories using Mem0
-  
-  **Fixes**
-  - Fix Async Execution
-  - Upgrade chroma and adjust embedder function generator
-  - Update CLI Watson supported models + docs
-  - Reduce level for Bandit
-  - Fixing all tests
-  
-  **Documentation**
-  - Update Docs
-</Update>
-
-<Update label="2024-11-13" description="v0.80.0">
-  **Fixes**
-  - Fixing Tokens callback replacement bug
-  - Fixing Step callback issue
-  - Add cached prompt tokens info on usage metrics
-  - Fix crew_train_success test
-</Update>

docs/common-room-tracking.js

@@ -0,0 +1,18 @@
+(function() {
+  if (typeof window === 'undefined') return;
+  if (typeof window.signals !== 'undefined') return;
+  var script = document.createElement('script');
+  script.src = 'https://cdn.cr-relay.com/v1/site/883520f4-c431-44be-80e7-e123a1ee7a2b/signals.js';
+  script.async = true;
+  window.signals = Object.assign(
+    [],
+    ['page', 'identify', 'form'].reduce(function (acc, method){
+      acc[method] = function () {
+        signals.push([method, arguments]);
+        return signals;
+      };
+     return acc;
+    }, {})
+  );
+  document.head.appendChild(script);
+})(); 

docs/concepts/cli.mdx

@@ -1,211 +0,0 @@
----
-title: CLI
-description: Learn how to use the CrewAI CLI to interact with CrewAI.
-icon: terminal
----
-
-# CrewAI CLI Documentation
-
-The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
-
-## Installation
-
-To use the CrewAI CLI, make sure you have CrewAI installed:
-
-```shell Terminal
-pip install crewai
-```
-
-## Basic Usage
-
-The basic structure of a CrewAI CLI command is:
-
-```shell Terminal
-crewai [COMMAND] [OPTIONS] [ARGUMENTS]
-```
-
-## Available Commands
-
-### 1. Create
-
-Create a new crew or flow.
-
-```shell Terminal
-crewai create [OPTIONS] TYPE NAME
-```
-
-- `TYPE`: Choose between "crew" or "flow"
-- `NAME`: Name of the crew or flow
-
-Example:
-```shell Terminal
-crewai create crew my_new_crew
-crewai create flow my_new_flow
-```
-
-### 2. Version
-
-Show the installed version of CrewAI.
-
-```shell Terminal
-crewai version [OPTIONS]
-```
-
-- `--tools`: (Optional) Show the installed version of CrewAI tools
-
-Example:
-```shell Terminal
-crewai version
-crewai version --tools
-```
-
-### 3. Train
-
-Train the crew for a specified number of iterations.
-
-```shell Terminal
-crewai train [OPTIONS]
-```
-
-- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
-- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
-
-Example:
-```shell Terminal
-crewai train -n 10 -f my_training_data.pkl
-```
-
-### 4. Replay
-
-Replay the crew execution from a specific task.
-
-```shell Terminal
-crewai replay [OPTIONS]
-```
-
-- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
-
-Example:
-```shell Terminal    
-crewai replay -t task_123456
-```
-
-### 5. Log-tasks-outputs
-
-Retrieve your latest crew.kickoff() task outputs.
-
-```shell Terminal
-crewai log-tasks-outputs
-```
-
-### 6. Reset-memories
-
-Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
-
-```shell Terminal
-crewai reset-memories [OPTIONS]
-```
-
-- `-l, --long`: Reset LONG TERM memory
-- `-s, --short`: Reset SHORT TERM memory
-- `-e, --entities`: Reset ENTITIES memory
-- `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
-- `-a, --all`: Reset ALL memories
-
-Example:
-```shell Terminal
-crewai reset-memories --long --short
-crewai reset-memories --all
-```
-
-### 7. Test
-
-Test the crew and evaluate the results.
-
-```shell Terminal
-crewai test [OPTIONS]
-```
-
-- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
-- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
-
-Example:
-```shell Terminal    
-crewai test -n 5 -m gpt-3.5-turbo
-```
-
-### 8. Run
-
-Run the crew or flow.
-
-```shell Terminal
-crewai run
-```
-
-<Note>
-Starting from version 0.103.0, the `crewai run` command can be used to run both standard crews and flows. For flows, it automatically detects the type from pyproject.toml and runs the appropriate command. This is now the recommended way to run both crews and flows.
-</Note>
-
-<Note>
-Make sure to run these commands from the directory where your CrewAI project is set up. 
-Some commands may require additional configuration or setup within your project structure.
-</Note>
-
-### 9. Chat
-
-Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
-
-After receiving the results, you can continue interacting with the assistant for further instructions or questions.
-
-```shell Terminal
-crewai chat
-```
-<Note>
-Ensure you execute these commands from your CrewAI project's root directory.
-</Note>
-<Note>
-IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
-
-```python
-@crew
-def crew(self) -> Crew:
-    return Crew(
-        agents=self.agents,
-        tasks=self.tasks,
-        process=Process.sequential,
-        verbose=True,
-        chat_llm="gpt-4o",  # LLM for chat orchestration
-    )
-```
-</Note>
-
-### 10. API Keys
-
-When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
-
-Once you've selected an LLM provider, you will be prompted for API keys.
-
-#### Initial API key providers
-
-The CLI will initially prompt for API keys for the following services:
-
-* OpenAI
-* Groq
-* Anthropic
-* Google Gemini
-* SambaNova
-
-When you select a provider, the CLI will prompt you to enter your API key.
-
-#### Other Options
-
-If you select option 6, you will be able to select from a list of LiteLLM supported providers.
-
-When you select a provider, the CLI will prompt you to enter the Key name and the API key.
-
-See the following link for each provider's key name:
-
-* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
-
-
-

docs/concepts/collaboration.mdx

@@ -1,52 +0,0 @@
----
-title: Collaboration
-description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.
-icon: screen-users
----
-
-## Collaboration Fundamentals      
-
-Collaboration in CrewAI is fundamental, enabling agents to combine their skills, share information, and assist each other in task execution, embodying a truly cooperative ecosystem.
-
-- **Information Sharing**: Ensures all agents are well-informed and can contribute effectively by sharing data and findings.
-- **Task Assistance**: Allows agents to seek help from peers with the required expertise for specific tasks.
-- **Resource Allocation**: Optimizes task execution through the efficient distribution and sharing of resources among agents.
-
-## Enhanced Attributes for Improved Collaboration
-
-The `Crew` class has been enriched with several attributes to support advanced functionalities:
-
-| Feature                                    | Description                                                                                                                                                                                                                  |
-|:-------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| **Language Model Management** (`manager_llm`, `function_calling_llm`) | Manages language models for executing tasks and tools. `manager_llm` is required for hierarchical processes, while `function_calling_llm` is optional with a default value for streamlined interactions.                    |
-| **Custom Manager Agent** (`manager_agent`) | Specifies a custom agent as the manager, replacing the default CrewAI manager.                                                                                                                                                |
-| **Process Flow** (`process`)           | Defines execution logic (e.g., sequential, hierarchical) for task distribution.                                                                                                                                                |
-| **Verbose Logging** (`verbose`)        | Provides detailed logging for monitoring and debugging. Accepts integer and boolean values to control verbosity level.                                                                                                       |
-| **Rate Limiting** (`max_rpm`)          | Limits requests per minute to optimize resource usage. Setting guidelines depend on task complexity and load.                                                                                                                 |
-| **Internationalization / Customization** (`language`, `prompt_file`) | Supports prompt customization for global usability. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json)                                                                                      |
-| **Execution and Output Handling** (`full_output`) | Controls output granularity, distinguishing between full and final outputs.                                                                                                                                                  |
-| **Callback and Telemetry** (`step_callback`, `task_callback`) | Enables step-wise and task-level execution monitoring and telemetry for performance analytics.                                                                                                                               |
-| **Crew Sharing** (`share_crew`)        | Allows sharing crew data with CrewAI for model improvement. Privacy implications and benefits should be considered.                                                                                                          |
-| **Usage Metrics** (`usage_metrics`)    | Logs all LLM usage metrics during task execution for performance insights.                                                                                                                           |
-| **Memory Usage** (`memory`)            | Enables memory for storing execution history, aiding in agent learning and task efficiency.                                                                                                                                  |
-| **Embedder Configuration** (`embedder`) | Configures the embedder for language understanding and generation, with support for provider customization.                                                                                                                   |
-| **Cache Management** (`cache`)         | Specifies whether to cache tool execution results, enhancing performance.                                                                                                                                                   |
-| **Output Logging** (`output_log_file`) | Defines the file path for logging crew execution output.                                                                                                                                                                      |
-| **Planning Mode** (`planning`)         | Enables action planning before task execution. Set `planning=True` to activate.                                                                                                                                              |
-| **Replay Feature** (`replay`)         | Provides CLI for listing tasks from the last run and replaying from specific tasks, aiding in task management and troubleshooting.                                                                                           |
-
-## Delegation (Dividing to Conquer)
-
-Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.
-
-## Implementing Collaboration and Delegation
-
-Setting up a crew involves defining the roles and capabilities of each agent. CrewAI seamlessly manages their interactions, ensuring efficient collaboration and delegation, with enhanced customization and monitoring features to adapt to various operational needs.
-
-## Example Scenario
-
-Consider a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The integration of advanced language model management and process flow attributes allows for more sophisticated interactions, such as the writer delegating complex research tasks to the researcher or querying specific information, thereby facilitating a seamless workflow.
-
-## Conclusion
-
-The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.

