Merge branch 'gl/feat/async-crew-support' into gl/feat/async-flow-kickoff

- add async support for tools
- add async tool tests
- improve tool decorator typing
- fix _run backward compatibility
- update docs and improve readability of docstrings

.env.test

@@ -0,0 +1,161 @@
+# =============================================================================
+# Test Environment Variables
+# =============================================================================
+# This file contains all environment variables needed to run tests locally
+# in a way that mimics the GitHub Actions CI environment.
+
+# =============================================================================
+
+# -----------------------------------------------------------------------------
+# LLM Provider API Keys
+# -----------------------------------------------------------------------------
+OPENAI_API_KEY=fake-api-key
+ANTHROPIC_API_KEY=fake-anthropic-key
+GEMINI_API_KEY=fake-gemini-key
+AZURE_API_KEY=fake-azure-key
+OPENROUTER_API_KEY=fake-openrouter-key
+
+# -----------------------------------------------------------------------------
+# AWS Credentials
+# -----------------------------------------------------------------------------
+AWS_ACCESS_KEY_ID=fake-aws-access-key
+AWS_SECRET_ACCESS_KEY=fake-aws-secret-key
+AWS_DEFAULT_REGION=us-east-1
+AWS_REGION_NAME=us-east-1
+
+# -----------------------------------------------------------------------------
+# Azure OpenAI Configuration
+# -----------------------------------------------------------------------------
+AZURE_ENDPOINT=https://fake-azure-endpoint.openai.azure.com
+AZURE_OPENAI_ENDPOINT=https://fake-azure-endpoint.openai.azure.com
+AZURE_OPENAI_API_KEY=fake-azure-openai-key
+AZURE_API_VERSION=2024-02-15-preview
+OPENAI_API_VERSION=2024-02-15-preview
+
+# -----------------------------------------------------------------------------
+# Google Cloud Configuration
+# -----------------------------------------------------------------------------
+#GOOGLE_CLOUD_PROJECT=fake-gcp-project
+#GOOGLE_CLOUD_LOCATION=us-central1
+
+# -----------------------------------------------------------------------------
+# OpenAI Configuration
+# -----------------------------------------------------------------------------
+OPENAI_BASE_URL=https://api.openai.com/v1
+OPENAI_API_BASE=https://api.openai.com/v1
+
+# -----------------------------------------------------------------------------
+# Search & Scraping Tool API Keys
+# -----------------------------------------------------------------------------
+SERPER_API_KEY=fake-serper-key
+EXA_API_KEY=fake-exa-key
+BRAVE_API_KEY=fake-brave-key
+FIRECRAWL_API_KEY=fake-firecrawl-key
+TAVILY_API_KEY=fake-tavily-key
+SERPAPI_API_KEY=fake-serpapi-key
+SERPLY_API_KEY=fake-serply-key
+LINKUP_API_KEY=fake-linkup-key
+PARALLEL_API_KEY=fake-parallel-key
+
+# -----------------------------------------------------------------------------
+# Exa Configuration
+# -----------------------------------------------------------------------------
+EXA_BASE_URL=https://api.exa.ai
+
+# -----------------------------------------------------------------------------
+# Web Scraping & Automation
+# -----------------------------------------------------------------------------
+BRIGHT_DATA_API_KEY=fake-brightdata-key
+BRIGHT_DATA_ZONE=fake-zone
+BRIGHTDATA_API_URL=https://api.brightdata.com
+BRIGHTDATA_DEFAULT_TIMEOUT=600
+BRIGHTDATA_DEFAULT_POLLING_INTERVAL=1
+
+OXYLABS_USERNAME=fake-oxylabs-user
+OXYLABS_PASSWORD=fake-oxylabs-pass
+
+SCRAPFLY_API_KEY=fake-scrapfly-key
+SCRAPEGRAPH_API_KEY=fake-scrapegraph-key
+
+BROWSERBASE_API_KEY=fake-browserbase-key
+BROWSERBASE_PROJECT_ID=fake-browserbase-project
+
+HYPERBROWSER_API_KEY=fake-hyperbrowser-key
+MULTION_API_KEY=fake-multion-key
+APIFY_API_TOKEN=fake-apify-token
+
+# -----------------------------------------------------------------------------
+# Database & Vector Store Credentials
+# -----------------------------------------------------------------------------
+SINGLESTOREDB_URL=mysql://fake:fake@localhost:3306/fake
+SINGLESTOREDB_HOST=localhost
+SINGLESTOREDB_PORT=3306
+SINGLESTOREDB_USER=fake-user
+SINGLESTOREDB_PASSWORD=fake-password
+SINGLESTOREDB_DATABASE=fake-database
+SINGLESTOREDB_CONNECT_TIMEOUT=30
+
+SNOWFLAKE_USER=fake-snowflake-user
+SNOWFLAKE_PASSWORD=fake-snowflake-password
+SNOWFLAKE_ACCOUNT=fake-snowflake-account
+SNOWFLAKE_WAREHOUSE=fake-snowflake-warehouse
+SNOWFLAKE_DATABASE=fake-snowflake-database
+SNOWFLAKE_SCHEMA=fake-snowflake-schema
+
+WEAVIATE_URL=http://localhost:8080
+WEAVIATE_API_KEY=fake-weaviate-key
+
+EMBEDCHAIN_DB_URI=sqlite:///test.db
+
+# Databricks Credentials
+DATABRICKS_HOST=https://fake-databricks.cloud.databricks.com
+DATABRICKS_TOKEN=fake-databricks-token
+DATABRICKS_CONFIG_PROFILE=fake-profile
+
+# MongoDB Credentials
+MONGODB_URI=mongodb://fake:fake@localhost:27017/fake
+
+# -----------------------------------------------------------------------------
+# CrewAI Platform & Enterprise
+# -----------------------------------------------------------------------------
+# setting CREWAI_PLATFORM_INTEGRATION_TOKEN causes these test to fail:
+#=========================== short test summary info ============================
+#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_platform_context_manager_basic_usage - AssertionError: assert 'fake-platform-token' is None
+# +  where 'fake-platform-token' = get_platform_integration_token()
+#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_context_var_isolation_between_tests - AssertionError: assert 'fake-platform-token' is None
+# +  where 'fake-platform-token' = get_platform_integration_token()
+#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_multiple_sequential_context_managers - AssertionError: assert 'fake-platform-token' is None
+# +  where 'fake-platform-token' = get_platform_integration_token()
+#CREWAI_PLATFORM_INTEGRATION_TOKEN=fake-platform-token
+CREWAI_PERSONAL_ACCESS_TOKEN=fake-personal-token
+CREWAI_PLUS_URL=https://fake.crewai.com
+
+# -----------------------------------------------------------------------------
+# Other Service API Keys
+# -----------------------------------------------------------------------------
+ZAPIER_API_KEY=fake-zapier-key
+PATRONUS_API_KEY=fake-patronus-key
+MINDS_API_KEY=fake-minds-key
+HF_TOKEN=fake-hf-token
+
+# -----------------------------------------------------------------------------
+# Feature Flags/Testing Modes
+# -----------------------------------------------------------------------------
+CREWAI_DISABLE_TELEMETRY=true
+OTEL_SDK_DISABLED=true
+CREWAI_TESTING=true
+CREWAI_TRACING_ENABLED=false
+
+# -----------------------------------------------------------------------------
+# Testing/CI Configuration
+# -----------------------------------------------------------------------------
+# VCR recording mode: "none" (default), "new_episodes", "all", "once"
+PYTEST_VCR_RECORD_MODE=none
+
+# Set to "true" by GitHub when running in GitHub Actions
+# GITHUB_ACTIONS=false
+
+# -----------------------------------------------------------------------------
+# Python Configuration
+# -----------------------------------------------------------------------------
+PYTHONUNBUFFERED=1

