Fix tool output token overflow issue

- Add max_tool_output_tokens parameter to Agent (default: 4096)
- Implement token counting and truncation utilities in agent_utils
- Truncate large tool outputs before appending to messages
- Update CONTEXT_LIMIT_ERRORS to recognize negative max_tokens error
- Add comprehensive tests for tool output truncation

Fixes #3843

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

.github/codeql/codeql-config.yml

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

.github/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

@@ -7,6 +7,8 @@ on:
     paths:
       - "uv.lock"
       - "pyproject.toml"
+  schedule:
+    - cron: "0 0 */5 * *"  # Run every 5 days at midnight UTC to prevent cache expiration
   workflow_dispatch:
 
 permissions:

.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/linter.yml

@@ -52,10 +52,11 @@ jobs:
       - name: Run Ruff on Changed Files
         if: ${{ steps.changed-files.outputs.files != '' }}
         run: |
-          echo "${{ steps.changed-files.outputs.files }}" \
-            | tr ' ' '\n' \
-            | grep -v 'src/crewai/cli/templates/' \
-            | xargs -I{} uv run ruff check "{}"
+         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'

.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/tests.yml

@@ -8,6 +8,14 @@ permissions:
 env:
   OPENAI_API_KEY: fake-api-key
   PYTHONUNBUFFERED: 1
+  BRAVE_API_KEY: fake-brave-key
+  SNOWFLAKE_USER: fake-snowflake-user
+  SNOWFLAKE_PASSWORD: fake-snowflake-password
+  SNOWFLAKE_ACCOUNT: fake-snowflake-account
+  SNOWFLAKE_WAREHOUSE: fake-snowflake-warehouse
+  SNOWFLAKE_DATABASE: fake-snowflake-database
+  SNOWFLAKE_SCHEMA: fake-snowflake-schema
+  EMBEDCHAIN_DB_URI: sqlite:///test.db
 
 jobs:
   tests:
@@ -56,13 +64,13 @@ jobs:
       - 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}"
-          
+          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"
@@ -74,8 +82,8 @@ jobs:
           #   echo "No test changes detected, using cached test durations for optimal splitting"
           #   DURATIONS_ARG="--durations-path=${DURATION_FILE}"
           # fi
-          
-          uv run pytest \
+
+          cd lib/crewai && uv run pytest \
             --block-network \
             --timeout=30 \
             -vv \
@@ -86,6 +94,19 @@ jobs:
             -n auto \
             --maxfail=3
 
+      - name: Run tool tests (group ${{ matrix.group }} of 8)
+        run: |
+          cd lib/crewai-tools && uv run pytest \
+            --block-network \
+            --timeout=30 \
+            -vv \
+            --splits 8 \
+            --group ${{ matrix.group }} \
+            --durations=10 \
+            -n auto \
+            --maxfail=3
+
+
       - name: Save uv caches
         if: steps.cache-restore.outputs.cache-hit != 'true'
         uses: actions/cache/save@v4

.gitignore

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

.pre-commit-config.yaml

@@ -3,17 +3,25 @@ repos:
     hooks:
       - id: ruff
         name: ruff
-        entry: uv run ruff check
+        entry: bash -c 'source .venv/bin/activate && uv run ruff check --config pyproject.toml "$@"' --
         language: system
+        pass_filenames: true
         types: [python]
       - id: ruff-format
         name: ruff-format
-        entry: uv run ruff format
+        entry: bash -c 'source .venv/bin/activate && uv run ruff format --config pyproject.toml "$@"' --
         language: system
+        pass_filenames: true
         types: [python]
       - id: mypy
         name: mypy
-        entry: uv run mypy
+        entry: bash -c 'source .venv/bin/activate && uv run mypy --config-file pyproject.toml "$@"' --
         language: system
+        pass_filenames: true
         types: [python]
-        exclude: ^tests/
+        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
+

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 AMP Suite
 
-CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
+CrewAI AMP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
 
 You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
 
@@ -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 AMP on-premise or in the cloud, depending on your security and compliance requirements.
 
-CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
+CrewAI AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
 intelligent automations.
 