docs/concepts/knowledge.mdx

@@ -1,626 +0,0 @@
----
-title: Knowledge
-description: What is knowledge in CrewAI and how to use it.
-icon: book
----
-
-## What is Knowledge?
-
-Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
-Think of it as giving your agents a reference library they can consult while working.
-
-<Info>
-  Key benefits of using Knowledge:
-  - Enhance agents with domain-specific information
-  - Support decisions with real-world data
-  - Maintain context across conversations
-  - Ground responses in factual information
-</Info>
-
-## Supported Knowledge Sources
-
-CrewAI supports various types of knowledge sources out of the box:
-
-<CardGroup cols={2}>
-  <Card title="Text Sources" icon="text">
-    - Raw strings
-    - Text files (.txt)
-    - PDF documents
-  </Card>
-  <Card title="Structured Data" icon="table">
-    - CSV files
-    - Excel spreadsheets
-    - JSON documents
-  </Card>
-</CardGroup>
-
-## Supported Knowledge Parameters
-
-| Parameter                    | Type                                | Required | Description                                                                                                                                           |
-| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `sources`                  | **List[BaseKnowledgeSource]**        | Yes      | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.           |
-| `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
-| `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |
-
-## Quickstart Example
-
-<Tip>
-For file-Based Knowledge Sources, make sure to place your files in a `knowledge` directory at the root of your project. 
-Also, use relative paths from the `knowledge` directory when creating the source.
-</Tip>
-
-Here's an example using string-based knowledge:
-
-```python Code
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-
-# Create a knowledge source
-content = "Users name is John. He is 30 years old and lives in San Francisco."
-string_source = StringKnowledgeSource(
-    content=content,
-)
-
-# Create an LLM with a temperature of 0 to ensure deterministic outputs
-llm = LLM(model="gpt-4o-mini", temperature=0)
-
-# Create an agent with the knowledge store
-agent = Agent(
-    role="About User",
-    goal="You know everything about the user.",
-    backstory="""You are a master at understanding people and their preferences.""",
-    verbose=True,
-    allow_delegation=False,
-    llm=llm,
-)
-task = Task(
-    description="Answer the following questions about the user: {question}",
-    expected_output="An answer to the question.",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    verbose=True,
-    process=Process.sequential,
-    knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
-)
-
-result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
-```
-
-
-Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including MD, PDF, DOCX, HTML, and more. 
-
-<Note>
-  You need to install `docling` for the following example to work: `uv add docling`
-</Note>
-
-
-
-```python Code
-from crewai import LLM, Agent, Crew, Process, Task
-from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
-
-# Create a knowledge source
-content_source = CrewDoclingSource(
-    file_paths=[
-        "https://lilianweng.github.io/posts/2024-11-28-reward-hacking",
-        "https://lilianweng.github.io/posts/2024-07-07-hallucination",
-    ],
-)
-
-# Create an LLM with a temperature of 0 to ensure deterministic outputs
-llm = LLM(model="gpt-4o-mini", temperature=0)
-
-# Create an agent with the knowledge store
-agent = Agent(
-    role="About papers",
-    goal="You know everything about the papers.",
-    backstory="""You are a master at understanding papers and their content.""",
-    verbose=True,
-    allow_delegation=False,
-    llm=llm,
-)
-task = Task(
-    description="Answer the following questions about the papers: {question}",
-    expected_output="An answer to the question.",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    verbose=True,
-    process=Process.sequential,
-    knowledge_sources=[
-        content_source
-    ],  # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
-)
-
-result = crew.kickoff(
-    inputs={
-        "question": "What is the reward hacking paper about? Be sure to provide sources."
-    }
-)
-```
-
-## More Examples
-
-Here are examples of how to use different types of knowledge sources:
-
-Note: Please ensure that you create the ./knowldge folder. All source files (e.g., .txt, .pdf, .xlsx, .json) should be placed in this folder for centralized management.
-
-### Text File Knowledge Source
-```python
-from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
-
-# Create a text file knowledge source
-text_source = TextFileKnowledgeSource(
-    file_paths=["document.txt", "another.txt"]
-)
-
-# Create crew with text file source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[text_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[text_source]
-)
-```
-
-### PDF Knowledge Source
-```python
-from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
-
-# Create a PDF knowledge source
-pdf_source = PDFKnowledgeSource(
-    file_paths=["document.pdf", "another.pdf"]
-)
-
-# Create crew with PDF knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[pdf_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[pdf_source]
-)
-```
-
-### CSV Knowledge Source
-```python
-from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
-
-# Create a CSV knowledge source
-csv_source = CSVKnowledgeSource(
-    file_paths=["data.csv"]
-)
-
-# Create crew with CSV knowledge source or on agent level
-agent = Agent(
-    ...
-    knowledge_sources=[csv_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[csv_source]
-)
-```
-
-### Excel Knowledge Source
-```python
-from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
-
-# Create an Excel knowledge source
-excel_source = ExcelKnowledgeSource(
-    file_paths=["spreadsheet.xlsx"]
-)
-
-# Create crew with Excel knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[excel_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[excel_source]
-)
-```
-
-### JSON Knowledge Source
-```python
-from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
-
-# Create a JSON knowledge source
-json_source = JSONKnowledgeSource(
-    file_paths=["data.json"]
-)
-
-# Create crew with JSON knowledge source on agents or crew level
-agent = Agent(
-    ...
-    knowledge_sources=[json_source]
-)
-
-crew = Crew(
-    ...
-    knowledge_sources=[json_source]
-)
-```
-
-## Knowledge Configuration
-
-### Chunking Configuration
-
-Knowledge sources automatically chunk content for better processing. 
-You can configure chunking behavior in your knowledge sources:
-
-```python
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-
-source = StringKnowledgeSource(
-    content="Your content here",
-    chunk_size=4000,      # Maximum size of each chunk (default: 4000)
-    chunk_overlap=200     # Overlap between chunks (default: 200)
-)
-```
-
-The chunking configuration helps in:
-- Breaking down large documents into manageable pieces
-- Maintaining context through chunk overlap
-- Optimizing retrieval accuracy
-
-### Embeddings Configuration
-
-You can also configure the embedder for the knowledge store. 
-This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
-The `embedder` parameter supports various embedding model providers that include:
-- `openai`: OpenAI's embedding models
-- `google`: Google's text embedding models
-- `azure`: Azure OpenAI embeddings
-- `ollama`: Local embeddings with Ollama
-- `vertexai`: Google Cloud VertexAI embeddings
-- `cohere`: Cohere's embedding models
-- `voyageai`: VoyageAI's embedding models
-- `bedrock`: AWS Bedrock embeddings
-- `huggingface`: Hugging Face models
-- `watson`: IBM Watson embeddings
-
-Here's an example of how to configure the embedder for the knowledge store using Google's `text-embedding-004` model:
-<CodeGroup>
-```python Example
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-import os
-
-# Get the GEMINI API key
-GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
-
-# Create a knowledge source
-content = "Users name is John. He is 30 years old and lives in San Francisco."
-string_source = StringKnowledgeSource(
-    content=content,
-)
-
-# Create an LLM with a temperature of 0 to ensure deterministic outputs
-gemini_llm = LLM(
-    model="gemini/gemini-1.5-pro-002",
-    api_key=GEMINI_API_KEY,
-    temperature=0,
-)
-
-# Create an agent with the knowledge store
-agent = Agent(
-    role="About User",
-    goal="You know everything about the user.",
-    backstory="""You are a master at understanding people and their preferences.""",
-    verbose=True,
-    allow_delegation=False,
-    llm=gemini_llm,
-    embedder={
-        "provider": "google",
-        "config": {
-            "model": "models/text-embedding-004",
-            "api_key": GEMINI_API_KEY,
-        }
-    }
-)
-
-task = Task(
-    description="Answer the following questions about the user: {question}",
-    expected_output="An answer to the question.",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    verbose=True,
-    process=Process.sequential,
-    knowledge_sources=[string_source],
-    embedder={
-        "provider": "google",
-        "config": {
-            "model": "models/text-embedding-004",
-            "api_key": GEMINI_API_KEY,
-        }
-    }
-)
-
-result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
-```
-```text Output
-# Agent: About User
-## Task: Answer the following questions about the user: What city does John live in and how old is he?
-
-# Agent: About User
-## Final Answer: 
-John is 30 years old and lives in San Francisco.
-```
-</CodeGroup>
-## Clearing Knowledge
-
-If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.
-
-```bash Command
-crewai reset-memories --knowledge
-```
-
-This is useful when you've updated your knowledge sources and want to ensure that the agents are using the most recent information.
-
-## Agent-Specific Knowledge
-
-While knowledge can be provided at the crew level using `crew.knowledge_sources`, individual agents can also have their own knowledge sources using the `knowledge_sources` parameter:
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-
-# Create agent-specific knowledge about a product
-product_specs = StringKnowledgeSource(
-    content="""The XPS 13 laptop features:
-    - 13.4-inch 4K display
-    - Intel Core i7 processor
-    - 16GB RAM
-    - 512GB SSD storage
-    - 12-hour battery life""",
-    metadata={"category": "product_specs"}
-)
-
-# Create a support agent with product knowledge
-support_agent = Agent(
-    role="Technical Support Specialist",
-    goal="Provide accurate product information and support.",
-    backstory="You are an expert on our laptop products and specifications.",
-    knowledge_sources=[product_specs]  # Agent-specific knowledge
-)
-
-# Create a task that requires product knowledge
-support_task = Task(
-    description="Answer this customer question: {question}",
-    agent=support_agent
-)
-
-# Create and run the crew
-crew = Crew(
-    agents=[support_agent],
-    tasks=[support_task]
-)
-
-# Get answer about the laptop's specifications
-result = crew.kickoff(
-    inputs={"question": "What is the storage capacity of the XPS 13?"}
-)
-```
-
-<Info>
-  Benefits of agent-specific knowledge:
-  - Give agents specialized information for their roles
-  - Maintain separation of concerns between agents
-  - Combine with crew-level knowledge for layered information access
-</Info>
-
-## Custom Knowledge Sources
-
-CrewAI allows you to create custom knowledge sources for any type of data by extending the `BaseKnowledgeSource` class. Let's create a practical example that fetches and processes space news articles.
-
-#### Space News Knowledge Source Example
-
-<CodeGroup>
-
-```python Code
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-import requests
-from datetime import datetime
-from typing import Dict, Any
-from pydantic import BaseModel, Field
-
-class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
-    """Knowledge source that fetches data from Space News API."""
-
-    api_endpoint: str = Field(description="API endpoint URL")
-    limit: int = Field(default=10, description="Number of articles to fetch")
-
-    def load_content(self) -> Dict[Any, str]:
-        """Fetch and format space news articles."""
-        try:
-            response = requests.get(
-                f"{self.api_endpoint}?limit={self.limit}"
-            )
-            response.raise_for_status()
-
-            data = response.json()
-            articles = data.get('results', [])
-
-            formatted_data = self.validate_content(articles)
-            return {self.api_endpoint: formatted_data}
-        except Exception as e:
-            raise ValueError(f"Failed to fetch space news: {str(e)}")
-
-    def validate_content(self, articles: list) -> str:
-        """Format articles into readable text."""
-        formatted = "Space News Articles:\n\n"
-        for article in articles:
-            formatted += f"""
-                Title: {article['title']}
-                Published: {article['published_at']}
-                Summary: {article['summary']}
-                News Site: {article['news_site']}
-                URL: {article['url']}
-                -------------------"""
-        return formatted
-
-    def add(self) -> None:
-        """Process and store the articles."""
-        content = self.load_content()
-        for _, text in content.items():
-            chunks = self._chunk_text(text)
-            self.chunks.extend(chunks)
-
-        self._save_documents()
-
-# Create knowledge source
-recent_news = SpaceNewsKnowledgeSource(
-    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
-    limit=10,
-)
-
-# Create specialized agent
-space_analyst = Agent(
-    role="Space News Analyst",
-    goal="Answer questions about space news accurately and comprehensively",
-    backstory="""You are a space industry analyst with expertise in space exploration,
-    satellite technology, and space industry trends. You excel at answering questions
-    about space news and providing detailed, accurate information.""",
-    knowledge_sources=[recent_news],
-    llm=LLM(model="gpt-4", temperature=0.0)
-)
-
-# Create task that handles user questions
-analysis_task = Task(
-    description="Answer this question about space news: {user_question}",
-    expected_output="A detailed answer based on the recent space news articles",
-    agent=space_analyst
-)
-
-# Create and run the crew
-crew = Crew(
-    agents=[space_analyst],
-    tasks=[analysis_task],
-    verbose=True,
-    process=Process.sequential
-)
-
-# Example usage
-result = crew.kickoff(
-    inputs={"user_question": "What are the latest developments in space exploration?"}
-)
-```
-
-```output Output
-# Agent: Space News Analyst
-## Task: Answer this question about space news: What are the latest developments in space exploration?
-
-
-# Agent: Space News Analyst
-## Final Answer:
-The latest developments in space exploration, based on recent space news articles, include the following:
-
-1. SpaceX has received the final regulatory approvals to proceed with the second integrated Starship/Super Heavy launch, scheduled for as soon as the morning of Nov. 17, 2023. This is a significant step in SpaceX's ambitious plans for space exploration and colonization. [Source: SpaceNews](https://spacenews.com/starship-cleared-for-nov-17-launch/)
-
-2. SpaceX has also informed the US Federal Communications Commission (FCC) that it plans to begin launching its first next-generation Starlink Gen2 satellites. This represents a major upgrade to the Starlink satellite internet service, which aims to provide high-speed internet access worldwide. [Source: Teslarati](https://www.teslarati.com/spacex-first-starlink-gen2-satellite-launch-2022/)
-
-3. AI startup Synthetaic has raised $15 million in Series B funding. The company uses artificial intelligence to analyze data from space and air sensors, which could have significant applications in space exploration and satellite technology. [Source: SpaceNews](https://spacenews.com/ai-startup-synthetaic-raises-15-million-in-series-b-funding/)
-
-4. The Space Force has formally established a unit within the U.S. Indo-Pacific Command, marking a permanent presence in the Indo-Pacific region. This could have significant implications for space security and geopolitics. [Source: SpaceNews](https://spacenews.com/space-force-establishes-permanent-presence-in-indo-pacific-region/)
-
-5. Slingshot Aerospace, a space tracking and data analytics company, is expanding its network of ground-based optical telescopes to increase coverage of low Earth orbit. This could improve our ability to track and analyze objects in low Earth orbit, including satellites and space debris. [Source: SpaceNews](https://spacenews.com/slingshots-space-tracking-network-to-extend-coverage-of-low-earth-orbit/)
-
-6. The National Natural Science Foundation of China has outlined a five-year project for researchers to study the assembly of ultra-large spacecraft. This could lead to significant advancements in spacecraft technology and space exploration capabilities. [Source: SpaceNews](https://spacenews.com/china-researching-challenges-of-kilometer-scale-ultra-large-spacecraft/)
-
-7. The Center for AEroSpace Autonomy Research (CAESAR) at Stanford University is focusing on spacecraft autonomy. The center held a kickoff event on May 22, 2024, to highlight the industry, academia, and government collaboration it seeks to foster. This could lead to significant advancements in autonomous spacecraft technology. [Source: SpaceNews](https://spacenews.com/stanford-center-focuses-on-spacecraft-autonomy/)
-```
-
-</CodeGroup>
-#### Key Components Explained
-
-1. **Custom Knowledge Source (`SpaceNewsKnowledgeSource`)**:
-
-   - Extends `BaseKnowledgeSource` for integration with CrewAI
-   - Configurable API endpoint and article limit
-   - Implements three key methods:
-     - `load_content()`: Fetches articles from the API
-     - `_format_articles()`: Structures the articles into readable text
-     - `add()`: Processes and stores the content
-
-2. **Agent Configuration**:
-
-   - Specialized role as a Space News Analyst
-   - Uses the knowledge source to access space news
-
-3. **Task Setup**:
-
-   - Takes a user question as input through `{user_question}`
-   - Designed to provide detailed answers based on the knowledge source
-
-4. **Crew Orchestration**:
-   - Manages the workflow between agent and task
-   - Handles input/output through the kickoff method
-
-This example demonstrates how to:
-
-- Create a custom knowledge source that fetches real-time data
-- Process and format external data for AI consumption
-- Use the knowledge source to answer specific user questions
-- Integrate everything seamlessly with CrewAI's agent system
-
-#### About the Spaceflight News API
-
-The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/docs/), which:
-
-- Provides free access to space-related news articles
-- Requires no authentication
-- Returns structured data about space news
-- Supports pagination and filtering
-
-You can customize the API query by modifying the endpoint URL:
-
-```python
-# Fetch more articles
-recent_news = SpaceNewsKnowledgeSource(
-    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
-    limit=20,  # Increase the number of articles
-)
-
-# Add search parameters
-recent_news = SpaceNewsKnowledgeSource(
-    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news
-    limit=10,
-)
-```
-
-## Best Practices
-
-<AccordionGroup>
-  <Accordion title="Content Organization">
-    - Keep chunk sizes appropriate for your content type
-    - Consider content overlap for context preservation
-    - Organize related information into separate knowledge sources
-  </Accordion>
-
-  <Accordion title="Performance Tips">
-    - Adjust chunk sizes based on content complexity 
-    - Configure appropriate embedding models
-    - Consider using local embedding providers for faster processing
-  </Accordion>
-</AccordionGroup>