.github/codeql/codeql-config.yml

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

.github/dependabot.yml

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

.github/security.md

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

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

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

.github/workflows/codeql.yml

@@ -0,0 +1,103 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+#
+# ******** NOTE ********
+# We have attempted to detect the languages in your repository. Please check
+# the `language` matrix defined below to confirm you have the correct set of
+# supported CodeQL languages.
+#
+name: "CodeQL Advanced"
+
+on:
+  push:
+    branches: [ "main" ]
+    paths-ignore:
+      - "lib/crewai/src/crewai/cli/templates/**"
+  pull_request:
+    branches: [ "main" ]
+    paths-ignore:
+      - "lib/crewai/src/crewai/cli/templates/**"
+
+jobs:
+  analyze:
+    name: Analyze (${{ matrix.language }})
+    # Runner size impacts CodeQL analysis time. To learn more, please see:
+    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
+    #   - https://gh.io/supported-runners-and-hardware-resources
+    #   - https://gh.io/using-larger-runners (GitHub.com only)
+    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
+    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
+    permissions:
+      # required for all workflows
+      security-events: write
+
+      # required to fetch internal or private CodeQL packs
+      packages: read
+
+      # only required for workflows in private repositories
+      actions: read
+      contents: read
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+        - language: actions
+          build-mode: none
+        - language: python
+          build-mode: none
+        # CodeQL supports the following values keywords for 'language': 'actions', 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'rust', 'swift'
+        # Use `c-cpp` to analyze code written in C, C++ or both
+        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
+        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
+        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
+        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
+        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
+        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    # Add any setup steps before running the `github/codeql-action/init` action.
+    # This includes steps like installing compilers or runtimes (`actions/setup-node`
+    # or others). This is typically only required for manual builds.
+    # - name: Setup runtime (example)
+    #   uses: actions/setup-example@v1
+
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v3
+      with:
+        languages: ${{ matrix.language }}
+        build-mode: ${{ matrix.build-mode }}
+        config-file: ./.github/codeql/codeql-config.yml
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+
+        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+        # queries: security-extended,security-and-quality
+
+    # If the analyze step fails for one of the languages you are analyzing with
+    # "We were unable to automatically build your code", modify the matrix above
+    # to set the build mode to "manual" for that language. Then modify this step
+    # to build your code.
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+    - if: matrix.build-mode == 'manual'
+      shell: bash
+      run: |
+        echo 'If you are using a "manual" build mode for one or more of the' \
+          'languages you are analyzing, replace this with the commands to build' \
+          'your code, for example:'
+        echo '  make bootstrap'
+        echo '  make release'
+        exit 1
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v3
+      with:
+        category: "/language:${{matrix.language}}"

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

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

.github/workflows/linter.yml

@@ -2,6 +2,9 @@ name: Lint
 
 on: [pull_request]
 
+permissions:
+  contents: read
+
 jobs:
   lint:
     runs-on: ubuntu-latest
@@ -15,8 +18,27 @@ jobs:
       - name: Fetch Target Branch
         run: git fetch origin $TARGET_BRANCH --depth=1
 
-      - name: Install Ruff
-        run: pip install ruff
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py3.11-
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: "3.11"
+          enable-cache: false
+
+      - name: Install dependencies
+        run: uv sync --all-groups --all-extras --no-install-project
 
       - name: Get Changed Python Files
         id: changed-files
@@ -30,4 +52,18 @@ jobs:
       - name: Run Ruff on Changed Files
         if: ${{ steps.changed-files.outputs.files != '' }}
         run: |
-          echo "${{ steps.changed-files.outputs.files }}" | tr " " "\n" | xargs -I{} ruff check "{}"
+         echo "${{ steps.changed-files.outputs.files }}" \
+           | tr ' ' '\n' \
+           | grep -v 'src/crewai/cli/templates/' \
+           | grep -v '/tests/' \
+           | xargs -I{} uv run ruff check "{}"
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}

.github/workflows/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/publish.yml

@@ -0,0 +1,81 @@
+name: Publish to PyPI
+
+on:
+  release:
+    types: [ published ]
+  workflow_dispatch:
+
+jobs:
+  build:
+    name: Build packages
+    runs-on: ubuntu-latest
+    permissions:
+        contents: read
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Build packages
+        run: |
+          uv build --all-packages
+          rm dist/.gitignore
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: dist
+          path: dist/
+
+  publish:
+    name: Publish to PyPI
+    needs: build
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/crewai
+    permissions:
+      id-token: write
+      contents: read
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: "3.12"
+          enable-cache: false
+
+      - name: Download artifacts
+        uses: actions/download-artifact@v4
+        with:
+          name: dist
+          path: dist
+
+      - name: Publish to PyPI
+        env:
+          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
+        run: |
+          failed=0
+          for package in dist/*; do
+            if [[ "$package" == *"crewai_devtools"* ]]; then
+              echo "Skipping private package: $package"
+              continue
+            fi
+            echo "Publishing $package"
+            if ! uv publish "$package"; then
+              echo "Failed to publish $package"
+              failed=1
+            fi
+          done
+          if [ $failed -eq 1 ]; then
+            echo "Some packages failed to publish"
+            exit 1
+          fi

.github/workflows/security-checker.yml

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

.github/workflows/tests.yml

@@ -3,32 +3,98 @@ name: Run Tests
 on: [pull_request]
 
 permissions:
-  contents: write
-
-env:
-  OPENAI_API_KEY: fake-api-key
+  contents: read
 
 jobs:
   tests:
+    name: tests (${{ matrix.python-version }})
     runs-on: ubuntu-latest
     timeout-minutes: 15
     strategy:
+      fail-fast: true
       matrix:
-        python-version: ['3.10', '3.11', '3.12']
+        python-version: ['3.10', '3.11', '3.12', '3.13']
+        group: [1, 2, 3, 4, 5, 6, 7, 8]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0  # Fetch all history for proper diff
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py${{ matrix.python-version }}-
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v3
+        uses: astral-sh/setup-uv@v6
         with:
-          enable-cache: true
-
-      - name: Set up Python ${{ matrix.python-version }}
-        run: uv python install ${{ matrix.python-version }}
+          version: "0.8.4"
+          python-version: ${{ matrix.python-version }}
+          enable-cache: false
 