 ## Table of contents
@@ -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 AMP offer?](#q-what-additional-features-does-crewai-amp-offer)
+- [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
+- [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-amp-for-free)
 
 ### Q: What exactly is CrewAI?
 
@@ -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 AMP 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 AMP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
 
-### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
+### Q: Is CrewAI AMP 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 AMP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
 
-### Q: Can I try CrewAI Enterprise for free?
+### Q: Can I try CrewAI AMP 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 AMP 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 AMP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
 
 ### Q: What programming languages does CrewAI support?
 

docs/docs.json

@@ -9,7 +9,22 @@
   },
   "favicon": "/images/favicon.svg",
   "contextual": {
-    "options": ["copy", "view", "chatgpt", "claude"]
+    "options": [
+      "copy",
+      "view",
+      "chatgpt",
+      "claude",
+      "perplexity",
+      "mcp",
+      "cursor",
+      "vscode",
+      {
+        "title": "Request a feature",
+        "description": "Join the discussion on GitHub to request a new feature",
+        "icon": "plus",
+        "href": "https://github.com/crewAIInc/crewAI/issues/new/choose"
+      }
+    ]
   },
   "navigation": {
     "languages": [
@@ -40,6 +55,16 @@
           ]
         },
         "tabs": [
+          {
+            "tab": "Home",
+            "icon": "house",
+            "groups": [
+              {
+                "group": "Welcome",
+                "pages": ["index"]
+              }
+            ]
+          },
           {
             "tab": "Documentation",
             "icon": "book-open",
@@ -109,6 +134,7 @@
                 "group": "MCP Integration",
                 "pages": [
                   "en/mcp/overview",
+                  "en/mcp/dsl-integration",
                   "en/mcp/stdio",
                   "en/mcp/sse",
                   "en/mcp/streamable-http",
@@ -225,9 +251,9 @@
                     "group": "Integrations",
                     "icon": "plug",
                     "pages": [
-                      "en/tools/tool-integrations/overview",
-                      "en/tools/tool-integrations/bedrockinvokeagenttool",
-                      "en/tools/tool-integrations/crewaiautomationtool"
+                      "en/tools/integration/overview",
+                      "en/tools/integration/bedrockinvokeagenttool",
+                      "en/tools/integration/crewaiautomationtool"
                     ]
                   },
                   {
@@ -246,8 +272,11 @@
               {
                 "group": "Observability",
                 "pages": [
+                  "en/observability/tracing",
                   "en/observability/overview",
                   "en/observability/arize-phoenix",
+                  "en/observability/braintrust",
+                  "en/observability/datadog",
                   "en/observability/langdb",
                   "en/observability/langfuse",
                   "en/observability/langtrace",
@@ -277,6 +306,7 @@
                   "en/learn/force-tool-output-as-result",
                   "en/learn/hierarchical-process",
                   "en/learn/human-input-on-execution",
+                  "en/learn/human-in-the-loop",
                   "en/learn/kickoff-async",
                   "en/learn/kickoff-for-each",
                   "en/learn/llm-connections",
@@ -293,7 +323,7 @@
             ]
           },
           {
-            "tab": "Enterprise",
+            "tab": "AMP",
             "icon": "briefcase",
             "groups": [
               {
@@ -301,15 +331,27 @@
                 "pages": ["en/enterprise/introduction"]
               },
               {
-                "group": "Features",
+                "group": "Build",
+                "pages": [
+                  "en/enterprise/features/automations",
+                  "en/enterprise/features/crew-studio",
+                  "en/enterprise/features/marketplace",
+                  "en/enterprise/features/agent-repositories",
+                  "en/enterprise/features/tools-and-integrations"
+                ]
+              },
+              {
+                "group": "Operate",
                 "pages": [
-                  "en/enterprise/features/rbac",
