feat: add --no-commit flag to bump command (#4087)

Allows updating version files without creating a commit, branch, or PR.

.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
@@ -30,7 +52,18 @@ jobs:
       - name: Run Ruff on Changed Files
         if: ${{ steps.changed-files.outputs.files != '' }}
         run: |
-          echo "${{ steps.changed-files.outputs.files }}" \
-            | tr ' ' '\n' \
-            | grep -v 'src/crewai/cli/templates/' \
-            | xargs -I{} ruff check "{}"
+         echo "${{ steps.changed-files.outputs.files }}" \
+           | tr ' ' '\n' \
+           | grep -v 'src/crewai/cli/templates/' \
+           | grep -v '/tests/' \
+           | xargs -I{} uv run ruff check "{}"
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}

.github/workflows/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 (${{ matrix.python-version }})
     runs-on: ubuntu-latest
     timeout-minutes: 15
     strategy:
+      fail-fast: true
       matrix:
         python-version: ['3.10', '3.11', '3.12', '3.13']
+        group: [1, 2, 3, 4, 5, 6, 7, 8]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0  # Fetch all history for proper diff
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py${{ 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

@@ -62,9 +62,9 @@
 With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
 standard for enterprise-ready AI automation.
 
-# CrewAI Enterprise Suite
+# CrewAI AOP Suite
 
-CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
+CrewAI AOP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
 
 You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
 
@@ -76,9 +76,9 @@ You can try one part of the suite the [Crew Control Plane for free](https://app.
 - **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
 - **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
 - **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
-- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
+- **On-premise and Cloud Deployment Options**: Deploy CrewAI AOP on-premise or in the cloud, depending on your security and compliance requirements.
 
-CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
+CrewAI AOP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
 intelligent automations.
 
 ## Table of contents
@@ -418,10 +418,10 @@ Choose CrewAI to easily build powerful, adaptable, and production-ready AI autom
 
 You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
 
-- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
+- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator)
 - [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
-- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
-- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
+- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner)
+- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis)
 
 ### Quick Tutorial
 
@@ -429,19 +429,19 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 ### Write Job Descriptions
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below:
 
 [![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
 
 ### Trip Planner
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below:
 
 [![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
 
 ### Stock Analysis
 
-[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
+[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below:
 
 [![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
 
@@ -674,9 +674,9 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
 
 ### Enterprise Features
 
-- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
-- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
-- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
+- [What additional features does CrewAI AOP offer?](#q-what-additional-features-does-crewai-amp-offer)
+- [Is CrewAI AOP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
+- [Can I try CrewAI AOP for free?](#q-can-i-try-crewai-amp-for-free)
 
 ### Q: What exactly is CrewAI?
 
@@ -732,17 +732,17 @@ A: Check out practical examples in the [CrewAI-examples repository](https://gith
 
 A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
 
-### Q: What additional features does CrewAI Enterprise offer?
+### Q: What additional features does CrewAI AOP offer?
 
-A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
+A: CrewAI AOP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
 
-### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
+### Q: Is CrewAI AOP available for cloud and on-premise deployments?
 
-A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
+A: Yes, CrewAI AOP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
 
-### Q: Can I try CrewAI Enterprise for free?
+### Q: Can I try CrewAI AOP for free?
 
-A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
+A: Yes, you can explore part of the CrewAI AOP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
 
 ### Q: Does CrewAI support fine-tuning or training custom models?
 
@@ -762,7 +762,7 @@ A: CrewAI is highly scalable, supporting simple automations and large-scale ente
 
 ### Q: Does CrewAI offer debugging and monitoring tools?
 
-A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
+A: Yes, CrewAI AOP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
 
 ### Q: What programming languages does CrewAI support?
 

conftest.py

@@ -0,0 +1,197 @@
+"""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."""
+    # Remove Content-Encoding to prevent decompression issues on replay
+    for encoding_header in ["Content-Encoding", "content-encoding"]:
+        response["headers"].pop(encoding_header, None)
+
+    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/api-reference/introduction.mdx

@@ -1,119 +0,0 @@
----
-title: "Introduction"
-description: "Complete reference for the CrewAI Enterprise REST API"
-icon: "code"
----
-
-# CrewAI Enterprise 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.
-
-## 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.
-  </Step>
-  
-  <Step title="Discover Required Inputs">
-    Use the `GET /inputs` endpoint to see what parameters your crew expects.
-  </Step>
-  
-  <Step title="Start a Crew Execution">
-    Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`.
-  </Step>
-  
-  <Step title="Monitor Progress">
-    Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
-  </Step>
-</Steps>
-
-## Authentication
-
-All API requests require authentication using a Bearer token. Include your token in the `Authorization` header:
-
-```bash
-curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
-  https://your-crew-url.crewai.com/inputs
-```
-
-### Token Types
-
-| Token Type | Scope | Use Case |
-|:-----------|:--------|:----------|
-| **Bearer Token** | Organization-level access | Full crew operations, ideal for server-to-server integration |
-| **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
-
-<Tip>
-You can find both token types in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
-</Tip>
-
-## Base URL
-
-Each deployed crew has its own unique API endpoint:
-
-```
-https://your-crew-name.crewai.com
-```
-
-Replace `your-crew-name` with your actual crew's URL from the dashboard.
-
-## Typical Workflow
-
-1. **Discovery**: Call `GET /inputs` to understand what your crew needs
-2. **Execution**: Submit inputs via `POST /kickoff` to start processing  
-3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
-4. **Results**: Extract the final output from the completed response
-
-## Error Handling
-
-The API uses standard HTTP status codes:
-
-| Code | Meaning |
-|------|:--------|
-| `200` | Success |
-| `400` | Bad Request - Invalid input format |
-| `401` | Unauthorized - Invalid bearer token |
-| `404` | Not Found - Resource doesn't exist |
-| `422` | Validation Error - Missing required inputs |
-| `500` | Server Error - Contact support |
-
-## Interactive Testing
-
-<Info>
-**Why no "Send" button?** Since each CrewAI Enterprise user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
-</Info>
-
-Each endpoint page shows you:
-- ✅ **Exact request format** with all parameters
-- ✅ **Response examples** for success and error cases  
-- ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.)
-- ✅ **Authentication examples** with proper Bearer token format
-
-### **To Test Your Actual API:**
-
-<CardGroup cols={2}>
-  <Card title="Copy cURL Examples" icon="terminal">
-    Copy the cURL examples and replace the URL + token with your real values
-  </Card>
-  <Card title="Use Postman/Insomnia" icon="play">
-    Import the examples into your preferred API testing tool
-  </Card>
-</CardGroup>
-
-**Example workflow:**
-1. **Copy this cURL example** from any endpoint page
-2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL  
-3. **Replace the Bearer token** with your real token from the dashboard
-4. **Run the request** in your terminal or API client
-
-## Need Help?
-
-<CardGroup cols={2}>
-  <Card title="Enterprise Support" icon="headset" href="mailto:support@crewai.com">
-    Get help with API integration and troubleshooting
-  </Card>
-  <Card title="Enterprise Dashboard" icon="chart-line" href="https://app.crewai.com">
-    Manage your crews and view execution logs
-  </Card>
-</CardGroup>

docs/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/concepts/agents.mdx

@@ -1,593 +0,0 @@
----
-title: Agents
-description: Detailed guide on creating and managing agents within the CrewAI framework.
-icon: robot
----
-
-## Overview of an Agent
-
-In the CrewAI framework, an `Agent` is an autonomous unit that can:
-- Perform specific tasks
-- Make decisions based on its role and goal
-- Use tools to accomplish objectives
-- Communicate and collaborate with other agents
-- Maintain memory of interactions
-- Delegate tasks when allowed
-
-<Tip>
-Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a `Researcher` agent might excel at gathering and analyzing information, while a `Writer` agent might be better at creating content.
-</Tip>
-
-<Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
-CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
-
-![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
-
-The Visual Agent Builder enables:
-- Intuitive agent configuration with form-based interfaces
-- Real-time testing and validation
-- Template library with pre-configured agent types
-- Easy customization of agent attributes and behaviors
-</Note>
-
-## Agent Attributes
-
-| Attribute                               | Parameter                | Type                          | Description                                                                                                          |
-| :-------------------------------------- | :----------------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
-| **Role**                                | `role`                   | `str`                         | Defines the agent's function and expertise within the crew.                                                           |
-| **Goal**                                | `goal`                   | `str`                         | The individual objective that guides the agent's decision-making.                                                     |
-| **Backstory**                           | `backstory`              | `str`                         | Provides context and personality to the agent, enriching interactions.                                                |
-| **LLM** _(optional)_                    | `llm`                    | `Union[str, LLM, Any]`        | Language model that powers the agent. Defaults to the model specified in `OPENAI_MODEL_NAME` or "gpt-4".              |
-| **Tools** _(optional)_                  | `tools`                  | `List[BaseTool]`              | Capabilities or functions available to the agent. Defaults to an empty list.                                          |
-| **Function Calling LLM** _(optional)_   | `function_calling_llm`   | `Optional[Any]`               | Language model for tool calling, overrides crew's LLM if specified.                                                   |
-| **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.                                                                         |
-| **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.                                                       |
-| **Cache** _(optional)_                  | `cache`                  | `bool`                        | Enable caching for tool usage. Default is True.                                                                       |
-| **System Template** _(optional)_        | `system_template`        | `Optional[str]`               | Custom system prompt template for the agent.                                                                          |
-| **Prompt Template** _(optional)_        | `prompt_template`        | `Optional[str]`               | Custom prompt template for the agent.                                                                                 |
-| **Response Template** _(optional)_      | `response_template`      | `Optional[str]`               | Custom response template for the agent.                                                                               |
-| **Allow Code Execution** _(optional)_   | `allow_code_execution`   | `Optional[bool]`              | Enable code execution for the agent. Default is False.                                                                |
-| **Max Retry Limit** _(optional)_        | `max_retry_limit`        | `int`                         | Maximum number of retries when an error occurs. Default is 2.                                                         |
-| **Respect Context Window** _(optional)_ | `respect_context_window` | `bool`                        | Keep messages under context window size by summarizing. Default is True.                                              |
-| **Code Execution Mode** _(optional)_    | `code_execution_mode`    | `Literal["safe", "unsafe"]`   | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'.                               |
-| **Multimodal** _(optional)_             | `multimodal`             | `bool`                        | Whether the agent supports multimodal capabilities. Default is False.                                                  |
-| **Inject Date** _(optional)_            | `inject_date`            | `bool`                        | Whether to automatically inject the current date into tasks. Default is False.                                         |
-| **Date Format** _(optional)_            | `date_format`            | `str`                         | Format string for date when inject_date is enabled. Default is "%Y-%m-%d" (ISO format).                               |
-| **Reasoning** _(optional)_              | `reasoning`              | `bool`                        | Whether the agent should reflect and create a plan before executing a task. Default is False.                         |
-| **Max Reasoning Attempts** _(optional)_ | `max_reasoning_attempts` | `Optional[int]`               | Maximum number of reasoning attempts before executing the task. If None, will try until ready.                         |
-| **Embedder** _(optional)_               | `embedder`               | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
-| **Knowledge Sources** _(optional)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent.                                                                     |
-| **Use System Prompt** _(optional)_      | `use_system_prompt`      | `Optional[bool]`              | Whether to use system prompt (for o1 model support). Default is True.                                                 |
-
-## Creating Agents
-
-There are two ways to create agents in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
-
-### YAML Configuration (Recommended)
-
-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.
-
-<Note>
-Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
-```python Code
-crew.kickoff(inputs={'topic': 'AI Agents'})
-```
-</Note>
-
-Here's an example of how to configure agents using YAML:
-
-```yaml agents.yaml
-# src/latest_ai_development/config/agents.yaml
-researcher:
-  role: >
-    {topic} Senior Data Researcher
-  goal: >
-    Uncover cutting-edge developments in {topic}
-  backstory: >
-    You're a seasoned researcher with a knack for uncovering the latest
-    developments in {topic}. Known for your ability to find the most relevant
-    information and present it in a clear and concise manner.
-
-reporting_analyst:
-  role: >
-    {topic} Reporting Analyst
-  goal: >
-    Create detailed reports based on {topic} data analysis and research findings
-  backstory: >
-    You're a meticulous analyst with a keen eye for detail. You're known for
-    your ability to turn complex data into clear and concise reports, making
-    it easy for others to understand and act on the information you provide.
-```
-
-To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`:
-
-```python Code
-# src/latest_ai_development/crew.py
-from crewai import Agent, Crew, Process
-from crewai.project import CrewBase, agent, crew
-from crewai_tools import SerperDevTool
-
-@CrewBase
-class LatestAiDevelopmentCrew():
-  """LatestAiDevelopment crew"""
-
-  agents_config = "config/agents.yaml"
-
-  @agent
-  def researcher(self) -> Agent:
-    return Agent(
-      config=self.agents_config['researcher'], # type: ignore[index]
-      verbose=True,
-      tools=[SerperDevTool()]
-    )
-
-  @agent
-  def reporting_analyst(self) -> Agent:
-    return Agent(
-      config=self.agents_config['reporting_analyst'], # type: ignore[index]
-      verbose=True
-    )
-```
-
-<Note>
-The names you use in your YAML files (`agents.yaml`) should match the method names in your Python code.
-</Note>
-
-### Direct Code Definition
-
-You can create agents directly in code by instantiating the `Agent` class. Here's a comprehensive example showing all available parameters:
-
-```python Code
-from crewai import Agent
-from crewai_tools import SerperDevTool
-
-# Create an agent with all available parameters
-agent = Agent(
-    role="Senior Data Scientist",
-    goal="Analyze and interpret complex datasets to provide actionable insights",
-    backstory="With over 10 years of experience in data science and machine learning, "
-              "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
-    verbose=False,  # Default: False
-    allow_delegation=False,  # Default: False
-    max_iter=20,  # Default: 20 iterations
-    max_rpm=None,  # Optional: Rate limit for API calls
-    max_execution_time=None,  # Optional: Maximum execution time in seconds
-    max_retry_limit=2,  # Default: 2 retries on error
-    allow_code_execution=False,  # Default: False
-    code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
-    respect_context_window=True,  # Default: True
-    use_system_prompt=True,  # Default: True
-    multimodal=False,  # Default: False
-    inject_date=False,  # Default: False
-    date_format="%Y-%m-%d",  # Default: ISO format
-    reasoning=False,  # Default: False
-    max_reasoning_attempts=None,  # Default: None
-    tools=[SerperDevTool()],  # Optional: List of tools
-    knowledge_sources=None,  # Optional: List of knowledge sources
-    embedder=None,  # Optional: Custom embedder configuration
-    system_template=None,  # Optional: Custom system prompt template
-    prompt_template=None,  # Optional: Custom prompt template
-    response_template=None,  # Optional: Custom response template
-    step_callback=None,  # Optional: Callback function for monitoring
-)
-```
-
-Let's break down some key parameter combinations for common use cases:
-
-#### Basic Research Agent
-```python Code
-research_agent = Agent(
-    role="Research Analyst",
-    goal="Find and summarize information about specific topics",
-    backstory="You are an experienced researcher with attention to detail",
-    tools=[SerperDevTool()],
-    verbose=True  # Enable logging for debugging
-)
-```
-
-#### Code Development Agent
-```python Code
-dev_agent = Agent(
-    role="Senior Python Developer",
-    goal="Write and debug Python code",
-    backstory="Expert Python developer with 10 years of experience",
-    allow_code_execution=True,
-    code_execution_mode="safe",  # Uses Docker for safety
-    max_execution_time=300,  # 5-minute timeout
-    max_retry_limit=3  # More retries for complex code tasks
-)
-```
-
-#### Long-Running Analysis Agent
-```python Code
-analysis_agent = Agent(
-    role="Data Analyst",
-    goal="Perform deep analysis of large datasets",
-    backstory="Specialized in big data analysis and pattern recognition",
-    memory=True,
-    respect_context_window=True,
-    max_rpm=10,  # Limit API calls
-    function_calling_llm="gpt-4o-mini"  # Cheaper model for tool calls
-)
-```
-
-#### Custom Template Agent
-```python Code
-custom_agent = Agent(
-    role="Customer Service Representative",
-    goal="Assist customers with their inquiries",
-    backstory="Experienced in customer support with a focus on satisfaction",
-    system_template="""<|start_header_id|>system<|end_header_id|>
-                        {{ .System }}<|eot_id|>""",
-    prompt_template="""<|start_header_id|>user<|end_header_id|>
-                        {{ .Prompt }}<|eot_id|>""",
-    response_template="""<|start_header_id|>assistant<|end_header_id|>
-                        {{ .Response }}<|eot_id|>""",
-)
-```
-
-#### Date-Aware Agent with Reasoning
-```python Code
-strategic_agent = Agent(
-    role="Market Analyst",
-    goal="Track market movements with precise date references and strategic planning",
-    backstory="Expert in time-sensitive financial analysis and strategic reporting",
-    inject_date=True,  # Automatically inject current date into tasks
-    date_format="%B %d, %Y",  # Format as "May 21, 2025"
-    reasoning=True,  # Enable strategic planning
-    max_reasoning_attempts=2,  # Limit planning iterations
-    verbose=True
-)
-```
-
-#### Reasoning Agent
-```python Code
-reasoning_agent = Agent(
-    role="Strategic Planner",
-    goal="Analyze complex problems and create detailed execution plans",
-    backstory="Expert strategic planner who methodically breaks down complex challenges",
-    reasoning=True,  # Enable reasoning and planning
-    max_reasoning_attempts=3,  # Limit reasoning attempts
-    max_iter=30,  # Allow more iterations for complex planning
-    verbose=True
-)
-```
-
-#### Multimodal Agent
-```python Code
-multimodal_agent = Agent(
-    role="Visual Content Analyst",
-    goal="Analyze and process both text and visual content",
-    backstory="Specialized in multimodal analysis combining text and image understanding",
-    multimodal=True,  # Enable multimodal capabilities
-    verbose=True
-)
-```
-
-### Parameter Details
-
-#### Critical Parameters
-- `role`, `goal`, and `backstory` are required and shape the agent's behavior
-- `llm` determines the language model used (default: OpenAI's GPT-4)
-
-#### Memory and Context
-- `memory`: Enable to maintain conversation history
-- `respect_context_window`: Prevents token limit issues
-- `knowledge_sources`: Add domain-specific knowledge bases
-
-#### Execution Control
-- `max_iter`: Maximum attempts before giving best answer
-- `max_execution_time`: Timeout in seconds
-- `max_rpm`: Rate limiting for API calls
-- `max_retry_limit`: Retries on error
-
-#### Code Execution
-- `allow_code_execution`: Must be True to run code
-- `code_execution_mode`:
-  - `"safe"`: Uses Docker (recommended for production)
-  - `"unsafe"`: Direct execution (use only in trusted environments)
-
-<Note>
-  This runs a default Docker image. If you want to configure the docker image, the checkout the Code Interpreter Tool in the tools section.
-  Add the code interpreter tool as a tool in the agent as a tool parameter.
-  </Note>
-
-#### Advanced Features
-- `multimodal`: Enable multimodal capabilities for processing text and visual content
-- `reasoning`: Enable agent to reflect and create plans before executing tasks
-- `inject_date`: Automatically inject current date into task descriptions
-
-#### Templates
-- `system_template`: Defines agent's core behavior
-- `prompt_template`: Structures input format
-- `response_template`: Formats agent responses
-
-<Note>
-When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting.
-</Note>  
-
-<Note>
-When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
-</Note>
-
-## Agent Tools
-
-Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from:
-- [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools)
-- [LangChain Tools](https://python.langchain.com/docs/integrations/tools)
-
-Here's how to add tools to an agent:
-
-```python Code
-from crewai import Agent
-from crewai_tools import SerperDevTool, WikipediaTools
-
-# Create tools
-search_tool = SerperDevTool()
-wiki_tool = WikipediaTools()
-
-# Add tools to agent
-researcher = Agent(
-    role="AI Technology Researcher",
-    goal="Research the latest AI developments",
-    tools=[search_tool, wiki_tool],
-    verbose=True
-)
-```
-
-## Agent Memory and Context
-
-Agents can maintain memory of their interactions and use context from previous tasks. This is particularly useful for complex workflows where information needs to be retained across multiple tasks.
-
-```python Code
-from crewai import Agent
-
-analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze and remember complex data patterns",
-    memory=True,  # Enable memory
-    verbose=True
-)
-```
-
-<Note>
-When `memory` is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks.
-</Note>
-
-## Context Window Management
-
-CrewAI includes sophisticated automatic context window management to handle situations where conversations exceed the language model's token limits. This powerful feature is controlled by the `respect_context_window` parameter.
-
-### How Context Window Management Works
-
-When an agent's conversation history grows too large for the LLM's context window, CrewAI automatically detects this situation and can either:
-
-1. **Automatically summarize content** (when `respect_context_window=True`)
-2. **Stop execution with an error** (when `respect_context_window=False`)
-
-### Automatic Context Handling (`respect_context_window=True`)
-
-This is the **default and recommended setting** for most use cases. When enabled, CrewAI will:
-
-```python Code
-# Agent with automatic context management (default)
-smart_agent = Agent(
-    role="Research Analyst",
-    goal="Analyze large documents and datasets",
-    backstory="Expert at processing extensive information",
-    respect_context_window=True,  # 🔑 Default: auto-handle context limits
-    verbose=True
-)
-```
-
-**What happens when context limits are exceeded:**
-- ⚠️ **Warning message**: `"Context length exceeded. Summarizing content to fit the model context window."`
-- 🔄 **Automatic summarization**: CrewAI intelligently summarizes the conversation history
-- ✅ **Continued execution**: Task execution continues seamlessly with the summarized context
-- 📝 **Preserved information**: Key information is retained while reducing token count
-
-### Strict Context Limits (`respect_context_window=False`)
-
-When you need precise control and prefer execution to stop rather than lose any information:
-
-```python Code
-# Agent with strict context limits
-strict_agent = Agent(
-    role="Legal Document Reviewer",
-    goal="Provide precise legal analysis without information loss",
-    backstory="Legal expert requiring complete context for accurate analysis",
-    respect_context_window=False,  # ❌ Stop execution on context limit
-    verbose=True
-)
-```
-
-**What happens when context limits are exceeded:**
-- ❌ **Error message**: `"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools."`
-- 🛑 **Execution stops**: Task execution halts immediately
-- 🔧 **Manual intervention required**: You need to modify your approach
-
-### Choosing the Right Setting
-
-#### Use `respect_context_window=True` (Default) when:
-- **Processing large documents** that might exceed context limits
-- **Long-running conversations** where some summarization is acceptable
-- **Research tasks** where general context is more important than exact details
-- **Prototyping and development** where you want robust execution
-
-```python Code
-# Perfect for document processing
-document_processor = Agent(
-    role="Document Analyst", 
-    goal="Extract insights from large research papers",
-    backstory="Expert at analyzing extensive documentation",
-    respect_context_window=True,  # Handle large documents gracefully
-    max_iter=50,  # Allow more iterations for complex analysis
-    verbose=True
-)
-```
-
-#### Use `respect_context_window=False` when:
-- **Precision is critical** and information loss is unacceptable
-- **Legal or medical tasks** requiring complete context
-- **Code review** where missing details could introduce bugs
-- **Financial analysis** where accuracy is paramount
-
-```python Code
-# Perfect for precision tasks
-precision_agent = Agent(
-    role="Code Security Auditor",
-    goal="Identify security vulnerabilities in code",
-    backstory="Security expert requiring complete code context",
-    respect_context_window=False,  # Prefer failure over incomplete analysis
-    max_retry_limit=1,  # Fail fast on context issues
-    verbose=True
-)
-```
-
-### Alternative Approaches for Large Data
-
-When dealing with very large datasets, consider these strategies:
-
-#### 1. Use RAG Tools
-```python Code
-from crewai_tools import RagTool
-
-# Create RAG tool for large document processing
-rag_tool = RagTool()
-
-rag_agent = Agent(
-    role="Research Assistant",
-    goal="Query large knowledge bases efficiently",
-    backstory="Expert at using RAG tools for information retrieval",
-    tools=[rag_tool],  # Use RAG instead of large context windows
-    respect_context_window=True,
-    verbose=True
-)
-```
-
-#### 2. Use Knowledge Sources
-```python Code
-# Use knowledge sources instead of large prompts
-knowledge_agent = Agent(
-    role="Knowledge Expert",
-    goal="Answer questions using curated knowledge",
-    backstory="Expert at leveraging structured knowledge sources",
-    knowledge_sources=[your_knowledge_sources],  # Pre-processed knowledge
-    respect_context_window=True,
-    verbose=True
-)
-```
-
-### Context Window Best Practices
-
-1. **Monitor Context Usage**: Enable `verbose=True` to see context management in action
-2. **Design for Efficiency**: Structure tasks to minimize context accumulation
-3. **Use Appropriate Models**: Choose LLMs with context windows suitable for your tasks
-4. **Test Both Settings**: Try both `True` and `False` to see which works better for your use case
-5. **Combine with RAG**: Use RAG tools for very large datasets instead of relying solely on context windows
-
-### Troubleshooting Context Issues
-
-**If you're getting context limit errors:**
-```python Code
-# Quick fix: Enable automatic handling
-agent.respect_context_window = True
-
-# Better solution: Use RAG tools for large data
-from crewai_tools import RagTool
-agent.tools = [RagTool()]
-
-# Alternative: Break tasks into smaller pieces
-# Or use knowledge sources instead of large prompts
-```
-
-**If automatic summarization loses important information:**
-```python Code
-# Disable auto-summarization and use RAG instead
-agent = Agent(
-    role="Detailed Analyst",
-    goal="Maintain complete information accuracy",
-    backstory="Expert requiring full context",
-    respect_context_window=False,  # No summarization
-    tools=[RagTool()],  # Use RAG for large data
-    verbose=True
-)
-```
-
-<Note>
-The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
-</Note>
-
-## Important Considerations and Best Practices
-
-### Security and Code Execution
-- When using `allow_code_execution`, be cautious with user input and always validate it
-- Use `code_execution_mode: "safe"` (Docker) in production environments
-- Consider setting appropriate `max_execution_time` limits to prevent infinite loops
-
-### Performance Optimization
-- Use `respect_context_window: true` to prevent token limit issues
-- Set appropriate `max_rpm` to avoid rate limiting
-- Enable `cache: true` to improve performance for repetitive tasks
-- Adjust `max_iter` and `max_retry_limit` based on task complexity
-
-### Memory and Context Management
-- 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
-
-### Advanced Features
-- Enable `reasoning: true` for agents that need to plan and reflect before executing complex tasks
-- Set appropriate `max_reasoning_attempts` to control planning iterations (None for unlimited attempts)
-- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
-- Customize the date format with `date_format` using standard Python datetime format codes
-- Enable `multimodal: true` for agents that need to process both text and visual content
-
-### Agent Collaboration
-- Enable `allow_delegation: true` when agents need to work together
-- Use `step_callback` to monitor and log agent interactions
-- Consider using different LLMs for different purposes:
-  - Main `llm` for complex reasoning
-  - `function_calling_llm` for efficient tool usage
-
-### Date Awareness and Reasoning
-- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
-- Customize the date format with `date_format` using standard Python datetime format codes
-- Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
-- Invalid date formats will be logged as warnings and will not modify the task description
-- Enable `reasoning: true` for complex tasks that benefit from upfront planning and reflection
-
-### Model Compatibility
-- Set `use_system_prompt: false` for older models that don't support system messages
-- Ensure your chosen `llm` supports the features you need (like function calling)
-
-## Troubleshooting Common Issues
-
-1. **Rate Limiting**: If you're hitting API rate limits:
-   - Implement appropriate `max_rpm`
-   - Use caching for repetitive operations
-   - Consider batching requests
-
-2. **Context Window Errors**: If you're exceeding context limits:
-   - Enable `respect_context_window`
-   - Use more efficient prompts
-   - Clear agent memory periodically
-
-3. **Code Execution Issues**: If code execution fails:
-   - Verify Docker is installed for safe mode
-   - Check execution permissions
-   - Review code sandbox settings
-
-4. **Memory Issues**: If agent responses seem inconsistent:
-   - Check knowledge source configuration
-   - Review conversation history management
-
-Remember that agents are most effective when configured according to their specific use case. Take time to understand your requirements and adjust these parameters accordingly.