       - name: Install the project
-        run: uv sync --dev --all-extras
+        run: uv sync --all-groups --all-extras
 
-      - name: Run tests
-        run: uv run pytest --block-network --timeout=60 -vv
+      - name: Restore test durations
+        uses: actions/cache/restore@v4
+        with:
+          path: .test_durations_py*
+          key: test-durations-py${{ matrix.python-version }}
+
+      - name: Run tests (group ${{ matrix.group }} of 8)
+        run: |
+          PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
+          DURATION_FILE="../../.test_durations_py${PYTHON_VERSION_SAFE}"
+
+          # Temporarily always skip cached durations to fix test splitting
+          # When durations don't match, pytest-split runs duplicate tests instead of splitting
+          echo "Using even test splitting (duration cache disabled until fix merged)"
+          DURATIONS_ARG=""
+
+          # Original logic (disabled temporarily):
+          # if [ ! -f "$DURATION_FILE" ]; then
+          #   echo "No cached durations found, tests will be split evenly"
+          #   DURATIONS_ARG=""
+          # elif git diff origin/${{ github.base_ref }}...HEAD --name-only 2>/dev/null | grep -q "^tests/.*\.py$"; then
+          #   echo "Test files have changed, skipping cached durations to avoid mismatches"
+          #   DURATIONS_ARG=""
+          # else
+          #   echo "No test changes detected, using cached test durations for optimal splitting"
+          #   DURATIONS_ARG="--durations-path=${DURATION_FILE}"
+          # fi
+
+          cd lib/crewai && uv run pytest \
+            -vv \
+            --splits 8 \
+            --group ${{ matrix.group }} \
+            $DURATIONS_ARG \
+            --durations=10 \
+            --maxfail=3
+
+      - name: Run tool tests (group ${{ matrix.group }} of 8)
+        run: |
+          cd lib/crewai-tools && uv run pytest \
+            -vv \
+            --splits 8 \
+            --group ${{ matrix.group }} \
+            --durations=10 \
+            --maxfail=3
+
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

.github/workflows/type-checker.yml

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

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

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

.gitignore

@@ -2,7 +2,6 @@
 .pytest_cache
 __pycache__
 dist/
-lib/
 .env
 assets/*
 .idea
@@ -21,9 +20,9 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log
 test_flow.html
 crewairules.mdc
 plan.md
 conceptual_plan.md
-build_image
+build_image
+chromadb-*.lock

.pre-commit-config.yaml

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

.ruff.toml

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

README.md

@@ -1,27 +1,70 @@
-<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/images/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>
 
-</div>
+<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>
+
+<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 AOP Suite
 
-CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations
-that require secure, scalable, and easy-to-manage agent-driven automation.
+CrewAI AOP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
 
 You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
 
@@ -33,23 +76,11 @@ You can try one part of the suite the [Crew Control Plane for free](https://app.
 - **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
 - **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
 - **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
-- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
+- **On-premise and Cloud Deployment Options**: Deploy CrewAI AOP on-premise or in the cloud, depending on your security and compliance requirements.
 
-CrewAI Enterprise is designed for enterprises seeking a powerful,
-reliable solution to transform complex business processes into efficient,
+CrewAI AOP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
 intelligent automations.
 
-<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)
@@ -88,7 +119,12 @@ 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:
 
@@ -125,7 +161,7 @@ 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:
 
@@ -367,7 +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.
@@ -382,10 +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
 
@@ -393,19 +429,19 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 ### Write Job Descriptions
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below:
 
 [![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
 
 ### Stock Analysis
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below:
 
 [![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 
@@ -638,9 +674,9 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
 
 ### Enterprise Features
 
-- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
-- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
-- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
+- [What additional features does CrewAI AOP offer?](#q-what-additional-features-does-crewai-amp-offer)
+- [Is CrewAI AOP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
+- [Can I try CrewAI AOP for free?](#q-can-i-try-crewai-amp-for-free)
 
 ### Q: What exactly is CrewAI?
 
@@ -696,17 +732,17 @@ A: Check out practical examples in the [CrewAI-examples repository](https://gith
 
 A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
 
-### Q: What additional features does CrewAI Enterprise offer?
+### Q: What additional features does CrewAI AOP offer?
 
-A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
+A: CrewAI AOP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
 
-### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
+### Q: Is CrewAI AOP available for cloud and on-premise deployments?
 
-A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
+A: Yes, CrewAI AOP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
 
-### Q: Can I try CrewAI Enterprise for free?
+### Q: Can I try CrewAI AOP for free?
 
-A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
+A: Yes, you can explore part of the CrewAI AOP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
 
 ### Q: Does CrewAI support fine-tuning or training custom models?
 
@@ -726,7 +762,7 @@ A: CrewAI is highly scalable, supporting simple automations and large-scale ente
 
 ### Q: Does CrewAI offer debugging and monitoring tools?
 
-A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
+A: Yes, CrewAI AOP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
 