docs/concepts/llamaindex-tools.mdx

@@ -1,71 +0,0 @@
----
-title: Using LlamaIndex Tools
-description: Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more.
-icon: toolbox
----
-
-## Using LlamaIndex Tools
-
-<Info>
-    CrewAI seamlessly integrates with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more. 
-</Info>
-
-Here are the available built-in tools offered by LlamaIndex.
-
-```python Code
-from crewai import Agent
-from crewai_tools import LlamaIndexTool
-
-# Example 1: Initialize from FunctionTool
-from llama_index.core.tools import FunctionTool
-
-your_python_function = lambda ...: ...
-og_tool = FunctionTool.from_defaults(
-    your_python_function, 
-    name="<name>", 
-    description='<description>'
-)
-tool = LlamaIndexTool.from_tool(og_tool)
-
-# Example 2: Initialize from LlamaHub Tools
-from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
-wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
-wolfram_tools = wolfram_spec.to_tool_list()
-tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
-
-# Example 3: Initialize Tool from a LlamaIndex Query Engine
-query_engine = index.as_query_engine()
-query_tool = LlamaIndexTool.from_query_engine(
-    query_engine,
-    name="Uber 2019 10K Query Tool",
-    description="Use this tool to lookup the 2019 Uber 10K Annual Report"
-)
-
-# Create and assign the tools to an agent
-agent = Agent(
-    role='Research Analyst',
-    goal='Provide up-to-date market analysis',
-    backstory='An expert analyst with a keen eye for market trends.',
-    tools=[tool, *tools, query_tool]
-)
-
-# rest of the code ...
-```
-
-## Steps to Get Started
-
-To effectively use the LlamaIndexTool, follow these steps:
-
-<Steps>
-    <Step title="Package Installation">
-    Make sure that `crewai[tools]` package is installed in your Python environment:
-    <CodeGroup>
-        ```shell Terminal
-        pip install 'crewai[tools]'
-        ```
-    </CodeGroup>
-    </Step>
-    <Step title="Install and Use LlamaIndex">
-    Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
-    </Step>
-</Steps>

docs/concepts/memory.mdx

@@ -1,574 +0,0 @@
----
-title: Memory
-description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
-icon: database
----
-
-## Introduction to Memory Systems in CrewAI
-
-The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. 
-This system comprises `short-term memory`, `long-term memory`, `entity memory`, and `contextual memory`, each serving a unique purpose in aiding agents to remember, 
-reason, and learn from past interactions.
-
-## Memory System Components
-
-| Component            | Description                                                                                                             |
-| :------------------- | :---------------------------------------------------------------------------------------------------------------------- |
-| **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.|
-| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
-| **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
-| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
-| **User Memory**      | Stores user-specific information and preferences, enhancing personalization and user experience. |
-
-## How Memory Systems Empower Agents
-
-1. **Contextual Awareness**: With short-term and contextual memory, agents gain the ability to maintain context over a conversation or task sequence, leading to more coherent and relevant responses.
-
-2. **Experience Accumulation**: Long-term memory allows agents to accumulate experiences, learning from past actions to improve future decision-making and problem-solving.
-
-3. **Entity Understanding**: By maintaining entity memory, agents can recognize and remember key entities, enhancing their ability to process and interact with complex information.
-
-## Implementing Memory in Your Crew
-
-When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
-By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. 
-The memory will use OpenAI embeddings by default, but you can change it by setting `embedder` to a different model. 
-It's also possible to initialize the memory instance with your own instance.
-
-The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG.
-The **Long-Term Memory** uses SQLite3 to store task results. Currently, there is no way to override these storage implementations.
-The data storage files are saved into a platform-specific location found using the appdirs package,
-and the name of the project can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
-
-### Example: Configuring Memory for a Crew
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-# Assemble your crew with memory capabilities
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True
-)
-```
-
-### Example: Use Custom Memory Instances e.g FAISS as the VectorDB
-
-```python Code
-from crewai import Crew, Process
-from crewai.memory import LongTermMemory, ShortTermMemory, EntityMemory
-from crewai.memory.storage.rag_storage import RAGStorage
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
-from typing import List, Optional
-
-# Assemble your crew with memory capabilities
-my_crew: Crew = Crew(
-    agents = [...],
-    tasks = [...],
-    process = Process.sequential,
-    memory = True,
-    # Long-term memory for persistent storage across sessions
-    long_term_memory = LongTermMemory(
-        storage=LTMSQLiteStorage(
-            db_path="/my_crew1/long_term_memory_storage.db"
-        )
-    ),
-    # Short-term memory for current context using RAG
-    short_term_memory = ShortTermMemory(
-        storage = RAGStorage(
-                embedder_config={
-                    "provider": "openai",
-                    "config": {
-                        "model": 'text-embedding-3-small'
-                    }
-                },
-                type="short_term",
-                path="/my_crew1/"
-            )
-        ),
-    ),
-    # Entity memory for tracking key information about entities
-    entity_memory = EntityMemory(
-        storage=RAGStorage(
-            embedder_config={
-                "provider": "openai",
-                "config": {
-                    "model": 'text-embedding-3-small'
-                }
-            },
-            type="short_term",
-            path="/my_crew1/"
-        )
-    ),
-    verbose=True,
-)
-```
-
-## Security Considerations
-
-When configuring memory storage:
-- Use environment variables for storage paths (e.g., `CREWAI_STORAGE_DIR`)
-- Never hardcode sensitive information like database credentials
-- Consider access permissions for storage directories
-- Use relative paths when possible to maintain portability
-
-Example using environment variables:
-```python
-import os
-from crewai import Crew
-from crewai.memory import LongTermMemory
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
-
-# Configure storage path using environment variable
-storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
-crew = Crew(
-    memory=True,
-    long_term_memory=LongTermMemory(
-        storage=LTMSQLiteStorage(
-            db_path="{storage_path}/memory.db".format(storage_path=storage_path)
-        )
-    )
-)
-```
-
-## Configuration Examples
-
-### Basic Memory Configuration
-```python
-from crewai import Crew
-from crewai.memory import LongTermMemory
-
-# Simple memory configuration
-crew = Crew(memory=True)  # Uses default storage locations
-```
-
-### Custom Storage Configuration
-```python
-from crewai import Crew
-from crewai.memory import LongTermMemory
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
-
-# Configure custom storage paths
-crew = Crew(
-    memory=True,
-    long_term_memory=LongTermMemory(
-        storage=LTMSQLiteStorage(db_path="./memory.db")
-    )
-)
-```
-
-## Integrating Mem0 for Enhanced User Memory
-
-[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 
-
-To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences.
-
-
-```python Code
-import os
-from crewai import Crew, Process
-from mem0 import MemoryClient
-
-# Set environment variables for Mem0
-os.environ["MEM0_API_KEY"] = "m0-xx"
-
-# Step 1: Record preferences based on past conversation or user input
-client = MemoryClient()
-messages = [
-    {"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."},
-    {"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"},
-    {"role": "user", "content": "I am more of a beach person than a mountain person."},
-    {"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"},
-    {"role": "user", "content": "I like Airbnb more."},
-]
-client.add(messages, user_id="john")
-
-# Step 2: Create a Crew with User Memory
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    verbose=True,
-    process=Process.sequential,
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john"},
-    },
-)
-```
-
-## Memory Configuration Options
-If you want to access a specific organization and project, you can set the `org_id` and `project_id` parameters in the memory configuration.
-
-```python Code
-from crewai import Crew
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    verbose=True,
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john", "org_id": "my_org_id", "project_id": "my_project_id"},
-    },
-)
-```
-
-## Additional Embedding Providers
-
-### Using OpenAI embeddings (already default)
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "model": 'text-embedding-3-small'
-        }
-    }
-)
-```
-Alternatively, you can directly pass the OpenAIEmbeddingFunction to the embedder parameter.
-
-Example:
-```python Code
-from crewai import Crew, Agent, Task, Process
-from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "model": 'text-embedding-3-small'
-        }
-    }
-)
-```
-
-### Using Ollama embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "ollama",
-        "config": {
-            "model": "mxbai-embed-large"
-        }
-    }
-)
-```
-
-### Using Google AI embeddings
-
-#### Prerequisites
-Before using Google AI embeddings, ensure you have:
-- Access to the Gemini API
-- The necessary API keys and permissions
-
-You will need to update your *pyproject.toml* dependencies:
-```YAML
-dependencies = [
-    "google-generativeai>=0.8.4", #main version in January/2025 - crewai v.0.100.0 and crewai-tools 0.33.0
-    "crewai[tools]>=0.100.0,<1.0.0"
-]
-```
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "google",
-        "config": {
-            "api_key": "<YOUR_API_KEY>",
-            "model": "<model_name>"
-        }
-    }
-)
-```
-
-### Using Azure OpenAI embeddings
-
-```python Code
-from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "api_key": "YOUR_API_KEY",
-            "api_base": "YOUR_API_BASE_PATH",
-            "api_version": "YOUR_API_VERSION",
-            "model_name": 'text-embedding-3-small'
-        }
-    }
-)
-```
-
-### Using Vertex AI embeddings
-
-```python Code
-from chromadb.utils.embedding_functions import GoogleVertexEmbeddingFunction
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "vertexai",
-        "config": {
-            "project_id"="YOUR_PROJECT_ID",
-            "region"="YOUR_REGION",
-            "api_key"="YOUR_API_KEY",
-            "model_name"="textembedding-gecko"
-        }
-    }
-)
-```
-
-### Using Cohere embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "cohere",
-        "config": {
-            "api_key": "YOUR_API_KEY",
-            "model": "<model_name>"
-        }
-    }
-)
-```
-### Using VoyageAI embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "voyageai",
-        "config": {
-            "api_key": "YOUR_API_KEY",
-            "model": "<model_name>"
-        }
-    }
-)
-```
-### Using HuggingFace embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "huggingface",
-        "config": {
-            "api_url": "<api_url>",
-        }
-    }
-)
-```
-
-### Using Watson embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-# Note: Ensure you have installed and imported `ibm_watsonx_ai` for Watson embeddings to work.
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "watson",
-        "config": {
-            "model": "<model_name>",
-            "api_url": "<api_url>",
-            "api_key": "<YOUR_API_KEY>",
-            "project_id": "<YOUR_PROJECT_ID>",
-        }
-    }
-)
-```
-
-### Using Amazon Bedrock embeddings
-
-```python Code
-# Note: Ensure you have installed `boto3` for Bedrock embeddings to work.
-
-import os
-import boto3
-from crewai import Crew, Agent, Task, Process
-
-boto3_session = boto3.Session(
-    region_name=os.environ.get("AWS_REGION_NAME"),
-    aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"),
-    aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY")
-)
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    embedder={
-    "provider": "bedrock",
-        "config":{
-            "session": boto3_session,
-            "model": "amazon.titan-embed-text-v2:0",
-            "vector_dimension": 1024
-        }
-    }
-    verbose=True
-)
-```
-
-### Adding Custom Embedding Function
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-from chromadb import Documents, EmbeddingFunction, Embeddings
-
-# Create a custom embedding function
-class CustomEmbedder(EmbeddingFunction):
-    def __call__(self, input: Documents) -> Embeddings:
-        # generate embeddings
-        return [1, 2, 3] # this is a dummy embedding
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "custom",
-        "config": {
-            "embedder": CustomEmbedder()
-        }
-    }
-)
-```
-
-### Resetting Memory via cli
-
-```shell
-crewai reset-memories [OPTIONS]
-```
-
-#### Resetting Memory Options
-
-| Option             | Description                      | Type           | Default |
-| :----------------- | :------------------------------- | :------------- | :------ |
-| `-l`, `--long`     | Reset LONG TERM memory.          | Flag (boolean) | False   |
-| `-s`, `--short`    | Reset SHORT TERM memory.         | Flag (boolean) | False   |
-| `-e`, `--entities` | Reset ENTITIES memory.           | Flag (boolean) | False   |
-| `-k`, `--kickoff-outputs` | Reset LATEST KICKOFF TASK OUTPUTS. | Flag (boolean) | False   |
-| `-kn`, `--knowledge` | Reset KNOWLEDEGE storage | Flag (boolean) | False   |
-| `-a`, `--all`      | Reset ALL memories.              | Flag (boolean) | False   |
-
-Note: To use the cli command you need to have your crew in a file called crew.py in the same directory.
-
-
-
-
-### Resetting Memory via crew object
-
-```python
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "custom",
-        "config": {
-            "embedder": CustomEmbedder()
-        }
-    }
-)
-
-my_crew.reset_memories(command_type = 'all') # Resets all the memory
-```
-
-#### Resetting Memory Options
-
-| Command Type       | Description                      |
-| :----------------- | :------------------------------- |
-| `long`             | Reset LONG TERM memory.          | 
-| `short`            | Reset SHORT TERM memory.         | 
-| `entities`         | Reset ENTITIES memory.           | 
-| `kickoff_outputs`  | Reset LATEST KICKOFF TASK OUTPUTS. |
-| `knowledge`        | Reset KNOWLEDGE memory.          |
-| `all`              | Reset ALL memories.              |
-
-
-## Benefits of Using CrewAI's Memory System
-
-- 🦾 **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
-- 🫡 **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
-- 🧠 **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
-
-## Conclusion
-
-Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, 
-you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

docs/concepts/training.mdx

@@ -1,67 +0,0 @@
----
-title: Training
-description: Learn how to train your CrewAI agents by giving them feedback early on and get consistent results.
-icon: dumbbell
----
-
-## Introduction
-
-The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
-By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
-
-During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. 
-This helps the agents improve their understanding, decision-making, and problem-solving abilities.
-
-### Training Your Crew Using the CLI
-
-To use the training feature, follow these steps:
-
-1. Open your terminal or command prompt.
-2. Navigate to the directory where your CrewAI project is located.
-3. Run the following command:
-
-```shell
-crewai train -n <n_iterations> <filename> (optional)
-```
-<Tip>
-  Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
-</Tip>
-
-### Training Your Crew Programmatically
-
-To train your crew programmatically, use the following steps:
-
-1. Define the number of iterations for training.
-2. Specify the input parameters for the training process.
-3. Execute the training command within a try-except block to handle potential errors.
-
-```python Code
-n_iterations = 2
-inputs = {"topic": "CrewAI Training"}
-filename = "your_model.pkl"
-
-try:
-    YourCrewName_Crew().crew().train(
-      n_iterations=n_iterations, 
-      inputs=inputs, 
-      filename=filename
-    )
-
-except Exception as e:
-    raise Exception(f"An error occurred while training the crew: {e}")
-```
-
-### Key Points to Note
-
-- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
-- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
-- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
-
-It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
-
-Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
-
-Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
-
-Happy training with CrewAI! 🚀
-  