docs/concepts/cli.mdx

@@ -1,315 +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.
-
-### 11. Organization Management
-
-Manage your CrewAI Enterprise 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 Enterprise to use these organization management commands.
-</Note>
-
-- **Create a deployment** (continued):
-    - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
-
-- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI 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/collaboration.mdx

@@ -1,362 +0,0 @@
----
-title: Collaboration
-description: How to enable agents to work together, delegate tasks, and communicate effectively within CrewAI teams.
-icon: screen-users
----
-
-## Overview
-
-Collaboration in CrewAI enables agents to work together as a team by delegating tasks and asking questions to leverage each other's expertise. When `allow_delegation=True`, agents automatically gain access to powerful collaboration tools.
-
-## Quick Start: Enable Collaboration
-
-```python
-from crewai import Agent, Crew, Task
-
-# Enable collaboration for agents
-researcher = Agent(
-    role="Research Specialist",
-    goal="Conduct thorough research on any topic",
-    backstory="Expert researcher with access to various sources",
-    allow_delegation=True,  # 🔑 Key setting for collaboration
-    verbose=True
-)
-
-writer = Agent(
-    role="Content Writer", 
-    goal="Create engaging content based on research",
-    backstory="Skilled writer who transforms research into compelling content",
-    allow_delegation=True,  # 🔑 Enables asking questions to other agents
-    verbose=True
-)
-
-# Agents can now collaborate automatically
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[...],
-    verbose=True
-)
-```
-
-## How Agent Collaboration Works
-
-When `allow_delegation=True`, CrewAI automatically provides agents with two powerful tools:
-
-### 1. **Delegate Work Tool**
-Allows agents to assign tasks to teammates with specific expertise.
-
-```python
-# Agent automatically gets this tool:
-# Delegate work to coworker(task: str, context: str, coworker: str)
-```
-
-### 2. **Ask Question Tool** 
-Enables agents to ask specific questions to gather information from colleagues.
-
-```python
-# Agent automatically gets this tool:
-# Ask question to coworker(question: str, context: str, coworker: str)
-```
-
-## Collaboration in Action
-
-Here's a complete example showing agents collaborating on a content creation task:
-
-```python
-from crewai import Agent, Crew, Task, Process
-
-# Create collaborative agents
-researcher = Agent(
-    role="Research Specialist",
-    goal="Find accurate, up-to-date information on any topic",
-    backstory="""You're a meticulous researcher with expertise in finding 
-    reliable sources and fact-checking information across various domains.""",
-    allow_delegation=True,
-    verbose=True
-)
-
-writer = Agent(
-    role="Content Writer",
-    goal="Create engaging, well-structured content",
-    backstory="""You're a skilled content writer who excels at transforming 
-    research into compelling, readable content for different audiences.""",
-    allow_delegation=True,
-    verbose=True
-)
-
-editor = Agent(
-    role="Content Editor",
-    goal="Ensure content quality and consistency",
-    backstory="""You're an experienced editor with an eye for detail, 
-    ensuring content meets high standards for clarity and accuracy.""",
-    allow_delegation=True,
-    verbose=True
-)
-
-# Create a task that encourages collaboration
-article_task = Task(
-    description="""Write a comprehensive 1000-word article about 'The Future of AI in Healthcare'.
-    
-    The article should include:
-    - Current AI applications in healthcare
-    - Emerging trends and technologies  
-    - Potential challenges and ethical considerations
-    - Expert predictions for the next 5 years
-    
-    Collaborate with your teammates to ensure accuracy and quality.""",
-    expected_output="A well-researched, engaging 1000-word article with proper structure and citations",
-    agent=writer  # Writer leads, but can delegate research to researcher
-)
-
-# Create collaborative crew
-crew = Crew(
-    agents=[researcher, writer, editor],
-    tasks=[article_task],
-    process=Process.sequential,
-    verbose=True
-)
-
-result = crew.kickoff()
-```
-
-## Collaboration Patterns
-
-### Pattern 1: Research → Write → Edit
-```python
-research_task = Task(
-    description="Research the latest developments in quantum computing",
-    expected_output="Comprehensive research summary with key findings and sources",
-    agent=researcher
-)
-
-writing_task = Task(
-    description="Write an article based on the research findings",
-    expected_output="Engaging 800-word article about quantum computing",
-    agent=writer,
-    context=[research_task]  # Gets research output as context
-)
-
-editing_task = Task(
-    description="Edit and polish the article for publication",
-    expected_output="Publication-ready article with improved clarity and flow",
-    agent=editor,
-    context=[writing_task]  # Gets article draft as context
-)
-```
-
-### Pattern 2: Collaborative Single Task
-```python
-collaborative_task = Task(
-    description="""Create a marketing strategy for a new AI product.
-    
-    Writer: Focus on messaging and content strategy
-    Researcher: Provide market analysis and competitor insights
-    
-    Work together to create a comprehensive strategy.""",
-    expected_output="Complete marketing strategy with research backing",
-    agent=writer  # Lead agent, but can delegate to researcher
-)
-```
-
-## Hierarchical Collaboration
-
-For complex projects, use a hierarchical process with a manager agent:
-
-```python
-from crewai import Agent, Crew, Task, Process
-
-# Manager agent coordinates the team
-manager = Agent(
-    role="Project Manager",
-    goal="Coordinate team efforts and ensure project success",
-    backstory="Experienced project manager skilled at delegation and quality control",
-    allow_delegation=True,
-    verbose=True
-)
-
-# Specialist agents
-researcher = Agent(
-    role="Researcher",
-    goal="Provide accurate research and analysis",
-    backstory="Expert researcher with deep analytical skills",
-    allow_delegation=False,  # Specialists focus on their expertise
-    verbose=True
-)
-
-writer = Agent(
-    role="Writer", 
-    goal="Create compelling content",
-    backstory="Skilled writer who creates engaging content",
-    allow_delegation=False,
-    verbose=True
-)
-
-# Manager-led task
-project_task = Task(
-    description="Create a comprehensive market analysis report with recommendations",
-    expected_output="Executive summary, detailed analysis, and strategic recommendations",
-    agent=manager  # Manager will delegate to specialists
-)
-
-# Hierarchical crew
-crew = Crew(
-    agents=[manager, researcher, writer],
-    tasks=[project_task],
-    process=Process.hierarchical,  # Manager coordinates everything
-    manager_llm="gpt-4o",  # Specify LLM for manager
-    verbose=True
-)
-```
-
-## Best Practices for Collaboration
-
-### 1. **Clear Role Definition**
-```python
-# ✅ Good: Specific, complementary roles
-researcher = Agent(role="Market Research Analyst", ...)
-writer = Agent(role="Technical Content Writer", ...)
-
-# ❌ Avoid: Overlapping or vague roles  
-agent1 = Agent(role="General Assistant", ...)
-agent2 = Agent(role="Helper", ...)
-```
-
-### 2. **Strategic Delegation Enabling**
-```python
-# ✅ Enable delegation for coordinators and generalists
-lead_agent = Agent(
-    role="Content Lead",
-    allow_delegation=True,  # Can delegate to specialists
-    ...
-)
-
-# ✅ Disable for focused specialists (optional)
-specialist_agent = Agent(
-    role="Data Analyst", 
-    allow_delegation=False,  # Focuses on core expertise
-    ...
-)
-```
-
-### 3. **Context Sharing**
-```python
-# ✅ Use context parameter for task dependencies
-writing_task = Task(
-    description="Write article based on research",
-    agent=writer,
-    context=[research_task],  # Shares research results
-    ...
-)
-```
-
-### 4. **Clear Task Descriptions**
-```python
-# ✅ Specific, actionable descriptions
-Task(
-    description="""Research competitors in the AI chatbot space.
-    Focus on: pricing models, key features, target markets.
-    Provide data in a structured format.""",
-    ...
-)
-
-# ❌ Vague descriptions that don't guide collaboration
-Task(description="Do some research about chatbots", ...)
-```
-
-## Troubleshooting Collaboration
-
-### Issue: Agents Not Collaborating
-**Symptoms:** Agents work in isolation, no delegation occurs
-```python
-# ✅ Solution: Ensure delegation is enabled
-agent = Agent(
-    role="...",
-    allow_delegation=True,  # This is required!
-    ...
-)
-```
-
-### Issue: Too Much Back-and-Forth
-**Symptoms:** Agents ask excessive questions, slow progress
-```python
-# ✅ Solution: Provide better context and specific roles
-Task(
-    description="""Write a technical blog post about machine learning.
-    
-    Context: Target audience is software developers with basic ML knowledge.
-    Length: 1200 words
-    Include: code examples, practical applications, best practices
-    
-    If you need specific technical details, delegate research to the researcher.""",
-    ...
-)
-```
-
-### Issue: Delegation Loops
-**Symptoms:** Agents delegate back and forth indefinitely
-```python
-# ✅ Solution: Clear hierarchy and responsibilities
-manager = Agent(role="Manager", allow_delegation=True)
-specialist1 = Agent(role="Specialist A", allow_delegation=False)  # No re-delegation
-specialist2 = Agent(role="Specialist B", allow_delegation=False)
-```
-
-## Advanced Collaboration Features
-
-### Custom Collaboration Rules
-```python
-# Set specific collaboration guidelines in agent backstory
-agent = Agent(
-    role="Senior Developer",
-    backstory="""You lead development projects and coordinate with team members.
-    
-    Collaboration guidelines:
-    - Delegate research tasks to the Research Analyst
-    - Ask the Designer for UI/UX guidance  
-    - Consult the QA Engineer for testing strategies
-    - Only escalate blocking issues to the Project Manager""",
-    allow_delegation=True
-)
-```
-
-### Monitoring Collaboration
-```python
-def track_collaboration(output):
-    """Track collaboration patterns"""
-    if "Delegate work to coworker" in output.raw:
-        print("🤝 Delegation occurred")
-    if "Ask question to coworker" in output.raw:
-        print("❓ Question asked")
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    step_callback=track_collaboration,  # Monitor collaboration
-    verbose=True
-)
-```
-
-## Memory and Learning
-
-Enable agents to remember past collaborations:
-
-```python
-agent = Agent(
-    role="Content Lead",
-    memory=True,  # Remembers past interactions
-    allow_delegation=True,
-    verbose=True
-)
-```
-
-With memory enabled, agents learn from previous collaborations and improve their delegation decisions over time.
-
-## Next Steps
-
-- **Try the examples**: Start with the basic collaboration example
-- **Experiment with roles**: Test different agent role combinations  
-- **Monitor interactions**: Use `verbose=True` to see collaboration in action
-- **Optimize task descriptions**: Clear tasks lead to better collaboration
-- **Scale up**: Try hierarchical processes for complex projects
-
-Collaboration transforms individual AI agents into powerful teams that can tackle complex, multi-faceted challenges together.

docs/concepts/crews.mdx

@@ -1,368 +0,0 @@
----
-title: Crews
-description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
-icon: people-group
----
-
-## Overview
-
-A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
-
-## Crew Attributes
-
-| Attribute                             | Parameters             | Description                                                                                                                                                                                                                                               |
-| :------------------------------------ | :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Tasks**                             | `tasks`                | A list of tasks assigned to the crew.                                                                                                                                                                                                                     |
-| **Agents**                            | `agents`               | A list of agents that are part of the crew.                                                                                                                                                                                                               |
-| **Process** _(optional)_              | `process`              | The process flow (e.g., sequential, hierarchical) the crew follows. Default is `sequential`.                                                                                                                                                              |
-| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution. Defaults to `False`.                                                                                                                                                                                    |
-| **Manager LLM** _(optional)_          | `manager_llm`          | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.**                                                                                                                                   |
-| **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.                                                                                                                                                                                             |
-| **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`.                                                               |
-| **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
-| **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defaults to `None`.                                                                      |
-| **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
-| **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
-| **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
-| **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
-
-<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.
-</Tip>
-
-## Creating Crews
-
-There are two ways to create crews in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
-
-### YAML Configuration (Recommended)
-
-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.
-
-#### Example Crew Class with Decorators
-
-```python code
-from crewai import Agent, Crew, Task, Process
-from crewai.project import CrewBase, agent, task, crew, before_kickoff, after_kickoff
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from typing import List
-
-@CrewBase
-class YourCrewName:
-    """Description of your crew"""
-
-    agents: List[BaseAgent]
-    tasks: List[Task]
-
-    # Paths to your YAML configuration files
-    # To see an example agent and task defined in YAML, checkout the following:
-    # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
-    # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    agents_config = 'config/agents.yaml' 
-    tasks_config = 'config/tasks.yaml' 
-
-    @before_kickoff
-    def prepare_inputs(self, inputs):
-        # Modify inputs before the crew starts
-        inputs['additional_data'] = "Some extra information"
-        return inputs
-
-    @after_kickoff
-    def process_output(self, output):
-        # Modify output after the crew finishes
-        output.raw += "\nProcessed after kickoff."
-        return output
-
-    @agent
-    def agent_one(self) -> Agent:
-        return Agent(
-            config=self.agents_config['agent_one'], # type: ignore[index]
-            verbose=True
-        )
-
-    @agent
-    def agent_two(self) -> Agent:
-        return Agent(
-            config=self.agents_config['agent_two'], # type: ignore[index]
-            verbose=True
-        )
-
-    @task
-    def task_one(self) -> Task:
-        return Task(
-            config=self.tasks_config['task_one'] # type: ignore[index]
-        )
-
-    @task
-    def task_two(self) -> Task:
-        return Task(
-            config=self.tasks_config['task_two'] # type: ignore[index]
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,  # Automatically collected by the @agent decorator
-            tasks=self.tasks,    # Automatically collected by the @task decorator. 
-            process=Process.sequential,
-            verbose=True,
-        )
-```
-
-How to run the above code:
-
-```python code
-YourCrewName().crew().kickoff(inputs={"any": "input here"})
-```
-
-<Note>
-Tasks will be executed in the order they are defined.
-</Note>
-
-The `CrewBase` class, along with these decorators, automates the collection of agents and tasks, reducing the need for manual management.
-
-#### Decorators overview from `annotations.py`
-
-CrewAI provides several decorators in the `annotations.py` file that are used to mark methods within your crew class for special handling:
-
-- `@CrewBase`: Marks the class as a crew base class.
-- `@agent`: Denotes a method that returns an `Agent` object.
-- `@task`: Denotes a method that returns a `Task` object.
-- `@crew`: Denotes the method that returns the `Crew` object.
-- `@before_kickoff`: (Optional) Marks a method to be executed before the crew starts.
-- `@after_kickoff`: (Optional) Marks a method to be executed after the crew finishes.
-
-These decorators help in organizing your crew's structure and automatically collecting agents and tasks without manually listing them.
-
-### Direct Code Definition (Alternative)
-
-Alternatively, you can define the crew directly in code without using YAML configuration files.
-
-```python code
-from crewai import Agent, Crew, Task, Process
-from crewai_tools import YourCustomTool
-
-class YourCrewName:
-    def agent_one(self) -> Agent:
-        return Agent(
-            role="Data Analyst",
-            goal="Analyze data trends in the market",
-            backstory="An experienced data analyst with a background in economics",
-            verbose=True,
-            tools=[YourCustomTool()]
-        )
-
-    def agent_two(self) -> Agent:
-        return Agent(
-            role="Market Researcher",
-            goal="Gather information on market dynamics",
-            backstory="A diligent researcher with a keen eye for detail",
-            verbose=True
-        )
-
-    def task_one(self) -> Task:
-        return Task(
-            description="Collect recent market data and identify trends.",
-            expected_output="A report summarizing key trends in the market.",
-            agent=self.agent_one()
-        )
-
-    def task_two(self) -> Task:
-        return Task(
-            description="Research factors affecting market dynamics.",
-            expected_output="An analysis of factors influencing the market.",
-            agent=self.agent_two()
-        )
-
-    def crew(self) -> Crew:
-        return Crew(
-            agents=[self.agent_one(), self.agent_two()],
-            tasks=[self.task_one(), self.task_two()],
-            process=Process.sequential,
-            verbose=True
-        )
-```
-How to run the above code:
-
-```python code
-YourCrewName().crew().kickoff(inputs={})
-```
-
-In this example:
-
-- Agents and tasks are defined directly within the class without decorators.
-- We manually create and manage the list of agents and tasks.
-- This approach provides more control but can be less maintainable for larger projects.
-
-## Crew Output
-
-The output of a crew in the CrewAI framework is encapsulated within the `CrewOutput` class.
-This class provides a structured way to access results of the crew's execution, including various formats such as raw strings, JSON, and Pydantic models.
-The `CrewOutput` includes the results from the final task output, token usage, and individual task outputs.
-
-### Crew Output Attributes
-
-| Attribute        | Parameters     | Type                       | Description                                                                                          |
-| :--------------- | :------------- | :------------------------- | :--------------------------------------------------------------------------------------------------- |
-| **Raw**          | `raw`          | `str`                      | The raw output of the crew. This is the default format for the output.                               |
-| **Pydantic**     | `pydantic`     | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the crew.                              |
-| **JSON Dict**    | `json_dict`    | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the crew.                                               |
-| **Tasks Output** | `tasks_output` | `List[TaskOutput]`         | A list of `TaskOutput` objects, each representing the output of a task in the crew.                  |
-| **Token Usage**  | `token_usage`  | `Dict[str, Any]`           | A summary of token usage, providing insights into the language model's performance during execution. |
-
-### Crew Output Methods and Properties
-
-| Method/Property | Description                                                                                       |
-| :-------------- | :------------------------------------------------------------------------------------------------ |
-| **json**        | Returns the JSON string representation of the crew output if the output format is JSON.           |
-| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
-| \***\*str\*\*** | Returns the string representation of the crew output, prioritizing Pydantic, then JSON, then raw. |
-
-### Accessing Crew Outputs
-
-Once a crew has been executed, its output can be accessed through the `output` attribute of the `Crew` object. The `CrewOutput` class provides various ways to interact with and present this output.
-
-#### Example
-
-```python Code
-# Example crew execution
-crew = Crew(
-    agents=[research_agent, writer_agent],
-    tasks=[research_task, write_article_task],
-    verbose=True
-)
-
-crew_output = crew.kickoff()
-
-# Accessing the crew output
-print(f"Raw Output: {crew_output.raw}")
-if crew_output.json_dict:
-    print(f"JSON Output: {json.dumps(crew_output.json_dict, indent=2)}")
-if crew_output.pydantic:
-    print(f"Pydantic Output: {crew_output.pydantic}")
-print(f"Tasks Output: {crew_output.tasks_output}")
-print(f"Token Usage: {crew_output.token_usage}")
-```
-
-## Accessing Crew Logs
-
-You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
-In case of `True(Boolean)` will save as `logs.txt`.
-
-In case of `output_log_file` is set as `False(Boolean)` or `None`, the logs will not be populated.
-
-```python Code
-# Save crew logs
-crew = Crew(output_log_file = True)  # Logs will be saved as logs.txt
-crew = Crew(output_log_file = file_name)  # Logs will be saved as file_name.txt
-crew = Crew(output_log_file = file_name.txt)  # Logs will be saved as file_name.txt
-crew = Crew(output_log_file = file_name.json)  # Logs will be saved as file_name.json
-```
-
-
-
-## Memory Utilization
-
-Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
-
-## Cache Utilization
-
-Caches can be employed to store the results of tools' execution, making the process more efficient by reducing the need to re-execute identical tasks.
-
-## Crew Usage Metrics
-
-After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.
-
-```python Code
-# Access the crew's usage metrics
-crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
-crew.kickoff()
-print(crew.usage_metrics)
-```
-
-## Crew Execution Process
-
-- **Sequential Process**: Tasks are executed one after another, allowing for a linear flow of work.
-- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. **Note**: A `manager_llm` or `manager_agent` is required for this process and it's essential for validating the process flow.
-
-### Kicking Off a Crew
-
-Once your crew is assembled, initiate the workflow with the `kickoff()` method. This starts the execution process according to the defined process flow.
-
-```python Code
-# Start the crew's task execution
-result = my_crew.kickoff()
-print(result)
-```
-
-### Different Ways to Kick Off a Crew
-
-Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
-
-- `kickoff()`: Starts the execution process according to the defined process flow.
-- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
-- `kickoff_async()`: Initiates the workflow asynchronously.
-- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.
-
-```python Code
-# Start the crew's task execution
-result = my_crew.kickoff()
-print(result)
-
-# Example of using kickoff_for_each
-inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
-results = my_crew.kickoff_for_each(inputs=inputs_array)
-for result in results:
-    print(result)
-
-# Example of using kickoff_async
-inputs = {'topic': 'AI in healthcare'}
-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 = 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.
-
-### Replaying from a Specific Task
-
-You can now replay from a specific task using our CLI command `replay`.
-
-The replay feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the `task_id` for the replay process.
-
-Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
-
-### Replaying from a Specific Task Using the CLI
-
-To use the replay 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:
-
-To view the latest kickoff task IDs, use:
-
-```shell
-crewai log-tasks-outputs
-```
-
-Then, to replay from a specific task, use:
-
-```shell
-crewai replay -t <task_id>
-```
-
-These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