 ### Q: What programming languages does CrewAI support?
 

conftest.py

@@ -0,0 +1,193 @@
+"""Pytest configuration for crewAI workspace."""
+
+from collections.abc import Generator
+import os
+from pathlib import Path
+import tempfile
+from typing import Any
+
+from dotenv import load_dotenv
+import pytest
+from vcr.request import Request  # type: ignore[import-untyped]
+
+
+env_test_path = Path(__file__).parent / ".env.test"
+load_dotenv(env_test_path, override=True)
+load_dotenv(override=True)
+
+
+@pytest.fixture(autouse=True, scope="function")
+def cleanup_event_handlers() -> Generator[None, Any, None]:
+    """Clean up event bus handlers after each test to prevent test pollution."""
+    yield
+
+    try:
+        from crewai.events.event_bus import crewai_event_bus
+
+        with crewai_event_bus._rwlock.w_locked():
+            crewai_event_bus._sync_handlers.clear()
+            crewai_event_bus._async_handlers.clear()
+    except Exception:  # noqa: S110
+        pass
+
+
+@pytest.fixture(autouse=True, scope="function")
+def setup_test_environment() -> Generator[None, Any, None]:
+    """Setup test environment for crewAI workspace."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        storage_dir = Path(temp_dir) / "crewai_test_storage"
+        storage_dir.mkdir(parents=True, exist_ok=True)
+
+        if not storage_dir.exists() or not storage_dir.is_dir():
+            raise RuntimeError(
+                f"Failed to create test storage directory: {storage_dir}"
+            )
+
+        try:
+            test_file = storage_dir / ".permissions_test"
+            test_file.touch()
+            test_file.unlink()
+        except (OSError, IOError) as e:
+            raise RuntimeError(
+                f"Test storage directory {storage_dir} is not writable: {e}"
+            ) from e
+
+        os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
+        os.environ["CREWAI_TESTING"] = "true"
+
+        try:
+            yield
+        finally:
+            os.environ.pop("CREWAI_TESTING", "true")
+            os.environ.pop("CREWAI_STORAGE_DIR", None)
+            os.environ.pop("CREWAI_DISABLE_TELEMETRY", "true")
+            os.environ.pop("OTEL_SDK_DISABLED", "true")
+            os.environ.pop("OPENAI_BASE_URL", "https://api.openai.com/v1")
+            os.environ.pop("OPENAI_API_BASE", "https://api.openai.com/v1")
+
+
+HEADERS_TO_FILTER = {
+    "authorization": "AUTHORIZATION-XXX",
+    "content-security-policy": "CSP-FILTERED",
+    "cookie": "COOKIE-XXX",
+    "set-cookie": "SET-COOKIE-XXX",
+    "permissions-policy": "PERMISSIONS-POLICY-XXX",
+    "referrer-policy": "REFERRER-POLICY-XXX",
+    "strict-transport-security": "STS-XXX",
+    "x-content-type-options": "X-CONTENT-TYPE-XXX",
+    "x-frame-options": "X-FRAME-OPTIONS-XXX",
+    "x-permitted-cross-domain-policies": "X-PERMITTED-XXX",
+    "x-request-id": "X-REQUEST-ID-XXX",
+    "x-runtime": "X-RUNTIME-XXX",
+    "x-xss-protection": "X-XSS-PROTECTION-XXX",
+    "x-stainless-arch": "X-STAINLESS-ARCH-XXX",
+    "x-stainless-os": "X-STAINLESS-OS-XXX",
+    "x-stainless-read-timeout": "X-STAINLESS-READ-TIMEOUT-XXX",
+    "cf-ray": "CF-RAY-XXX",
+    "etag": "ETAG-XXX",
+    "Strict-Transport-Security": "STS-XXX",
+    "access-control-expose-headers": "ACCESS-CONTROL-XXX",
+    "openai-organization": "OPENAI-ORG-XXX",
+    "openai-project": "OPENAI-PROJECT-XXX",
+    "x-ratelimit-limit-requests": "X-RATELIMIT-LIMIT-REQUESTS-XXX",
+    "x-ratelimit-limit-tokens": "X-RATELIMIT-LIMIT-TOKENS-XXX",
+    "x-ratelimit-remaining-requests": "X-RATELIMIT-REMAINING-REQUESTS-XXX",
+    "x-ratelimit-remaining-tokens": "X-RATELIMIT-REMAINING-TOKENS-XXX",
+    "x-ratelimit-reset-requests": "X-RATELIMIT-RESET-REQUESTS-XXX",
+    "x-ratelimit-reset-tokens": "X-RATELIMIT-RESET-TOKENS-XXX",
+    "x-goog-api-key": "X-GOOG-API-KEY-XXX",
+    "api-key": "X-API-KEY-XXX",
+    "User-Agent": "X-USER-AGENT-XXX",
+    "apim-request-id:": "X-API-CLIENT-REQUEST-ID-XXX",
+    "azureml-model-session": "AZUREML-MODEL-SESSION-XXX",
+    "x-ms-client-request-id": "X-MS-CLIENT-REQUEST-ID-XXX",
+    "x-ms-region": "X-MS-REGION-XXX",
+    "apim-request-id": "APIM-REQUEST-ID-XXX",
+    "x-api-key": "X-API-KEY-XXX",
+    "anthropic-organization-id": "ANTHROPIC-ORGANIZATION-ID-XXX",
+    "request-id": "REQUEST-ID-XXX",
+    "anthropic-ratelimit-input-tokens-limit": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX",
+    "anthropic-ratelimit-input-tokens-remaining": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX",
+    "anthropic-ratelimit-input-tokens-reset": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX",
+    "anthropic-ratelimit-output-tokens-limit": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX",
+    "anthropic-ratelimit-output-tokens-remaining": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX",
+    "anthropic-ratelimit-output-tokens-reset": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX",
+    "anthropic-ratelimit-tokens-limit": "ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX",
+    "anthropic-ratelimit-tokens-remaining": "ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX",
+    "anthropic-ratelimit-tokens-reset": "ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX",
+    "x-amz-date": "X-AMZ-DATE-XXX",
+    "amz-sdk-invocation-id": "AMZ-SDK-INVOCATION-ID-XXX",
+    "accept-encoding": "ACCEPT-ENCODING-XXX",
+    "x-amzn-requestid": "X-AMZN-REQUESTID-XXX",
+    "x-amzn-RequestId": "X-AMZN-REQUESTID-XXX",
+}
+
+
+def _filter_request_headers(request: Request) -> Request:  # type: ignore[no-any-unimported]
+    """Filter sensitive headers from request before recording."""
+    for header_name, replacement in HEADERS_TO_FILTER.items():
+        for variant in [header_name, header_name.upper(), header_name.title()]:
+            if variant in request.headers:
+                request.headers[variant] = [replacement]
+
+    request.method = request.method.upper()
+    return request
+
+
+def _filter_response_headers(response: dict[str, Any]) -> dict[str, Any]:
+    """Filter sensitive headers from response before recording."""
+    for header_name, replacement in HEADERS_TO_FILTER.items():
+        for variant in [header_name, header_name.upper(), header_name.title()]:
+            if variant in response["headers"]:
+                response["headers"][variant] = [replacement]
+    return response
+
+
+@pytest.fixture(scope="module")
+def vcr_cassette_dir(request: Any) -> str:
+    """Generate cassette directory path based on test module location.
+
+    Organizes cassettes to mirror test directory structure within each package:
+    lib/crewai/tests/llms/google/test_google.py -> lib/crewai/tests/cassettes/llms/google/
+    lib/crewai-tools/tests/tools/test_search.py -> lib/crewai-tools/tests/cassettes/tools/
+    """
+    test_file = Path(request.fspath)
+
+    for parent in test_file.parents:
+        if parent.name in ("crewai", "crewai-tools") and parent.parent.name == "lib":
+            package_root = parent
+            break
+    else:
+        package_root = test_file.parent
+
+    tests_root = package_root / "tests"
+    test_dir = test_file.parent
+
+    if test_dir != tests_root:
+        relative_path = test_dir.relative_to(tests_root)
+        cassette_dir = tests_root / "cassettes" / relative_path
+    else:
+        cassette_dir = tests_root / "cassettes"
+
+    cassette_dir.mkdir(parents=True, exist_ok=True)
+
+    return str(cassette_dir)
+
+
+@pytest.fixture(scope="module")
+def vcr_config(vcr_cassette_dir: str) -> dict[str, Any]:
+    """Configure VCR with organized cassette storage."""
+    config = {
+        "cassette_library_dir": vcr_cassette_dir,
+        "record_mode": os.getenv("PYTEST_VCR_RECORD_MODE", "once"),
+        "filter_headers": [(k, v) for k, v in HEADERS_TO_FILTER.items()],
+        "before_record_request": _filter_request_headers,
+        "before_record_response": _filter_response_headers,
+        "filter_query_parameters": ["key"],
+        "match_on": ["method", "scheme", "host", "port", "path"],
+    }
+
+    if os.getenv("GITHUB_ACTIONS") == "true":
+        config["record_mode"] = "none"
+
+    return config