docs/custom_llm.md

@@ -1,642 +0,0 @@
-# Custom LLM Implementations
-
-CrewAI now supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to create your own LLM implementations that don't rely on litellm's authentication mechanism.
-
-## Using Custom LLM Implementations
-
-To create a custom LLM implementation, you need to:
-
-1. Inherit from the `BaseLLM` abstract base class
-2. Implement the required methods:
-   - `call()`: The main method to call the LLM with messages
-   - `supports_function_calling()`: Whether the LLM supports function calling
-   - `supports_stop_words()`: Whether the LLM supports stop words
-   - `get_context_window_size()`: The context window size of the LLM
-
-## Example: Basic Custom LLM
-
-```python
-from crewai import BaseLLM
-from typing import Any, Dict, List, Optional, Union
-
-class CustomLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()  # Initialize the base class to set default attributes
-        if not api_key or not isinstance(api_key, str):
-            raise ValueError("Invalid API key: must be a non-empty string")
-        if not endpoint or not isinstance(endpoint, str):
-            raise ValueError("Invalid endpoint URL: must be a non-empty string")
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.stop = []  # You can customize stop words if needed
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Call the LLM with the given messages.
-        
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas for function calling.
-            callbacks: Optional list of callback functions.
-            available_functions: Optional dict mapping function names to callables.
-            
-        Returns:
-            Either a text response from the LLM or the result of a tool function call.
-            
-        Raises:
-            TimeoutError: If the LLM request times out.
-            RuntimeError: If the LLM request fails for other reasons.
-            ValueError: If the response format is invalid.
-        """
-        # Implement your own logic to call the LLM
-        # For example, using requests:
-        import requests
-        
-        try:
-            headers = {
-                "Authorization": f"Bearer {self.api_key}",
-                "Content-Type": "application/json"
-            }
-            
-            # Convert string message to proper format if needed
-            if isinstance(messages, str):
-                messages = [{"role": "user", "content": messages}]
-            
-            data = {
-                "messages": messages,
-                "tools": tools
-            }
-            
-            response = requests.post(
-                self.endpoint, 
-                headers=headers, 
-                json=data,
-                timeout=30  # Set a reasonable timeout
-            )
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            return response.json()["choices"][0]["message"]["content"]
-        except requests.Timeout:
-            raise TimeoutError("LLM request timed out")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-        except (KeyError, IndexError, ValueError) as e:
-            raise ValueError(f"Invalid response format: {str(e)}")
-        
-    def supports_function_calling(self) -> bool:
-        """Check if the LLM supports function calling.
-        
-        Returns:
-            True if the LLM supports function calling, False otherwise.
-        """
-        # Return True if your LLM supports function calling
-        return True
-        
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-        
-        Returns:
-            True if the LLM supports stop words, False otherwise.
-        """
-        # Return True if your LLM supports stop words
-        return True
-        
-    def get_context_window_size(self) -> int:
-        """Get the context window size of the LLM.
-        
-        Returns:
-            The context window size as an integer.
-        """
-        # Return the context window size of your LLM
-        return 8192
-```
-
-## Error Handling Best Practices
-
-When implementing custom LLMs, it's important to handle errors properly to ensure robustness and reliability. Here are some best practices:
-
-### 1. Implement Try-Except Blocks for API Calls
-
-Always wrap API calls in try-except blocks to handle different types of errors:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    try:
-        # API call implementation
-        response = requests.post(
-            self.endpoint,
-            headers=self.headers,
-            json=self.prepare_payload(messages),
-            timeout=30  # Set a reasonable timeout
-        )
-        response.raise_for_status()  # Raise an exception for HTTP errors
-        return response.json()["choices"][0]["message"]["content"]
-    except requests.Timeout:
-        raise TimeoutError("LLM request timed out")
-    except requests.RequestException as e:
-        raise RuntimeError(f"LLM request failed: {str(e)}")
-    except (KeyError, IndexError, ValueError) as e:
-        raise ValueError(f"Invalid response format: {str(e)}")
-```
-
-### 2. Implement Retry Logic for Transient Failures
-
-For transient failures like network issues or rate limiting, implement retry logic with exponential backoff:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    import time
-    
-    max_retries = 3
-    retry_delay = 1  # seconds
-    
-    for attempt in range(max_retries):
-        try:
-            response = requests.post(
-                self.endpoint,
-                headers=self.headers,
-                json=self.prepare_payload(messages),
-                timeout=30
-            )
-            response.raise_for_status()
-            return response.json()["choices"][0]["message"]["content"]
-        except (requests.Timeout, requests.ConnectionError) as e:
-            if attempt < max_retries - 1:
-                time.sleep(retry_delay * (2 ** attempt))  # Exponential backoff
-                continue
-            raise TimeoutError(f"LLM request failed after {max_retries} attempts: {str(e)}")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-```
-
-### 3. Validate Input Parameters
-
-Always validate input parameters to prevent runtime errors:
-
-```python
-def __init__(self, api_key: str, endpoint: str):
-    super().__init__()
-    if not api_key or not isinstance(api_key, str):
-        raise ValueError("Invalid API key: must be a non-empty string")
-    if not endpoint or not isinstance(endpoint, str):
-        raise ValueError("Invalid endpoint URL: must be a non-empty string")
-    self.api_key = api_key
-    self.endpoint = endpoint
-```
-
-### 4. Handle Authentication Errors Gracefully
-
-Provide clear error messages for authentication failures:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    try:
-        response = requests.post(self.endpoint, headers=self.headers, json=data)
-        if response.status_code == 401:
-            raise ValueError("Authentication failed: Invalid API key or token")
-        elif response.status_code == 403:
-            raise ValueError("Authorization failed: Insufficient permissions")
-        response.raise_for_status()
-        # Process response
-    except Exception as e:
-        # Handle error
-        raise
-```
-
-## Example: JWT-based Authentication
-
-For services that use JWT-based authentication instead of API keys, you can implement a custom LLM like this:
-
-```python
-from crewai import BaseLLM, Agent, Task
-from typing import Any, Dict, List, Optional, Union
-
-class JWTAuthLLM(BaseLLM):
-    def __init__(self, jwt_token: str, endpoint: str):
-        super().__init__()  # Initialize the base class to set default attributes
-        if not jwt_token or not isinstance(jwt_token, str):
-            raise ValueError("Invalid JWT token: must be a non-empty string")
-        if not endpoint or not isinstance(endpoint, str):
-            raise ValueError("Invalid endpoint URL: must be a non-empty string")
-        self.jwt_token = jwt_token
-        self.endpoint = endpoint
-        self.stop = []  # You can customize stop words if needed
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Call the LLM with JWT authentication.
-        
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas for function calling.
-            callbacks: Optional list of callback functions.
-            available_functions: Optional dict mapping function names to callables.
-            
-        Returns:
-            Either a text response from the LLM or the result of a tool function call.
-            
-        Raises:
-            TimeoutError: If the LLM request times out.
-            RuntimeError: If the LLM request fails for other reasons.
-            ValueError: If the response format is invalid.
-        """
-        # Implement your own logic to call the LLM with JWT authentication
-        import requests
-        
-        try:
-            headers = {
-                "Authorization": f"Bearer {self.jwt_token}",
-                "Content-Type": "application/json"
-            }
-            
-            # Convert string message to proper format if needed
-            if isinstance(messages, str):
-                messages = [{"role": "user", "content": messages}]
-            
-            data = {
-                "messages": messages,
-                "tools": tools
-            }
-            
-            response = requests.post(
-                self.endpoint,
-                headers=headers,
-                json=data,
-                timeout=30  # Set a reasonable timeout
-            )
-            
-            if response.status_code == 401:
-                raise ValueError("Authentication failed: Invalid JWT token")
-            elif response.status_code == 403:
-                raise ValueError("Authorization failed: Insufficient permissions")
-                
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            return response.json()["choices"][0]["message"]["content"]
-        except requests.Timeout:
-            raise TimeoutError("LLM request timed out")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-        except (KeyError, IndexError, ValueError) as e:
-            raise ValueError(f"Invalid response format: {str(e)}")
-        
-    def supports_function_calling(self) -> bool:
-        """Check if the LLM supports function calling.
-        
-        Returns:
-            True if the LLM supports function calling, False otherwise.
-        """
-        return True
-        
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-        
-        Returns:
-            True if the LLM supports stop words, False otherwise.
-        """
-        return True
-        
-    def get_context_window_size(self) -> int:
-        """Get the context window size of the LLM.
-        
-        Returns:
-            The context window size as an integer.
-        """
-        return 8192
-```
-
-## Troubleshooting
-
-Here are some common issues you might encounter when implementing custom LLMs and how to resolve them:
-
-### 1. Authentication Failures
-
-**Symptoms**: 401 Unauthorized or 403 Forbidden errors
-
-**Solutions**:
-- Verify that your API key or JWT token is valid and not expired
-- Check that you're using the correct authentication header format
-- Ensure that your token has the necessary permissions
-
-### 2. Timeout Issues
-
-**Symptoms**: Requests taking too long or timing out
-
-**Solutions**:
-- Implement timeout handling as shown in the examples
-- Use retry logic with exponential backoff
-- Consider using a more reliable network connection
-
-### 3. Response Parsing Errors
-
-**Symptoms**: KeyError, IndexError, or ValueError when processing responses
-
-**Solutions**:
-- Validate the response format before accessing nested fields
-- Implement proper error handling for malformed responses
-- Check the API documentation for the expected response format
-
-### 4. Rate Limiting
-
-**Symptoms**: 429 Too Many Requests errors
-
-**Solutions**:
-- Implement rate limiting in your custom LLM
-- Add exponential backoff for retries
-- Consider using a token bucket algorithm for more precise rate control
-
-## Advanced Features
-
-### Logging
-
-Adding logging to your custom LLM can help with debugging and monitoring:
-
-```python
-import logging
-from typing import Any, Dict, List, Optional, Union
-
-class LoggingLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.logger = logging.getLogger("crewai.llm.custom")
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        self.logger.info(f"Calling LLM with {len(messages) if isinstance(messages, list) else 1} messages")
-        try:
-            # API call implementation
-            response = self._make_api_call(messages, tools)
-            self.logger.debug(f"LLM response received: {response[:100]}...")
-            return response
-        except Exception as e:
-            self.logger.error(f"LLM call failed: {str(e)}")
-            raise
-```
-
-### Rate Limiting
-
-Implementing rate limiting can help avoid overwhelming the LLM API:
-
-```python
-import time
-from typing import Any, Dict, List, Optional, Union
-
-class RateLimitedLLM(BaseLLM):
-    def __init__(
-        self, 
-        api_key: str, 
-        endpoint: str, 
-        requests_per_minute: int = 60
-    ):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.requests_per_minute = requests_per_minute
-        self.request_times: List[float] = []
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        self._enforce_rate_limit()
-        # Record this request time
-        self.request_times.append(time.time())
-        # Make the actual API call
-        return self._make_api_call(messages, tools)
-        
-    def _enforce_rate_limit(self) -> None:
-        """Enforce the rate limit by waiting if necessary."""
-        now = time.time()
-        # Remove request times older than 1 minute
-        self.request_times = [t for t in self.request_times if now - t < 60]
-        
-        if len(self.request_times) >= self.requests_per_minute:
-            # Calculate how long to wait
-            oldest_request = min(self.request_times)
-            wait_time = 60 - (now - oldest_request)
-            if wait_time > 0:
-                time.sleep(wait_time)
-```
-
-### Metrics Collection
-
-Collecting metrics can help you monitor your LLM usage:
-
-```python
-import time
-from typing import Any, Dict, List, Optional, Union
-
-class MetricsCollectingLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.metrics: Dict[str, Any] = {
-            "total_calls": 0,
-            "total_tokens": 0,
-            "errors": 0,
-            "latency": []
-        }
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        start_time = time.time()
-        self.metrics["total_calls"] += 1
-        
-        try:
-            response = self._make_api_call(messages, tools)
-            # Estimate tokens (simplified)
-            if isinstance(messages, str):
-                token_estimate = len(messages) // 4
-            else:
-                token_estimate = sum(len(m.get("content", "")) // 4 for m in messages)
-            self.metrics["total_tokens"] += token_estimate
-            return response
-        except Exception as e:
-            self.metrics["errors"] += 1
-            raise
-        finally:
-            latency = time.time() - start_time
-            self.metrics["latency"].append(latency)
-            
-    def get_metrics(self) -> Dict[str, Any]:
-        """Return the collected metrics."""
-        avg_latency = sum(self.metrics["latency"]) / len(self.metrics["latency"]) if self.metrics["latency"] else 0
-        return {
-            **self.metrics,
-            "avg_latency": avg_latency
-        }
-```
-
-## Advanced Usage: Function Calling
-
-If your LLM supports function calling, you can implement the function calling logic in your custom LLM:
-
-```python
-import json
-from typing import Any, Dict, List, Optional, Union
-
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    import requests
-    
-    try:
-        headers = {
-            "Authorization": f"Bearer {self.jwt_token}",
-            "Content-Type": "application/json"
-        }
-        
-        # Convert string message to proper format if needed
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
-        
-        data = {
-            "messages": messages,
-            "tools": tools
-        }
-        
-        response = requests.post(
-            self.endpoint,
-            headers=headers,
-            json=data,
-            timeout=30
-        )
-        response.raise_for_status()
-        response_data = response.json()
-        
-        # Check if the LLM wants to call a function
-        if response_data["choices"][0]["message"].get("tool_calls"):
-            tool_calls = response_data["choices"][0]["message"]["tool_calls"]
-            
-            # Process each tool call
-            for tool_call in tool_calls:
-                function_name = tool_call["function"]["name"]
-                function_args = json.loads(tool_call["function"]["arguments"])
-                
-                if available_functions and function_name in available_functions:
-                    function_to_call = available_functions[function_name]
-                    function_response = function_to_call(**function_args)
-                    
-                    # Add the function response to the messages
-                    messages.append({
-                        "role": "tool",
-                        "tool_call_id": tool_call["id"],
-                        "name": function_name,
-                        "content": str(function_response)
-                    })
-            
-            # Call the LLM again with the updated messages
-            return self.call(messages, tools, callbacks, available_functions)
-        
-        # Return the text response if no function call
-        return response_data["choices"][0]["message"]["content"]
-    except requests.Timeout:
-        raise TimeoutError("LLM request timed out")
-    except requests.RequestException as e:
-        raise RuntimeError(f"LLM request failed: {str(e)}")
-    except (KeyError, IndexError, ValueError) as e:
-        raise ValueError(f"Invalid response format: {str(e)}")
-```
-
-## Using Your Custom LLM with CrewAI
-
-Once you've implemented your custom LLM, you can use it with CrewAI agents and crews:
-
-```python
-from crewai import Agent, Task, Crew
-from typing import Dict, Any
-
-# Create your custom LLM instance
-jwt_llm = JWTAuthLLM(
-    jwt_token="your.jwt.token", 
-    endpoint="https://your-llm-endpoint.com/v1/chat/completions"
-)
-
-# Use it with an agent
-agent = Agent(
-    role="Research Assistant",
-    goal="Find information on a topic",
-    backstory="You are a research assistant tasked with finding information.",
-    llm=jwt_llm,
-)
-
-# Create a task for the agent
-task = Task(
-    description="Research the benefits of exercise",
-    agent=agent,
-    expected_output="A summary of the benefits of exercise",
-)
-
-# Execute the task
-result = agent.execute_task(task)
-print(result)
-
-# Or use it with a crew
-crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    manager_llm=jwt_llm,  # Use your custom LLM for the manager
-)
-
-# Run the crew
-result = crew.kickoff()
-print(result)
-```
-
-## Implementing Your Own Authentication Mechanism
-
-The `BaseLLM` class allows you to implement any authentication mechanism you need, not just JWT or API keys. You can use:
-
-- OAuth tokens
-- Client certificates
-- Custom headers
-- Session-based authentication
-- Any other authentication method required by your LLM provider
-
-Simply implement the appropriate authentication logic in your custom LLM class.