docs/concepts/event-listener.mdx

@@ -1,374 +0,0 @@
----
-title: 'Event Listeners'
-description: 'Tap into CrewAI events to build custom integrations and monitoring'
-icon: spinner
----
-
-## Overview
-
-CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
-
-## How It Works
-
-CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:
-
-1. **CrewAIEventsBus**: A singleton event bus that manages event registration and emission
-2. **BaseEvent**: Base class for all events in the system
-3. **BaseEventListener**: Abstract base class for creating custom event listeners
-
-When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
-
-<Note type="info" title="Enterprise Enhancement: Prompt Tracing">
-CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
-
-![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
-
-With Prompt Tracing you can:
-- View the complete history of all prompts sent to your LLM
-- Track token usage and costs
-- Debug agent reasoning failures
-- Share prompt sequences with your team
-- Compare different prompt strategies
-- Export traces for compliance and auditing
-</Note>
-
-## Creating a Custom Event Listener
-
-To create a custom event listener, you need to:
-
-1. Create a class that inherits from `BaseEventListener`
-2. Implement the `setup_listeners` method
-3. Register handlers for the events you're interested in
-4. Create an instance of your listener in the appropriate file
-
-Here's a simple example of a custom event listener class:
-
-```python
-from crewai.utilities.events import (
-    CrewKickoffStartedEvent,
-    CrewKickoffCompletedEvent,
-    AgentExecutionCompletedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-
-class MyCustomListener(BaseEventListener):
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_started(source, event):
-            print(f"Crew '{event.crew_name}' has started execution!")
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_completed(source, event):
-            print(f"Crew '{event.crew_name}' has completed execution!")
-            print(f"Output: {event.output}")
-
-        @crewai_event_bus.on(AgentExecutionCompletedEvent)
-        def on_agent_execution_completed(source, event):
-            print(f"Agent '{event.agent.role}' completed task")
-            print(f"Output: {event.output}")
-```
-
-## Properly Registering Your Listener
-
-Simply defining your listener class isn't enough. You need to create an instance of it and ensure it's imported in your application. This ensures that:
-
-1. The event handlers are registered with the event bus
-2. The listener instance remains in memory (not garbage collected)
-3. The listener is active when events are emitted
-
-### Option 1: Import and Instantiate in Your Crew or Flow Implementation
-
-The most important thing is to create an instance of your listener in the file where your Crew or Flow is defined and executed:
-
-#### For Crew-based Applications
-
-Create and import your listener at the top of your Crew implementation file:
-
-```python
-# In your crew.py file
-from crewai import Agent, Crew, Task
-from my_listeners import MyCustomListener
-
-# Create an instance of your listener
-my_listener = MyCustomListener()
-
-class MyCustomCrew:
-    # Your crew implementation...
-
-    def crew(self):
-        return Crew(
-            agents=[...],
-            tasks=[...],
-            # ...
-        )
-```
-
-#### For Flow-based Applications
-
-Create and import your listener at the top of your Flow implementation file:
-
-```python
-# In your main.py or flow.py file
-from crewai.flow import Flow, listen, start
-from my_listeners import MyCustomListener
-
-# Create an instance of your listener
-my_listener = MyCustomListener()
-
-class MyCustomFlow(Flow):
-    # Your flow implementation...
-
-    @start()
-    def first_step(self):
-        # ...
-```
-
-This ensures that your listener is loaded and active when your Crew or Flow is executed.
-
-### Option 2: Create a Package for Your Listeners
-
-For a more structured approach, especially if you have multiple listeners:
-
-1. Create a package for your listeners:
-
-```
-my_project/
-  ├── listeners/
-  │   ├── __init__.py
-  │   ├── my_custom_listener.py
-  │   └── another_listener.py
-```
-
-2. In `my_custom_listener.py`, define your listener class and create an instance:
-
-```python
-# my_custom_listener.py
-from crewai.utilities.events.base_event_listener import BaseEventListener
-# ... import events ...
-
-class MyCustomListener(BaseEventListener):
-    # ... implementation ...
-
-# Create an instance of your listener
-my_custom_listener = MyCustomListener()
-```
-
-3. In `__init__.py`, import the listener instances to ensure they're loaded:
-
-```python
-# __init__.py
-from .my_custom_listener import my_custom_listener
-from .another_listener import another_listener
-
-# Optionally export them if you need to access them elsewhere
-__all__ = ['my_custom_listener', 'another_listener']
-```
-
-4. Import your listeners package in your Crew or Flow file:
-
-```python
-# In your crew.py or flow.py file
-import my_project.listeners  # This loads all your listeners
-
-class MyCustomCrew:
-    # Your crew implementation...
-```
-
-This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
-
-```python
-# src/crewai/utilities/events/third_party/__init__.py
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
-
-## Available Event Types
-
-CrewAI provides a wide range of events that you can listen for:
-
-### Crew Events
-
-- **CrewKickoffStartedEvent**: Emitted when a Crew starts execution
-- **CrewKickoffCompletedEvent**: Emitted when a Crew completes execution
-- **CrewKickoffFailedEvent**: Emitted when a Crew fails to complete execution
-- **CrewTestStartedEvent**: Emitted when a Crew starts testing
-- **CrewTestCompletedEvent**: Emitted when a Crew completes testing
-- **CrewTestFailedEvent**: Emitted when a Crew fails to complete testing
-- **CrewTrainStartedEvent**: Emitted when a Crew starts training
-- **CrewTrainCompletedEvent**: Emitted when a Crew completes training
-- **CrewTrainFailedEvent**: Emitted when a Crew fails to complete training
-
-### Agent Events
-
-- **AgentExecutionStartedEvent**: Emitted when an Agent starts executing a task
-- **AgentExecutionCompletedEvent**: Emitted when an Agent completes executing a task
-- **AgentExecutionErrorEvent**: Emitted when an Agent encounters an error during execution
-
-### Task Events
-
-- **TaskStartedEvent**: Emitted when a Task starts execution
-- **TaskCompletedEvent**: Emitted when a Task completes execution
-- **TaskFailedEvent**: Emitted when a Task fails to complete execution
-- **TaskEvaluationEvent**: Emitted when a Task is evaluated
-
-### Tool Usage Events
-
-- **ToolUsageStartedEvent**: Emitted when a tool execution is started
-- **ToolUsageFinishedEvent**: Emitted when a tool execution is completed
-- **ToolUsageErrorEvent**: Emitted when a tool execution encounters an error
-- **ToolValidateInputErrorEvent**: Emitted when a tool input validation encounters an error
-- **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
-- **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
-
-### Knowledge Events
-
-- **KnowledgeRetrievalStartedEvent**: Emitted when a knowledge retrieval is started
-- **KnowledgeRetrievalCompletedEvent**: Emitted when a knowledge retrieval is completed
-- **KnowledgeQueryStartedEvent**: Emitted when a knowledge query is started
-- **KnowledgeQueryCompletedEvent**: Emitted when a knowledge query is completed
-- **KnowledgeQueryFailedEvent**: Emitted when a knowledge query fails
-- **KnowledgeSearchQueryFailedEvent**: Emitted when a knowledge search query fails
-
-### Flow Events
-
-- **FlowCreatedEvent**: Emitted when a Flow is created
-- **FlowStartedEvent**: Emitted when a Flow starts execution
-- **FlowFinishedEvent**: Emitted when a Flow completes execution
-- **FlowPlotEvent**: Emitted when a Flow is plotted
-- **MethodExecutionStartedEvent**: Emitted when a Flow method starts execution
-- **MethodExecutionFinishedEvent**: Emitted when a Flow method completes execution
-- **MethodExecutionFailedEvent**: Emitted when a Flow method fails to complete execution
-
-### LLM Events
-
-- **LLMCallStartedEvent**: Emitted when an LLM call starts
-- **LLMCallCompletedEvent**: Emitted when an LLM call completes
-- **LLMCallFailedEvent**: Emitted when an LLM call fails
-- **LLMStreamChunkEvent**: Emitted for each chunk received during streaming LLM responses
-
-## Event Handler Structure
-
-Each event handler receives two parameters:
-
-1. **source**: The object that emitted the event
-2. **event**: The event instance, containing event-specific data
-
-The structure of the event object depends on the event type, but all events inherit from `BaseEvent` and include:
-
-- **timestamp**: The time when the event was emitted
-- **type**: A string identifier for the event type
-
-Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
-
-## Real-World Example: Integration with AgentOps
-
-CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
-
-```python
-from typing import Optional
-
-from crewai.utilities.events import (
-    CrewKickoffCompletedEvent,
-    ToolUsageErrorEvent,
-    ToolUsageStartedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
-from crewai.utilities.events.task_events import TaskEvaluationEvent
-
-try:
-    import agentops
-    AGENTOPS_INSTALLED = True
-except ImportError:
-    AGENTOPS_INSTALLED = False
-
-class AgentOpsListener(BaseEventListener):
-    tool_event: Optional["agentops.ToolEvent"] = None
-    session: Optional["agentops.Session"] = None
-
-    def __init__(self):
-        super().__init__()
-
-    def setup_listeners(self, crewai_event_bus):
-        if not AGENTOPS_INSTALLED:
-            return
-
-        @crewai_event_bus.on(CrewKickoffStartedEvent)
-        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
-            self.session = agentops.init()
-            for agent in source.agents:
-                if self.session:
-                    self.session.create_agent(
-                        name=agent.role,
-                        agent_id=str(agent.id),
-                    )
-
-        @crewai_event_bus.on(CrewKickoffCompletedEvent)
-        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
-            if self.session:
-                self.session.end_session(
-                    end_state="Success",
-                    end_state_reason="Finished Execution",
-                )
-
-        @crewai_event_bus.on(ToolUsageStartedEvent)
-        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
-            self.tool_event = agentops.ToolEvent(name=event.tool_name)
-            if self.session:
-                self.session.record(self.tool_event)
-
-        @crewai_event_bus.on(ToolUsageErrorEvent)
-        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
-            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
-```
-
-This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
-
-The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
-
-```python
-from .agentops_listener import agentops_listener
-```
-
-This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
-
-## Advanced Usage: Scoped Handlers
-
-For temporary event handling (useful for testing or specific operations), you can use the `scoped_handlers` context manager:
-
-```python
-from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent
-
-with crewai_event_bus.scoped_handlers():
-    @crewai_event_bus.on(CrewKickoffStartedEvent)
-    def temp_handler(source, event):
-        print("This handler only exists within this context")
-
-    # Do something that emits events
-
-# Outside the context, the temporary handler is removed
-```
-
-## Use Cases
-
-Event listeners can be used for a variety of purposes:
-
-1. **Logging and Monitoring**: Track the execution of your Crew and log important events
-2. **Analytics**: Collect data about your Crew's performance and behavior
-3. **Debugging**: Set up temporary listeners to debug specific issues
-4. **Integration**: Connect CrewAI with external systems like monitoring platforms, databases, or notification services
-5. **Custom Behavior**: Trigger custom actions based on specific events
-
-## Best Practices
-
-1. **Keep Handlers Light**: Event handlers should be lightweight and avoid blocking operations
-2. **Error Handling**: Include proper error handling in your event handlers to prevent exceptions from affecting the main execution
-3. **Cleanup**: If your listener allocates resources, ensure they're properly cleaned up
-4. **Selective Listening**: Only listen for events you actually need to handle
-5. **Testing**: Test your event listeners in isolation to ensure they behave as expected
-
-By leveraging CrewAI's event system, you can extend its functionality and integrate it seamlessly with your existing infrastructure.