docs/changelog.mdx

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

docs/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

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

docs/concepts/knowledge.mdx

@@ -1,760 +0,0 @@
----
-title: Knowledge
-description: What is knowledge in CrewAI and how to use it.
-icon: book
----
-
-## Overview
-
-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
-
-<ParamField body="sources" type="List[BaseKnowledgeSource]" required="Yes"> 
-    List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content. 
-</ParamField>
-<ParamField body="collection_name" type="str"> 
-    Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to \"knowledge\" if not provided. 
-</ParamField>
-<ParamField body="storage" type="Optional[KnowledgeStorage]"> 
-Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created. 
-</ParamField>
-
-<Tip>
-Unlike retrieval from a vector database using a tool, agents preloaded with knowledge will not need a retrieval persona or task.
-Simply add the relevant knowledge sources your agent or crew needs to function.
-
-Knowledge sources can be added at the agent or crew level.
-Crew level knowledge sources will be used by **all agents** in the crew.
-Agent level knowledge sources will be used by the **specific agent** that is preloaded with the knowledge.
-</Tip>
-
-## 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."
-    }
-)
-```
-
-## Knowledge Configuration
-
-You can configure the knowledge configuration for the crew or agent.
-
-```python Code
-from crewai.knowledge.knowledge_config import KnowledgeConfig
-
-knowledge_config = KnowledgeConfig(results_limit=10, score_threshold=0.5)
-
-agent = Agent(
-    ...
-    knowledge_config=knowledge_config
-)
-```
-
-<Tip>
-  `results_limit`: is the number of relevant documents to return. Default is 3.
-  `score_threshold`: is the minimum score for a document to be considered relevant. Default is 0.35.
-</Tip>
-
-## 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>
-
-## Query Rewriting
-
-CrewAI implements an intelligent query rewriting mechanism to optimize knowledge retrieval. When an agent needs to search through knowledge sources, the raw task prompt is automatically transformed into a more effective search query.
-
-### How Query Rewriting Works
-
-1. When an agent executes a task with knowledge sources available, the `_get_knowledge_search_query` method is triggered
-2. The agent's LLM is used to transform the original task prompt into an optimized search query
-3. This optimized query is then used to retrieve relevant information from knowledge sources
-
-### Benefits of Query Rewriting
-
-<CardGroup cols={2}>
-  <Card title="Improved Retrieval Accuracy" icon="bullseye-arrow">
-    By focusing on key concepts and removing irrelevant content, query rewriting helps retrieve more relevant information.
-  </Card>
-  <Card title="Context Awareness" icon="brain">
-    The rewritten queries are designed to be more specific and context-aware for vector database retrieval.
-  </Card>
-</CardGroup>
-
-### Implementation Details
-
-Query rewriting happens transparently using a system prompt that instructs the LLM to:
-
-- Focus on key words of the intended task
-- Make the query more specific and context-aware
-- Remove irrelevant content like output format instructions
-- Generate only the rewritten query without preamble or postamble
-
-<Tip>
-  This mechanism is fully automatic and requires no configuration from users. The agent's LLM is used to perform the query rewriting, so using a more capable LLM can improve the quality of rewritten queries.
-</Tip>
-
-## Knowledge Events
-
-CrewAI emits events during the knowledge retrieval process that you can listen for using the event system. These events allow you to monitor, debug, and analyze how knowledge is being retrieved and used by your agents.
-
-### Available Knowledge Events
-
-- **KnowledgeRetrievalStartedEvent**: Emitted when an agent starts retrieving knowledge from sources
-- **KnowledgeRetrievalCompletedEvent**: Emitted when knowledge retrieval is completed, including the query used and the retrieved content
-- **KnowledgeQueryStartedEvent**: Emitted when a query to knowledge sources begins
-- **KnowledgeQueryCompletedEvent**: Emitted when a query completes successfully
-- **KnowledgeQueryFailedEvent**: Emitted when a query to knowledge sources fails
-- **KnowledgeSearchQueryFailedEvent**: Emitted when a search query fails
-
-### Example: Monitoring Knowledge Retrieval
-
-```python
-from crewai.utilities.events import (
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-
-class KnowledgeMonitorListener(BaseEventListener):
-    def setup_listeners(self, crewai_event_bus):
-        @crewai_event_bus.on(KnowledgeRetrievalStartedEvent)
-        def on_knowledge_retrieval_started(source, event):
-            print(f"Agent '{event.agent.role}' started retrieving knowledge")
-            
-        @crewai_event_bus.on(KnowledgeRetrievalCompletedEvent)
-        def on_knowledge_retrieval_completed(source, event):
-            print(f"Agent '{event.agent.role}' completed knowledge retrieval")
-            print(f"Query: {event.query}")
-            print(f"Retrieved {len(event.retrieved_knowledge)} knowledge chunks")
-
-# Create an instance of your listener
-knowledge_monitor = KnowledgeMonitorListener()
-```
-
-For more information on using events, see the [Event Listeners](https://docs.crewai.com/concepts/event-listener) documentation.
-
-### Example
-
-```python
-# Original task prompt
-task_prompt = "Answer the following questions about the user's favorite movies: What movie did John watch last week? Format your answer in JSON."
-
-# Behind the scenes, this might be rewritten as:
-rewritten_query = "What movies did John watch last week?"
-```
-
-The rewritten query is more focused on the core information need and removes irrelevant instructions about output formatting.
-
-## 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?"}
-)
-
-# Resetting the agent specific knowledge via crew object
-crew.reset_memories(command_type = 'agent_knowledge')
-
-# Resetting the agent specific knowledge via CLI
-crewai reset-memories --agent-knowledge 
-crewai reset-memories -akn
-```
-
-<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>
-
-  <Accordion title="One Time Knowledge">
-    - With the typical file structure provided by CrewAI, knowledge sources are embedded every time the kickoff is triggered.
-    - If the knowledge sources are large, this leads to inefficiency and increased latency, as the same data is embedded each time.
-    - To resolve this, directly initialize the knowledge parameter instead of the knowledge_sources parameter.
-    - Link to the issue to get complete idea [Github Issue](https://github.com/crewAIInc/crewAI/issues/2755)
-  </Accordion>  
-</AccordionGroup>