docs/en/api-reference/inputs.mdx

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

docs/en/api-reference/introduction.mdx

@@ -0,0 +1,120 @@
+---
+title: "Introduction"
+description: "Complete reference for the CrewAI AMP REST API"
+icon: "code"
+mode: "wide"
+---
+
+# CrewAI AMP API
+
+Welcome to the CrewAI AMP API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
+
+## Quick Start
+
+<Steps>
+  <Step title="Get Your API Credentials">
+    Navigate to your crew's detail page in the CrewAI AMP dashboard and copy your Bearer Token from the Status tab.
+  </Step>
+
+  <Step title="Discover Required Inputs">
+    Use the `GET /inputs` endpoint to see what parameters your crew expects.
+  </Step>
+
+  <Step title="Start a Crew Execution">
+    Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`.
+  </Step>
+
+  <Step title="Monitor Progress">
+    Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
+  </Step>
+</Steps>
+
+## Authentication
+
+All API requests require authentication using a Bearer token. Include your token in the `Authorization` header:
+
+```bash
+curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
+  https://your-crew-url.crewai.com/inputs
+```
+
+### Token Types
+
+| Token Type | Scope | Use Case |
+|:-----------|:--------|:----------|
+| **Bearer Token** | Organization-level access | Full crew operations, ideal for server-to-server integration |
+| **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
+
+<Tip>
+You can find both token types in the Status tab of your crew's detail page in the CrewAI AMP dashboard.
+</Tip>
+
+## Base URL
+
+Each deployed crew has its own unique API endpoint:
+
+```
+https://your-crew-name.crewai.com
+```
+
+Replace `your-crew-name` with your actual crew's URL from the dashboard.
+
+## Typical Workflow
+
+1. **Discovery**: Call `GET /inputs` to understand what your crew needs
+2. **Execution**: Submit inputs via `POST /kickoff` to start processing
+3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
+4. **Results**: Extract the final output from the completed response
+
+## Error Handling
+
+The API uses standard HTTP status codes:
+
+| Code | Meaning |
+|------|:--------|
+| `200` | Success |
+| `400` | Bad Request - Invalid input format |
+| `401` | Unauthorized - Invalid bearer token |
+| `404` | Not Found - Resource doesn't exist |
+| `422` | Validation Error - Missing required inputs |
+| `500` | Server Error - Contact support |
+
+## Interactive Testing
+
+<Info>
+**Why no "Send" button?** Since each CrewAI AMP user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
+</Info>
+
+Each endpoint page shows you:
+- ✅ **Exact request format** with all parameters
+- ✅ **Response examples** for success and error cases
+- ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.)
+- ✅ **Authentication examples** with proper Bearer token format
+
+### **To Test Your Actual API:**
+
+<CardGroup cols={2}>
+  <Card title="Copy cURL Examples" icon="terminal">
+    Copy the cURL examples and replace the URL + token with your real values
+  </Card>
+  <Card title="Use Postman/Insomnia" icon="play">
+    Import the examples into your preferred API testing tool
+  </Card>
+</CardGroup>
+
+**Example workflow:**
+1. **Copy this cURL example** from any endpoint page
+2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL
+3. **Replace the Bearer token** with your real token from the dashboard
+4. **Run the request** in your terminal or API client
+
+## Need Help?
+
+<CardGroup cols={2}>
+  <Card title="Enterprise Support" icon="headset" href="mailto:support@crewai.com">
+    Get help with API integration and troubleshooting
+  </Card>
+  <Card title="Enterprise Dashboard" icon="chart-line" href="https://app.crewai.com">
+    Manage your crews and view execution logs
+  </Card>
+</CardGroup>

docs/en/api-reference/kickoff.mdx

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

docs/en/api-reference/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

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

docs/en/concepts/agents.mdx

@@ -2,6 +2,7 @@
 title: Agents
 description: Detailed guide on creating and managing agents within the CrewAI framework.
 icon: robot
+mode: "wide"
 ---
 
 ## Overview of an Agent
@@ -18,6 +19,18 @@ In the CrewAI framework, an `Agent` is an autonomous unit that can:
 Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a `Researcher` agent might excel at gathering and analyzing information, while a `Writer` agent might be better at creating content.
 </Tip>
 
+<Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
+CrewAI AMP includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
+
+![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
+
+The Visual Agent Builder enables:
+- Intuitive agent configuration with form-based interfaces
+- Real-time testing and validation
+- Template library with pre-configured agent types
+- Easy customization of agent attributes and behaviors
+</Note>
+
 ## Agent Attributes
 
 | Attribute                               | Parameter                | Type                          | Description                                                                                                          |
@@ -31,7 +44,6 @@ Think of an agent as a specialized team member with specific skills, expertise,
 | **Max Iterations** _(optional)_         | `max_iter`               | `int`                         | Maximum iterations before the agent must provide its best answer. Default is 20.                                      |
 | **Max RPM** _(optional)_                | `max_rpm`                | `Optional[int]`               | Maximum requests per minute to avoid rate limits.                                                                     |
 | **Max Execution Time** _(optional)_     | `max_execution_time`     | `Optional[int]`               | Maximum time (in seconds) for task execution.                                                                         |
-| **Memory** _(optional)_                 | `memory`                 | `bool`                        | Whether the agent should maintain memory of interactions. Default is True.                                            |
 | **Verbose** _(optional)_                | `verbose`                | `bool`                        | Enable detailed execution logs for debugging. Default is False.                                                       |
 | **Allow Delegation** _(optional)_       | `allow_delegation`       | `bool`                        | Allow the agent to delegate tasks to other agents. Default is False.                                                  |
 | **Step Callback** _(optional)_          | `step_callback`          | `Optional[Any]`               | Function called after each agent step, overrides crew callback.                                                       |
@@ -43,6 +55,11 @@ Think of an agent as a specialized team member with specific skills, expertise,
 | **Max Retry Limit** _(optional)_        | `max_retry_limit`        | `int`                         | Maximum number of retries when an error occurs. Default is 2.                                                         |
 | **Respect Context Window** _(optional)_ | `respect_context_window` | `bool`                        | Keep messages under context window size by summarizing. Default is True.                                              |
 | **Code Execution Mode** _(optional)_    | `code_execution_mode`    | `Literal["safe", "unsafe"]`   | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'.                               |
+| **Multimodal** _(optional)_             | `multimodal`             | `bool`                        | Whether the agent supports multimodal capabilities. Default is False.                                                  |
+| **Inject Date** _(optional)_            | `inject_date`            | `bool`                        | Whether to automatically inject the current date into tasks. Default is False.                                         |
+| **Date Format** _(optional)_            | `date_format`            | `str`                         | Format string for date when inject_date is enabled. Default is "%Y-%m-%d" (ISO format).                               |
+| **Reasoning** _(optional)_              | `reasoning`              | `bool`                        | Whether the agent should reflect and create a plan before executing a task. Default is False.                         |
+| **Max Reasoning Attempts** _(optional)_ | `max_reasoning_attempts` | `Optional[int]`               | Maximum number of reasoning attempts before executing the task. If None, will try until ready.                         |
 | **Embedder** _(optional)_               | `embedder`               | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
 | **Knowledge Sources** _(optional)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent.                                                                     |
 | **Use System Prompt** _(optional)_      | `use_system_prompt`      | `Optional[bool]`              | Whether to use system prompt (for o1 model support). Default is True.                                                 |
@@ -55,7 +72,7 @@ There are two ways to create agents in CrewAI: using **YAML configuration (recom
 
 Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
+After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
 
 <Note>
 Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
@@ -106,7 +123,7 @@ class LatestAiDevelopmentCrew():
   @agent
   def researcher(self) -> Agent:
     return Agent(
-      config=self.agents_config['researcher'],
+      config=self.agents_config['researcher'], # type: ignore[index]
       verbose=True,
       tools=[SerperDevTool()]
     )
@@ -114,7 +131,7 @@ class LatestAiDevelopmentCrew():
   @agent
   def reporting_analyst(self) -> Agent:
     return Agent(
-      config=self.agents_config['reporting_analyst'],
+      config=self.agents_config['reporting_analyst'], # type: ignore[index]
       verbose=True
     )
 ```
@@ -139,7 +156,6 @@ agent = Agent(
               "you excel at finding patterns in complex datasets.",
     llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
     function_calling_llm=None,  # Optional: Separate LLM for tool calling
-    memory=True,  # Default: True
     verbose=False,  # Default: False
     allow_delegation=False,  # Default: False
     max_iter=20,  # Default: 20 iterations
@@ -150,6 +166,11 @@ agent = Agent(
     code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
     respect_context_window=True,  # Default: True
     use_system_prompt=True,  # Default: True
+    multimodal=False,  # Default: False
+    inject_date=False,  # Default: False
+    date_format="%Y-%m-%d",  # Default: ISO format
+    reasoning=False,  # Default: False
+    max_reasoning_attempts=None,  # Default: None
     tools=[SerperDevTool()],  # Optional: List of tools
     knowledge_sources=None,  # Optional: List of knowledge sources
     embedder=None,  # Optional: Custom embedder configuration
@@ -214,6 +235,44 @@ custom_agent = Agent(
 )
 ```
 
+#### Date-Aware Agent with Reasoning
+```python Code
+strategic_agent = Agent(
+    role="Market Analyst",
+    goal="Track market movements with precise date references and strategic planning",
+    backstory="Expert in time-sensitive financial analysis and strategic reporting",
+    inject_date=True,  # Automatically inject current date into tasks
+    date_format="%B %d, %Y",  # Format as "May 21, 2025"
+    reasoning=True,  # Enable strategic planning
+    max_reasoning_attempts=2,  # Limit planning iterations
+    verbose=True
+)
+```
+
+#### Reasoning Agent
+```python Code
+reasoning_agent = Agent(
+    role="Strategic Planner",
+    goal="Analyze complex problems and create detailed execution plans",
+    backstory="Expert strategic planner who methodically breaks down complex challenges",
+    reasoning=True,  # Enable reasoning and planning
+    max_reasoning_attempts=3,  # Limit reasoning attempts
+    max_iter=30,  # Allow more iterations for complex planning
+    verbose=True
+)
+```
+
+#### Multimodal Agent
+```python Code
+multimodal_agent = Agent(
+    role="Visual Content Analyst",
+    goal="Analyze and process both text and visual content",
+    backstory="Specialized in multimodal analysis combining text and image understanding",
+    multimodal=True,  # Enable multimodal capabilities
+    verbose=True
+)
+```
+
 ### Parameter Details
 
 #### Critical Parameters
@@ -233,17 +292,31 @@ custom_agent = Agent(
 
 #### Code Execution
 - `allow_code_execution`: Must be True to run code
-- `code_execution_mode`: 
+- `code_execution_mode`:
   - `"safe"`: Uses Docker (recommended for production)
   - `"unsafe"`: Direct execution (use only in trusted environments)
 
+<Note>
+  This runs a default Docker image. If you want to configure the docker image, the checkout the Code Interpreter Tool in the tools section.
+  Add the code interpreter tool as a tool in the agent as a tool parameter.
+  </Note>
+
+#### Advanced Features
+- `multimodal`: Enable multimodal capabilities for processing text and visual content
+- `reasoning`: Enable agent to reflect and create plans before executing tasks
+- `inject_date`: Automatically inject current date into task descriptions
+
 #### Templates
 - `system_template`: Defines agent's core behavior
 - `prompt_template`: Structures input format
 - `response_template`: Formats agent responses
 