docs/concepts/flows.mdx

@@ -1,910 +0,0 @@
----
-title: Flows
-description: Learn how to create and manage AI workflows using CrewAI Flows.
-icon: arrow-progress
----
-
-## Overview
-
-CrewAI Flows is a powerful feature designed to streamline the creation and management of AI workflows. Flows allow developers to combine and coordinate coding tasks and Crews efficiently, providing a robust framework for building sophisticated AI automations.
-
-Flows allow you to create structured, event-driven workflows. They provide a seamless way to connect multiple tasks, manage state, and control the flow of execution in your AI applications. With Flows, you can easily design and implement multi-step processes that leverage the full potential of CrewAI's capabilities.
-
-1. **Simplified Workflow Creation**: Easily chain together multiple Crews and tasks to create complex AI workflows.
-
-2. **State Management**: Flows make it super easy to manage and share state between different tasks in your workflow.
-
-3. **Event-Driven Architecture**: Built on an event-driven model, allowing for dynamic and responsive workflows.
-
-4. **Flexible Control Flow**: Implement conditional logic, loops, and branching within your workflows.
-
-## Getting Started
-
-Let's create a simple Flow where you will use OpenAI to generate a random city in one task and then use that city to generate a fun fact in another task.
-
-```python Code
-
-from crewai.flow.flow import Flow, listen, start
-from dotenv import load_dotenv
-from litellm import completion
-
-
-class ExampleFlow(Flow):
-    model = "gpt-4o-mini"
-
-    @start()
-    def generate_city(self):
-        print("Starting flow")
-        # Each flow state automatically gets a unique ID
-        print(f"Flow State ID: {self.state['id']}")
-
-        response = completion(
-            model=self.model,
-            messages=[
-                {
-                    "role": "user",
-                    "content": "Return the name of a random city in the world.",
-                },
-            ],
-        )
-
-        random_city = response["choices"][0]["message"]["content"]
-        # Store the city in our state
-        self.state["city"] = random_city
-        print(f"Random City: {random_city}")
-
-        return random_city
-
-    @listen(generate_city)
-    def generate_fun_fact(self, random_city):
-        response = completion(
-            model=self.model,
-            messages=[
-                {
-                    "role": "user",
-                    "content": f"Tell me a fun fact about {random_city}",
-                },
-            ],
-        )
-
-        fun_fact = response["choices"][0]["message"]["content"]
-        # Store the fun fact in our state
-        self.state["fun_fact"] = fun_fact
-        return fun_fact
-
-
-
-flow = ExampleFlow()
-flow.plot()
-result = flow.kickoff()
-
-print(f"Generated fun fact: {result}")
-```
-![Flow Visual image](/images/crewai-flow-1.png)
-In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
-
-Each Flow instance automatically receives a unique identifier (UUID) in its state, which helps track and manage flow executions. The state can also store additional data (like the generated city and fun fact) that persists throughout the flow's execution.
-
-When you run the Flow, it will:
-1. Generate a unique ID for the flow state
-2. Generate a random city and store it in the state
-3. Generate a fun fact about that city and store it in the state
-4. Print the results to the console
-
-The state's unique ID and stored data can be useful for tracking flow executions and maintaining context between tasks.
-
-**Note:** Ensure you have set up your `.env` file to store your `OPENAI_API_KEY`. This key is necessary for authenticating requests to the OpenAI API.
-
-### @start()
-
-The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
-
-### @listen()
-
-The `@listen()` decorator is used to mark a method as a listener for the output of another task in the Flow. The method decorated with `@listen()` will be executed when the specified task emits an output. The method can access the output of the task it is listening to as an argument.
-
-#### Usage
-
-The `@listen()` decorator can be used in several ways:
-
-1. **Listening to a Method by Name**: You can pass the name of the method you want to listen to as a string. When that method completes, the listener method will be triggered.
-
-   ```python Code
-   @listen("generate_city")
-   def generate_fun_fact(self, random_city):
-       # Implementation
-   ```
-
-2. **Listening to a Method Directly**: You can pass the method itself. When that method completes, the listener method will be triggered.
-   ```python Code
-   @listen(generate_city)
-   def generate_fun_fact(self, random_city):
-       # Implementation
-   ```
-
-### Flow Output
-
-Accessing and handling the output of a Flow is essential for integrating your AI workflows into larger applications or systems. CrewAI Flows provide straightforward mechanisms to retrieve the final output, access intermediate results, and manage the overall state of your Flow.
-
-#### Retrieving the Final Output
-
-When you run a Flow, the final output is determined by the last method that completes. The `kickoff()` method returns the output of this final method.
-
-Here's how you can access the final output:
-
-<CodeGroup>
-```python Code
-from crewai.flow.flow import Flow, listen, start
-
-class OutputExampleFlow(Flow):
-    @start()
-    def first_method(self):
-        return "Output from first_method"
-
-    @listen(first_method)
-    def second_method(self, first_output):
-        return f"Second method received: {first_output}"
-
-
-flow = OutputExampleFlow()
-flow.plot("my_flow_plot")
-final_output = flow.kickoff()
-
-print("---- Final Output ----")
-print(final_output)
-```
-
-```text Output
----- Final Output ----
-Second method received: Output from first_method
-```
-
-</CodeGroup>
-![Flow Visual image](/images/crewai-flow-2.png)
-
-In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow.
-The `kickoff()` method will return the final output, which is then printed to the console. The `plot()` method will generate the HTML file, which will help you understand the flow.
-
-#### Accessing and Updating State
-
-In addition to retrieving the final output, you can also access and update the state within your Flow. The state can be used to store and share data between different methods in the Flow. After the Flow has run, you can access the state to retrieve any information that was added or updated during the execution.
-
-Here's an example of how to update and access the state:
-
-<CodeGroup>
-
-```python Code
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-class ExampleState(BaseModel):
-    counter: int = 0
-    message: str = ""
-
-class StateExampleFlow(Flow[ExampleState]):
-
-    @start()
-    def first_method(self):
-        self.state.message = "Hello from first_method"
-        self.state.counter += 1
-
-    @listen(first_method)
-    def second_method(self):
-        self.state.message += " - updated by second_method"
-        self.state.counter += 1
-        return self.state.message
-
-flow = StateExampleFlow()
-flow.plot("my_flow_plot")
-final_output = flow.kickoff()
-print(f"Final Output: {final_output}")
-print("Final State:")
-print(flow.state)
-```
-
-```text Output
-Final Output: Hello from first_method - updated by second_method
-Final State:
-counter=2 message='Hello from first_method - updated by second_method'
-```
-
-</CodeGroup>
-
-![Flow Visual image](/images/crewai-flow-2.png)
-
-In this example, the state is updated by both `first_method` and `second_method`.
-After the Flow has run, you can access the final state to see the updates made by these methods.
-
-By ensuring that the final method's output is returned and providing access to the state, CrewAI Flows make it easy to integrate the results of your AI workflows into larger applications or systems,
-while also maintaining and accessing the state throughout the Flow's execution.
-
-## Flow State Management
-
-Managing state effectively is crucial for building reliable and maintainable AI workflows. CrewAI Flows provides robust mechanisms for both unstructured and structured state management,
-allowing developers to choose the approach that best fits their application's needs.
-
-### Unstructured State Management
-
-In unstructured state management, all state is stored in the `state` attribute of the `Flow` class.
-This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema.
-Even with unstructured states, CrewAI Flows automatically generates and maintains a unique identifier (UUID) for each state instance.
-
-```python Code
-from crewai.flow.flow import Flow, listen, start
-
-class UnstructuredExampleFlow(Flow):
-
-    @start()
-    def first_method(self):
-        # The state automatically includes an 'id' field
-        print(f"State ID: {self.state['id']}")
-        self.state['counter'] = 0
-        self.state['message'] = "Hello from structured flow"
-
-    @listen(first_method)
-    def second_method(self):
-        self.state['counter'] += 1
-        self.state['message'] += " - updated"
-
-    @listen(second_method)
-    def third_method(self):
-        self.state['counter'] += 1
-        self.state['message'] += " - updated again"
-
-        print(f"State after third_method: {self.state}")
-
-
-flow = UnstructuredExampleFlow()
-flow.plot("my_flow_plot")
-flow.kickoff()
-```
-
-![Flow Visual image](/images/crewai-flow-3.png)
-
-**Note:** The `id` field is automatically generated and preserved throughout the flow's execution. You don't need to manage or set it manually, and it will be maintained even when updating the state with new data.
-
-**Key Points:**
-
-- **Flexibility:** You can dynamically add attributes to `self.state` without predefined constraints.
-- **Simplicity:** Ideal for straightforward workflows where state structure is minimal or varies significantly.
-
-### Structured State Management
-
-Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow.
-By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments.
-
-Each state in CrewAI Flows automatically receives a unique identifier (UUID) to help track and manage state instances. This ID is automatically generated and managed by the Flow system.
-
-```python Code
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-
-class ExampleState(BaseModel):
-    # Note: 'id' field is automatically added to all states
-    counter: int = 0
-    message: str = ""
-
-
-class StructuredExampleFlow(Flow[ExampleState]):
-
-    @start()
-    def first_method(self):
-        # Access the auto-generated ID if needed
-        print(f"State ID: {self.state.id}")
-        self.state.message = "Hello from structured flow"
-
-    @listen(first_method)
-    def second_method(self):
-        self.state.counter += 1
-        self.state.message += " - updated"
-
-    @listen(second_method)
-    def third_method(self):
-        self.state.counter += 1
-        self.state.message += " - updated again"
-
-        print(f"State after third_method: {self.state}")
-
-
-flow = StructuredExampleFlow()
-flow.kickoff()
-```
-
-![Flow Visual image](/images/crewai-flow-3.png)
-
-**Key Points:**
-
-- **Defined Schema:** `ExampleState` clearly outlines the state structure, enhancing code readability and maintainability.
-- **Type Safety:** Leveraging Pydantic ensures that state attributes adhere to the specified types, reducing runtime errors.
-- **Auto-Completion:** IDEs can provide better auto-completion and error checking based on the defined state model.
-
-### Choosing Between Unstructured and Structured State Management
-
-- **Use Unstructured State Management when:**
-
-  - The workflow's state is simple or highly dynamic.
-  - Flexibility is prioritized over strict state definitions.
-  - Rapid prototyping is required without the overhead of defining schemas.
-
-- **Use Structured State Management when:**
-  - The workflow requires a well-defined and consistent state structure.
-  - Type safety and validation are important for your application's reliability.
-  - You want to leverage IDE features like auto-completion and type checking for better developer experience.
-
-By providing both unstructured and structured state management options, CrewAI Flows empowers developers to build AI workflows that are both flexible and robust, catering to a wide range of application requirements.
-
-## Flow Persistence
-
-The @persist decorator enables automatic state persistence in CrewAI Flows, allowing you to maintain flow state across restarts or different workflow executions. This decorator can be applied at either the class level or method level, providing flexibility in how you manage state persistence.
-
-### Class-Level Persistence
-
-When applied at the class level, the @persist decorator automatically persists all flow method states:
-
-```python
-@persist  # Using SQLiteFlowPersistence by default
-class MyFlow(Flow[MyState]):
-    @start()
-    def initialize_flow(self):
-        # This method will automatically have its state persisted
-        self.state.counter = 1
-        print("Initialized flow. State ID:", self.state.id)
-
-    @listen(initialize_flow)
-    def next_step(self):
-        # The state (including self.state.id) is automatically reloaded
-        self.state.counter += 1
-        print("Flow state is persisted. Counter:", self.state.counter)
-```
-
-### Method-Level Persistence
-
-For more granular control, you can apply @persist to specific methods:
-
-```python
-class AnotherFlow(Flow[dict]):
-    @persist  # Persists only this method's state
-    @start()
-    def begin(self):
-        if "runs" not in self.state:
-            self.state["runs"] = 0
-        self.state["runs"] += 1
-        print("Method-level persisted runs:", self.state["runs"])
-```
-
-### How It Works
-
-1. **Unique State Identification**
-   - Each flow state automatically receives a unique UUID
-   - The ID is preserved across state updates and method calls
-   - Supports both structured (Pydantic BaseModel) and unstructured (dictionary) states
-
-2. **Default SQLite Backend**
-   - SQLiteFlowPersistence is the default storage backend
-   - States are automatically saved to a local SQLite database
-   - Robust error handling ensures clear messages if database operations fail
-
-3. **Error Handling**
-   - Comprehensive error messages for database operations
-   - Automatic state validation during save and load
-   - Clear feedback when persistence operations encounter issues
-
-### Important Considerations
-
-- **State Types**: Both structured (Pydantic BaseModel) and unstructured (dictionary) states are supported
-- **Automatic ID**: The `id` field is automatically added if not present
-- **State Recovery**: Failed or restarted flows can automatically reload their previous state
-- **Custom Implementation**: You can provide your own FlowPersistence implementation for specialized storage needs
-
-### Technical Advantages
-
-1. **Precise Control Through Low-Level Access**
-   - Direct access to persistence operations for advanced use cases
-   - Fine-grained control via method-level persistence decorators
-   - Built-in state inspection and debugging capabilities
-   - Full visibility into state changes and persistence operations
-
-2. **Enhanced Reliability**
-   - Automatic state recovery after system failures or restarts
-   - Transaction-based state updates for data integrity
-   - Comprehensive error handling with clear error messages
-   - Robust validation during state save and load operations
-
-3. **Extensible Architecture**
-   - Customizable persistence backend through FlowPersistence interface
-   - Support for specialized storage solutions beyond SQLite
-   - Compatible with both structured (Pydantic) and unstructured (dict) states
-   - Seamless integration with existing CrewAI flow patterns
-
-The persistence system's architecture emphasizes technical precision and customization options, allowing developers to maintain full control over state management while benefiting from built-in reliability features.
-
-## Flow Control
-
-### Conditional Logic: `or`
-
-The `or_` function in Flows allows you to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
-
-<CodeGroup>
-
-```python Code
-from crewai.flow.flow import Flow, listen, or_, start
-
-class OrExampleFlow(Flow):
-
-    @start()
-    def start_method(self):
-        return "Hello from the start method"
-
-    @listen(start_method)
-    def second_method(self):
-        return "Hello from the second method"
-
-    @listen(or_(start_method, second_method))
-    def logger(self, result):
-        print(f"Logger: {result}")
-
-
-
-flow = OrExampleFlow()
-flow.plot("my_flow_plot")
-flow.kickoff()
-```
-
-```text Output
-Logger: Hello from the start method
-Logger: Hello from the second method
-```
-
-</CodeGroup>
-
-![Flow Visual image](/images/crewai-flow-4.png)
-
-When you run this Flow, the `logger` method will be triggered by the output of either the `start_method` or the `second_method`.
-The `or_` function is used to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
-
-### Conditional Logic: `and`
-
-The `and_` function in Flows allows you to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
-
-<CodeGroup>
-
-```python Code
-from crewai.flow.flow import Flow, and_, listen, start
-
-class AndExampleFlow(Flow):
-
-    @start()
-    def start_method(self):
-        self.state["greeting"] = "Hello from the start method"
-
-    @listen(start_method)
-    def second_method(self):
-        self.state["joke"] = "What do computers eat? Microchips."
-
-    @listen(and_(start_method, second_method))
-    def logger(self):
-        print("---- Logger ----")
-        print(self.state)
-
-flow = AndExampleFlow()
-flow.plot()
-flow.kickoff()
-```
-
-```text Output
----- Logger ----
-{'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'}
-```
-
-</CodeGroup>
-
-![Flow Visual image](/images/crewai-flow-5.png)
-
-When you run this Flow, the `logger` method will be triggered only when both the `start_method` and the `second_method` emit an output.
-The `and_` function is used to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
-
-### Router
-
-The `@router()` decorator in Flows allows you to define conditional routing logic based on the output of a method.
-You can specify different routes based on the output of the method, allowing you to control the flow of execution dynamically.
-
-<CodeGroup>
-
-```python Code
-import random
-from crewai.flow.flow import Flow, listen, router, start
-from pydantic import BaseModel
-
-class ExampleState(BaseModel):
-    success_flag: bool = False
-
-class RouterFlow(Flow[ExampleState]):
-
-    @start()
-    def start_method(self):
-        print("Starting the structured flow")
-        random_boolean = random.choice([True, False])
-        self.state.success_flag = random_boolean
-
-    @router(start_method)
-    def second_method(self):
-        if self.state.success_flag:
-            return "success"
-        else:
-            return "failed"
-
-    @listen("success")
-    def third_method(self):
-        print("Third method running")
-
-    @listen("failed")
-    def fourth_method(self):
-        print("Fourth method running")
-
-
-flow = RouterFlow()
-flow.plot("my_flow_plot")
-flow.kickoff()
-```
-
-```text Output
-Starting the structured flow
-Third method running
-Fourth method running
-```
-
-</CodeGroup>
-
-![Flow Visual image](/images/crewai-flow-6.png)
-
-In the above example, the `start_method` generates a random boolean value and sets it in the state.
-The `second_method` uses the `@router()` decorator to define conditional routing logic based on the value of the boolean.
-If the boolean is `True`, the method returns `"success"`, and if it is `False`, the method returns `"failed"`.
-The `third_method` and `fourth_method` listen to the output of the `second_method` and execute based on the returned value.
-
-When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
-
-## Adding Agents to Flows
-
-Agents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use an Agent within a flow to perform market research:
-
-```python
-import asyncio
-from typing import Any, Dict, List
-
-from crewai_tools import SerperDevTool
-from pydantic import BaseModel, Field
-
-from crewai.agent import Agent
-from crewai.flow.flow import Flow, listen, start
-
-
-# Define a structured output format
-class MarketAnalysis(BaseModel):
-    key_trends: List[str] = Field(description="List of identified market trends")
-    market_size: str = Field(description="Estimated market size")
-    competitors: List[str] = Field(description="Major competitors in the space")
-
-
-# Define flow state
-class MarketResearchState(BaseModel):
-    product: str = ""
-    analysis: MarketAnalysis | None = None
-
-
-# Create a flow class
-class MarketResearchFlow(Flow[MarketResearchState]):
-    @start()
-    def initialize_research(self) -> Dict[str, Any]:
-        print(f"Starting market research for {self.state.product}")
-        return {"product": self.state.product}
-
-    @listen(initialize_research)
-    async def analyze_market(self) -> Dict[str, Any]:
-        # Create an Agent for market research
-        analyst = Agent(
-            role="Market Research Analyst",
-            goal=f"Analyze the market for {self.state.product}",
-            backstory="You are an experienced market analyst with expertise in "
-            "identifying market trends and opportunities.",
-            tools=[SerperDevTool()],
-            verbose=True,
-        )
-
-        # Define the research query
-        query = f"""
-        Research the market for {self.state.product}. Include:
-        1. Key market trends
-        2. Market size
-        3. Major competitors
-
-        Format your response according to the specified structure.
-        """
-
-        # Execute the analysis with structured output format
-        result = await analyst.kickoff_async(query, response_format=MarketAnalysis)
-        if result.pydantic:
-            print("result", result.pydantic)
-        else:
-            print("result", result)
-
-        # Return the analysis to update the state
-        return {"analysis": result.pydantic}
-
-    @listen(analyze_market)
-    def present_results(self, analysis) -> None:
-        print("\nMarket Analysis Results")
-        print("=====================")
-
-        if isinstance(analysis, dict):
-            # If we got a dict with 'analysis' key, extract the actual analysis object
-            market_analysis = analysis.get("analysis")
-        else:
-            market_analysis = analysis
-
-        if market_analysis and isinstance(market_analysis, MarketAnalysis):
-            print("\nKey Market Trends:")
-            for trend in market_analysis.key_trends:
-                print(f"- {trend}")
-
-            print(f"\nMarket Size: {market_analysis.market_size}")
-
-            print("\nMajor Competitors:")
-            for competitor in market_analysis.competitors:
-                print(f"- {competitor}")
-        else:
-            print("No structured analysis data available.")
-            print("Raw analysis:", analysis)
-
-
-# Usage example
-async def run_flow():
-    flow = MarketResearchFlow()
-    flow.plot("MarketResearchFlowPlot")
-    result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
-    return result
-
-
-# Run the flow
-if __name__ == "__main__":
-    asyncio.run(run_flow())
-```
-
-![Flow Visual image](/images/crewai-flow-7.png)
-
-This example demonstrates several key features of using Agents in flows:
-
-1. **Structured Output**: Using Pydantic models to define the expected output format (`MarketAnalysis`) ensures type safety and structured data throughout the flow.
-
-2. **State Management**: The flow state (`MarketResearchState`) maintains context between steps and stores both inputs and outputs.
-
-3. **Tool Integration**: Agents can use tools (like `WebsiteSearchTool`) to enhance their capabilities.
-
-## Adding Crews to Flows
-
-Creating a flow with multiple crews in CrewAI is straightforward.
-
-You can generate a new CrewAI project that includes all the scaffolding needed to create a flow with multiple crews by running the following command:
-
-```bash
-crewai create flow name_of_flow
-```
-
-This command will generate a new CrewAI project with the necessary folder structure. The generated project includes a prebuilt crew called `poem_crew` that is already working. You can use this crew as a template by copying, pasting, and editing it to create other crews.
-
-### Folder Structure
-
-After running the `crewai create flow name_of_flow` command, you will see a folder structure similar to the following:
-
-| Directory/File         | Description                                                        |
-| :--------------------- | :----------------------------------------------------------------- |
-| `name_of_flow/`        | Root directory for the flow.                                       |
-| ├── `crews/`           | Contains directories for specific crews.                           |
-| │ └── `poem_crew/`     | Directory for the "poem_crew" with its configurations and scripts. |
-| │ ├── `config/`        | Configuration files directory for the "poem_crew".                 |
-| │ │ ├── `agents.yaml`  | YAML file defining the agents for "poem_crew".                     |
-| │ │ └── `tasks.yaml`   | YAML file defining the tasks for "poem_crew".                      |
-| │ ├── `poem_crew.py`   | Script for "poem_crew" functionality.                              |
-| ├── `tools/`           | Directory for additional tools used in the flow.                   |
-| │ └── `custom_tool.py` | Custom tool implementation.                                        |
-| ├── `main.py`          | Main script for running the flow.                                  |
-| ├── `README.md`        | Project description and instructions.                              |
-| ├── `pyproject.toml`   | Configuration file for project dependencies and settings.          |
-| └── `.gitignore`       | Specifies files and directories to ignore in version control.      |
-
-### Building Your Crews
-
-In the `crews` folder, you can define multiple crews. Each crew will have its own folder containing configuration files and the crew definition file. For example, the `poem_crew` folder contains:
-
-- `config/agents.yaml`: Defines the agents for the crew.
-- `config/tasks.yaml`: Defines the tasks for the crew.
-- `poem_crew.py`: Contains the crew definition, including agents, tasks, and the crew itself.
-
-You can copy, paste, and edit the `poem_crew` to create other crews.
-
-### Connecting Crews in `main.py`
-
-The `main.py` file is where you create your flow and connect the crews together. You can define your flow by using the `Flow` class and the decorators `@start` and `@listen` to specify the flow of execution.
-
-Here's an example of how you can connect the `poem_crew` in the `main.py` file:
-
-```python Code
-#!/usr/bin/env python
-from random import randint
-
-from pydantic import BaseModel
-from crewai.flow.flow import Flow, listen, start
-from .crews.poem_crew.poem_crew import PoemCrew
-
-class PoemState(BaseModel):
-    sentence_count: int = 1
-    poem: str = ""
-
-class PoemFlow(Flow[PoemState]):
-
-    @start()
-    def generate_sentence_count(self):
-        print("Generating sentence count")
-        self.state.sentence_count = randint(1, 5)
-
-    @listen(generate_sentence_count)
-    def generate_poem(self):
-        print("Generating poem")
-        result = PoemCrew().crew().kickoff(inputs={"sentence_count": self.state.sentence_count})
-
-        print("Poem generated", result.raw)
-        self.state.poem = result.raw
-
-    @listen(generate_poem)
-    def save_poem(self):
-        print("Saving poem")
-        with open("poem.txt", "w") as f:
-            f.write(self.state.poem)
-
-def kickoff():
-    poem_flow = PoemFlow()
-    poem_flow.kickoff()
-
-
-def plot():
-    poem_flow = PoemFlow()
-    poem_flow.plot("PoemFlowPlot")
-
-if __name__ == "__main__":
-    kickoff()
-    plot()
-```
-
-In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method. The PoemFlowPlot will be generated by `plot()` method.
-
-![Flow Visual image](/images/crewai-flow-8.png)
-
-### Running the Flow
-
-(Optional) Before running the flow, you can install the dependencies by running:
-
-```bash
-crewai install
-```
-
-Once all of the dependencies are installed, you need to activate the virtual environment by running:
-
-```bash
-source .venv/bin/activate
-```
-
-After activating the virtual environment, you can run the flow by executing one of the following commands:
-
-```bash
-crewai flow kickoff
-```
-
-or
-
-```bash
-uv run kickoff
-```
-
-The flow will execute, and you should see the output in the console.
-
-## Plot Flows
-
-Visualizing your AI workflows can provide valuable insights into the structure and execution paths of your flows. CrewAI offers a powerful visualization tool that allows you to generate interactive plots of your flows, making it easier to understand and optimize your AI workflows.
-
-### What are Plots?
-
-Plots in CrewAI are graphical representations of your AI workflows. They display the various tasks, their connections, and the flow of data between them. This visualization helps in understanding the sequence of operations, identifying bottlenecks, and ensuring that the workflow logic aligns with your expectations.
-
-### How to Generate a Plot
-
-CrewAI provides two convenient methods to generate plots of your flows:
-
-#### Option 1: Using the `plot()` Method
-
-If you are working directly with a flow instance, you can generate a plot by calling the `plot()` method on your flow object. This method will create an HTML file containing the interactive plot of your flow.
-
-```python Code
-# Assuming you have a flow instance
-flow.plot("my_flow_plot")
-```
-
-This will generate a file named `my_flow_plot.html` in your current directory. You can open this file in a web browser to view the interactive plot.
-
-#### Option 2: Using the Command Line
-
-If you are working within a structured CrewAI project, you can generate a plot using the command line. This is particularly useful for larger projects where you want to visualize the entire flow setup.
-
-```bash
-crewai flow plot
-```
-
-This command will generate an HTML file with the plot of your flow, similar to the `plot()` method. The file will be saved in your project directory, and you can open it in a web browser to explore the flow.
-
-### Understanding the Plot
-
-The generated plot will display nodes representing the tasks in your flow, with directed edges indicating the flow of execution. The plot is interactive, allowing you to zoom in and out, and hover over nodes to see additional details.
-
-By visualizing your flows, you can gain a clearer understanding of the workflow's structure, making it easier to debug, optimize, and communicate your AI processes to others.
-
-### Conclusion
-
-Plotting your flows is a powerful feature of CrewAI that enhances your ability to design and manage complex AI workflows. Whether you choose to use the `plot()` method or the command line, generating plots will provide you with a visual representation of your workflows, aiding in both development and presentation.
-
-## Next Steps
-
-If you're interested in exploring additional examples of flows, we have a variety of recommendations in our examples repository. Here are four specific flow examples, each showcasing unique use cases to help you match your current problem type to a specific example:
-
-1. **Email Auto Responder Flow**: This example demonstrates an infinite loop where a background job continually runs to automate email responses. It's a great use case for tasks that need to be performed repeatedly without manual intervention. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow)
-
-2. **Lead Score Flow**: This flow showcases adding human-in-the-loop feedback and handling different conditional branches using the router. It's an excellent example of how to incorporate dynamic decision-making and human oversight into your workflows. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/lead-score-flow)
-
-3. **Write a Book Flow**: This example excels at chaining multiple crews together, where the output of one crew is used by another. Specifically, one crew outlines an entire book, and another crew generates chapters based on the outline. Eventually, everything is connected to produce a complete book. This flow is perfect for complex, multi-step processes that require coordination between different tasks. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/write_a_book_with_flows)
-
-4. **Meeting Assistant Flow**: This flow demonstrates how to broadcast one event to trigger multiple follow-up actions. For instance, after a meeting is completed, the flow can update a Trello board, send a Slack message, and save the results. It's a great example of handling multiple outcomes from a single event, making it ideal for comprehensive task management and notification systems. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow)
-
-By exploring these examples, you can gain insights into how to leverage CrewAI Flows for various use cases, from automating repetitive tasks to managing complex, multi-step processes with dynamic decision-making and human feedback.
-
-Also, check out our YouTube video on how to use flows in CrewAI below!
-
-<iframe
-  width="560"
-  height="315"
-  src="https://www.youtube.com/embed/MTb5my6VOT8"
-  title="YouTube video player"
-  frameborder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowfullscreen
-></iframe>
-
-## Running Flows
-
-There are two ways to run a flow:
-
-### Using the Flow API
-
-You can run a flow programmatically by creating an instance of your flow class and calling the `kickoff()` method:
-
-```python
-flow = ExampleFlow()
-result = flow.kickoff()
-```
-
-### Using the CLI
-
-Starting from version 0.103.0, you can run flows using the `crewai run` command:
-
-```shell
-crewai run
-```
-
-This command automatically detects if your project is a flow (based on the `type = "flow"` setting in your pyproject.toml) and runs it accordingly. This is the recommended way to run flows from the command line.
-
-For backward compatibility, you can also use:
-
-```shell
-crewai flow kickoff
-```
-
-However, the `crewai run` command is now the preferred method as it works for both crews and flows.