-                  "en/enterprise/features/tool-repository",
-                  "en/enterprise/features/webhook-streaming",
                   "en/enterprise/features/traces",
-                  "en/enterprise/features/hallucination-guardrail",
-                  "en/enterprise/features/integrations",
-                  "en/enterprise/features/agent-repositories"
+                  "en/enterprise/features/webhook-streaming",
+                  "en/enterprise/features/hallucination-guardrail"
+                ]
+              },
+              {
+                "group": "Manage",
+                "pages": [
+                  "en/enterprise/features/rbac"
                 ]
               },
               {
@@ -321,10 +363,20 @@
                   "en/enterprise/integrations/github",
                   "en/enterprise/integrations/gmail",
                   "en/enterprise/integrations/google_calendar",
+                  "en/enterprise/integrations/google_contacts",
+                  "en/enterprise/integrations/google_docs",
+                  "en/enterprise/integrations/google_drive",
                   "en/enterprise/integrations/google_sheets",
+                  "en/enterprise/integrations/google_slides",
                   "en/enterprise/integrations/hubspot",
                   "en/enterprise/integrations/jira",
                   "en/enterprise/integrations/linear",
+                  "en/enterprise/integrations/microsoft_excel",
+                  "en/enterprise/integrations/microsoft_onedrive",
+                  "en/enterprise/integrations/microsoft_outlook",
+                  "en/enterprise/integrations/microsoft_sharepoint",
+                  "en/enterprise/integrations/microsoft_teams",
+                  "en/enterprise/integrations/microsoft_word",
                   "en/enterprise/integrations/notion",
                   "en/enterprise/integrations/salesforce",
                   "en/enterprise/integrations/shopify",
@@ -333,6 +385,22 @@
                   "en/enterprise/integrations/zendesk"
                 ]
               },
+              {
+                "group": "Triggers",
+                "pages": [
+                  "en/enterprise/guides/automation-triggers",
+                  "en/enterprise/guides/gmail-trigger",
+                  "en/enterprise/guides/google-calendar-trigger",
+                  "en/enterprise/guides/google-drive-trigger",
+                  "en/enterprise/guides/outlook-trigger",
+                  "en/enterprise/guides/onedrive-trigger",
+                  "en/enterprise/guides/microsoft-teams-trigger",
+                  "en/enterprise/guides/slack-trigger",
+                  "en/enterprise/guides/hubspot-trigger",
+                  "en/enterprise/guides/salesforce-trigger",
+                  "en/enterprise/guides/zapier-trigger"
+                ]
+              },
               {
                 "group": "How-To Guides",
                 "pages": [
@@ -341,16 +409,13 @@
                   "en/enterprise/guides/kickoff-crew",
                   "en/enterprise/guides/update-crew",
                   "en/enterprise/guides/enable-crew-studio",
+                  "en/enterprise/guides/capture_telemetry_logs",
                   "en/enterprise/guides/azure-openai-setup",
-                  "en/enterprise/guides/automation-triggers",
-                  "en/enterprise/guides/hubspot-trigger",
+                  "en/enterprise/guides/tool-repository",
                   "en/enterprise/guides/react-component-export",
-                  "en/enterprise/guides/salesforce-trigger",
-                  "en/enterprise/guides/slack-trigger",
                   "en/enterprise/guides/team-management",
-                  "en/enterprise/guides/webhook-automation",
                   "en/enterprise/guides/human-in-the-loop",
-                  "en/enterprise/guides/zapier-trigger"
+                  "en/enterprise/guides/webhook-automation"
                 ]
               },
               {
@@ -369,6 +434,7 @@
                   "en/api-reference/introduction",
                   "en/api-reference/inputs",
                   "en/api-reference/kickoff",
+                  "en/api-reference/resume",
                   "en/api-reference/status"
                 ]
               }
@@ -423,6 +489,16 @@
           ]
         },
         "tabs": [
+          {
+            "tab": "Início",
+            "icon": "house",
+            "groups": [
+              {
+                "group": "Bem-vindo",
+                "pages": ["pt-BR/index"]
+              }