 <Note>
-When using custom templates, you can use variables like `{role}`, `{goal}`, and `{input}` in your templates. These will be automatically populated during execution.
+When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting.
+</Note>
+
+<Note>
+When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
 </Note>
 
 ## Agent Tools
@@ -290,6 +363,267 @@ analyst = Agent(
 When `memory` is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks.
 </Note>
 
+## Context Window Management
+
+CrewAI includes sophisticated automatic context window management to handle situations where conversations exceed the language model's token limits. This powerful feature is controlled by the `respect_context_window` parameter.
+
+### How Context Window Management Works
+
+When an agent's conversation history grows too large for the LLM's context window, CrewAI automatically detects this situation and can either:
+
+1. **Automatically summarize content** (when `respect_context_window=True`)
+2. **Stop execution with an error** (when `respect_context_window=False`)
+
+### Automatic Context Handling (`respect_context_window=True`)
+
+This is the **default and recommended setting** for most use cases. When enabled, CrewAI will:
+
+```python Code
+# Agent with automatic context management (default)
+smart_agent = Agent(
+    role="Research Analyst",
+    goal="Analyze large documents and datasets",
+    backstory="Expert at processing extensive information",
+    respect_context_window=True,  # 🔑 Default: auto-handle context limits
+    verbose=True
+)
+```
+
+**What happens when context limits are exceeded:**
+- ⚠️ **Warning message**: `"Context length exceeded. Summarizing content to fit the model context window."`
+- 🔄 **Automatic summarization**: CrewAI intelligently summarizes the conversation history
+- ✅ **Continued execution**: Task execution continues seamlessly with the summarized context
+- 📝 **Preserved information**: Key information is retained while reducing token count
+
+### Strict Context Limits (`respect_context_window=False`)
+
+When you need precise control and prefer execution to stop rather than lose any information:
+
+```python Code
+# Agent with strict context limits
+strict_agent = Agent(
+    role="Legal Document Reviewer",
+    goal="Provide precise legal analysis without information loss",
+    backstory="Legal expert requiring complete context for accurate analysis",
+    respect_context_window=False,  # ❌ Stop execution on context limit
+    verbose=True
+)
+```
+
+**What happens when context limits are exceeded:**
+- ❌ **Error message**: `"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools."`
+- 🛑 **Execution stops**: Task execution halts immediately
+- 🔧 **Manual intervention required**: You need to modify your approach
+
+### Choosing the Right Setting
+
+#### Use `respect_context_window=True` (Default) when:
+- **Processing large documents** that might exceed context limits
+- **Long-running conversations** where some summarization is acceptable
+- **Research tasks** where general context is more important than exact details
+- **Prototyping and development** where you want robust execution
+
+```python Code
+# Perfect for document processing
+document_processor = Agent(
+    role="Document Analyst",
+    goal="Extract insights from large research papers",
+    backstory="Expert at analyzing extensive documentation",
+    respect_context_window=True,  # Handle large documents gracefully
+    max_iter=50,  # Allow more iterations for complex analysis
+    verbose=True
+)
+```
+
+#### Use `respect_context_window=False` when:
+- **Precision is critical** and information loss is unacceptable
+- **Legal or medical tasks** requiring complete context
+- **Code review** where missing details could introduce bugs
+- **Financial analysis** where accuracy is paramount
+
+```python Code
+# Perfect for precision tasks
+precision_agent = Agent(
+    role="Code Security Auditor",
+    goal="Identify security vulnerabilities in code",
+    backstory="Security expert requiring complete code context",
+    respect_context_window=False,  # Prefer failure over incomplete analysis
+    max_retry_limit=1,  # Fail fast on context issues
+    verbose=True
+)
+```
+
+### Alternative Approaches for Large Data
+
+When dealing with very large datasets, consider these strategies:
+
+#### 1. Use RAG Tools
+```python Code
+from crewai_tools import RagTool
+
+# Create RAG tool for large document processing
+rag_tool = RagTool()
+
+rag_agent = Agent(
+    role="Research Assistant",
+    goal="Query large knowledge bases efficiently",
+    backstory="Expert at using RAG tools for information retrieval",
+    tools=[rag_tool],  # Use RAG instead of large context windows
+    respect_context_window=True,
+    verbose=True
+)
+```
+
+#### 2. Use Knowledge Sources
+```python Code
+# Use knowledge sources instead of large prompts
+knowledge_agent = Agent(
+    role="Knowledge Expert",
+    goal="Answer questions using curated knowledge",
+    backstory="Expert at leveraging structured knowledge sources",
+    knowledge_sources=[your_knowledge_sources],  # Pre-processed knowledge
+    respect_context_window=True,
+    verbose=True
+)
+```
+
+### Context Window Best Practices
+
+1. **Monitor Context Usage**: Enable `verbose=True` to see context management in action
+2. **Design for Efficiency**: Structure tasks to minimize context accumulation
+3. **Use Appropriate Models**: Choose LLMs with context windows suitable for your tasks
+4. **Test Both Settings**: Try both `True` and `False` to see which works better for your use case
+5. **Combine with RAG**: Use RAG tools for very large datasets instead of relying solely on context windows
+
+### Troubleshooting Context Issues
+
+**If you're getting context limit errors:**
+```python Code
+# Quick fix: Enable automatic handling
+agent.respect_context_window = True
+
+# Better solution: Use RAG tools for large data
+from crewai_tools import RagTool
+agent.tools = [RagTool()]
+
+# Alternative: Break tasks into smaller pieces
+# Or use knowledge sources instead of large prompts
+```
+
+**If automatic summarization loses important information:**
+```python Code
+# Disable auto-summarization and use RAG instead
+agent = Agent(
+    role="Detailed Analyst",
+    goal="Maintain complete information accuracy",
+    backstory="Expert requiring full context",
+    respect_context_window=False,  # No summarization
+    tools=[RagTool()],  # Use RAG for large data
+    verbose=True
+)
+```
+
+<Note>
+The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
+</Note>
+
+## Direct Agent Interaction with `kickoff()`
+
+Agents can be used directly without going through a task or crew workflow using the `kickoff()` method. This provides a simpler way to interact with an agent when you don't need the full crew orchestration capabilities.
+
+### How `kickoff()` Works
+
+The `kickoff()` method allows you to send messages directly to an agent and get a response, similar to how you would interact with an LLM but with all the agent's capabilities (tools, reasoning, etc.).
+
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool
+
+# Create an agent
+researcher = Agent(
+    role="AI Technology Researcher",
+    goal="Research the latest AI developments",
+    tools=[SerperDevTool()],
+    verbose=True
+)
+
+# Use kickoff() to interact directly with the agent
+result = researcher.kickoff("What are the latest developments in language models?")
+
+# Access the raw response
+print(result.raw)
+```
+
+### Parameters and Return Values
+
+| Parameter         | Type                                | Description                                                               |
+| :---------------- | :---------------------------------- | :------------------------------------------------------------------------ |
+| `messages`        | `Union[str, List[Dict[str, str]]]`  | Either a string query or a list of message dictionaries with role/content |
+| `response_format` | `Optional[Type[Any]]`               | Optional Pydantic model for structured output                             |
+
+The method returns a `LiteAgentOutput` object with the following properties:
+
+- `raw`: String containing the raw output text
+- `pydantic`: Parsed Pydantic model (if a `response_format` was provided)
+- `agent_role`: Role of the agent that produced the output
+- `usage_metrics`: Token usage metrics for the execution
+
+### Structured Output
+
+You can get structured output by providing a Pydantic model as the `response_format`:
+
+```python Code
+from pydantic import BaseModel
+from typing import List
+
+class ResearchFindings(BaseModel):
+    main_points: List[str]
+    key_technologies: List[str]
+    future_predictions: str
+
+# Get structured output
+result = researcher.kickoff(
+    "Summarize the latest developments in AI for 2025",
+    response_format=ResearchFindings
+)
+
+# Access structured data
+print(result.pydantic.main_points)
+print(result.pydantic.future_predictions)
+```
+
+### Multiple Messages
+
+You can also provide a conversation history as a list of message dictionaries:
+
+```python Code
+messages = [
+    {"role": "user", "content": "I need information about large language models"},
+    {"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
+    {"role": "user", "content": "What are the latest developments in 2025?"}
+]
+
+result = researcher.kickoff(messages)
+```
+
+### Async Support
+
+An asynchronous version is available via `kickoff_async()` with the same parameters:
+
+```python Code
+import asyncio
+
+async def main():
+    result = await researcher.kickoff_async("What are the latest developments in AI?")
+    print(result.raw)
+
+asyncio.run(main())
+```
+
+<Note>
+The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler execution flow while preserving all of the agent's configuration (role, goal, backstory, tools, etc.).
+</Note>
+
 ## Important Considerations and Best Practices
 
 ### Security and Code Execution
@@ -304,11 +638,17 @@ When `memory` is enabled, the agent will maintain context across multiple intera
 - Adjust `max_iter` and `max_retry_limit` based on task complexity
 
 ### Memory and Context Management
-- Use `memory: true` for tasks requiring historical context
 - Leverage `knowledge_sources` for domain-specific information
-- Configure `embedder_config` when using custom embedding models
+- Configure `embedder` when using custom embedding models
 - Use custom templates (`system_template`, `prompt_template`, `response_template`) for fine-grained control over agent behavior
 
+### Advanced Features
+- Enable `reasoning: true` for agents that need to plan and reflect before executing complex tasks
+- Set appropriate `max_reasoning_attempts` to control planning iterations (None for unlimited attempts)
+- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
+- Customize the date format with `date_format` using standard Python datetime format codes
+- Enable `multimodal: true` for agents that need to process both text and visual content
+
 ### Agent Collaboration
 - Enable `allow_delegation: true` when agents need to work together
 - Use `step_callback` to monitor and log agent interactions
@@ -316,6 +656,13 @@ When `memory` is enabled, the agent will maintain context across multiple intera
   - Main `llm` for complex reasoning
   - `function_calling_llm` for efficient tool usage
 
+### Date Awareness and Reasoning
+- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
+- Customize the date format with `date_format` using standard Python datetime format codes
+- Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
+- Invalid date formats will be logged as warnings and will not modify the task description
+- Enable `reasoning: true` for complex tasks that benefit from upfront planning and reflection
+
 ### Model Compatibility
 - Set `use_system_prompt: false` for older models that don't support system messages
 - Ensure your chosen `llm` supports the features you need (like function calling)
@@ -338,7 +685,6 @@ When `memory` is enabled, the agent will maintain context across multiple intera
    - Review code sandbox settings
 
 4. **Memory Issues**: If agent responses seem inconsistent:
-   - Verify memory is enabled
    - Check knowledge source configuration
    - Review conversation history management
 