docs/concepts/llms.mdx

@@ -1,887 +0,0 @@
----
-title: 'LLMs'
-description: 'A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects'
-icon: 'microchip-ai'
----
-
-## Overview
-
-CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
-
-
-## What are LLMs?
-
-Large Language Models (LLMs) are the core intelligence behind CrewAI agents. They enable agents to understand context, make decisions, and generate human-like responses. Here's what you need to know:
-
-<CardGroup cols={2}>
-  <Card title="LLM Basics" icon="brain">
-    Large Language Models are AI systems trained on vast amounts of text data. They power the intelligence of your CrewAI agents, enabling them to understand and generate human-like text.
-  </Card>
-  <Card title="Context Window" icon="window">
-    The context window determines how much text an LLM can process at once. Larger windows (e.g., 128K tokens) allow for more context but may be more expensive and slower.
-  </Card>
-  <Card title="Temperature" icon="temperature-three-quarters">
-    Temperature (0.0 to 1.0) controls response randomness. Lower values (e.g., 0.2) produce more focused, deterministic outputs, while higher values (e.g., 0.8) increase creativity and variability.
-  </Card>
-  <Card title="Provider Selection" icon="server">
-    Each LLM provider (e.g., OpenAI, Anthropic, Google) offers different models with varying capabilities, pricing, and features. Choose based on your needs for accuracy, speed, and cost.
-  </Card>
-</CardGroup>
-
-## Setting up your LLM
-
-There are different places in CrewAI code where you can specify the model to use. Once you specify the model you are using, you will need to provide the configuration (like an API key) for each of the model providers you use. See the [provider configuration examples](#provider-configuration-examples) section for your provider.
-
-<Tabs>
-  <Tab title="1. Environment Variables">
-    The simplest way to get started. Set the model in your environment directly, through an `.env` file or in your app code. If you used `crewai create` to bootstrap your project, it will be set already.
-
-    ```bash .env
-    MODEL=model-id  # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...
-
-    # Be sure to set your API keys here too. See the Provider
-    # section below.
-    ```
-
-    <Warning>
-      Never commit API keys to version control. Use environment files (.env) or your system's secret management.
-    </Warning>
-  </Tab>
-  <Tab title="2. YAML Configuration">
-    Create a YAML file to define your agent configurations. This method is great for version control and team collaboration:
-
-    ```yaml agents.yaml {6}
-    researcher:
-        role: Research Specialist
-        goal: Conduct comprehensive research and analysis
-        backstory: A dedicated research professional with years of experience
-        verbose: true
-        llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
-        # (see provider configuration examples below for more)
-    ```
-
-    <Info>
-      The YAML configuration allows you to:
-      - Version control your agent settings
-      - Easily switch between different models
-      - Share configurations across team members
-      - Document model choices and their purposes
-    </Info>
-  </Tab>
-  <Tab title="3. Direct Code">
-    For maximum flexibility, configure LLMs directly in your Python code:
-
-    ```python {4,8}
-    from crewai import LLM
-
-    # Basic configuration
-    llm = LLM(model="model-id-here")  # gpt-4o, gemini-2.0-flash, anthropic/claude...
-
-    # Advanced configuration with detailed parameters
-    llm = LLM(
-        model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
-        temperature=0.7,        # Higher for more creative outputs
-        timeout=120,            # Seconds to wait for response
-        max_tokens=4000,        # Maximum length of response
-        top_p=0.9,              # Nucleus sampling parameter
-        frequency_penalty=0.1 , # Reduce repetition
-        presence_penalty=0.1,   # Encourage topic diversity
-        response_format={"type": "json"},  # For structured outputs
-        seed=42                 # For reproducible results
-    )
-    ```
-
-    <Info>
-      Parameter explanations:
-      - `temperature`: Controls randomness (0.0-1.0)
-      - `timeout`: Maximum wait time for response
-      - `max_tokens`: Limits response length
-      - `top_p`: Alternative to temperature for sampling
-      - `frequency_penalty`: Reduces word repetition
-      - `presence_penalty`: Encourages new topics
-      - `response_format`: Specifies output structure
-      - `seed`: Ensures consistent outputs
-    </Info>
-  </Tab>
-</Tabs>
-
-## Provider Configuration Examples
-
-CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
-In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.
-
-<AccordionGroup>
-  <Accordion title="OpenAI">
-    Set the following environment variables in your `.env` file:
-
-    ```toml Code
-    # Required
-    OPENAI_API_KEY=sk-...
-
-    # Optional
-    OPENAI_API_BASE=<custom-base-url>
-    OPENAI_ORGANIZATION=<your-org-id>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="openai/gpt-4", # call model by provider/model_name
-        temperature=0.8,
-        max_tokens=150,
-        top_p=0.9,
-        frequency_penalty=0.1,
-        presence_penalty=0.1,
-        stop=["END"],
-        seed=42
-    )
-    ```
-
-    OpenAI is one of the leading providers of LLMs with a wide range of models and features.
-
-    | Model               | Context Window   | Best For                                      |
-    |---------------------|------------------|-----------------------------------------------|
-    | GPT-4               | 8,192 tokens     | High-accuracy tasks, complex reasoning        |
-    | GPT-4 Turbo         | 128,000 tokens   | Long-form content, document analysis          |
-    | GPT-4o & GPT-4o-mini  | 128,000 tokens  | Cost-effective large context processing       |
-    | o3-mini             | 200,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1-mini             | 128,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1-preview          | 128,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1                  | 200,000 tokens   | Fast reasoning, complex reasoning             |
-  </Accordion>
-
-  <Accordion title="Meta-Llama">
-    Meta's Llama API provides access to Meta's family of large language models.
-    The API is available through the [Meta Llama API](https://llama.developer.meta.com?utm_source=partner-crewai&utm_medium=website).
-    Set the following environment variables in your `.env` file:
-
-    ```toml Code
-    # Meta Llama API Key Configuration
-    LLAMA_API_KEY=LLM|your_api_key_here
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    from crewai import LLM
-
-    # Initialize Meta Llama LLM
-    llm = LLM(
-        model="meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8",
-        temperature=0.8,
-        stop=["END"],
-        seed=42
-    )
-    ```
-
-    All models listed here https://llama.developer.meta.com/docs/models/ are supported.
-
-    | Model ID | Input context length | Output context length | Input Modalities | Output Modalities |
-    | --- | --- | --- | --- | --- |
-    | `meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8` | 128k | 4028 | Text, Image | Text |
-    | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Text, Image | Text |
-    | `meta_llama/Llama-3.3-70B-Instruct` | 128k | 4028 | Text | Text |
-    | `meta_llama/Llama-3.3-8B-Instruct` | 128k | 4028 | Text | Text |
-  </Accordion>
-
-  <Accordion title="Anthropic">
-    ```toml Code
-    # Required
-    ANTHROPIC_API_KEY=sk-ant-...
-
-    # Optional
-    ANTHROPIC_API_BASE=<custom-base-url>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="anthropic/claude-3-sonnet-20240229-v1:0",
-        temperature=0.7
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="Google (Gemini API)">
-    Set your API key in your `.env` file. If you need a key, or need to find an
-    existing key, check [AI Studio](https://aistudio.google.com/apikey).
-
-    ```toml .env
-    # https://ai.google.dev/gemini-api/docs/api-key
-    GEMINI_API_KEY=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="gemini/gemini-2.0-flash",
-        temperature=0.7,
-    )
-    ```
-
-    ### Gemini models
-
-    Google offers a range of powerful models optimized for different use cases.
-
-    | Model                          | Context Window | Best For                                                          |
-    |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
-    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
-    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
-
-    The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
-
-    ### Gemma
-
-    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
-
-    | Model          | Context Window |
-    |----------------|----------------|
-    | gemma-3-1b-it  | 32k tokens     |
-    | gemma-3-4b-it  | 32k tokens     |
-    | gemma-3-12b-it | 32k tokens     |
-    | gemma-3-27b-it | 128k tokens    |
-
-  </Accordion>
-  <Accordion title="Google (Vertex AI)">
-    Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
-    ```python Code
-    import json
-
-    file_path = 'path/to/vertex_ai_service_account.json'
-
-    # Load the JSON file
-    with open(file_path, 'r') as file:
-        vertex_credentials = json.load(file)
-
-    # Convert the credentials to a JSON string
-    vertex_credentials_json = json.dumps(vertex_credentials)
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="gemini/gemini-1.5-pro-latest",
-        temperature=0.7,
-        vertex_credentials=vertex_credentials_json
-    )
-    ```
-
-    Google offers a range of powerful models optimized for different use cases:
-
-    | Model                          | Context Window | Best For                                                          |
-    |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
-    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
-    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
-  </Accordion>
-
-  <Accordion title="Azure">
-    ```toml Code
-    # Required
-    AZURE_API_KEY=<your-api-key>
-    AZURE_API_BASE=<your-resource-url>
-    AZURE_API_VERSION=<api-version>
-
-    # Optional
-    AZURE_AD_TOKEN=<your-azure-ad-token>
-    AZURE_API_TYPE=<your-azure-api-type>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="azure/gpt-4",
-        api_version="2023-05-15"
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="AWS Bedrock">
-    ```toml Code
-    AWS_ACCESS_KEY_ID=<your-access-key>
-    AWS_SECRET_ACCESS_KEY=<your-secret-key>
-    AWS_DEFAULT_REGION=<your-region>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
-    )
-    ```
-
-    Before using Amazon Bedrock, make sure you have boto3 installed in your environment
-
-    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API, enabling secure and responsible AI application development.
-
-    | Model                   | Context Window       | Best For                                                          |
-    |-------------------------|----------------------|-------------------------------------------------------------------|
-    | Amazon Nova Pro         | Up to 300k tokens    | High-performance, model balancing accuracy, speed, and cost-effectiveness across diverse tasks. |
-    | Amazon Nova Micro       | Up to 128k tokens    | High-performance, cost-effective text-only model optimized for lowest latency responses. |
-    | Amazon Nova Lite        | Up to 300k tokens    | High-performance, affordable multimodal processing for images, video, and text with real-time capabilities. |
-    | Claude 3.7 Sonnet       | Up to 128k tokens    | High-performance, best for complex reasoning, coding & AI agents |
-    | Claude 3.5 Sonnet v2    | Up to 200k tokens    | State-of-the-art model specialized in software engineering, agentic capabilities, and computer interaction at optimized cost. |
-    | Claude 3.5 Sonnet       | Up to 200k tokens    | High-performance model delivering superior intelligence and reasoning across diverse tasks with optimal speed-cost balance. |
-    | Claude 3.5 Haiku        | Up to 200k tokens    | Fast, compact multimodal model optimized for quick responses and seamless human-like interactions |
-    | Claude 3 Sonnet         | Up to 200k tokens    | Multimodal model balancing intelligence and speed for high-volume deployments. |
-    | Claude 3 Haiku          | Up to 200k tokens    | Compact, high-speed multimodal model optimized for quick responses and natural conversational interactions |
-    | Claude 3 Opus           | Up to 200k tokens    | Most advanced multimodal model exceling at complex tasks with human-like reasoning and superior contextual understanding. |
-    | Claude 2.1              | Up to 200k tokens    | Enhanced version with expanded context window, improved reliability, and reduced hallucinations for long-form and RAG applications |
-    | Claude                  | Up to 100k tokens    | Versatile model excelling in sophisticated dialogue, creative content, and precise instruction following. |
-    | Claude Instant          | Up to 100k tokens    | Fast, cost-effective model for everyday tasks like dialogue, analysis, summarization, and document Q&A |
-    | Llama 3.1 405B Instruct | Up to 128k tokens    | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
-    | Llama 3.1 70B Instruct  | Up to 128k tokens    | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | Llama 3.1 8B Instruct   | Up to 128k tokens    | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
-    | Llama 3 70B Instruct    | Up to 8k tokens      | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | Llama 3 8B Instruct     | Up to 8k tokens      | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
-    | Titan Text G1 - Lite    | Up to 4k tokens      | Lightweight, cost-effective model optimized for English tasks and fine-tuning with focus on summarization and content generation. |
-    | Titan Text G1 - Express | Up to 8k tokens      | Versatile model for general language tasks, chat, and RAG applications with support for English and 100+ languages. |
-    | Cohere Command          | Up to 4k tokens      | Model specialized in following user commands and delivering practical enterprise solutions. |
-    | Jurassic-2 Mid          | Up to 8,191 tokens   | Cost-effective model balancing quality and affordability for diverse language tasks like Q&A, summarization, and content generation. |
-    | Jurassic-2 Ultra        | Up to 8,191 tokens   | Model for advanced text generation and comprehension, excelling in complex tasks like analysis and content creation. |
-    | Jamba-Instruct          | Up to 256k tokens    | Model with extended context window optimized for cost-effective text generation, summarization, and Q&A. |
-    | Mistral 7B Instruct     | Up to 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
-    | Mistral 8x7B Instruct   | Up to 32k tokens     | An MOE LLM that follows instructions, completes requests, and generates creative text. |
-
-  </Accordion>
-
-  <Accordion title="Amazon SageMaker">
-    ```toml Code
-    AWS_ACCESS_KEY_ID=<your-access-key>
-    AWS_SECRET_ACCESS_KEY=<your-secret-key>
-    AWS_DEFAULT_REGION=<your-region>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="sagemaker/<my-endpoint>"
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="Mistral">
-    Set the following environment variables in your `.env` file:
-    ```toml Code
-    MISTRAL_API_KEY=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="mistral/mistral-large-latest",
-        temperature=0.7
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="Nvidia NIM">
-    Set the following environment variables in your `.env` file:
-    ```toml Code
-    NVIDIA_API_KEY=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="nvidia_nim/meta/llama3-70b-instruct",
-        temperature=0.7
-    )
-    ```
-
-    Nvidia NIM provides a comprehensive suite of models for various use cases, from general-purpose tasks to specialized applications.
-
-    | Model                                                                   | Context Window | Best For                                                          |
-    |-------------------------------------------------------------------------|----------------|-------------------------------------------------------------------|
-    | nvidia/mistral-nemo-minitron-8b-8k-instruct                              | 8,192 tokens   | State-of-the-art small language model delivering superior accuracy for chatbot, virtual assistants, and content generation. |
-    | nvidia/nemotron-4-mini-hindi-4b-instruct                                 | 4,096 tokens   | A bilingual Hindi-English SLM for on-device inference, tailored specifically for Hindi Language. |
-    | nvidia/llama-3.1-nemotron-70b-instruct                                  | 128k tokens    | Customized for enhanced helpfulness in responses                  |
-    | nvidia/llama3-chatqa-1.5-8b                                                | 128k tokens    | Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines. |
-    | nvidia/llama3-chatqa-1.5-70b                                               | 128k tokens    | Advanced LLM to generate high-quality, context-aware responses for chatbots and search engines. |
-    | nvidia/vila                                                             | 128k tokens    | Multi-modal vision-language model that understands text/img/video and creates informative responses |
-    | nvidia/neva-22                                                          | 4,096 tokens   | Multi-modal vision-language model that understands text/images and generates informative responses |
-    | nvidia/nemotron-mini-4b-instruct                                         | 8,192 tokens   | General-purpose tasks |
-    | nvidia/usdcode-llama3-70b-instruct                                       | 128k tokens    | State-of-the-art LLM that answers OpenUSD knowledge queries and generates USD-Python code. |
-    | nvidia/nemotron-4-340b-instruct                                          | 4,096 tokens   | Creates diverse synthetic data that mimics the characteristics of real-world data. |
-    | meta/codellama-70b                                                      | 100k tokens    | LLM capable of generating code from natural language and vice versa. |
-    | meta/llama2-70b                                                         | 4,096 tokens   | Cutting-edge large language AI model capable of generating text and code in response to prompts. |
-    | meta/llama3-8b-instruct                                                | 8,192 tokens   | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
-    | meta/llama3-70b-instruct                                               | 8,192 tokens   | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | meta/llama-3.1-8b-instruct                                             | 128k tokens    | Advanced state-of-the-art model with language understanding, superior reasoning, and text generation. |
-    | meta/llama-3.1-70b-instruct                                            | 128k tokens    | Powers complex conversations with superior contextual understanding, reasoning and text generation. |
-    | meta/llama-3.1-405b-instruct                                           | 128k tokens    | Advanced LLM for synthetic data generation, distillation, and inference for chatbots, coding, and domain-specific tasks. |
-    | meta/llama-3.2-1b-instruct                                             | 128k tokens    | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
-    | meta/llama-3.2-3b-instruct                                             | 128k tokens    | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
-    | meta/llama-3.2-11b-vision-instruct                                     | 128k tokens    | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
-    | meta/llama-3.2-90b-vision-instruct                                     | 128k tokens    | Advanced state-of-the-art small language model with language understanding, superior reasoning, and text generation. |
-    | google/gemma-7b                                                        | 8,192 tokens   | Cutting-edge text generation model text understanding, transformation, and code generation. |
-    | google/gemma-2b                                                        | 8,192 tokens   | Cutting-edge text generation model text understanding, transformation, and code generation. |
-    | google/codegemma-7b                                                    | 8,192 tokens   | Cutting-edge model built on Google's Gemma-7B specialized for code generation and code completion. |
-    | google/codegemma-1.1-7b                                               | 8,192 tokens   | Advanced programming model for code generation, completion, reasoning, and instruction following. |
-    | google/recurrentgemma-2b                                              | 8,192 tokens   | Novel recurrent architecture based language model for faster inference when generating long sequences. |
-    | google/gemma-2-9b-it                                                  | 8,192 tokens   | Cutting-edge text generation model text understanding, transformation, and code generation. |
-    | google/gemma-2-27b-it                                                 | 8,192 tokens   | Cutting-edge text generation model text understanding, transformation, and code generation. |
-    | google/gemma-2-2b-it                                                  | 8,192 tokens   | Cutting-edge text generation model text understanding, transformation, and code generation. |
-    | google/deplot                                                         | 512 tokens     | One-shot visual language understanding model that translates images of plots into tables. |
-    | google/paligemma                                                      | 8,192 tokens   | Vision language model adept at comprehending text and visual inputs to produce informative responses. |
-    | mistralai/mistral-7b-instruct-v0.2                                   | 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
-    | mistralai/mixtral-8x7b-instruct-v0.1                                 | 8,192 tokens   | An MOE LLM that follows instructions, completes requests, and generates creative text. |
-    | mistralai/mistral-large                                              | 4,096 tokens   | Creates diverse synthetic data that mimics the characteristics of real-world data. |
-    | mistralai/mixtral-8x22b-instruct-v0.1                               | 8,192 tokens   | Creates diverse synthetic data that mimics the characteristics of real-world data. |
-    | mistralai/mistral-7b-instruct-v0.3                                  | 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
-    | nv-mistralai/mistral-nemo-12b-instruct                              | 128k tokens    | Most advanced language model for reasoning, code, multilingual tasks; runs on a single GPU. |
-    | mistralai/mamba-codestral-7b-v0.1                                   | 256k tokens    | Model for writing and interacting with code across a wide range of programming languages and tasks. |
-    | microsoft/phi-3-mini-128k-instruct                                  | 128K tokens    | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3-mini-4k-instruct                                    | 4,096 tokens   | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3-small-8k-instruct                                   | 8,192 tokens   | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3-small-128k-instruct                                 | 128K tokens    | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3-medium-4k-instruct                                  | 4,096 tokens   | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3-medium-128k-instruct                                | 128K tokens    | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
-    | microsoft/phi-3.5-mini-instruct                                     | 128K tokens    | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
-    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecture to deliver compute efficient content generation |
-    | microsoft/kosmos-2                                                  | 1,024 tokens   | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
-    | microsoft/phi-3-vision-128k-instruct                               | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
-    | microsoft/phi-3.5-vision-instruct                                  | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
-    | databricks/dbrx-instruct                                           | 12k tokens     | A general-purpose LLM with state-of-the-art performance in language understanding, coding, and RAG. |
-    | snowflake/arctic                                                   | 1,024 tokens   | Delivers high efficiency inference for enterprise applications focused on SQL generation and coding. |
-    | aisingapore/sea-lion-7b-instruct                                  | 4,096 tokens   | LLM to represent and serve the linguistic and cultural diversity of Southeast Asia |
-    | ibm/granite-8b-code-instruct                                      | 4,096 tokens   | Software programming LLM for code generation, completion, explanation, and multi-turn conversion. |
-    | ibm/granite-34b-code-instruct                                     | 8,192 tokens   | Software programming LLM for code generation, completion, explanation, and multi-turn conversion. |
-    | ibm/granite-3.0-8b-instruct                                       | 4,096 tokens   | Advanced Small Language Model supporting RAG, summarization, classification, code, and agentic AI |
-    | ibm/granite-3.0-3b-a800m-instruct                                | 4,096 tokens   | Highly efficient Mixture of Experts model for RAG, summarization, entity extraction, and classification |
-    | mediatek/breeze-7b-instruct                                       | 4,096 tokens   | Creates diverse synthetic data that mimics the characteristics of real-world data. |
-    | upstage/solar-10.7b-instruct                                      | 4,096 tokens   | Excels in NLP tasks, particularly in instruction-following, reasoning, and mathematics. |
-    | writer/palmyra-med-70b-32k                                        | 32k tokens     | Leading LLM for accurate, contextually relevant responses in the medical domain. |
-    | writer/palmyra-med-70b                                            | 32k tokens     | Leading LLM for accurate, contextually relevant responses in the medical domain. |
-    | writer/palmyra-fin-70b-32k                                        | 32k tokens     | Specialized LLM for financial analysis, reporting, and data processing |
-    | 01-ai/yi-large                                                    | 32k tokens     | Powerful model trained on English and Chinese for diverse tasks including chatbot and creative writing. |
-    | deepseek-ai/deepseek-coder-6.7b-instruct                         | 2k tokens      | Powerful coding model offering advanced capabilities in code generation, completion, and infilling |
-    | rakuten/rakutenai-7b-instruct                                     | 1,024 tokens   | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
-    | rakuten/rakutenai-7b-chat                                         | 1,024 tokens   | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
-    | baichuan-inc/baichuan2-13b-chat                                  | 4,096 tokens   | Support Chinese and English chat, coding, math, instruction following, solving quizzes |
-  </Accordion>
-
-  <Accordion title="Local NVIDIA NIM Deployed using WSL2">
-
-    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
-    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services.
-    Perfect for development, testing, or production scenarios where data privacy or offline capabilities are required.
-
-    Here is a step-by-step guide to setting up a local NVIDIA NIM model:
-
-    1. Follow installation instructions from [NVIDIA Website](https://docs.nvidia.com/nim/wsl2/latest/getting-started.html)
-
-    2. Install the local model. For Llama 3.1-8b follow [instructions](https://build.nvidia.com/meta/llama-3_1-8b-instruct/deploy)
-
-    3. Configure your crewai local models:
-
-    ```python Code
-    from crewai.llm import LLM
-
-    local_nvidia_nim_llm = LLM(
-        model="openai/meta/llama-3.1-8b-instruct", # it's an openai-api compatible model
-        base_url="http://localhost:8000/v1",
-        api_key="<your_api_key|any text if you have not configured it>", # api_key is required, but you can use any text
-    )
-
-    # Then you can use it in your crew:
-
-    @CrewBase
-    class MyCrew():
-        # ...
-
-        @agent
-        def researcher(self) -> Agent:
-            return Agent(
-                config=self.agents_config['researcher'], # type: ignore[index]
-                llm=local_nvidia_nim_llm
-            )
-
-        # ...
-    ```
-  </Accordion>
-
-  <Accordion title="Groq">
-    Set the following environment variables in your `.env` file:
-
-    ```toml Code
-    GROQ_API_KEY=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="groq/llama-3.2-90b-text-preview",
-        temperature=0.7
-    )
-    ```
-    | Model             | Context Window   | Best For                                   |