docs/concepts/memory.mdx

@@ -1,387 +0,0 @@
----
-title: Memory
-description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
-icon: database
----
-
-## Overview
-
-The CrewAI framework provides a sophisticated memory system designed to significantly enhance AI agent capabilities. CrewAI offers **three distinct memory approaches** that serve different use cases:
-
-1. **Basic Memory System** - Built-in short-term, long-term, and entity memory
-2. **User Memory** - User-specific memory with Mem0 integration (legacy approach)  
-3. **External Memory** - Standalone external memory providers (new approach)
-
-## 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. |
-
-## 1. Basic Memory System (Recommended)
-
-The simplest and most commonly used approach. Enable memory for your crew with a single parameter:
-
-### Quick Start
-```python
-from crewai import Crew, Agent, Task, Process
-
-# Enable basic memory system
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,  # Enables short-term, long-term, and entity memory
-    verbose=True
-)
-```
-
-### How It Works
-- **Short-Term Memory**: Uses ChromaDB with RAG for current context
-- **Long-Term Memory**: Uses SQLite3 to store task results across sessions
-- **Entity Memory**: Uses RAG to track entities (people, places, concepts)
-- **Storage Location**: Platform-specific location via `appdirs` package
-- **Custom Storage Directory**: Set `CREWAI_STORAGE_DIR` environment variable
-
-### Custom Embedder Configuration
-```python
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "model": "text-embedding-3-small"
-        }
-    }
-)
-```
-
-### Custom Storage Paths
-```python
-import os
-from crewai import Crew
-from crewai.memory import LongTermMemory
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
-
-# Configure custom storage location
-crew = Crew(
-    memory=True,
-    long_term_memory=LongTermMemory(
-        storage=LTMSQLiteStorage(
-            db_path=os.getenv("CREWAI_STORAGE_DIR", "./storage") + "/memory.db"
-        )
-    )
-)
-```
-
-## 2. User Memory with Mem0 (Legacy)
-
-<Warning>
-**Legacy Approach**: While fully functional, this approach is considered legacy. For new projects requiring user-specific memory, consider using External Memory instead.
-</Warning>
-
-User Memory integrates with [Mem0](https://mem0.ai/) to provide user-specific memory that persists across sessions and integrates with the crew's contextual memory system.
-
-### Prerequisites
-```bash
-pip install mem0ai
-```
-
-### Mem0 Cloud Configuration
-```python
-import os
-from crewai import Crew, Process
-
-# Set your Mem0 API key
-os.environ["MEM0_API_KEY"] = "m0-your-api-key"
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,  # Required for contextual memory integration
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john"},
-        "user_memory": {}  # Required - triggers user memory initialization
-    },
-    process=Process.sequential,
-    verbose=True
-)
-```
-
-### Advanced Mem0 Configuration
-```python
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {
-            "user_id": "john",
-            "org_id": "my_org_id",        # Optional
-            "project_id": "my_project_id", # Optional
-            "api_key": "custom-api-key"    # Optional - overrides env var
-        },
-        "user_memory": {}
-    }
-)
-```
-
-### Local Mem0 Configuration
-```python
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {
-            "user_id": "john",
-            "local_mem0_config": {
-                "vector_store": {
-                    "provider": "qdrant",
-                    "config": {"host": "localhost", "port": 6333}
-                },
-                "llm": {
-                    "provider": "openai",
-                    "config": {"api_key": "your-api-key", "model": "gpt-4"}
-                },
-                "embedder": {
-                    "provider": "openai",
-                    "config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}
-                }
-            }
-        },
-        "user_memory": {}
-    }
-)
-```
-
-## 3. External Memory (New Approach)
-
-External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing.
-
-### Basic External Memory with Mem0
-```python
-import os
-from crewai import Agent, Crew, Process, Task
-from crewai.memory.external.external_memory import ExternalMemory
-
-os.environ["MEM0_API_KEY"] = "your-api-key"
-
-# Create external memory instance
-external_memory = ExternalMemory(
-    embedder_config={
-        "provider": "mem0", 
-        "config": {"user_id": "U-123"}
-    }
-)
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    external_memory=external_memory,  # Separate from basic memory
-    process=Process.sequential,
-    verbose=True
-)
-```
-
-### Custom Storage Implementation
-```python
-from crewai.memory.external.external_memory import ExternalMemory
-from crewai.memory.storage.interface import Storage
-
-class CustomStorage(Storage):
-    def __init__(self):
-        self.memories = []
-
-    def save(self, value, metadata=None, agent=None):
-        self.memories.append({
-            "value": value, 
-            "metadata": metadata, 
-            "agent": agent
-        })
-
-    def search(self, query, limit=10, score_threshold=0.5):
-        # Implement your search logic here
-        return [m for m in self.memories if query.lower() in str(m["value"]).lower()]
-
-    def reset(self):
-        self.memories = []
-
-# Use custom storage
-external_memory = ExternalMemory(storage=CustomStorage())
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    external_memory=external_memory
-)
-```
-
-## Memory System Comparison
-
-| Feature | Basic Memory | User Memory (Legacy) | External Memory |
-|---------|-------------|---------------------|----------------|
-| **Setup Complexity** | Simple | Medium | Medium |
-| **Integration** | Built-in contextual | Contextual + User-specific | Standalone |
-| **Storage** | Local files | Mem0 Cloud/Local | Custom/Mem0 |
-| **Cross-session** | ✅ | ✅ | ✅ |
-| **User-specific** | ❌ | ✅ | ✅ |
-| **Custom providers** | Limited | Mem0 only | Any provider |
-| **Recommended for** | Most use cases | Legacy projects | Specialized needs |
-
-## Supported Embedding Providers
-
-### OpenAI (Default)
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "openai",
-        "config": {"model": "text-embedding-3-small"}
-    }
-)
-```
-
-### Ollama
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "ollama",
-        "config": {"model": "mxbai-embed-large"}
-    }
-)
-```
-
-### Google AI
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "google",
-        "config": {
-            "api_key": "your-api-key",
-            "model": "text-embedding-004"
-        }
-    }
-)
-```
-
-### Azure OpenAI
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "api_key": "your-api-key",
-            "api_base": "https://your-resource.openai.azure.com/",
-            "api_version": "2023-05-15",
-            "model_name": "text-embedding-3-small"
-        }
-    }
-)
-```
-
-### Vertex AI
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "vertexai",
-        "config": {
-            "project_id": "your-project-id",
-            "region": "your-region",
-            "api_key": "your-api-key",
-            "model_name": "textembedding-gecko"
-        }
-    }
-)
-```
-
-## Security Best Practices
-
-### Environment Variables
-```python
-import os
-from crewai import Crew
-
-# Store sensitive data in environment variables
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "api_key": os.getenv("OPENAI_API_KEY"),
-            "model": "text-embedding-3-small"
-        }
-    }
-)
-```
-
-### Storage Security
-```python
-import os
-from crewai import Crew
-from crewai.memory import LongTermMemory
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
-
-# Use secure storage paths
-storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
-os.makedirs(storage_path, mode=0o700, exist_ok=True)  # Restricted permissions
-
-crew = Crew(
-    memory=True,
-    long_term_memory=LongTermMemory(
-        storage=LTMSQLiteStorage(
-            db_path=f"{storage_path}/memory.db"
-        )
-    )
-)
-```
-
-## Troubleshooting
-
-### Common Issues
-
-**Memory not persisting between sessions?**
-- Check `CREWAI_STORAGE_DIR` environment variable
-- Ensure write permissions to storage directory
-- Verify memory is enabled with `memory=True`
-
-**Mem0 authentication errors?**
-- Verify `MEM0_API_KEY` environment variable is set
-- Check API key permissions on Mem0 dashboard
-- Ensure `mem0ai` package is installed
-
-**High memory usage with large datasets?**
-- Consider using External Memory with custom storage
-- Implement pagination in custom storage search methods
-- Use smaller embedding models for reduced memory footprint
-
-### Performance Tips
-
-- Use `memory=True` for most use cases (simplest and fastest)
-- Only use User Memory if you need user-specific persistence
-- Consider External Memory for high-scale or specialized requirements
-- Choose smaller embedding models for faster processing
-- Set appropriate search limits to control memory retrieval size
-
-## 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
----
-
-## Overview
-
-The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
-By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
-
-During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. 
-This helps the agents improve their understanding, decision-making, and problem-solving abilities.
-
-### Training Your Crew Using the CLI
-
-To use the training feature, follow these steps:
-
-1. Open your terminal or command prompt.
-2. Navigate to the directory where your CrewAI project is located.
-3. Run the following command:
-
-```shell
-crewai train -n <n_iterations> <filename> (optional)
-```
-<Tip>
-  Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
-</Tip>
-
-### Training Your Crew Programmatically
-
-To train your crew programmatically, use the following steps:
-
-1. Define the number of iterations for training.
-2. Specify the input parameters for the training process.
-3. Execute the training command within a try-except block to handle potential errors.
-
-```python Code
-n_iterations = 2
-inputs = {"topic": "CrewAI Training"}
-filename = "your_model.pkl"
-
-try:
-    YourCrewName_Crew().crew().train(
-      n_iterations=n_iterations, 
-      inputs=inputs, 
-      filename=filename
-    )
-
-except Exception as e:
-    raise Exception(f"An error occurred while training the crew: {e}")
-```
-
-### Key Points to Note
-
-- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
-- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
-- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
-
-It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
-
-Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
-
-Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
-
-Happy training with CrewAI! 🚀
-  