docs/en/concepts/cli.mdx

@@ -0,0 +1,411 @@
+---
+title: CLI
+description: Learn how to use the CrewAI CLI to interact with CrewAI.
+icon: terminal
+mode: "wide"
+---
+
+<Warning>Since release 0.140.0, CrewAI AMP started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
+
+## Overview
+
+The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
+
+## Installation
+
+To use the CrewAI CLI, make sure you have CrewAI installed:
+
+```shell Terminal
+pip install crewai
+```
+
+## Basic Usage
+
+The basic structure of a CrewAI CLI command is:
+
+```shell Terminal
+crewai [COMMAND] [OPTIONS] [ARGUMENTS]
+```
+
+## Available Commands
+
+### 1. Create
+
+Create a new crew or flow.
+
+```shell Terminal
+crewai create [OPTIONS] TYPE NAME
+```
+
+- `TYPE`: Choose between "crew" or "flow"
+- `NAME`: Name of the crew or flow
+
+Example:
+```shell Terminal
+crewai create crew my_new_crew
+crewai create flow my_new_flow
+```
+
+### 2. Version
+
+Show the installed version of CrewAI.
+
+```shell Terminal
+crewai version [OPTIONS]
+```
+
+- `--tools`: (Optional) Show the installed version of CrewAI tools
+
+Example:
+```shell Terminal
+crewai version
+crewai version --tools
+```
+
+### 3. Train
+
+Train the crew for a specified number of iterations.
+
+```shell Terminal
+crewai train [OPTIONS]
+```
+
+- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
+- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
+
+Example:
+```shell Terminal
+crewai train -n 10 -f my_training_data.pkl
+```
+
+### 4. Replay
+
+Replay the crew execution from a specific task.
+
+```shell Terminal
+crewai replay [OPTIONS]
+```
+
+- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
+
+Example:
+```shell Terminal
+crewai replay -t task_123456
+```
+
+### 5. Log-tasks-outputs
+
+Retrieve your latest crew.kickoff() task outputs.
+
+```shell Terminal
+crewai log-tasks-outputs
+```
+
+### 6. Reset-memories
+
+Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
+
+```shell Terminal
+crewai reset-memories [OPTIONS]
+```
+
+- `-l, --long`: Reset LONG TERM memory
+- `-s, --short`: Reset SHORT TERM memory
+- `-e, --entities`: Reset ENTITIES memory
+- `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
+- `-kn, --knowledge`: Reset KNOWLEDGE storage
+- `-akn, --agent-knowledge`: Reset AGENT KNOWLEDGE storage
+- `-a, --all`: Reset ALL memories
+
+Example:
+```shell Terminal
+crewai reset-memories --long --short
+crewai reset-memories --all
+```
+
+### 7. Test
+
+Test the crew and evaluate the results.
+
+```shell Terminal
+crewai test [OPTIONS]
+```
+
+- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
+- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
+
+Example:
+```shell Terminal
+crewai test -n 5 -m gpt-3.5-turbo
+```
+
+### 8. Run
+
+Run the crew or flow.
+
+```shell Terminal
+crewai run
+```
+
+<Note>
+Starting from version 0.103.0, the `crewai run` command can be used to run both standard crews and flows. For flows, it automatically detects the type from pyproject.toml and runs the appropriate command. This is now the recommended way to run both crews and flows.
+</Note>
+
+<Note>
+Make sure to run these commands from the directory where your CrewAI project is set up.
+Some commands may require additional configuration or setup within your project structure.
+</Note>
+
+### 9. Chat
+
+Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
+
+After receiving the results, you can continue interacting with the assistant for further instructions or questions.
+
+```shell Terminal
+crewai chat
+```
+<Note>
+Ensure you execute these commands from your CrewAI project's root directory.
+</Note>
+<Note>
+IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
+
+```python
+@crew
+def crew(self) -> Crew:
+    return Crew(
+        agents=self.agents,
+        tasks=self.tasks,
+        process=Process.sequential,
+        verbose=True,
+        chat_llm="gpt-4o",  # LLM for chat orchestration
+    )
+```
+</Note>
+
+### 10. Deploy
+
+Deploy the crew or flow to [CrewAI AMP](https://app.crewai.com).
+
+- **Authentication**: You need to be authenticated to deploy to CrewAI AMP.
+    You can login or create an account with:
+    ```shell Terminal
+    crewai login
+    ```
+
+- **Create a deployment**: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.
+    ```shell Terminal
+    crewai deploy create
+    ```
+    - Reads your local project configuration.
+    - Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
+
+### 11. Organization Management
+
+Manage your CrewAI AMP organizations.
+
+```shell Terminal
+crewai org [COMMAND] [OPTIONS]
+```
+
+#### Commands:
+
+- `list`: List all organizations you belong to
+```shell Terminal
+crewai org list
+```
+
+- `current`: Display your currently active organization
+```shell Terminal
+crewai org current
+```
+
+- `switch`: Switch to a specific organization
+```shell Terminal
+crewai org switch <organization_id>
+```
+
+<Note>
+You must be authenticated to CrewAI AMP to use these organization management commands.
+</Note>
+
+- **Create a deployment** (continued):
+    - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
+
+- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI AMP.
+    ```shell Terminal
+    crewai deploy push
+    ```
+    - Initiates the deployment process on the CrewAI AMP platform.
+    - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
+
+- **Deployment Status**: You can check the status of your deployment with:
+    ```shell Terminal
+    crewai deploy status
+    ```
+    This fetches the latest deployment status of your most recent deployment attempt (e.g., `Building Images for Crew`, `Deploy Enqueued`, `Online`).
+
+- **Deployment Logs**: You can check the logs of your deployment with:
+    ```shell Terminal
+    crewai deploy logs
+    ```
+    This streams the deployment logs to your terminal.
+
+- **List deployments**: You can list all your deployments with:
+    ```shell Terminal
+    crewai deploy list
+    ```
+    This lists all your deployments.
+
+- **Delete a deployment**: You can delete a deployment with:
+    ```shell Terminal
+    crewai deploy remove
+    ```
+    This deletes the deployment from the CrewAI AMP platform.
+
+- **Help Command**: You can get help with the CLI with:
+    ```shell Terminal
+    crewai deploy --help
+    ```
+    This shows the help message for the CrewAI Deploy CLI.
+
+Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AMP](http://app.crewai.com) using the CLI.
+
+<iframe
+  className="w-full aspect-video rounded-xl"
+  src="https://www.youtube.com/embed/3EqSV-CYDZA"
+  title="CrewAI Deployment Guide"
+  frameBorder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+  allowFullScreen
+></iframe>
+
+### 11. Login
+
+Authenticate with CrewAI AMP 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.
+
+Once you've selected an LLM provider and model, you will be prompted for API keys.
+
+#### Available LLM Providers
+
+Here's a list of the most popular LLM providers suggested by the CLI:
+
+* OpenAI
+* Groq
+* Anthropic
+* Google Gemini
+* SambaNova
+
+When you select a provider, the CLI will then show you available models for that provider and prompt you to enter your API key.
+
+#### Other Options
+
+If you select "other", you will be able to select from a list of LiteLLM supported providers.
+
+When you select a provider, the CLI will prompt you to enter the Key name and the API key.
+
+See the following link for each provider's key name:
+
+* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
+
+### 13. Configuration Management
+
+Manage CLI configuration settings for CrewAI.
+
+```shell Terminal
+crewai config [COMMAND] [OPTIONS]
+```
+
+#### Commands:
+
+- `list`: Display all CLI configuration parameters
+```shell Terminal
+crewai config list
+```
+
+- `set`: Set a CLI configuration parameter
+```shell Terminal
+crewai config set <key> <value>
+```
+
+- `reset`: Reset all CLI configuration parameters to default values
+```shell Terminal
+crewai config reset
+```
+
+#### Available Configuration Parameters
+
+- `enterprise_base_url`: Base URL of the CrewAI AMP instance
+- `oauth2_provider`: OAuth2 provider used for authentication (e.g., workos, okta, auth0)
+- `oauth2_audience`: OAuth2 audience value, typically used to identify the target API or resource
+- `oauth2_client_id`: OAuth2 client ID issued by the provider, used during authentication requests
+- `oauth2_domain`: OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens
+
+#### Examples
+
+Display current configuration:
+```shell Terminal
+crewai config list
+```
+
+Example output:
+| Setting             | Value                    | Description                                                 |
+| :------------------ | :----------------------- | :---------------------------------------------------------- |
+| enterprise_base_url | https://app.crewai.com   | Base URL of the CrewAI AMP instance                  |
+| org_name            | Not set                  | Name of the currently active organization                   |
+| org_uuid            | Not set                  | UUID of the currently active organization                   |
+| oauth2_provider     | workos                   | OAuth2 provider (e.g., workos, okta, auth0)                 |
+| oauth2_audience     | client_01YYY             | Audience identifying the target API/resource                |
+| oauth2_client_id    | client_01XXX             | OAuth2 client ID issued by the provider                     |
+| oauth2_domain       | login.crewai.com         | Provider domain (e.g., your-org.auth0.com)                  |
+
+Set the enterprise base URL:
+```shell Terminal
+crewai config set enterprise_base_url https://my-enterprise.crewai.com
+```
+
+Set OAuth2 provider:
+```shell Terminal
+crewai config set oauth2_provider auth0
+```
+
+Set OAuth2 domain:
+```shell Terminal
+crewai config set oauth2_domain my-company.auth0.com
+```
+
+Reset all configuration to defaults:
+```shell Terminal
+crewai config reset
+```
+
+<Tip>
+After resetting configuration, re-run `crewai login` to authenticate again.
+</Tip>
+
+<Tip>
+CrewAI CLI handles authentication to the Tool Repository automatically when adding packages to your project. Just append `crewai` before any `uv` command to use it. E.g. `crewai uv add requests`. For more information, see [Tool Repository](https://docs.crewai.com/enterprise/features/tool-repository) docs.
+</Tip>
+
+<Note>
+Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
+</Note>

docs/en/concepts/collaboration.mdx

@@ -0,0 +1,363 @@
+---
+title: Collaboration
+description: How to enable agents to work together, delegate tasks, and communicate effectively within CrewAI teams.
+icon: screen-users
+mode: "wide"
+---
+
+## Overview
+
+Collaboration in CrewAI enables agents to work together as a team by delegating tasks and asking questions to leverage each other's expertise. When `allow_delegation=True`, agents automatically gain access to powerful collaboration tools.
+
+## Quick Start: Enable Collaboration
+
+```python
+from crewai import Agent, Crew, Task
+
+# Enable collaboration for agents
+researcher = Agent(
+    role="Research Specialist",
+    goal="Conduct thorough research on any topic",
+    backstory="Expert researcher with access to various sources",
+    allow_delegation=True,  # 🔑 Key setting for collaboration
+    verbose=True
+)
+
+writer = Agent(
+    role="Content Writer", 
+    goal="Create engaging content based on research",
+    backstory="Skilled writer who transforms research into compelling content",
+    allow_delegation=True,  # 🔑 Enables asking questions to other agents
+    verbose=True
+)
+
+# Agents can now collaborate automatically
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[...],
+    verbose=True
+)
+```
+
+## How Agent Collaboration Works
+
+When `allow_delegation=True`, CrewAI automatically provides agents with two powerful tools:
+
+### 1. **Delegate Work Tool**
+Allows agents to assign tasks to teammates with specific expertise.
+
+```python
+# Agent automatically gets this tool:
+# Delegate work to coworker(task: str, context: str, coworker: str)
+```
+
+### 2. **Ask Question Tool** 
+Enables agents to ask specific questions to gather information from colleagues.
+
+```python
+# Agent automatically gets this tool:
+# Ask question to coworker(question: str, context: str, coworker: str)
+```
+
+## Collaboration in Action
+
+Here's a complete example showing agents collaborating on a content creation task:
+
+```python
+from crewai import Agent, Crew, Task, Process
+
+# Create collaborative agents
+researcher = Agent(
+    role="Research Specialist",
+    goal="Find accurate, up-to-date information on any topic",
+    backstory="""You're a meticulous researcher with expertise in finding 
+    reliable sources and fact-checking information across various domains.""",
+    allow_delegation=True,
+    verbose=True
+)
+
+writer = Agent(
+    role="Content Writer",
+    goal="Create engaging, well-structured content",
+    backstory="""You're a skilled content writer who excels at transforming 
+    research into compelling, readable content for different audiences.""",
+    allow_delegation=True,
+    verbose=True
+)
+
+editor = Agent(
+    role="Content Editor",
+    goal="Ensure content quality and consistency",
+    backstory="""You're an experienced editor with an eye for detail, 
+    ensuring content meets high standards for clarity and accuracy.""",
+    allow_delegation=True,
+    verbose=True
+)
+
+# Create a task that encourages collaboration
+article_task = Task(
+    description="""Write a comprehensive 1000-word article about 'The Future of AI in Healthcare'.
+    
+    The article should include:
+    - Current AI applications in healthcare
+    - Emerging trends and technologies  
+    - Potential challenges and ethical considerations
+    - Expert predictions for the next 5 years
+    
+    Collaborate with your teammates to ensure accuracy and quality.""",
+    expected_output="A well-researched, engaging 1000-word article with proper structure and citations",
+    agent=writer  # Writer leads, but can delegate research to researcher
+)
+
+# Create collaborative crew
+crew = Crew(
+    agents=[researcher, writer, editor],
+    tasks=[article_task],
+    process=Process.sequential,
+    verbose=True
+)
+
+result = crew.kickoff()
+```
+
+## Collaboration Patterns
+
+### Pattern 1: Research → Write → Edit
+```python
+research_task = Task(
+    description="Research the latest developments in quantum computing",
+    expected_output="Comprehensive research summary with key findings and sources",
+    agent=researcher
+)
+
+writing_task = Task(
+    description="Write an article based on the research findings",
+    expected_output="Engaging 800-word article about quantum computing",
+    agent=writer,
+    context=[research_task]  # Gets research output as context
+)
+
+editing_task = Task(
+    description="Edit and polish the article for publication",
+    expected_output="Publication-ready article with improved clarity and flow",
+    agent=editor,
+    context=[writing_task]  # Gets article draft as context
+)
+```
+
+### Pattern 2: Collaborative Single Task
+```python
+collaborative_task = Task(
+    description="""Create a marketing strategy for a new AI product.
+    
+    Writer: Focus on messaging and content strategy
+    Researcher: Provide market analysis and competitor insights
+    
+    Work together to create a comprehensive strategy.""",
+    expected_output="Complete marketing strategy with research backing",
+    agent=writer  # Lead agent, but can delegate to researcher
+)
+```
+
+## Hierarchical Collaboration
+
+For complex projects, use a hierarchical process with a manager agent:
+
+```python
+from crewai import Agent, Crew, Task, Process
+
+# Manager agent coordinates the team
+manager = Agent(
+    role="Project Manager",
+    goal="Coordinate team efforts and ensure project success",
+    backstory="Experienced project manager skilled at delegation and quality control",
+    allow_delegation=True,
+    verbose=True
+)
+
+# Specialist agents
+researcher = Agent(
+    role="Researcher",
+    goal="Provide accurate research and analysis",
+    backstory="Expert researcher with deep analytical skills",
+    allow_delegation=False,  # Specialists focus on their expertise
+    verbose=True
+)
+
+writer = Agent(
+    role="Writer", 
+    goal="Create compelling content",
+    backstory="Skilled writer who creates engaging content",
+    allow_delegation=False,
+    verbose=True
+)
+
+# Manager-led task
+project_task = Task(
+    description="Create a comprehensive market analysis report with recommendations",
+    expected_output="Executive summary, detailed analysis, and strategic recommendations",
+    agent=manager  # Manager will delegate to specialists
+)
+
+# Hierarchical crew
+crew = Crew(
+    agents=[manager, researcher, writer],
+    tasks=[project_task],
+    process=Process.hierarchical,  # Manager coordinates everything
+    manager_llm="gpt-4o",  # Specify LLM for manager
+    verbose=True
+)
+```
+
+## Best Practices for Collaboration
+
+### 1. **Clear Role Definition**
+```python
+# ✅ Good: Specific, complementary roles
+researcher = Agent(role="Market Research Analyst", ...)
+writer = Agent(role="Technical Content Writer", ...)
+
+# ❌ Avoid: Overlapping or vague roles  
+agent1 = Agent(role="General Assistant", ...)
+agent2 = Agent(role="Helper", ...)
+```
+
+### 2. **Strategic Delegation Enabling**
+```python
+# ✅ Enable delegation for coordinators and generalists
+lead_agent = Agent(
+    role="Content Lead",
+    allow_delegation=True,  # Can delegate to specialists
+    ...
+)
+
+# ✅ Disable for focused specialists (optional)
+specialist_agent = Agent(
+    role="Data Analyst", 
+    allow_delegation=False,  # Focuses on core expertise
+    ...
+)
+```
+
+### 3. **Context Sharing**
+```python
+# ✅ Use context parameter for task dependencies
+writing_task = Task(
+    description="Write article based on research",
+    agent=writer,
+    context=[research_task],  # Shares research results
+    ...
+)
+```
+
+### 4. **Clear Task Descriptions**
+```python
+# ✅ Specific, actionable descriptions
+Task(
+    description="""Research competitors in the AI chatbot space.
+    Focus on: pricing models, key features, target markets.
+    Provide data in a structured format.""",
+    ...
+)
+
+# ❌ Vague descriptions that don't guide collaboration
+Task(description="Do some research about chatbots", ...)
+```
+
+## Troubleshooting Collaboration
+
+### Issue: Agents Not Collaborating
+**Symptoms:** Agents work in isolation, no delegation occurs
+```python
+# ✅ Solution: Ensure delegation is enabled
+agent = Agent(
+    role="...",
+    allow_delegation=True,  # This is required!
+    ...
+)
+```
+
+### Issue: Too Much Back-and-Forth
+**Symptoms:** Agents ask excessive questions, slow progress
+```python
+# ✅ Solution: Provide better context and specific roles
+Task(
+    description="""Write a technical blog post about machine learning.
+    
+    Context: Target audience is software developers with basic ML knowledge.
+    Length: 1200 words
+    Include: code examples, practical applications, best practices
+    
+    If you need specific technical details, delegate research to the researcher.""",
+    ...
+)
+```
+
+### Issue: Delegation Loops
+**Symptoms:** Agents delegate back and forth indefinitely
+```python
+# ✅ Solution: Clear hierarchy and responsibilities
+manager = Agent(role="Manager", allow_delegation=True)
+specialist1 = Agent(role="Specialist A", allow_delegation=False)  # No re-delegation
+specialist2 = Agent(role="Specialist B", allow_delegation=False)
+```
+
+## Advanced Collaboration Features
+
+### Custom Collaboration Rules
+```python
+# Set specific collaboration guidelines in agent backstory
+agent = Agent(
+    role="Senior Developer",
+    backstory="""You lead development projects and coordinate with team members.
+    
+    Collaboration guidelines:
+    - Delegate research tasks to the Research Analyst
+    - Ask the Designer for UI/UX guidance  
+    - Consult the QA Engineer for testing strategies
+    - Only escalate blocking issues to the Project Manager""",
+    allow_delegation=True
+)
+```
+
+### Monitoring Collaboration
+```python
+def track_collaboration(output):
+    """Track collaboration patterns"""
+    if "Delegate work to coworker" in output.raw:
+        print("🤝 Delegation occurred")
+    if "Ask question to coworker" in output.raw:
+        print("❓ Question asked")
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    step_callback=track_collaboration,  # Monitor collaboration
+    verbose=True
+)
+```
+
+## Memory and Learning
+
+Enable agents to remember past collaborations:
+
+```python
+agent = Agent(
+    role="Content Lead",
+    memory=True,  # Remembers past interactions
+    allow_delegation=True,
+    verbose=True
+)
+```
+
+With memory enabled, agents learn from previous collaborations and improve their delegation decisions over time.
+
+## Next Steps
+
+- **Try the examples**: Start with the basic collaboration example
+- **Experiment with roles**: Test different agent role combinations  
+- **Monitor interactions**: Use `verbose=True` to see collaboration in action
+- **Optimize task descriptions**: Clear tasks lead to better collaboration
+- **Scale up**: Try hierarchical processes for complex projects
+
+Collaboration transforms individual AI agents into powerful teams that can tackle complex, multi-faceted challenges together.