+            ]
+          },
           {
             "tab": "Documentação",
             "icon": "book-open",
@@ -496,6 +572,7 @@
                 "group": "Integração MCP",
                 "pages": [
                   "pt-BR/mcp/overview",
+                  "pt-BR/mcp/dsl-integration",
                   "pt-BR/mcp/stdio",
                   "pt-BR/mcp/sse",
                   "pt-BR/mcp/streamable-http",
@@ -598,12 +675,12 @@
                     ]
                   },
                   {
-                    "group": "Integrações",
+                    "group": "Integrations",
                     "icon": "plug",
                     "pages": [
-                      "pt-BR/tools/tool-integrations/overview",
-                      "pt-BR/tools/tool-integrations/bedrockinvokeagenttool",
-                      "pt-BR/tools/tool-integrations/crewaiautomationtool"
+                      "pt-BR/tools/integration/overview",
+                      "pt-BR/tools/integration/bedrockinvokeagenttool",
+                      "pt-BR/tools/integration/crewaiautomationtool"
                     ]
                   },
                   {
@@ -623,6 +700,8 @@
                 "pages": [
                   "pt-BR/observability/overview",
                   "pt-BR/observability/arize-phoenix",
+                  "pt-BR/observability/braintrust",
+                  "pt-BR/observability/datadog",
                   "pt-BR/observability/langdb",
                   "pt-BR/observability/langfuse",
                   "pt-BR/observability/langtrace",
@@ -651,6 +730,7 @@
                   "pt-BR/learn/force-tool-output-as-result",
                   "pt-BR/learn/hierarchical-process",
                   "pt-BR/learn/human-input-on-execution",
+                  "pt-BR/learn/human-in-the-loop",
                   "pt-BR/learn/kickoff-async",
                   "pt-BR/learn/kickoff-for-each",
                   "pt-BR/learn/llm-connections",
@@ -667,7 +747,7 @@
             ]
           },
           {
-            "tab": "Enterprise",
+            "tab": "AMP",
             "icon": "briefcase",
             "groups": [
               {
@@ -675,14 +755,27 @@
                 "pages": ["pt-BR/enterprise/introduction"]
               },
               {
-                "group": "Funcionalidades",
+                "group": "Construir",
+                "pages": [
+                  "pt-BR/enterprise/features/automations",
+                  "pt-BR/enterprise/features/crew-studio",
+                  "pt-BR/enterprise/features/marketplace",
+                  "pt-BR/enterprise/features/agent-repositories",
+                  "pt-BR/enterprise/features/tools-and-integrations"
+                ]
+              },
+              {
+                "group": "Operar",
                 "pages": [
-                  "pt-BR/enterprise/features/rbac",
-                  "pt-BR/enterprise/features/tool-repository",
-                  "pt-BR/enterprise/features/webhook-streaming",
                   "pt-BR/enterprise/features/traces",
-                  "pt-BR/enterprise/features/hallucination-guardrail",
-                  "pt-BR/enterprise/features/integrations"
+                  "pt-BR/enterprise/features/webhook-streaming",
+                  "pt-BR/enterprise/features/hallucination-guardrail"
+                ]
+              },
+              {
+                "group": "Gerenciar",
+                "pages": [
+                  "pt-BR/enterprise/features/rbac"
                 ]
               },
               {
@@ -694,10 +787,20 @@
                   "pt-BR/enterprise/integrations/github",
                   "pt-BR/enterprise/integrations/gmail",
                   "pt-BR/enterprise/integrations/google_calendar",
+                  "pt-BR/enterprise/integrations/google_contacts",
+                  "pt-BR/enterprise/integrations/google_docs",
+                  "pt-BR/enterprise/integrations/google_drive",
                   "pt-BR/enterprise/integrations/google_sheets",
+                  "pt-BR/enterprise/integrations/google_slides",
                   "pt-BR/enterprise/integrations/hubspot",
                   "pt-BR/enterprise/integrations/jira",
                   "pt-BR/enterprise/integrations/linear",
+                  "pt-BR/enterprise/integrations/microsoft_excel",
+                  "pt-BR/enterprise/integrations/microsoft_onedrive",
+                  "pt-BR/enterprise/integrations/microsoft_outlook",
+                  "pt-BR/enterprise/integrations/microsoft_sharepoint",