-    |-------------------|------------------|--------------------------------------------|
-    | Llama 3.1 70B/8B  | 131,072 tokens   | High-performance, large context tasks      |
-    | Llama 3.2 Series  | 8,192 tokens     | General-purpose tasks                      |
-    | Mixtral 8x7B      | 32,768 tokens    | Balanced performance and context           |
-  </Accordion>
-
-  <Accordion title="IBM watsonx.ai">
-    Set the following environment variables in your `.env` file:
-    ```toml Code
-    # Required
-    WATSONX_URL=<your-url>
-    WATSONX_APIKEY=<your-apikey>
-    WATSONX_PROJECT_ID=<your-project-id>
-
-    # Optional
-    WATSONX_TOKEN=<your-token>
-    WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="watsonx/meta-llama/llama-3-1-70b-instruct",
-        base_url="https://api.watsonx.ai/v1"
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="Ollama (Local LLMs)">
-    1. Install Ollama: [ollama.ai](https://ollama.ai/)
-    2. Run a model: `ollama run llama3`
-    3. Configure:
-
-    ```python Code
-    llm = LLM(
-        model="ollama/llama3:70b",
-        base_url="http://localhost:11434"
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="Fireworks AI">
-    Set the following environment variables in your `.env` file:
-    ```toml Code
-    FIREWORKS_API_KEY=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
-        temperature=0.7
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="Perplexity AI">
-    Set the following environment variables in your `.env` file:
-    ```toml Code
-    PERPLEXITY_API_KEY=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="llama-3.1-sonar-large-128k-online",
-        base_url="https://api.perplexity.ai/"
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="Hugging Face">
-    Set the following environment variables in your `.env` file:
-    ```toml Code
-    HF_TOKEN=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct"
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="SambaNova">
-    Set the following environment variables in your `.env` file:
-
-    ```toml Code
-    SAMBANOVA_API_KEY=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="sambanova/Meta-Llama-3.1-8B-Instruct",
-        temperature=0.7
-    )
-    ```
-    | Model              | Context Window         | Best For                                     |
-    |--------------------|------------------------|----------------------------------------------|
-    | Llama 3.1 70B/8B   | Up to 131,072 tokens   | High-performance, large context tasks        |
-    | Llama 3.1 405B     | 8,192 tokens           | High-performance and output quality          |
-    | Llama 3.2 Series   | 8,192 tokens           | General-purpose, multimodal tasks            |
-    | Llama 3.3 70B      | Up to 131,072 tokens   | High-performance and output quality          |
-    | Qwen2 familly      | 8,192 tokens           | High-performance and output quality          |
-  </Accordion>
-
-  <Accordion title="Cerebras">
-    Set the following environment variables in your `.env` file:
-    ```toml Code
-    # Required
-    CEREBRAS_API_KEY=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="cerebras/llama3.1-70b",
-        temperature=0.7,
-        max_tokens=8192
-    )
-    ```
-
-    <Info>
-      Cerebras features:
-      - Fast inference speeds
-      - Competitive pricing
-      - Good balance of speed and quality
-      - Support for long context windows
-    </Info>
-  </Accordion>
-
-  <Accordion title="Open Router">
-    Set the following environment variables in your `.env` file:
-    ```toml Code
-    OPENROUTER_API_KEY=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="openrouter/deepseek/deepseek-r1",
-        base_url="https://openrouter.ai/api/v1",
-        api_key=OPENROUTER_API_KEY
-    )
-    ```
-
-    <Info>
-      Open Router models:
-      - openrouter/deepseek/deepseek-r1
-      - openrouter/deepseek/deepseek-chat
-    </Info>
-  </Accordion>
-</AccordionGroup>
-
-## Streaming Responses
-
-CrewAI supports streaming responses from LLMs, allowing your application to receive and process outputs in real-time as they're generated.
-
-<Tabs>
-  <Tab title="Basic Setup">
-    Enable streaming by setting the `stream` parameter to `True` when initializing your LLM:
-
-    ```python
-    from crewai import LLM
-
-    # Create an LLM with streaming enabled
-    llm = LLM(
-        model="openai/gpt-4o",
-        stream=True  # Enable streaming
-    )
-    ```
-
-    When streaming is enabled, responses are delivered in chunks as they're generated, creating a more responsive user experience.
-  </Tab>
-
-  <Tab title="Event Handling">
-    CrewAI emits events for each chunk received during streaming:
-
-    ```python
-    from crewai.utilities.events import (
-      LLMStreamChunkEvent
-    )
-    from crewai.utilities.events.base_event_listener import BaseEventListener
-
-    class MyCustomListener(BaseEventListener):
-        def setup_listeners(self, crewai_event_bus):
-            @crewai_event_bus.on(LLMStreamChunkEvent)
-            def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
-              # Process each chunk as it arrives
-              print(f"Received chunk: {event.chunk}")
-
-    my_listener = MyCustomListener()
-    ```
-
-    <Tip>
-      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details 
-    </Tip>
-  </Tab>
-</Tabs>
-
-## Structured LLM Calls
-
-CrewAI supports structured responses from LLM calls by allowing you to define a `response_format` using a Pydantic model. This enables the framework to automatically parse and validate the output, making it easier to integrate the response into your application without manual post-processing.
-
-For example, you can define a Pydantic model to represent the expected response structure and pass it as the `response_format` when instantiating the LLM. The model will then be used to convert the LLM output into a structured Python object.
-
-```python Code
-from crewai import LLM
-
-class Dog(BaseModel):
-    name: str
-    age: int
-    breed: str
-
-
-llm = LLM(model="gpt-4o", response_format=Dog)
-
-response = llm.call(
-    "Analyze the following messages and return the name, age, and breed. "
-    "Meet Kona! She is 3 years old and is a black german shepherd."
-)
-print(response)
-
-# Output:
-# Dog(name='Kona', age=3, breed='black german shepherd')
-```
-
-## Advanced Features and Optimization
-
-Learn how to get the most out of your LLM configuration:
-
-<AccordionGroup>
-  <Accordion title="Context Window Management">
-    CrewAI includes smart context management features:
-
-    ```python
-    from crewai import LLM
-
-    # CrewAI automatically handles:
-    # 1. Token counting and tracking
-    # 2. Content summarization when needed
-    # 3. Task splitting for large contexts
-
-    llm = LLM(
-        model="gpt-4",
-        max_tokens=4000,  # Limit response length
-    )
-    ```
-
-    <Info>
-      Best practices for context management:
-      1. Choose models with appropriate context windows
-      2. Pre-process long inputs when possible
-      3. Use chunking for large documents
-      4. Monitor token usage to optimize costs
-    </Info>
-  </Accordion>
-
-  <Accordion title="Performance Optimization">
-    <Steps>
-      <Step title="Token Usage Optimization">
-        Choose the right context window for your task:
-        - Small tasks (up to 4K tokens): Standard models
-        - Medium tasks (between 4K-32K): Enhanced models
-        - Large tasks (over 32K): Large context models
-
-        ```python
-        # Configure model with appropriate settings
-        llm = LLM(
-            model="openai/gpt-4-turbo-preview",
-            temperature=0.7,    # Adjust based on task
-            max_tokens=4096,    # Set based on output needs
-            timeout=300        # Longer timeout for complex tasks
-        )
-        ```
-        <Tip>
-          - Lower temperature (0.1 to 0.3) for factual responses
-          - Higher temperature (0.7 to 0.9) for creative tasks
-        </Tip>
-      </Step>
-
-      <Step title="Best Practices">
-        1. Monitor token usage
-        2. Implement rate limiting
-        3. Use caching when possible
-        4. Set appropriate max_tokens limits
-      </Step>
-    </Steps>
-
-    <Info>
-      Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
-    </Info>
-  </Accordion>
-  
-  <Accordion title="Drop Additional Parameters">
-    CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
-    For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:
-
-    ```python
-    from crewai import LLM
-    import os
-
-    os.environ["OPENAI_API_KEY"] = "<api-key>"
-
-    o3_llm = LLM(
-        model="o3",
-        drop_params=True,
-        additional_drop_params=["stop"]
-    )
-    ```
-  </Accordion>
-</AccordionGroup>
-
-## Common Issues and Solutions
-
-<Tabs>
-  <Tab title="Authentication">
-    <Warning>
-      Most authentication issues can be resolved by checking API key format and environment variable names.
-    </Warning>
-
-    ```bash
-    # OpenAI
-    OPENAI_API_KEY=sk-...
-
-    # Anthropic
-    ANTHROPIC_API_KEY=sk-ant-...
-    ```
-  </Tab>
-  <Tab title="Model Names">
-    <Check>
-      Always include the provider prefix in model names
-    </Check>
-
-    ```python
-    # Correct
-    llm = LLM(model="openai/gpt-4")
-
-    # Incorrect
-    llm = LLM(model="gpt-4")
-    ```
-  </Tab>
-  <Tab title="Context Length">
-    <Tip>
-      Use larger context models for extensive tasks
-    </Tip>
-
-    ```python
-    # Large context model
-    llm = LLM(model="openai/gpt-4o")  # 128K tokens
-    ```
-  </Tab>
-</Tabs>

docs/concepts/memory.mdx

@@ -1,992 +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
-
-## Storage Location Transparency
-
-<Info>
-**Understanding Storage Locations**: CrewAI uses platform-specific directories to store memory and knowledge files following OS conventions. Understanding these locations helps with production deployments, backups, and debugging.
-</Info>
-
-### Where CrewAI Stores Files
-
-By default, CrewAI uses the `appdirs` library to determine storage locations following platform conventions. Here's exactly where your files are stored:
-
-#### Default Storage Locations by Platform
-
-**macOS:**
-```
-~/Library/Application Support/CrewAI/{project_name}/
-├── knowledge/           # Knowledge base ChromaDB files
-├── short_term_memory/   # Short-term memory ChromaDB files  
-├── long_term_memory/    # Long-term memory ChromaDB files
-├── entities/            # Entity memory ChromaDB files
-└── long_term_memory_storage.db  # SQLite database
-```
-
-**Linux:**
-```
-~/.local/share/CrewAI/{project_name}/
-├── knowledge/
-├── short_term_memory/
-├── long_term_memory/
-├── entities/
-└── long_term_memory_storage.db
-```
-
-**Windows:**
-```
-C:\Users\{username}\AppData\Local\CrewAI\{project_name}\
-├── knowledge\
-├── short_term_memory\
-├── long_term_memory\
-├── entities\
-└── long_term_memory_storage.db
-```
-
-### Finding Your Storage Location
-
-To see exactly where CrewAI is storing files on your system:
-
-```python
-from crewai.utilities.paths import db_storage_path
-import os
-
-# Get the base storage path
-storage_path = db_storage_path()
-print(f"CrewAI storage location: {storage_path}")
-
-# List all CrewAI storage directories
-if os.path.exists(storage_path):
-    print("\nStored files and directories:")
-    for item in os.listdir(storage_path):
-        item_path = os.path.join(storage_path, item)
-        if os.path.isdir(item_path):
-            print(f"📁 {item}/")
-            # Show ChromaDB collections
-            if os.path.exists(item_path):
-                for subitem in os.listdir(item_path):
-                    print(f"   └── {subitem}")
-        else:
-            print(f"📄 {item}")
-else:
-    print("No CrewAI storage directory found yet.")
-```
-
-### Controlling Storage Locations
-
-#### Option 1: Environment Variable (Recommended)
-```python
-import os
-from crewai import Crew
-
-# Set custom storage location
-os.environ["CREWAI_STORAGE_DIR"] = "./my_project_storage"
-
-# All memory and knowledge will now be stored in ./my_project_storage/
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True
-)
-```
-
-#### Option 2: 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
-custom_storage_path = "./storage"
-os.makedirs(custom_storage_path, exist_ok=True)
-
-crew = Crew(
-    memory=True,
-    long_term_memory=LongTermMemory(
-        storage=LTMSQLiteStorage(
-            db_path=f"{custom_storage_path}/memory.db"
-        )
-    )
-)
-```
-
-#### Option 3: Project-Specific Storage
-```python
-import os
-from pathlib import Path
-
-# Store in project directory
-project_root = Path(__file__).parent
-storage_dir = project_root / "crewai_storage"
-
-os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
-
-# Now all storage will be in your project directory
-```
-
-### Embedding Provider Defaults
-
-<Info>
-**Default Embedding Provider**: CrewAI defaults to OpenAI embeddings for consistency and reliability. You can easily customize this to match your LLM provider or use local embeddings.
-</Info>
-
-#### Understanding Default Behavior
-```python
-# When using Claude as your LLM...
-from crewai import Agent, LLM
-
-agent = Agent(
-    role="Analyst",
-    goal="Analyze data",
-    backstory="Expert analyst",
-    llm=LLM(provider="anthropic", model="claude-3-sonnet")  # Using Claude
-)
-
-# CrewAI will use OpenAI embeddings by default for consistency
-# You can easily customize this to match your preferred provider
-```
-
-#### Customizing Embedding Providers
-```python
-from crewai import Crew
-
-# Option 1: Match your LLM provider
-crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    memory=True,
-    embedder={
-        "provider": "anthropic",  # Match your LLM provider
-        "config": {
-            "api_key": "your-anthropic-key",
-            "model": "text-embedding-3-small"
-        }
-    }
-)
-
-# Option 2: Use local embeddings (no external API calls)
-crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    memory=True,
-    embedder={
-        "provider": "ollama",
-        "config": {"model": "mxbai-embed-large"}
-    }
-)
-```
-
-### Debugging Storage Issues
-
-#### Check Storage Permissions
-```python
-import os
-from crewai.utilities.paths import db_storage_path
-
-storage_path = db_storage_path()
-print(f"Storage path: {storage_path}")
-print(f"Path exists: {os.path.exists(storage_path)}")
-print(f"Is writable: {os.access(storage_path, os.W_OK) if os.path.exists(storage_path) else 'Path does not exist'}")
-
-# Create with proper permissions
-if not os.path.exists(storage_path):
-    os.makedirs(storage_path, mode=0o755, exist_ok=True)
-    print(f"Created storage directory: {storage_path}")
-```
-
-#### Inspect ChromaDB Collections
-```python
-import chromadb
-from crewai.utilities.paths import db_storage_path
-
-# Connect to CrewAI's ChromaDB
-storage_path = db_storage_path()
-chroma_path = os.path.join(storage_path, "knowledge")
-
-if os.path.exists(chroma_path):
-    client = chromadb.PersistentClient(path=chroma_path)
-    collections = client.list_collections()
-    
-    print("ChromaDB Collections:")
-    for collection in collections:
-        print(f"  - {collection.name}: {collection.count()} documents")
-else:
-    print("No ChromaDB storage found")
-```
-
-#### Reset Storage (Debugging)
-```python
-from crewai import Crew
-
-# Reset all memory storage
-crew = Crew(agents=[...], tasks=[...], memory=True)
-
-# Reset specific memory types
-crew.reset_memories(command_type='short')     # Short-term memory
-crew.reset_memories(command_type='long')      # Long-term memory  
-crew.reset_memories(command_type='entity')    # Entity memory
-crew.reset_memories(command_type='knowledge') # Knowledge storage
-```
-
-### Production Best Practices
-
-1. **Set `CREWAI_STORAGE_DIR`** to a known location in production for better control
-2. **Choose explicit embedding providers** to match your LLM setup
-3. **Monitor storage directory size** for large-scale deployments
-4. **Include storage directories** in your backup strategy
-5. **Set appropriate file permissions** (0o755 for directories, 0o644 for files)
-6. **Use project-relative paths** for containerized deployments
-
-### Common Storage Issues
-
-**"ChromaDB permission denied" errors:**
-```bash
-# Fix permissions
-chmod -R 755 ~/.local/share/CrewAI/
-```
-
-**"Database is locked" errors:**
-```python
-# Ensure only one CrewAI instance accesses storage
-import fcntl
-import os
-
-storage_path = db_storage_path()
-lock_file = os.path.join(storage_path, ".crewai.lock")
-
-with open(lock_file, 'w') as f:
-    fcntl.flock(f.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
-    # Your CrewAI code here
-```
-
-**Storage not persisting between runs:**
-```python
-# Verify storage location is consistent
-import os
-print("CREWAI_STORAGE_DIR:", os.getenv("CREWAI_STORAGE_DIR"))
-print("Current working directory:", os.getcwd())
-print("Computed storage path:", db_storage_path())
-```
-
-## Custom Embedder Configuration
-
-CrewAI supports multiple embedding providers to give you flexibility in choosing the best option for your use case. Here's a comprehensive guide to configuring different embedding providers for your memory system.
-
-### Why Choose Different Embedding Providers?
-
-- **Cost Optimization**: Local embeddings (Ollama) are free after initial setup
-- **Privacy**: Keep your data local with Ollama or use your preferred cloud provider
-- **Performance**: Some models work better for specific domains or languages
-- **Consistency**: Match your embedding provider with your LLM provider
-- **Compliance**: Meet specific regulatory or organizational requirements
-
-### OpenAI Embeddings (Default)
-
-OpenAI provides reliable, high-quality embeddings that work well for most use cases.
-
-```python
-from crewai import Crew
-
-# Basic OpenAI configuration (uses environment OPENAI_API_KEY)
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    memory=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "model": "text-embedding-3-small"  # or "text-embedding-3-large"
-        }
-    }
-)
-
-# Advanced OpenAI configuration
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "api_key": "your-openai-api-key",  # Optional: override env var
-            "model": "text-embedding-3-large",
-            "dimensions": 1536,  # Optional: reduce dimensions for smaller storage
-            "organization_id": "your-org-id"  # Optional: for organization accounts
-        }
-    }
-)
-```
-
-### Azure OpenAI Embeddings
-
-For enterprise users with Azure OpenAI deployments.
-
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "openai",  # Use openai provider for Azure
-        "config": {
-            "api_key": "your-azure-api-key",
-            "api_base": "https://your-resource.openai.azure.com/",
-            "api_type": "azure",
-            "api_version": "2023-05-15",
-            "model": "text-embedding-3-small",
-            "deployment_id": "your-deployment-name"  # Azure deployment name
-        }
-    }
-)
-```
-
-### Google AI Embeddings
-
-Use Google's text embedding models for integration with Google Cloud services.
-
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "google",
-        "config": {
-            "api_key": "your-google-api-key",
-            "model": "text-embedding-004"  # or "text-embedding-preview-0409"
-        }
-    }
-)
-```
-
-### Vertex AI Embeddings
-
-For Google Cloud users with Vertex AI access.
-
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "vertexai",
-        "config": {
-            "project_id": "your-gcp-project-id",
-            "region": "us-central1",  # or your preferred region
-            "api_key": "your-service-account-key",
-            "model_name": "textembedding-gecko"
-        }
-    }
-)
-```
-
-### Ollama Embeddings (Local)
-
-Run embeddings locally for privacy and cost savings.
-
-```python
-# First, install and run Ollama locally, then pull an embedding model:
-# ollama pull mxbai-embed-large
-
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "ollama",
-        "config": {
-            "model": "mxbai-embed-large",  # or "nomic-embed-text"
-            "url": "http://localhost:11434/api/embeddings"  # Default Ollama URL
-        }
-    }
-)
-
-# For custom Ollama installations
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "ollama",
-        "config": {
-            "model": "mxbai-embed-large",
-            "url": "http://your-ollama-server:11434/api/embeddings"
-        }
-    }
-)
-```
-
-### Cohere Embeddings
-
-Use Cohere's embedding models for multilingual support.
-
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "cohere",
-        "config": {
-            "api_key": "your-cohere-api-key",
-            "model": "embed-english-v3.0"  # or "embed-multilingual-v3.0"
-        }
-    }
-)
-```
-
-### VoyageAI Embeddings
-
-High-performance embeddings optimized for retrieval tasks.
-
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "voyageai",
-        "config": {
-            "api_key": "your-voyage-api-key",
-            "model": "voyage-large-2",  # or "voyage-code-2" for code
-            "input_type": "document"  # or "query"
-        }
-    }
-)
-```
-
-### AWS Bedrock Embeddings
-
-For AWS users with Bedrock access.
-
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "bedrock",
-        "config": {
-            "aws_access_key_id": "your-access-key",
-            "aws_secret_access_key": "your-secret-key",
-            "region_name": "us-east-1",
-            "model": "amazon.titan-embed-text-v1"
-        }
-    }
-)
-```
-
-### Hugging Face Embeddings
-
-Use open-source models from Hugging Face.
-
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "huggingface",
-        "config": {
-            "api_key": "your-hf-token",  # Optional for public models
-            "model": "sentence-transformers/all-MiniLM-L6-v2",
-            "api_url": "https://api-inference.huggingface.co"  # or your custom endpoint
-        }
-    }
-)
-```
-
-### IBM Watson Embeddings
-
-For IBM Cloud users.
-
-```python
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "watson",
-        "config": {
-            "api_key": "your-watson-api-key",
-            "url": "your-watson-instance-url",
-            "model": "ibm/slate-125m-english-rtrvr"
-        }
-    }
-)
-```
-
-### Choosing the Right Embedding Provider
-
-| Provider | Best For | Pros | Cons |
-|:---------|:----------|:------|:------|
-| **OpenAI** | General use, reliability | High quality, well-tested | Cost, requires API key |
-| **Ollama** | Privacy, cost savings | Free, local, private | Requires local setup |
-| **Google AI** | Google ecosystem | Good performance | Requires Google account |
-| **Azure OpenAI** | Enterprise, compliance | Enterprise features | Complex setup |
-| **Cohere** | Multilingual content | Great language support | Specialized use case |
-| **VoyageAI** | Retrieval tasks | Optimized for search | Newer provider |
-
-### Environment Variable Configuration
-
-For security, store API keys in environment variables:
-
-```python
-import os
-
-# Set environment variables
-os.environ["OPENAI_API_KEY"] = "your-openai-key"
-os.environ["GOOGLE_API_KEY"] = "your-google-key"
-os.environ["COHERE_API_KEY"] = "your-cohere-key"
-
-# Use without exposing keys in code
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "model": "text-embedding-3-small"
-            # API key automatically loaded from environment
-        }
-    }
-)
-```
-
-### Testing Different Embedding Providers
-
-Compare embedding providers for your specific use case:
-
-```python
-from crewai import Crew
-from crewai.utilities.paths import db_storage_path
-
-# Test different providers with the same data
-providers_to_test = [
-    {
-        "name": "OpenAI",
-        "config": {
-            "provider": "openai",
-            "config": {"model": "text-embedding-3-small"}
-        }
-    },
-    {
-        "name": "Ollama",
-        "config": {
-            "provider": "ollama", 
-            "config": {"model": "mxbai-embed-large"}
-        }
-    }
-]
-
-for provider in providers_to_test:
-    print(f"\nTesting {provider['name']} embeddings...")
-    
-    # Create crew with specific embedder
-    crew = Crew(
-        agents=[...],
-        tasks=[...],
-        memory=True,
-        embedder=provider['config']
-    )
-    
-    # Run your test and measure performance
-    result = crew.kickoff()
-    print(f"{provider['name']} completed successfully")
-```
-
-### Troubleshooting Embedding Issues
-
-**Model not found errors:**
-```python
-# Verify model availability
-from crewai.utilities.embedding_configurator import EmbeddingConfigurator
-
-configurator = EmbeddingConfigurator()
-try:
-    embedder = configurator.configure_embedder({
-        "provider": "ollama",
-        "config": {"model": "mxbai-embed-large"}
-    })
-    print("Embedder configured successfully")
-except Exception as e:
-    print(f"Configuration error: {e}")
-```
-
-**API key issues:**
-```python
-import os
-
-# Check if API keys are set
-required_keys = ["OPENAI_API_KEY", "GOOGLE_API_KEY", "COHERE_API_KEY"]
-for key in required_keys:
-    if os.getenv(key):
-        print(f"✅ {key} is set")
-    else:
-        print(f"❌ {key} is not set")
-```
-
-**Performance comparison:**
-```python
-import time
-
-def test_embedding_performance(embedder_config, test_text="This is a test document"):
-    start_time = time.time()
-    
-    crew = Crew(
-        agents=[...],
-        tasks=[...],
-        memory=True,
-        embedder=embedder_config
-    )
-    
-    # Simulate memory operation
-    crew.kickoff()
-    
-    end_time = time.time()
-    return end_time - start_time
-
-# Compare performance
-openai_time = test_embedding_performance({
-    "provider": "openai",
-    "config": {"model": "text-embedding-3-small"}
-})
-
-ollama_time = test_embedding_performance({
-    "provider": "ollama", 
-    "config": {"model": "mxbai-embed-large"}
-})
-
-print(f"OpenAI: {openai_time:.2f}s")
-print(f"Ollama: {ollama_time:.2f}s")
-```
-
-## 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/planning.mdx

@@ -1,154 +0,0 @@
----
-title: Planning
-description: Learn how to add planning to your CrewAI Crew and improve their performance.
-icon: ruler-combined
----
-
-## Overview
-
-The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration, 
-all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.
-
-### Using the Planning Feature
-
-Getting started with the planning feature is very easy, the only step required is to add `planning=True` to your Crew:
-
-<CodeGroup>
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-# Assemble your crew with planning capabilities
-my_crew = Crew(
-    agents=self.agents,
-    tasks=self.tasks,
-    process=Process.sequential,
-    planning=True,
-)
-```
-</CodeGroup>
-
-From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
-
-<Warning>
-When planning is enabled, crewAI will use `gpt-4o-mini` as the default LLM for planning, which requires a valid OpenAI API key. Since your agents might be using different LLMs, this could cause confusion if you don't have an OpenAI API key configured or if you're experiencing unexpected behavior related to LLM API calls.
-</Warning>
-
-#### Planning LLM
-
-Now you can define the LLM that will be used to plan the tasks. 
-
-When running the base case example, you will see something like the output below, which represents the output of the `AgentPlanner` 
-responsible for creating the step-by-step logic to add to the Agents' tasks.
-
-<CodeGroup>
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-# Assemble your crew with planning capabilities and custom LLM
-my_crew = Crew(
-    agents=self.agents,
-    tasks=self.tasks,
-    process=Process.sequential,
-    planning=True,
-    planning_llm="gpt-4o"
-)
-
-# Run the crew
-my_crew.kickoff()
-```
-
-```markdown Result
-[2024-07-15 16:49:11][INFO]: Planning the crew execution
-**Step-by-Step Plan for Task Execution**
-
-**Task Number 1: Conduct a thorough research about AI LLMs**
-
-**Agent:** AI LLMs Senior Data Researcher
-
-**Agent Goal:** Uncover cutting-edge developments in AI LLMs
-
-**Task Expected Output:** A list with 10 bullet points of the most relevant information about AI LLMs
-
-**Task Tools:** None specified
-
-**Agent Tools:** None specified
-
-**Step-by-Step Plan:**
-
-1. **Define Research Scope:**
-
-   - Determine the specific areas of AI LLMs to focus on, such as advancements in architecture, use cases, ethical considerations, and performance metrics.
-
-2. **Identify Reliable Sources:**
-
-   - List reputable sources for AI research, including academic journals, industry reports, conferences (e.g., NeurIPS, ACL), AI research labs (e.g., OpenAI, Google AI), and online databases (e.g., IEEE Xplore, arXiv).
-
-3. **Collect Data:**
-
-   - Search for the latest papers, articles, and reports published in 2024 and early 2025.
-   - Use keywords like "Large Language Models 2025", "AI LLM advancements", "AI ethics 2025", etc.
-
-4. **Analyze Findings:**
-
-   - Read and summarize the key points from each source.
-   - Highlight new techniques, models, and applications introduced in the past year.
-
-5. **Organize Information:**
-
-   - Categorize the information into relevant topics (e.g., new architectures, ethical implications, real-world applications).
-   - Ensure each bullet point is concise but informative.
-
-6. **Create the List:**
-
-   - Compile the 10 most relevant pieces of information into a bullet point list.
-   - Review the list to ensure clarity and relevance.