docs/en/concepts/crews.mdx

@@ -2,9 +2,10 @@
 title: Crews
 description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
 icon: people-group
+mode: "wide"
 ---
 
-## What is a Crew?
+## Overview
 
 A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
 
@@ -20,21 +21,18 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
 | **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
 | **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
-| **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
-| **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
-| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
-| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |                                                                                                                                                                                       |
 | **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
 | **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
-| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                      |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`.                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defaults to `None`.                                                                      |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
+| **Knowledge Sources** _(optional)_    | `knowledge_sources`    | Knowledge sources available at the crew level, accessible to all the agents.                                                                                                                                                                                    |
 
 <Tip>
 **Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
@@ -48,25 +46,29 @@ There are two ways to create crews in CrewAI: using **YAML configuration (recomm
 
 Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
+After creating your CrewAI project as outlined in the [Installation](/en/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
 
 #### Example Crew Class with Decorators
 
 ```python code
 from crewai import Agent, Crew, Task, Process
 from crewai.project import CrewBase, agent, task, crew, before_kickoff, after_kickoff
-
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
 
 @CrewBase
 class YourCrewName:
     """Description of your crew"""
 
+    agents: List[BaseAgent]
+    tasks: List[Task]
+
     # Paths to your YAML configuration files
     # To see an example agent and task defined in YAML, checkout the following:
     # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
     # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    agents_config = 'config/agents.yaml' 
-    tasks_config = 'config/tasks.yaml' 
+    agents_config = 'config/agents.yaml'
+    tasks_config = 'config/tasks.yaml'
 
     @before_kickoff
     def prepare_inputs(self, inputs):
@@ -83,39 +85,45 @@ class YourCrewName:
     @agent
     def agent_one(self) -> Agent:
         return Agent(
-            config=self.agents_config['agent_one'],
+            config=self.agents_config['agent_one'], # type: ignore[index]
             verbose=True
         )
 
     @agent
     def agent_two(self) -> Agent:
         return Agent(
-            config=self.agents_config['agent_two'],
+            config=self.agents_config['agent_two'], # type: ignore[index]
             verbose=True
         )
 
     @task
     def task_one(self) -> Task:
         return Task(
-            config=self.tasks_config['task_one']
+            config=self.tasks_config['task_one'] # type: ignore[index]
         )
 
     @task
     def task_two(self) -> Task:
         return Task(
-            config=self.tasks_config['task_two']
+            config=self.tasks_config['task_two'] # type: ignore[index]
         )
 
     @crew
     def crew(self) -> Crew:
         return Crew(
             agents=self.agents,  # Automatically collected by the @agent decorator
-            tasks=self.tasks,    # Automatically collected by the @task decorator. 
+            tasks=self.tasks,    # Automatically collected by the @task decorator.
             process=Process.sequential,
             verbose=True,
         )
 ```
 
+How to run the above code:
+
+```python code
+YourCrewName().crew().kickoff(inputs={"any": "input here"})
+```
+
 <Note>
 Tasks will be executed in the order they are defined.
 </Note>
@@ -183,6 +191,11 @@ class YourCrewName:
             verbose=True
         )
 ```
+How to run the above code:
+
+```python code
+YourCrewName().crew().kickoff(inputs={})
+```
 
 In this example:
 
@@ -245,7 +258,7 @@ print(f"Token Usage: {crew_output.token_usage}")
 You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
 In case of `True(Boolean)` will save as `logs.txt`.
 
-In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.
+In case of `output_log_file` is set as `False(Boolean)` or `None`, the logs will not be populated.
 
 ```python Code
 # Save crew logs
@@ -313,12 +326,12 @@ for result in results:
 
 # Example of using kickoff_async
 inputs = {'topic': 'AI in healthcare'}
-async_result = my_crew.kickoff_async(inputs=inputs)
+async_result = await my_crew.kickoff_async(inputs=inputs)
 print(async_result)
 
 # Example of using kickoff_for_each_async
 inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
-async_results = my_crew.kickoff_for_each_async(inputs=inputs_array)
+async_results = await my_crew.kickoff_for_each_async(inputs=inputs_array)
 for async_result in async_results:
     print(async_result)
 ```

docs/en/concepts/event-listener.mdx

@@ -2,9 +2,10 @@
 title: 'Event Listeners'
 description: 'Tap into CrewAI events to build custom integrations and monitoring'
 icon: spinner
+mode: "wide"
 ---
 
-# Event Listeners
+## Overview
 
 CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
 
@@ -13,11 +14,25 @@ CrewAI provides a powerful event system that allows you to listen for and react
 CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:
 
 1. **CrewAIEventsBus**: A singleton event bus that manages event registration and emission
-2. **CrewEvent**: Base class for all events in the system
+2. **BaseEvent**: Base class for all events in the system
 3. **BaseEventListener**: Abstract base class for creating custom event listeners
 
 When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
 
+<Note type="info" title="Enterprise Enhancement: Prompt Tracing">
+CrewAI AMP provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
+
+![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
+
+With Prompt Tracing you can:
+- View the complete history of all prompts sent to your LLM
+- Track token usage and costs
+- Debug agent reasoning failures
+- Share prompt sequences with your team
+- Compare different prompt strategies
+- Export traces for compliance and auditing
+</Note>
+
 ## Creating a Custom Event Listener
 
 To create a custom event listener, you need to:
@@ -30,27 +45,27 @@ 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):
         super().__init__()
-    
+
     def setup_listeners(self, crewai_event_bus):
         @crewai_event_bus.on(CrewKickoffStartedEvent)
         def on_crew_started(source, event):
             print(f"Crew '{event.crew_name}' has started execution!")
-        
+
         @crewai_event_bus.on(CrewKickoffCompletedEvent)
         def on_crew_completed(source, event):
             print(f"Crew '{event.crew_name}' has completed execution!")
             print(f"Output: {event.output}")
-        
+
         @crewai_event_bus.on(AgentExecutionCompletedEvent)
         def on_agent_execution_completed(source, event):
             print(f"Agent '{event.agent.role}' completed task")
@@ -83,7 +98,7 @@ my_listener = MyCustomListener()
 
 class MyCustomCrew:
     # Your crew implementation...
-    
+
     def crew(self):
         return Crew(
             agents=[...],
@@ -106,7 +121,7 @@ my_listener = MyCustomListener()
 
 class MyCustomFlow(Flow):
     # Your flow implementation...
-    
+
     @start()
     def first_step(self):
         # ...
@@ -132,7 +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):
@@ -163,14 +178,7 @@ class MyCustomCrew:
     # Your crew implementation...
 ```
 
-This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
+This is how third-party event listeners are registered in the CrewAI codebase.
 
 ## Available Event Types
 
@@ -210,6 +218,20 @@ CrewAI provides a wide range of events that you can listen for:
 - **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
 - **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
 
+### Knowledge Events
+
+- **KnowledgeRetrievalStartedEvent**: Emitted when a knowledge retrieval is started
+- **KnowledgeRetrievalCompletedEvent**: Emitted when a knowledge retrieval is completed
+- **KnowledgeQueryStartedEvent**: Emitted when a knowledge query is started
+- **KnowledgeQueryCompletedEvent**: Emitted when a knowledge query is completed
+- **KnowledgeQueryFailedEvent**: Emitted when a knowledge query fails
+- **KnowledgeSearchQueryFailedEvent**: Emitted when a knowledge search query fails
+
+### LLM Guardrail Events
+
+- **LLMGuardrailStartedEvent**: Emitted when a guardrail validation starts. Contains details about the guardrail being applied and retry count.
+- **LLMGuardrailCompletedEvent**: Emitted when a guardrail validation completes. Contains details about validation success/failure, results, and error messages if any.
+
 ### Flow Events
 
 - **FlowCreatedEvent**: Emitted when a Flow is created
@@ -227,6 +249,17 @@ CrewAI provides a wide range of events that you can listen for:
 - **LLMCallFailedEvent**: Emitted when an LLM call fails
 - **LLMStreamChunkEvent**: Emitted for each chunk received during streaming LLM responses
 
+### Memory Events
+
+- **MemoryQueryStartedEvent**: Emitted when a memory query is started. Contains the query, limit, and optional score threshold.
+- **MemoryQueryCompletedEvent**: Emitted when a memory query is completed successfully. Contains the query, results, limit, score threshold, and query execution time.
+- **MemoryQueryFailedEvent**: Emitted when a memory query fails. Contains the query, limit, score threshold, and error message.
+- **MemorySaveStartedEvent**: Emitted when a memory save operation is started. Contains the value to be saved, metadata, and optional agent role.
+- **MemorySaveCompletedEvent**: Emitted when a memory save operation is completed successfully. Contains the saved value, metadata, agent role, and save execution time.
+- **MemorySaveFailedEvent**: Emitted when a memory save operation fails. Contains the value, metadata, agent role, and error message.
+- **MemoryRetrievalStartedEvent**: Emitted when memory retrieval for a task prompt starts. Contains the optional task ID.
+- **MemoryRetrievalCompletedEvent**: Emitted when memory retrieval for a task prompt completes successfully. Contains the task ID, memory content, and retrieval execution time.
+
 ## Event Handler Structure
 
 Each event handler receives two parameters:
@@ -234,99 +267,28 @@ Each event handler receives two parameters:
 1. **source**: The object that emitted the event
 2. **event**: The event instance, containing event-specific data
 
-The structure of the event object depends on the event type, but all events inherit from `CrewEvent` and include:
+The structure of the event object depends on the event type, but all events inherit from `BaseEvent` and include:
 
 - **timestamp**: The time when the event was emitted
 - **type**: A string identifier for the event type
 
 Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
 
-## Real-World Example: Integration with AgentOps
-
-CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
-
-The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
 
 ## Advanced Usage: Scoped Handlers
 
 For temporary event handling (useful for testing or specific operations), you can use the `scoped_handlers` context manager:
 
 ```python
-from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
+from crewai.events import crewai_event_bus, CrewKickoffStartedEvent
 
 with crewai_event_bus.scoped_handlers():
     @crewai_event_bus.on(CrewKickoffStartedEvent)
     def temp_handler(source, event):
         print("This handler only exists within this context")
-    
+
     # Do something that emits events
-    
+
 # Outside the context, the temporary handler is removed
 ```