+                  "pt-BR/enterprise/integrations/microsoft_teams",
+                  "pt-BR/enterprise/integrations/microsoft_word",
                   "pt-BR/enterprise/integrations/notion",
                   "pt-BR/enterprise/integrations/salesforce",
                   "pt-BR/enterprise/integrations/shopify",
@@ -715,14 +818,26 @@
                   "pt-BR/enterprise/guides/update-crew",
                   "pt-BR/enterprise/guides/enable-crew-studio",
                   "pt-BR/enterprise/guides/azure-openai-setup",
-                  "pt-BR/enterprise/guides/automation-triggers",
-                  "pt-BR/enterprise/guides/hubspot-trigger",
+                  "pt-BR/enterprise/guides/tool-repository",
                   "pt-BR/enterprise/guides/react-component-export",
-                  "pt-BR/enterprise/guides/salesforce-trigger",
-                  "pt-BR/enterprise/guides/slack-trigger",
                   "pt-BR/enterprise/guides/team-management",
-                  "pt-BR/enterprise/guides/webhook-automation",
                   "pt-BR/enterprise/guides/human-in-the-loop",
+                  "pt-BR/enterprise/guides/webhook-automation"
+                ]
+              },
+              {
+                "group": "Triggers",
+                "pages": [
+                  "pt-BR/enterprise/guides/automation-triggers",
+                  "pt-BR/enterprise/guides/gmail-trigger",
+                  "pt-BR/enterprise/guides/google-calendar-trigger",
+                  "pt-BR/enterprise/guides/google-drive-trigger",
+                  "pt-BR/enterprise/guides/outlook-trigger",
+                  "pt-BR/enterprise/guides/onedrive-trigger",
+                  "pt-BR/enterprise/guides/microsoft-teams-trigger",
+                  "pt-BR/enterprise/guides/slack-trigger",
+                  "pt-BR/enterprise/guides/hubspot-trigger",
+                  "pt-BR/enterprise/guides/salesforce-trigger",
                   "pt-BR/enterprise/guides/zapier-trigger"
                 ]
               },
@@ -744,6 +859,7 @@
                   "pt-BR/api-reference/introduction",
                   "pt-BR/api-reference/inputs",
                   "pt-BR/api-reference/kickoff",
+                  "pt-BR/api-reference/resume",
                   "pt-BR/api-reference/status"
                 ]
               }
@@ -798,6 +914,16 @@
           ]
         },
         "tabs": [
+          {
+            "tab": "홈",
+            "icon": "house",
+            "groups": [
+              {
+                "group": "환영합니다",
+                "pages": ["ko/index"]
+              }
+            ]
+          },
           {
             "tab": "기술 문서",
             "icon": "book-open",
@@ -867,6 +993,7 @@
                 "group": "MCP 통합",
                 "pages": [
                   "ko/mcp/overview",
+                  "ko/mcp/dsl-integration",
                   "ko/mcp/stdio",
                   "ko/mcp/sse",
                   "ko/mcp/streamable-http",
@@ -976,17 +1103,16 @@
                       "ko/tools/cloud-storage/overview",
                       "ko/tools/cloud-storage/s3readertool",
                       "ko/tools/cloud-storage/s3writertool",
-                      "ko/tools/cloud-storage/bedrockinvokeagenttool",
                       "ko/tools/cloud-storage/bedrockkbretriever"
                     ]
                   },
                   {
-                    "group": "통합",
+                    "group": "Integrations",
                     "icon": "plug",
                     "pages": [
-                      "ko/tools/tool-integrations/overview",
-                      "ko/tools/tool-integrations/bedrockinvokeagenttool",
-                      "ko/tools/tool-integrations/crewaiautomationtool"
+                      "ko/tools/integration/overview",
+                      "ko/tools/integration/bedrockinvokeagenttool",
+                      "ko/tools/integration/crewaiautomationtool"
                     ]
                   },
                   {
@@ -1007,6 +1133,8 @@
                 "pages": [
                   "ko/observability/overview",
                   "ko/observability/arize-phoenix",
+                  "ko/observability/braintrust",
+                  "ko/observability/datadog",
                   "ko/observability/langdb",
                   "ko/observability/langfuse",
                   "ko/observability/langtrace",
@@ -1035,6 +1163,7 @@
                   "ko/learn/force-tool-output-as-result",
                   "ko/learn/hierarchical-process",
                   "ko/learn/human-input-on-execution",