-
-**Expected Output:**
-
-A list with 10 bullet points of the most relevant information about AI LLMs.
-
----
-
-**Task Number 2: Review the context you got and expand each topic into a full section for a report**
-
-**Agent:** AI LLMs Reporting Analyst
-
-**Agent Goal:** Create detailed reports based on AI LLMs data analysis and research findings
-
-**Task Expected Output:** A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'
-
-**Task Tools:** None specified
-
-**Agent Tools:** None specified
-
-**Step-by-Step Plan:**
-
-1. **Review the Bullet Points:**
-   - Carefully read through the list of 10 bullet points provided by the AI LLMs Senior Data Researcher.
-
-2. **Outline the Report:**
-   - Create an outline with each bullet point as a main section heading.
-   - Plan sub-sections under each main heading to cover different aspects of the topic.
-
-3. **Research Further Details:**
-   - For each bullet point, conduct additional research if necessary to gather more detailed information.
-   - Look for case studies, examples, and statistical data to support each section.
-
-4. **Write Detailed Sections:**
-   - Expand each bullet point into a comprehensive section.
-   - Ensure each section includes an introduction, detailed explanation, examples, and a conclusion.
-   - Use markdown formatting for headings, subheadings, lists, and emphasis.
-
-5. **Review and Edit:**
-   - Proofread the report for clarity, coherence, and correctness.
-   - Make sure the report flows logically from one section to the next.
-   - Format the report according to markdown standards.
-
-6. **Finalize the Report:**
-   - Ensure the report is complete with all sections expanded and detailed.
-   - Double-check formatting and make any necessary adjustments.
-
-**Expected Output:**
-A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
-```
-</CodeGroup>

docs/concepts/processes.mdx

@@ -1,66 +0,0 @@
----
-title: Processes
-description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
-icon: bars-staggered
----
-
-## Overview
-
-<Tip>
-  Processes orchestrate the execution of tasks by agents, akin to project management in human teams. 
-  These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.
-</Tip>
-
-## Process Implementations
-
-- **Sequential**: Executes tasks sequentially, ensuring tasks are completed in an orderly progression.
-- **Hierarchical**: Organizes tasks in a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command. A manager language model (`manager_llm`) or a custom manager agent (`manager_agent`) must be specified in the crew to enable the hierarchical process, facilitating the creation and management of tasks by the manager.
-- **Consensual Process (Planned)**: Aiming for collaborative decision-making among agents on task execution, this process type introduces a democratic approach to task management within CrewAI. It is planned for future development and is not currently implemented in the codebase.
-
-## The Role of Processes in Teamwork
-Processes enable individual agents to operate as a cohesive unit, streamlining their efforts to achieve common objectives with efficiency and coherence.
-
-## Assigning Processes to a Crew
-To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. For a hierarchical process, ensure to define `manager_llm` or `manager_agent` for the manager agent.
-
-```python
-from crewai import Crew, Process
-
-# Example: Creating a crew with a sequential process
-crew = Crew(
-    agents=my_agents,
-    tasks=my_tasks,
-    process=Process.sequential
-)
-
-# Example: Creating a crew with a hierarchical process
-# Ensure to provide a manager_llm or manager_agent
-crew = Crew(
-    agents=my_agents,
-    tasks=my_tasks,
-    process=Process.hierarchical,
-    manager_llm="gpt-4o"
-    # or
-    # manager_agent=my_manager_agent
-)
-```
-**Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object, and for the hierarchical process, either `manager_llm` or `manager_agent` is also required.
-
-## Sequential Process
-
-This method mirrors dynamic team workflows, progressing through tasks in a thoughtful and systematic manner. Task execution follows the predefined order in the task list, with the output of one task serving as context for the next.
-
-To customize task context, utilize the `context` parameter in the `Task` class to specify outputs that should be used as context for subsequent tasks.
-
-## Hierarchical Process
-
-Emulates a corporate hierarchy, CrewAI allows specifying a custom manager agent or automatically creates one, requiring the specification of a manager language model (`manager_llm`). This agent oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities, reviews outputs, and assesses task completion.
-
-## Process Class: Detailed Overview
-
-The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`). The consensual process is planned for future inclusion, emphasizing our commitment to continuous development and innovation.
-
-## Conclusion
-
-The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. 
-This documentation has been updated to reflect the latest features, enhancements, and the planned integration of the Consensual Process, ensuring users have access to the most current and comprehensive information.

docs/concepts/reasoning.mdx

@@ -1,147 +0,0 @@
----
-title: Reasoning
-description: "Learn how to enable and use agent reasoning to improve task execution."
-icon: brain
----
-
-## Overview
-
-Agent reasoning is a feature that allows agents to reflect on a task and create a plan before execution. This helps agents approach tasks more methodically and ensures they're ready to perform the assigned work.
-
-## Usage
-
-To enable reasoning for an agent, simply set `reasoning=True` when creating the agent:
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze complex datasets and provide insights",
-    backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
-    reasoning=True,  # Enable reasoning
-    max_reasoning_attempts=3  # Optional: Set a maximum number of reasoning attempts
-)
-```
-
-## How It Works
-
-When reasoning is enabled, before executing a task, the agent will:
-
-1. Reflect on the task and create a detailed plan
-2. Evaluate whether it's ready to execute the task
-3. Refine the plan as necessary until it's ready or max_reasoning_attempts is reached
-4. Inject the reasoning plan into the task description before execution
-
-This process helps the agent break down complex tasks into manageable steps and identify potential challenges before starting.
-
-## Configuration Options
-
-<ParamField body="reasoning" type="bool" default="False"> 
-    Enable or disable reasoning 
-</ParamField>
-
-<ParamField body="max_reasoning_attempts" type="int" default="None"> 
-    Maximum number of attempts to refine the plan before proceeding with execution. If None (default), the agent will continue refining until it's ready. 
-</ParamField>
-
-## Example
-
-Here's a complete example:
-
-```python
-from crewai import Agent, Task, Crew
-
-# Create an agent with reasoning enabled
-analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze data and provide insights",
-    backstory="You are an expert data analyst.",
-    reasoning=True,
-    max_reasoning_attempts=3  # Optional: Set a limit on reasoning attempts
-)
-
-# Create a task
-analysis_task = Task(
-    description="Analyze the provided sales data and identify key trends.",
-    expected_output="A report highlighting the top 3 sales trends.",
-    agent=analyst
-)
-
-# Create a crew and run the task
-crew = Crew(agents=[analyst], tasks=[analysis_task])
-result = crew.kickoff()
-
-print(result)
-```
-
-## Error Handling
-
-The reasoning process is designed to be robust, with error handling built in. If an error occurs during reasoning, the agent will proceed with executing the task without the reasoning plan. This ensures that tasks can still be executed even if the reasoning process fails.
-
-Here's how to handle potential errors in your code:
-
-```python
-from crewai import Agent, Task
-import logging
-
-# Set up logging to capture any reasoning errors
-logging.basicConfig(level=logging.INFO)
-
-# Create an agent with reasoning enabled
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze data and provide insights",
-    reasoning=True,
-    max_reasoning_attempts=3
-)
-
-# Create a task
-task = Task(
-    description="Analyze the provided sales data and identify key trends.",
-    expected_output="A report highlighting the top 3 sales trends.",
-    agent=agent
-)
-
-# Execute the task
-# If an error occurs during reasoning, it will be logged and execution will continue
-result = agent.execute_task(task)
-```
-
-## Example Reasoning Output
-
-Here's an example of what a reasoning plan might look like for a data analysis task:
-
-```
-Task: Analyze the provided sales data and identify key trends.
-
-Reasoning Plan:
-I'll analyze the sales data to identify the top 3 trends.
-
-1. Understanding of the task:
-   I need to analyze sales data to identify key trends that would be valuable for business decision-making.
-
-2. Key steps I'll take:
-   - First, I'll examine the data structure to understand what fields are available
-   - Then I'll perform exploratory data analysis to identify patterns
-   - Next, I'll analyze sales by time periods to identify temporal trends
-   - I'll also analyze sales by product categories and customer segments
-   - Finally, I'll identify the top 3 most significant trends
-
-3. Approach to challenges:
-   - If the data has missing values, I'll decide whether to fill or filter them
-   - If the data has outliers, I'll investigate whether they're valid data points or errors
-   - If trends aren't immediately obvious, I'll apply statistical methods to uncover patterns
-
-4. Use of available tools:
-   - I'll use data analysis tools to explore and visualize the data
-   - I'll use statistical tools to identify significant patterns
-   - I'll use knowledge retrieval to access relevant information about sales analysis
-
-5. Expected outcome:
-   A concise report highlighting the top 3 sales trends with supporting evidence from the data.
-
-READY: I am ready to execute the task.
-```
-
-This reasoning plan helps the agent organize its approach to the task, consider potential challenges, and ensure it delivers the expected output.

docs/concepts/tasks.mdx

@@ -1,999 +0,0 @@
----
-title: Tasks
-description: Detailed guide on managing and creating tasks within the CrewAI framework.
-icon: list-check
----
-
-## Overview
-
-In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
-
-Tasks provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
-
-Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
-
-<Note type="info" title="Enterprise Enhancement: Visual Task Builder">
-CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
-
-![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png)
-
-The Visual Task Builder enables:
-- Drag-and-drop task creation
-- Visual task dependencies and flow
-- Real-time testing and validation
-- Easy sharing and collaboration
-</Note>
-
-### Task Execution Flow
-
-Tasks can be executed in two ways:
-- **Sequential**: Tasks are executed in the order they are defined
-- **Hierarchical**: Tasks are assigned to agents based on their roles and expertise
-
-The execution flow is defined when creating the crew:
-```python Code
-crew = Crew(
-    agents=[agent1, agent2],
-    tasks=[task1, task2],
-    process=Process.sequential  # or Process.hierarchical
-)
-```
-
-## Task Attributes
-
-| Attribute                        | Parameters        | Type                          | Description                                                                                                          |
-| :------------------------------- | :---------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
-| **Description**                  | `description`     | `str`                         | A clear, concise statement of what the task entails.                                                                 |
-| **Expected Output**              | `expected_output` | `str`                         | A detailed description of what the task's completion looks like.                                                     |
-| **Name** _(optional)_            | `name`            | `Optional[str]`               | A name identifier for the task.                                                                                      |
-| **Agent** _(optional)_           | `agent`           | `Optional[BaseAgent]`         | The agent responsible for executing the task.                                                                        |
-| **Tools** _(optional)_           | `tools`           | `List[BaseTool]`              | The tools/resources the agent is limited to use for this task.                                                       |
-| **Context** _(optional)_         | `context`         | `Optional[List["Task"]]`      | Other tasks whose outputs will be used as context for this task.                                                     |
-| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]`              | Whether the task should be executed asynchronously. Defaults to False.                                               |
-| **Human Input** _(optional)_     | `human_input`     | `Optional[bool]`              | Whether the task should have a human review the final answer of the agent. Defaults to False.                        |
-| **Markdown** _(optional)_        | `markdown`        | `Optional[bool]`              | Whether the task should instruct the agent to return the final answer formatted in Markdown. Defaults to False.      |
-| **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Task-specific configuration parameters.                                                                              |
-| **Output File** _(optional)_     | `output_file`     | `Optional[str]`               | File path for storing the task output.                                                                               |
-| **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
-| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
-| **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
-
-## Creating Tasks
-
-There are two ways to create tasks in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
-
-### YAML Configuration (Recommended)
-
-Using YAML configuration provides a cleaner, more maintainable way to define tasks. We strongly recommend using this approach to define tasks in your CrewAI projects.
-
-After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
-
-<Note>
-Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
-```python Code
-crew.kickoff(inputs={'topic': 'AI Agents'})
-```
-</Note>
-
-Here's an example of how to configure tasks using YAML:
-
-```yaml tasks.yaml
-research_task:
-  description: >
-    Conduct a thorough research about {topic}
-    Make sure you find any interesting and relevant information given
-    the current year is 2025.
-  expected_output: >
-    A list with 10 bullet points of the most relevant information about {topic}
-  agent: researcher
-
-reporting_task:
-  description: >
-    Review the context you got and expand each topic into a full section for a report.
-    Make sure the report is detailed and contains any and all relevant information.
-  expected_output: >
-    A fully fledge reports with the mains topics, each with a full section of information.
-    Formatted as markdown without '```'
-  agent: reporting_analyst
-  markdown: true
-  output_file: report.md
-```
-
-To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`:
-
-```python crew.py
-# src/latest_ai_development/crew.py
-
-from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
-from crewai_tools import SerperDevTool
-
-@CrewBase
-class LatestAiDevelopmentCrew():
-  """LatestAiDevelopment crew"""
-
-  @agent
-  def researcher(self) -> Agent:
-    return Agent(
-      config=self.agents_config['researcher'], # type: ignore[index]
-      verbose=True,
-      tools=[SerperDevTool()]
-    )
-
-  @agent
-  def reporting_analyst(self) -> Agent:
-    return Agent(
-      config=self.agents_config['reporting_analyst'], # type: ignore[index]
-      verbose=True
-    )
-
-  @task
-  def research_task(self) -> Task:
-    return Task(
-      config=self.tasks_config['research_task'] # type: ignore[index]
-    )
-
-  @task
-  def reporting_task(self) -> Task:
-    return Task(
-      config=self.tasks_config['reporting_task'] # type: ignore[index]
-    )
-
-  @crew
-  def crew(self) -> Crew:
-    return Crew(
-      agents=[
-        self.researcher(),
-        self.reporting_analyst()
-      ],
-      tasks=[
-        self.research_task(),
-        self.reporting_task()
-      ],
-      process=Process.sequential
-    )
-```
-
-<Note>
-The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code.
-</Note>
-
-### Direct Code Definition (Alternative)
-
-Alternatively, you can define tasks directly in your code without using YAML configuration:
-
-```python task.py
-from crewai import Task
-
-research_task = Task(
-    description="""
-        Conduct a thorough research about AI Agents.
-        Make sure you find any interesting and relevant information given
-        the current year is 2025.
-    """,
-    expected_output="""
-        A list with 10 bullet points of the most relevant information about AI Agents
-    """,
-    agent=researcher
-)
-
-reporting_task = Task(
-    description="""
-        Review the context you got and expand each topic into a full section for a report.
-        Make sure the report is detailed and contains any and all relevant information.
-    """,
-    expected_output="""
-        A fully fledge reports with the mains topics, each with a full section of information.
-    """,
-    agent=reporting_analyst,
-    markdown=True,  # Enable markdown formatting for the final output
-    output_file="report.md"
-)
-```
-
-<Tip>
-  Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
-</Tip>
-
-## Task Output
-
-Understanding task outputs is crucial for building effective AI workflows. CrewAI provides a structured way to handle task results through the `TaskOutput` class, which supports multiple output formats and can be easily passed between tasks.
-
-The output of a task in CrewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw output, JSON, and Pydantic models.
-
-By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` will only include the `pydantic` or `json_dict` output if the original `Task` object was configured with `output_pydantic` or `output_json`, respectively.
-
-### Task Output Attributes
-
-| Attribute         | Parameters      | Type                       | Description                                                                                        |
-| :---------------- | :-------------- | :------------------------- | :------------------------------------------------------------------------------------------------- |
-| **Description**   | `description`   | `str`                      | Description of the task.                                                                           |
-| **Summary**       | `summary`       | `Optional[str]`            | Summary of the task, auto-generated from the first 10 words of the description.                    |
-| **Raw**           | `raw`           | `str`                      | The raw output of the task. This is the default format for the output.                             |
-| **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the task.                            |
-| **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
-| **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
-| **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
-
-### Task Methods and Properties
-
-| Method/Property | Description                                                                                       |
-| :-------------- | :------------------------------------------------------------------------------------------------ |
-| **json**        | Returns the JSON string representation of the task output if the output format is JSON.           |
-| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
-| **str**         | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
-
-### Accessing Task Outputs
-
-Once a task has been executed, its output can be accessed through the `output` attribute of the `Task` object. The `TaskOutput` class provides various ways to interact with and present this output.
-
-#### Example
-
-```python Code
-# Example task
-task = Task(
-    description='Find and summarize the latest AI news',
-    expected_output='A bullet list summary of the top 5 most important AI news',
-    agent=research_agent,
-    tools=[search_tool]
-)
-
-# Execute the crew
-crew = Crew(
-    agents=[research_agent],
-    tasks=[task],
-    verbose=True
-)
-
-result = crew.kickoff()
-
-# Accessing the task output
-task_output = task.output
-
-print(f"Task Description: {task_output.description}")
-print(f"Task Summary: {task_output.summary}")
-print(f"Raw Output: {task_output.raw}")
-if task_output.json_dict:
-    print(f"JSON Output: {json.dumps(task_output.json_dict, indent=2)}")
-if task_output.pydantic:
-    print(f"Pydantic Output: {task_output.pydantic}")
-```
-
-## Markdown Output Formatting
-
-The `markdown` parameter enables automatic markdown formatting for task outputs. When set to `True`, the task will instruct the agent to format the final answer using proper Markdown syntax.
-
-### Using Markdown Formatting
-
-```python Code
-# Example task with markdown formatting enabled
-formatted_task = Task(
-    description="Create a comprehensive report on AI trends",
-    expected_output="A well-structured report with headers, sections, and bullet points",
-    agent=reporter_agent,
-    markdown=True  # Enable automatic markdown formatting
-)
-```
-
-When `markdown=True`, the agent will receive additional instructions to format the output using:
-- `#` for headers
-- `**text**` for bold text  
-- `*text*` for italic text
-- `-` or `*` for bullet points
-- `` `code` `` for inline code
-- ``` ```language ``` for code blocks
-
-### YAML Configuration with Markdown
-
-```yaml tasks.yaml
-analysis_task:
-  description: >
-    Analyze the market data and create a detailed report
-  expected_output: >
-    A comprehensive analysis with charts and key findings
-  agent: analyst
-  markdown: true  # Enable markdown formatting
-  output_file: analysis.md
-```
-
-### Benefits of Markdown Output
-
-- **Consistent Formatting**: Ensures all outputs follow proper markdown conventions
-- **Better Readability**: Structured content with headers, lists, and emphasis
-- **Documentation Ready**: Output can be directly used in documentation systems
-- **Cross-Platform Compatibility**: Markdown is universally supported
-
-<Note>
-The markdown formatting instructions are automatically added to the task prompt when `markdown=True`, so you don't need to specify formatting requirements in your task description.
-</Note>
-
-## Task Dependencies and Context
-
-Tasks can depend on the output of other tasks using the `context` attribute. For example:
-
-```python Code
-research_task = Task(
-    description="Research the latest developments in AI",
-    expected_output="A list of recent AI developments",
-    agent=researcher
-)
-
-analysis_task = Task(
-    description="Analyze the research findings and identify key trends",
-    expected_output="Analysis report of AI trends",
-    agent=analyst,
-    context=[research_task]  # This task will wait for research_task to complete
-)
-```
-
-## Task Guardrails
-
-Task guardrails provide a way to validate and transform task outputs before they
-are passed to the next task. This feature helps ensure data quality and provides
-feedback to agents when their output doesn't meet specific criteria.
-
-### Using Task Guardrails
-
-To add a guardrail to a task, provide a validation function through the `guardrail` parameter:
-
-```python Code
-from typing import Tuple, Union, Dict, Any
-from crewai import TaskOutput
-
-def validate_blog_content(result: TaskOutput) -> Tuple[bool, Any]:
-    """Validate blog content meets requirements."""
-    try:
-        # Check word count
-        word_count = len(result.split())
-        if word_count > 200:
-            return (False, "Blog content exceeds 200 words")
-
-        # Additional validation logic here
-        return (True, result.strip())
-    except Exception as e:
-        return (False, "Unexpected error during validation")
-
-blog_task = Task(
-    description="Write a blog post about AI",
-    expected_output="A blog post under 200 words",
-    agent=blog_agent,
-    guardrail=validate_blog_content  # Add the guardrail function
-)
-```
-
-### Guardrail Function Requirements
-
-1. **Function Signature**:
-   - Must accept exactly one parameter (the task output)
-   - Should return a tuple of `(bool, Any)`
-   - Type hints are recommended but optional
-
-2. **Return Values**:
-   - On success: it returns a tuple of `(bool, Any)`. For example: `(True, validated_result)`
-   - On Failure: it returns a tuple of `(bool, str)`. For example: `(False, "Error message explain the failure")`
-
-### LLMGuardrail
-
-The `LLMGuardrail` class offers a robust mechanism for validating task outputs.
-
-### Error Handling Best Practices
-
-1. **Structured Error Responses**:
-```python Code
-from crewai import TaskOutput, LLMGuardrail
-
-def validate_with_context(result: TaskOutput) -> Tuple[bool, Any]:
-    try:
-        # Main validation logic
-        validated_data = perform_validation(result)
-        return (True, validated_data)
-    except ValidationError as e:
-        return (False, f"VALIDATION_ERROR: {str(e)}")
-    except Exception as e:
-        return (False, str(e))
-```
-
-2. **Error Categories**:
-   - Use specific error codes
-   - Include relevant context
-   - Provide actionable feedback
-
-3. **Validation Chain**:
-```python Code
-from typing import Any, Dict, List, Tuple, Union
-from crewai import TaskOutput
-
-def complex_validation(result: TaskOutput) -> Tuple[bool, Any]:
-    """Chain multiple validation steps."""
-    # Step 1: Basic validation
-    if not result:
-        return (False, "Empty result")
-
-    # Step 2: Content validation
-    try:
-        validated = validate_content(result)
-        if not validated:
-            return (False, "Invalid content")
-
-        # Step 3: Format validation
-        formatted = format_output(validated)
-        return (True, formatted)
-    except Exception as e:
-        return (False, str(e))
-```
-
-### Handling Guardrail Results
-
-When a guardrail returns `(False, error)`:
-1. The error is sent back to the agent
-2. The agent attempts to fix the issue
-3. The process repeats until:
-   - The guardrail returns `(True, result)`
-   - Maximum retries are reached
-
-Example with retry handling:
-```python Code
-from typing import Optional, Tuple, Union
-from crewai import TaskOutput, Task
-
-def validate_json_output(result: TaskOutput) -> Tuple[bool, Any]:
-    """Validate and parse JSON output."""
-    try:
-        # Try to parse as JSON
-        data = json.loads(result)
-        return (True, data)
-    except json.JSONDecodeError as e:
-        return (False, "Invalid JSON format")
-
-task = Task(
-    description="Generate a JSON report",
-    expected_output="A valid JSON object",
-    agent=analyst,
-    guardrail=validate_json_output,
-    max_retries=3  # Limit retry attempts
-)
-```
-
-## Getting Structured Consistent Outputs from Tasks
-
-<Note>
-It's also important to note that the output of the final task of a crew becomes the final output of the actual crew itself.
-</Note>
-
-### Using `output_pydantic`
-The `output_pydantic` property allows you to define a Pydantic model that the task output should conform to. This ensures that the output is not only structured but also validated according to the Pydantic model.
-
-Here's an example demonstrating how to use output_pydantic:
-
-```python Code
-import json
-
-from crewai import Agent, Crew, Process, Task
-from pydantic import BaseModel
-
-
-class Blog(BaseModel):
-    title: str
-    content: str
-
-
-blog_agent = Agent(
-    role="Blog Content Generator Agent",
-    goal="Generate a blog title and content",
-    backstory="""You are an expert content creator, skilled in crafting engaging and informative blog posts.""",
-    verbose=False,
-    allow_delegation=False,
-    llm="gpt-4o",
-)
-
-task1 = Task(
-    description="""Create a blog title and content on a given topic. Make sure the content is under 200 words.""",
-    expected_output="A compelling blog title and well-written content.",
-    agent=blog_agent,
-    output_pydantic=Blog,
-)
-
-# Instantiate your crew with a sequential process
-crew = Crew(
-    agents=[blog_agent],
-    tasks=[task1],
-    verbose=True,
-    process=Process.sequential,
-)
-
-result = crew.kickoff()
-
-# Option 1: Accessing Properties Using Dictionary-Style Indexing
-print("Accessing Properties - Option 1")
-title = result["title"]
-content = result["content"]
-print("Title:", title)
-print("Content:", content)
-
-# Option 2: Accessing Properties Directly from the Pydantic Model
-print("Accessing Properties - Option 2")
-title = result.pydantic.title
-content = result.pydantic.content
-print("Title:", title)
-print("Content:", content)
-
-# Option 3: Accessing Properties Using the to_dict() Method
-print("Accessing Properties - Option 3")
-output_dict = result.to_dict()
-title = output_dict["title"]
-content = output_dict["content"]