docs/en/api-reference/inputs.mdx

@@ -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

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

docs/en/api-reference/kickoff.mdx

@@ -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
@@ -19,7 +20,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 </Tip>
 
 <Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
-CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
+CrewAI AOP includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
 
 ![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
 
@@ -43,7 +44,6 @@ The Visual Agent Builder enables:
 | **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.                                                       |
@@ -72,7 +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:
@@ -156,7 +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
@@ -297,6 +296,11 @@ multimodal_agent = Agent(
   - `"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
@@ -309,7 +313,7 @@ multimodal_agent = Agent(
 
 <Note>
 When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting.
-</Note>  
+</Note>
 
 <Note>
 When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
@@ -422,7 +426,7 @@ strict_agent = Agent(
 ```python Code
 # Perfect for document processing
 document_processor = Agent(
-    role="Document Analyst", 
+    role="Document Analyst",
     goal="Extract insights from large research papers",
     backstory="Expert at analyzing extensive documentation",
     respect_context_window=True,  # Handle large documents gracefully
@@ -523,6 +527,103 @@ agent = Agent(
 The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
 </Note>
 
+## Direct Agent Interaction with `kickoff()`
+
+Agents can be used directly without going through a task or crew workflow using the `kickoff()` method. This provides a simpler way to interact with an agent when you don't need the full crew orchestration capabilities.
+
+### How `kickoff()` Works
+
+The `kickoff()` method allows you to send messages directly to an agent and get a response, similar to how you would interact with an LLM but with all the agent's capabilities (tools, reasoning, etc.).
+
+```python Code
+from crewai import Agent
+from crewai_tools import SerperDevTool
+
+# Create an agent
+researcher = Agent(
+    role="AI Technology Researcher",
+    goal="Research the latest AI developments",
+    tools=[SerperDevTool()],
+    verbose=True
+)
+
+# Use kickoff() to interact directly with the agent
+result = researcher.kickoff("What are the latest developments in language models?")
+
+# Access the raw response
+print(result.raw)
+```
+
+### Parameters and Return Values
+
+| Parameter         | Type                                | Description                                                               |
+| :---------------- | :---------------------------------- | :------------------------------------------------------------------------ |
+| `messages`        | `Union[str, List[Dict[str, str]]]`  | Either a string query or a list of message dictionaries with role/content |
+| `response_format` | `Optional[Type[Any]]`               | Optional Pydantic model for structured output                             |
+
+The method returns a `LiteAgentOutput` object with the following properties:
+
+- `raw`: String containing the raw output text
+- `pydantic`: Parsed Pydantic model (if a `response_format` was provided)
+- `agent_role`: Role of the agent that produced the output
+- `usage_metrics`: Token usage metrics for the execution
+
+### Structured Output
+
+You can get structured output by providing a Pydantic model as the `response_format`:
+
+```python Code
+from pydantic import BaseModel
+from typing import List
+
+class ResearchFindings(BaseModel):
+    main_points: List[str]
+    key_technologies: List[str]
+    future_predictions: str
+
+# Get structured output
+result = researcher.kickoff(
+    "Summarize the latest developments in AI for 2025",
+    response_format=ResearchFindings
+)
+
+# Access structured data
+print(result.pydantic.main_points)
+print(result.pydantic.future_predictions)
+```
+
+### Multiple Messages
+
+You can also provide a conversation history as a list of message dictionaries:
+
+```python Code
+messages = [
+    {"role": "user", "content": "I need information about large language models"},
+    {"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
+    {"role": "user", "content": "What are the latest developments in 2025?"}
+]
+
+result = researcher.kickoff(messages)
+```
+
+### Async Support
+
+An asynchronous version is available via `kickoff_async()` with the same parameters:
+
+```python Code
+import asyncio
+
+async def main():
+    result = await researcher.kickoff_async("What are the latest developments in AI?")
+    print(result.raw)
+
+asyncio.run(main())
+```
+
+<Note>
+The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler execution flow while preserving all of the agent's configuration (role, goal, backstory, tools, etc.).
+</Note>
+
 ## Important Considerations and Best Practices
 
 ### Security and Code Execution
@@ -537,7 +638,6 @@ The context window management feature works automatically in the background. You
 - 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` when using custom embedding models
 - Use custom templates (`system_template`, `prompt_template`, `response_template`) for fine-grained control over agent behavior
@@ -585,7 +685,6 @@ The context window management feature works automatically in the background. You
    - 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 +1,482 @@
+---
+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 AOP started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
+
+## Overview
+
+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 AOP](https://app.crewai.com).
+
+- **Authentication**: You need to be authenticated to deploy to CrewAI AOP.
+    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 AOP 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 AOP to use these organization management commands.
+</Note>
+
+- **Create a deployment** (continued):
+    - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
+
+- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI AOP.
+    ```shell Terminal
+    crewai deploy push
+    ```
+    - Initiates the deployment process on the CrewAI AOP platform.
+    - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
+
+- **Deployment Status**: You can check the status of your deployment with:
+    ```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 AOP 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 AOP](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 AOP using a secure device code flow (no email entry required).
+
+```shell Terminal
+crewai login
+```
+
+What happens:
+- A verification URL and short code are displayed in your terminal
+- Your browser opens to the verification URL
+- Enter/confirm the code to complete authentication
+
+Notes:
+- The OAuth2 provider and domain are configured via `crewai config` (defaults use `login.crewai.com`)
+- After successful login, the CLI also attempts to authenticate to the Tool Repository automatically
+- If you reset your configuration, run `crewai login` again to re-authenticate
+
+### 12. API Keys
+
+When running ```crewai create crew``` command, the CLI will show you a list of available LLM providers to choose from, followed by model selection for your chosen provider.
+