-print("Title:", title)
-print("Content:", content)
-
-# Option 4: Printing the Entire Blog Object
-print("Accessing Properties - Option 5")
-print("Blog:", result)
-
-```
-In this example:
-* A Pydantic model Blog is defined with title and content fields.
-* The task task1 uses the output_pydantic property to specify that its output should conform to the Blog model.
-* After executing the crew, you can access the structured output in multiple ways as shown.
-
-#### Explanation of Accessing the Output
-1. Dictionary-Style Indexing: You can directly access the fields using result["field_name"]. This works because the CrewOutput class implements the __getitem__ method.
-2. Directly from Pydantic Model: Access the attributes directly from the result.pydantic object.
-3. Using to_dict() Method: Convert the output to a dictionary and access the fields.
-4. Printing the Entire Object: Simply print the result object to see the structured output.
-
-### Using `output_json`
-The `output_json` property allows you to define the expected output in JSON format. This ensures that the task's output is a valid JSON structure that can be easily parsed and used in your application.
-
-Here's an example demonstrating how to use `output_json`:
-
-```python Code
-import json
-
-from crewai import Agent, Crew, Process, Task
-from pydantic import BaseModel
-
-
-# Define the Pydantic model for the blog
-class Blog(BaseModel):
-    title: str
-    content: str
-
-
-# Define the agent
-blog_agent = Agent(
-    role="Blog Content Generator Agent",
-    goal="Generate a blog title and content",
-    backstory="""You are an expert content creator, skilled in crafting engaging and informative blog posts.""",
-    verbose=False,
-    allow_delegation=False,
-    llm="gpt-4o",
-)
-
-# Define the task with output_json set to the Blog model
-task1 = Task(
-    description="""Create a blog title and content on a given topic. Make sure the content is under 200 words.""",
-    expected_output="A JSON object with 'title' and 'content' fields.",
-    agent=blog_agent,
-    output_json=Blog,
-)
-
-# Instantiate the crew with a sequential process
-crew = Crew(
-    agents=[blog_agent],
-    tasks=[task1],
-    verbose=True,
-    process=Process.sequential,
-)
-
-# Kickoff the crew to execute the task
-result = crew.kickoff()
-
-# Option 1: Accessing Properties Using Dictionary-Style Indexing
-print("Accessing Properties - Option 1")
-title = result["title"]
-content = result["content"]
-print("Title:", title)
-print("Content:", content)
-
-# Option 2: Printing the Entire Blog Object
-print("Accessing Properties - Option 2")
-print("Blog:", result)
-```
-
-In this example:
-* A Pydantic model Blog is defined with title and content fields, which is used to specify the structure of the JSON output.
-* The task task1 uses the output_json property to indicate that it expects a JSON output conforming to the Blog model.
-* After executing the crew, you can access the structured JSON output in two ways as shown.
-
-#### Explanation of Accessing the Output
-
-1. Accessing Properties Using Dictionary-Style Indexing: You can access the fields directly using result["field_name"]. This is possible because the CrewOutput class implements the __getitem__ method, allowing you to treat the output like a dictionary. In this option, we're retrieving the title and content from the result.
-2. Printing the Entire Blog Object: By printing result, you get the string representation of the CrewOutput object. Since the __str__ method is implemented to return the JSON output, this will display the entire output as a formatted string representing the Blog object.
-
----
-
-By using output_pydantic or output_json, you ensure that your tasks produce outputs in a consistent and structured format, making it easier to process and utilize the data within your application or across multiple tasks.
-
-## Integrating Tools with Tasks
-
-Leverage tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools) for enhanced task performance and agent interaction.
-
-## Creating a Task with Tools
-
-```python Code
-import os
-os.environ["OPENAI_API_KEY"] = "Your Key"
-os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
-
-from crewai import Agent, Task, Crew
-from crewai_tools import SerperDevTool
-
-research_agent = Agent(
-  role='Researcher',
-  goal='Find and summarize the latest AI news',
-  backstory="""You're a researcher at a large company.
-  You're responsible for analyzing data and providing insights
-  to the business.""",
-  verbose=True
-)
-
-# to perform a semantic search for a specified query from a text's content across the internet
-search_tool = SerperDevTool()
-
-task = Task(
-  description='Find and summarize the latest AI news',
-  expected_output='A bullet list summary of the top 5 most important AI news',
-  agent=research_agent,
-  tools=[search_tool]
-)
-
-crew = Crew(
-    agents=[research_agent],
-    tasks=[task],
-    verbose=True
-)
-
-result = crew.kickoff()
-print(result)
-```
-
-This demonstrates how tasks with specific tools can override an agent's default set for tailored task execution.
-
-## Referring to Other Tasks
-
-In CrewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output, including multiple, should be used as context for another task.
-
-This is useful when you have a task that depends on the output of another task that is not performed immediately after it. This is done through the `context` attribute of the task:
-
-```python Code
-# ...
-
-research_ai_task = Task(
-    description="Research the latest developments in AI",
-    expected_output="A list of recent AI developments",
-    async_execution=True,
-    agent=research_agent,
-    tools=[search_tool]
-)
-
-research_ops_task = Task(
-    description="Research the latest developments in AI Ops",
-    expected_output="A list of recent AI Ops developments",
-    async_execution=True,
-    agent=research_agent,
-    tools=[search_tool]
-)
-
-write_blog_task = Task(
-    description="Write a full blog post about the importance of AI and its latest news",
-    expected_output="Full blog post that is 4 paragraphs long",
-    agent=writer_agent,
-    context=[research_ai_task, research_ops_task]
-)
-
-#...
-```
-
-## Asynchronous Execution
-
-You can define a task to be executed asynchronously. This means that the crew will not wait for it to be completed to continue with the next task. This is useful for tasks that take a long time to be completed, or that are not crucial for the next tasks to be performed.
-
-You can then use the `context` attribute to define in a future task that it should wait for the output of the asynchronous task to be completed.
-
-```python Code
-#...
-
-list_ideas = Task(
-    description="List of 5 interesting ideas to explore for an article about AI.",
-    expected_output="Bullet point list of 5 ideas for an article.",
-    agent=researcher,
-    async_execution=True # Will be executed asynchronously
-)
-
-list_important_history = Task(
-    description="Research the history of AI and give me the 5 most important events.",
-    expected_output="Bullet point list of 5 important events.",
-    agent=researcher,
-    async_execution=True # Will be executed asynchronously
-)
-
-write_article = Task(
-    description="Write an article about AI, its history, and interesting ideas.",
-    expected_output="A 4 paragraph article about AI.",
-    agent=writer,
-    context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
-)
-
-#...
-```
-
-## Callback Mechanism
-
-The callback function is executed after the task is completed, allowing for actions or notifications to be triggered based on the task's outcome.
-
-```python Code
-# ...
-
-def callback_function(output: TaskOutput):
-    # Do something after the task is completed
-    # Example: Send an email to the manager
-    print(f"""
-        Task completed!
-        Task: {output.description}
-        Output: {output.raw}
-    """)
-
-research_task = Task(
-    description='Find and summarize the latest AI news',
-    expected_output='A bullet list summary of the top 5 most important AI news',
-    agent=research_agent,
-    tools=[search_tool],
-    callback=callback_function
-)
-
-#...
-```
-
-## Accessing a Specific Task Output
-
-Once a crew finishes running, you can access the output of a specific task by using the `output` attribute of the task object:
-
-```python Code
-# ...
-task1 = Task(
-    description='Find and summarize the latest AI news',
-    expected_output='A bullet list summary of the top 5 most important AI news',
-    agent=research_agent,
-    tools=[search_tool]
-)
-
-#...
-
-crew = Crew(
-    agents=[research_agent],
-    tasks=[task1, task2, task3],
-    verbose=True
-)
-
-result = crew.kickoff()
-
-# Returns a TaskOutput object with the description and results of the task
-print(f"""
-    Task completed!
-    Task: {task1.output.description}
-    Output: {task1.output.raw}
-""")
-```
-
-## Tool Override Mechanism
-
-Specifying tools in a task allows for dynamic adaptation of agent capabilities, emphasizing CrewAI's flexibility.
-
-## Error Handling and Validation Mechanisms
-
-While creating and executing tasks, certain validation mechanisms are in place to ensure the robustness and reliability of task attributes. These include but are not limited to:
-
-- Ensuring only one output type is set per task to maintain clear output expectations.
-- Preventing the manual assignment of the `id` attribute to uphold the integrity of the unique identifier system.
-
-These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
-
-## Task Guardrails
-
-Task guardrails provide a powerful way to validate, transform, or filter task outputs before they are passed to the next task. Guardrails are optional functions that execute before the next task starts, allowing you to ensure that task outputs meet specific requirements or formats.
-
-### Basic Usage
-
-#### Define your own logic to validate
-
-```python Code
-from typing import Tuple, Union
-from crewai import Task
-
-def validate_json_output(result: str) -> Tuple[bool, Union[dict, str]]:
-    """Validate that the output is valid JSON."""
-    try:
-        json_data = json.loads(result)
-        return (True, json_data)
-    except json.JSONDecodeError:
-        return (False, "Output must be valid JSON")
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail=validate_json_output
-)
-```
-
-#### Leverage a no-code approach for validation
-
-```python Code
-from crewai import Task
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail="Ensure the response is a valid JSON object"
-)
-```
-
-#### Using YAML
-
-```yaml
-research_task:
-  ...
-  guardrail: make sure each bullet contains a minimum of 100 words
-  ...
-```
-
-```python Code
-@CrewBase
-class InternalCrew:
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
-
-    ...
-    @task
-    def research_task(self):
-        return Task(config=self.tasks_config["research_task"])  # type: ignore[index]
-    ...
-```
-
-
-#### Use custom models for code generation
-
-```python Code
-from crewai import Task
-from crewai.llm import LLM
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail=LLMGuardrail(
-        description="Ensure the response is a valid JSON object",
-        llm=LLM(model="gpt-4o-mini"),
-    )
-)
-```
-
-### How Guardrails Work
-
-1. **Optional Attribute**: Guardrails are an optional attribute at the task level, allowing you to add validation only where needed.
-2. **Execution Timing**: The guardrail function is executed before the next task starts, ensuring valid data flow between tasks.
-3. **Return Format**: Guardrails must return a tuple of `(success, data)`:
-   - If `success` is `True`, `data` is the validated/transformed result
-   - If `success` is `False`, `data` is the error message
-4. **Result Routing**:
-   - On success (`True`), the result is automatically passed to the next task
-   - On failure (`False`), the error is sent back to the agent to generate a new answer
-
-### Common Use Cases
-
-#### Data Format Validation
-```python Code
-def validate_email_format(result: str) -> Tuple[bool, Union[str, str]]:
-    """Ensure the output contains a valid email address."""
-    import re
-    email_pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
-    if re.match(email_pattern, result.strip()):
-        return (True, result.strip())
-    return (False, "Output must be a valid email address")
-```
-
-#### Content Filtering
-```python Code
-def filter_sensitive_info(result: str) -> Tuple[bool, Union[str, str]]:
-    """Remove or validate sensitive information."""
-    sensitive_patterns = ['SSN:', 'password:', 'secret:']
-    for pattern in sensitive_patterns:
-        if pattern.lower() in result.lower():
-            return (False, f"Output contains sensitive information ({pattern})")
-    return (True, result)
-```
-
-#### Data Transformation
-```python Code
-def normalize_phone_number(result: str) -> Tuple[bool, Union[str, str]]:
-    """Ensure phone numbers are in a consistent format."""
-    import re
-    digits = re.sub(r'\D', '', result)
-    if len(digits) == 10:
-        formatted = f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
-        return (True, formatted)
-    return (False, "Output must be a 10-digit phone number")
-```
-
-### Advanced Features
-
-#### Chaining Multiple Validations
-```python Code
-def chain_validations(*validators):
-    """Chain multiple validators together."""
-    def combined_validator(result):
-        for validator in validators:
-            success, data = validator(result)
-            if not success:
-                return (False, data)
-            result = data
-        return (True, result)
-    return combined_validator
-
-# Usage
-task = Task(
-    description="Get user contact info",
-    expected_output="Email and phone",
-    guardrail=chain_validations(
-        validate_email_format,
-        filter_sensitive_info
-    )
-)
-```
-
-#### Custom Retry Logic
-```python Code
-task = Task(
-    description="Generate data",
-    expected_output="Valid data",
-    guardrail=validate_data,
-    max_retries=5  # Override default retry limit
-)
-```
-
-## Creating Directories when Saving Files
-
-You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
-
-```python Code
-# ...
-
-save_output_task = Task(
-    description='Save the summarized AI news to a file',
-    expected_output='File saved successfully',
-    agent=research_agent,
-    tools=[file_save_tool],
-    output_file='outputs/ai_news_summary.txt',
-    create_directory=True
-)
-
-#...
-```
-
-Check out the video below to see how to use structured outputs in CrewAI:
-
-<iframe
-  width="560"
-  height="315"
-  src="https://www.youtube.com/embed/dNpKQk5uxHw"
-  title="YouTube video player"
-  frameborder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowfullscreen
-></iframe>
-
-## Conclusion
-
-Tasks are the driving force behind the actions of agents in CrewAI.
-By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit.
-Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential,
-ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

docs/concepts/testing.mdx

@@ -1,48 +0,0 @@
----
-title: Testing
-description: Learn how to test your CrewAI Crew and evaluate their performance.
-icon: vial
----
-
-## Overview
-
-Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. With crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
-
-### Using the Testing Feature
-
-We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model`, which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
-
-```bash
-crewai test
-```
-
-If you want to run more iterations or use a different model, you can specify the parameters like this:
-
-```bash
-crewai test --n_iterations 5 --model gpt-4o
-```
-
-or using the short forms:
-
-```bash
-crewai test -n 5 -m gpt-4o
-```
-
-When you run the `crewai test` command, the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
-
-A table of scores at the end will show the performance of the crew in terms of the following metrics:
-
-<center>**Tasks Scores (1-10 Higher is better)**</center>
-
-| Tasks/Crew/Agents  | Run 1 | Run 2 | Avg. Total | Agents                         | Additional Info                  |
-|:------------------|:-----:|:-----:|:----------:|:------------------------------:|:---------------------------------|
-| Task 1        |  9.0  |  9.5  |    **9.2**     | Professional Insights          |                                  |
-|                    |       |       |            | Researcher                     |                                  |
-| Task 2        |  9.0  | 10.0  |    **9.5**     | Company Profile Investigator   |                                  |
-| Task 3        |  9.0  |  9.0  |    **9.0**     | Automation Insights            |                                  |
-|                    |       |       |            | Specialist                     |                                  |
-| Task 4        |  9.0  |  9.0  |    **9.0**     | Final Report Compiler          | Automation Insights Specialist   |
-| Crew         | 9.00  | 9.38  |    **9.2**     |                                |                                  |
-| Execution Time (s) |  126  |  145  |    **135**     |                                |                                  |
-
-The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole.

docs/concepts/tools.mdx

@@ -1,285 +0,0 @@
----
-title: Tools
-description: Understanding and leveraging tools within the CrewAI framework for agent collaboration and task execution.
-icon: screwdriver-wrench
----
-
-## Overview
-
-CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
-This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
-
-## What is a Tool?
-
-A tool in CrewAI is a skill or function that agents can utilize to perform various actions.
-This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools),
-enabling everything from simple searches to complex interactions and effective teamwork among agents.
-
-<Note type="info" title="Enterprise Enhancement: Tools Repository">
-CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
-
-The Enterprise Tools Repository includes:
-- Pre-built connectors for popular enterprise systems
-- Custom tool creation interface
-- Version control and sharing capabilities
-- Security and compliance features
-</Note>
-
-## Key Characteristics of Tools
-
-- **Utility**: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
-- **Integration**: Boosts agent capabilities by seamlessly integrating tools into their workflow.
-- **Customizability**: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
-- **Error Handling**: Incorporates robust error handling mechanisms to ensure smooth operation.
-- **Caching Mechanism**: Features intelligent caching to optimize performance and reduce redundant operations.
-- **Asynchronous Support**: Handles both synchronous and asynchronous tools, enabling non-blocking operations.
-
-## Using CrewAI Tools
-
-To enhance your agents' capabilities with crewAI tools, begin by installing our extra tools package:
-
-```bash
-pip install 'crewai[tools]'
-```
-
-Here's an example demonstrating their use:
-
-```python Code
-import os
-from crewai import Agent, Task, Crew
-# Importing crewAI tools
-from crewai_tools import (
-    DirectoryReadTool,
-    FileReadTool,
-    SerperDevTool,
-    WebsiteSearchTool
-)
-
-# Set up API keys
-os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
-os.environ["OPENAI_API_KEY"] = "Your Key"
-
-# Instantiate tools
-docs_tool = DirectoryReadTool(directory='./blog-posts')
-file_tool = FileReadTool()
-search_tool = SerperDevTool()
-web_rag_tool = WebsiteSearchTool()
-
-# Create agents
-researcher = Agent(
-    role='Market Research Analyst',
-    goal='Provide up-to-date market analysis of the AI industry',
-    backstory='An expert analyst with a keen eye for market trends.',
-    tools=[search_tool, web_rag_tool],
-    verbose=True
-)
-
-writer = Agent(
-    role='Content Writer',
-    goal='Craft engaging blog posts about the AI industry',
-    backstory='A skilled writer with a passion for technology.',
-    tools=[docs_tool, file_tool],
-    verbose=True
-)
-
-# Define tasks
-research = Task(
-    description='Research the latest trends in the AI industry and provide a summary.',
-    expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
-    agent=researcher
-)
-
-write = Task(
-    description='Write an engaging blog post about the AI industry, based on the research analyst's summary. Draw inspiration from the latest blog posts in the directory.',
-    expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
-    agent=writer,
-    output_file='blog-posts/new_post.md'  # The final blog post will be saved here
-)
-
-# Assemble a crew with planning enabled
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research, write],
-    verbose=True,
-    planning=True,  # Enable planning feature
-)
-
-# Execute tasks
-crew.kickoff()
-```
-
-## Available CrewAI Tools
-
-- **Error Handling**: All tools are built with error handling capabilities, allowing agents to gracefully manage exceptions and continue their tasks.
-- **Caching Mechanism**: All tools support caching, enabling agents to efficiently reuse previously obtained results, reducing the load on external resources and speeding up the execution time. You can also define finer control over the caching mechanism using the `cache_function` attribute on the tool.
-
-Here is a list of the available tools and their descriptions:
-
-| Tool                             | Description                                                                                    |
-| :------------------------------- | :--------------------------------------------------------------------------------------------- |
-| **ApifyActorsTool**              | A tool that integrates Apify Actors with your workflows for web scraping and automation tasks. |
-| **BrowserbaseLoadTool**          | A tool for interacting with and extracting data from web browsers.                             |
-| **CodeDocsSearchTool**           | A RAG tool optimized for searching through code documentation and related technical documents. |
-| **CodeInterpreterTool**          | A tool for interpreting python code.                                                           |
-| **ComposioTool**                 | Enables use of Composio tools.                                                                 |
-| **CSVSearchTool**                | A RAG tool designed for searching within CSV files, tailored to handle structured data.        |
-| **DALL-E Tool**                  | A tool for generating images using the DALL-E API.                                             |
-| **DirectorySearchTool**          | A RAG tool for searching within directories, useful for navigating through file systems.       |
-| **DOCXSearchTool**               | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.          |
-| **DirectoryReadTool**            | Facilitates reading and processing of directory structures and their contents.                 |
-| **EXASearchTool**                | A tool designed for performing exhaustive searches across various data sources.                |
-| **FileReadTool**                 | Enables reading and extracting data from files, supporting various file formats.               |
-| **FirecrawlSearchTool**          | A tool to search webpages using Firecrawl and return the results.                              |
-| **FirecrawlCrawlWebsiteTool**    | A tool for crawling webpages using Firecrawl.                                                  |
-| **FirecrawlScrapeWebsiteTool**   | A tool for scraping webpages URL using Firecrawl and returning its contents.                   |
-| **GithubSearchTool**             | A RAG tool for searching within GitHub repositories, useful for code and documentation search. |
-| **SerperDevTool**                | A specialized tool for development purposes, with specific functionalities under development.  |
-| **TXTSearchTool**                | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.      |
-| **JSONSearchTool**               | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
-| **LlamaIndexTool**               | Enables the use of LlamaIndex tools.                                                           |
-| **MDXSearchTool**                | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.       |
-| **PDFSearchTool**                | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
-| **PGSearchTool**                 | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
-| **Vision Tool**                  | A tool for generating images using the DALL-E API.                                             |
-| **RagTool**                      | A general-purpose RAG tool capable of handling various data sources and types.                 |
-| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.         |
-| **ScrapeWebsiteTool**            | Facilitates scraping entire websites, ideal for comprehensive data collection.                 |
-| **WebsiteSearchTool**            | A RAG tool for searching website content, optimized for web data extraction.                   |
-| **XMLSearchTool**                | A RAG tool designed for searching within XML files, suitable for structured data formats.      |
-| **YoutubeChannelSearchTool**     | A RAG tool for searching within YouTube channels, useful for video content analysis.           |
-| **YoutubeVideoSearchTool**       | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.          |
-
-## Creating your own Tools
-
-<Tip>
-  Developers can craft `custom tools` tailored for their agent's needs or
-  utilize pre-built options.
-</Tip>
-
-There are two main ways for one to create a CrewAI tool:
-
-### Subclassing `BaseTool`
-
-```python Code
-from crewai.tools import BaseTool
-from pydantic import BaseModel, Field
-
-class MyToolInput(BaseModel):
-    """Input schema for MyCustomTool."""
-    argument: str = Field(..., description="Description of the argument.")
-
-class MyCustomTool(BaseTool):
-    name: str = "Name of my tool"
-    description: str = "What this tool does. It's vital for effective utilization."
-    args_schema: Type[BaseModel] = MyToolInput
-
-    def _run(self, argument: str) -> str:
-        # Your tool's logic here
-        return "Tool's result"
-```
-
-## Asynchronous Tool Support
-
-CrewAI supports asynchronous tools, allowing you to implement tools that perform non-blocking operations like network requests, file I/O, or other async operations without blocking the main execution thread.
-
-### Creating Async Tools
-
-You can create async tools in two ways:
-
-#### 1. Using the `tool` Decorator with Async Functions
-
-```python Code
-from crewai.tools import tool
-
-@tool("fetch_data_async")
-async def fetch_data_async(query: str) -> str:
-    """Asynchronously fetch data based on the query."""
-    # Simulate async operation
-    await asyncio.sleep(1)
-    return f"Data retrieved for {query}"
-```
-
-#### 2. Implementing Async Methods in Custom Tool Classes
-
-```python Code
-from crewai.tools import BaseTool
-
-class AsyncCustomTool(BaseTool):
-    name: str = "async_custom_tool"
-    description: str = "An asynchronous custom tool"
-    
-    async def _run(self, query: str = "") -> str:
-        """Asynchronously run the tool"""
-        # Your async implementation here
-        await asyncio.sleep(1)
-        return f"Processed {query} asynchronously"
-```
-
-### Using Async Tools
-
-Async tools work seamlessly in both standard Crew workflows and Flow-based workflows:
-
-```python Code
-# In standard Crew
-agent = Agent(role="researcher", tools=[async_custom_tool])
-
-# In Flow
-class MyFlow(Flow):
-    @start()
-    async def begin(self):
-        crew = Crew(agents=[agent])
-        result = await crew.kickoff_async()
-        return result
-```
-
-The CrewAI framework automatically handles the execution of both synchronous and asynchronous tools, so you don't need to worry about how to call them differently.
-
-### Utilizing the `tool` Decorator
-
-```python Code
-from crewai.tools import tool
-@tool("Name of my tool")
-def my_tool(question: str) -> str:
-    """Clear description for what this tool is useful for, your agent will need this information to use it."""
-    # Function logic here
-    return "Result from your custom tool"
-```
-
-### Custom Caching Mechanism
-
-<Tip>
-  Tools can optionally implement a `cache_function` to fine-tune caching
-  behavior. This function determines when to cache results based on specific
-  conditions, offering granular control over caching logic.
-</Tip>
-
-```python Code
-from crewai.tools import tool
-
-@tool
-def multiplication_tool(first_number: int, second_number: int) -> str:
-    """Useful for when you need to multiply two numbers together."""
-    return first_number * second_number
-
-def cache_func(args, result):
-    # In this case, we only cache the result if it's a multiple of 2
-    cache = result % 2 == 0
-    return cache
-
-multiplication_tool.cache_function = cache_func
-
-writer1 = Agent(
-        role="Writer",
-        goal="You write lessons of math for kids.",
-        backstory="You're an expert in writing and you love to teach kids but you know nothing of math.",
-        tools=[multiplication_tool],
-        allow_delegation=False,
-    )
-    #...
-```
-
-## Conclusion
-
-Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively.
-When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling,
-caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.