+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 AOP instance
+- `oauth2_provider`: OAuth2 provider used for authentication (e.g., workos, okta, auth0)
+- `oauth2_audience`: OAuth2 audience value, typically used to identify the target API or resource
+- `oauth2_client_id`: OAuth2 client ID issued by the provider, used during authentication requests
+- `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 AOP instance                  |
+| org_name            | Not set                  | Name of the currently active organization                   |
+| org_uuid            | Not set                  | UUID of the currently active organization                   |
+| oauth2_provider     | workos                   | OAuth2 provider (e.g., workos, okta, auth0)                 |
+| oauth2_audience     | client_01YYY             | Audience identifying the target API/resource                |
+| oauth2_client_id    | client_01XXX             | OAuth2 client ID issued by the provider                     |
+| oauth2_domain       | login.crewai.com         | Provider domain (e.g., your-org.auth0.com)                  |
+
+Set the enterprise base URL:
+```shell Terminal
+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>
+
+### 14. Trace Management
+
+Manage trace collection preferences for your Crew and Flow executions.
+
+```shell Terminal
+crewai traces [COMMAND]
+```
+
+#### Commands:
+
+- `enable`: Enable trace collection for crew/flow executions
+```shell Terminal
+crewai traces enable
+```
+
+- `disable`: Disable trace collection for crew/flow executions
+```shell Terminal
+crewai traces disable
+```
+
+- `status`: Show current trace collection status
+```shell Terminal
+crewai traces status
+```
+
+#### How Tracing Works
+
+Trace collection is controlled by checking three settings in priority order:
+
+1. **Explicit flag in code** (highest priority - can enable OR disable):
+   ```python
+   crew = Crew(agents=[...], tasks=[...], tracing=True)   # Always enable
+   crew = Crew(agents=[...], tasks=[...], tracing=False)  # Always disable
+   crew = Crew(agents=[...], tasks=[...])                 # Check lower priorities (default)
+   ```
+   - `tracing=True` will **always enable** tracing (overrides everything)
+   - `tracing=False` will **always disable** tracing (overrides everything)
+   - `tracing=None` or omitted will check lower priority settings
+
+2. **Environment variable** (second priority):
+   ```env
+   CREWAI_TRACING_ENABLED=true
+   ```
+   - Checked only if `tracing` is not explicitly set to `True` or `False` in code
+   - Set to `true` or `1` to enable tracing
+
+3. **User preference** (lowest priority):
+   ```shell Terminal
+   crewai traces enable
+   ```
+   - Checked only if `tracing` is not set in code and `CREWAI_TRACING_ENABLED` is not set to `true`
+   - Running `crewai traces enable` is sufficient to enable tracing by itself
+
+<Note>
+**To enable tracing**, use any one of these methods:
+- Set `tracing=True` in your Crew/Flow code, OR
+- Add `CREWAI_TRACING_ENABLED=true` to your `.env` file, OR
+- Run `crewai traces enable`
+
+**To disable tracing**, use any ONE of these methods:
+- Set `tracing=False` in your Crew/Flow code (overrides everything), OR
+- Remove or set to `false` the `CREWAI_TRACING_ENABLED` env var, OR
+- Run `crewai traces disable`
+
+Higher priority settings override lower ones.
+</Note>
+
+<Tip>
+For more information about tracing, see the [Tracing documentation](/observability/tracing).
+</Tip>
+
+<Tip>
+CrewAI CLI handles authentication to the Tool Repository automatically when adding packages to your project. Just append `crewai` before any `uv` command to use it. E.g. `crewai uv add requests`. For more information, see [Tool Repository](https://docs.crewai.com/enterprise/features/tool-repository) docs.
+</Tip>
+
+<Note>
+Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
+</Note>

docs/en/concepts/collaboration.mdx

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

docs/en/concepts/crews.mdx

@@ -2,6 +2,7 @@
 title: Crews
 description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
 icon: people-group
+mode: "wide"
 ---
 
 ## Overview
@@ -20,8 +21,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
 | **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
 | **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
-| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
-| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |                                                                                                                                                                                       |
 | **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
 | **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
@@ -32,6 +32,8 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
+| **Knowledge Sources** _(optional)_    | `knowledge_sources`    | Knowledge sources available at the crew level, accessible to all the agents.                                                                                                                                                                                    |
+| **Stream** _(optional)_               | `stream`               | Enable streaming output to receive real-time updates during crew execution. Returns a `CrewStreamingOutput` object that can be iterated for chunks. Defaults to `False`.                                                                                    |
 
 <Tip>
 **Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
@@ -45,7 +47,7 @@ There are two ways to create crews in CrewAI: using **YAML configuration (recomm
 
 Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/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
 
@@ -66,8 +68,8 @@ class YourCrewName:
     # To see an example agent and task defined in YAML, checkout the following:
     # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
     # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    agents_config = 'config/agents.yaml' 
-    tasks_config = 'config/tasks.yaml' 
+    agents_config = 'config/agents.yaml'
+    tasks_config = 'config/tasks.yaml'
 
     @before_kickoff
     def prepare_inputs(self, inputs):
@@ -111,7 +113,7 @@ class YourCrewName:
     def crew(self) -> Crew:
         return Crew(
             agents=self.agents,  # Automatically collected by the @agent decorator
-            tasks=self.tasks,    # Automatically collected by the @task decorator. 
+            tasks=self.tasks,    # Automatically collected by the @task decorator.
             process=Process.sequential,
             verbose=True,
         )
@@ -325,18 +327,41 @@ 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)
 ```
 
 These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs.
 
+### Streaming Crew Execution
+
+For real-time visibility into crew execution, you can enable streaming to receive output as it's generated:
+
+```python Code
+# Enable streaming
+crew = Crew(
+    agents=[researcher],
+    tasks=[task],
+    stream=True
+)
+
+# Iterate over streaming output
+streaming = crew.kickoff(inputs={"topic": "AI"})
+for chunk in streaming:
+    print(chunk.content, end="", flush=True)
+
+# Access final result
+result = streaming.result
+```
+
+Learn more about streaming in the [Streaming Crew Execution](/en/learn/streaming-crew-execution) guide.
+
 ### Replaying from a Specific Task
 
 You can now replay from a specific task using our CLI command `replay`.