+                  "ko/learn/human-in-the-loop",
                   "ko/learn/kickoff-async",
                   "ko/learn/kickoff-for-each",
                   "ko/learn/llm-connections",
@@ -1059,15 +1188,27 @@
                 "pages": ["ko/enterprise/introduction"]
               },
               {
-                "group": "특징",
+                "group": "빌드",
+                "pages": [
+                  "ko/enterprise/features/automations",
+                  "ko/enterprise/features/crew-studio",
+                  "ko/enterprise/features/marketplace",
+                  "ko/enterprise/features/agent-repositories",
+                  "ko/enterprise/features/tools-and-integrations"
+                ]
+              },
+              {
+                "group": "운영",
                 "pages": [
-                  "ko/enterprise/features/rbac",
-                  "ko/enterprise/features/tool-repository",
-                  "ko/enterprise/features/webhook-streaming",
                   "ko/enterprise/features/traces",
-                  "ko/enterprise/features/hallucination-guardrail",
-                  "ko/enterprise/features/integrations",
-                  "ko/enterprise/features/agent-repositories"
+                  "ko/enterprise/features/webhook-streaming",
+                  "ko/enterprise/features/hallucination-guardrail"
+                ]
+              },
+              {
+                "group": "관리",
+                "pages": [
+                  "ko/enterprise/features/rbac"
                 ]
               },
               {
@@ -1079,10 +1220,20 @@
                   "ko/enterprise/integrations/github",
                   "ko/enterprise/integrations/gmail",
                   "ko/enterprise/integrations/google_calendar",
+                  "ko/enterprise/integrations/google_contacts",
+                  "ko/enterprise/integrations/google_docs",
+                  "ko/enterprise/integrations/google_drive",
                   "ko/enterprise/integrations/google_sheets",
+                  "ko/enterprise/integrations/google_slides",
                   "ko/enterprise/integrations/hubspot",
                   "ko/enterprise/integrations/jira",
                   "ko/enterprise/integrations/linear",
+                  "ko/enterprise/integrations/microsoft_excel",
+                  "ko/enterprise/integrations/microsoft_onedrive",
+                  "ko/enterprise/integrations/microsoft_outlook",
+                  "ko/enterprise/integrations/microsoft_sharepoint",
+                  "ko/enterprise/integrations/microsoft_teams",
+                  "ko/enterprise/integrations/microsoft_word",
                   "ko/enterprise/integrations/notion",
                   "ko/enterprise/integrations/salesforce",
                   "ko/enterprise/integrations/shopify",
@@ -1100,14 +1251,26 @@
                   "ko/enterprise/guides/update-crew",
                   "ko/enterprise/guides/enable-crew-studio",
                   "ko/enterprise/guides/azure-openai-setup",
-                  "ko/enterprise/guides/automation-triggers",
-                  "ko/enterprise/guides/hubspot-trigger",
+                  "ko/enterprise/guides/tool-repository",
                   "ko/enterprise/guides/react-component-export",
-                  "ko/enterprise/guides/salesforce-trigger",
-                  "ko/enterprise/guides/slack-trigger",
                   "ko/enterprise/guides/team-management",
-                  "ko/enterprise/guides/webhook-automation",
                   "ko/enterprise/guides/human-in-the-loop",
+                  "ko/enterprise/guides/webhook-automation"
+                ]
+              },
+              {
+                "group": "트리거",
+                "pages": [
+                  "ko/enterprise/guides/automation-triggers",
+                  "ko/enterprise/guides/gmail-trigger",
+                  "ko/enterprise/guides/google-calendar-trigger",
+                  "ko/enterprise/guides/google-drive-trigger",
+                  "ko/enterprise/guides/outlook-trigger",
+                  "ko/enterprise/guides/onedrive-trigger",
+                  "ko/enterprise/guides/microsoft-teams-trigger",
+                  "ko/enterprise/guides/slack-trigger",
+                  "ko/enterprise/guides/hubspot-trigger",
+                  "ko/enterprise/guides/salesforce-trigger",
                   "ko/enterprise/guides/zapier-trigger"
                 ]
               },
@@ -1127,6 +1290,7 @@
                   "ko/api-reference/introduction",
                   "ko/api-reference/inputs",
                   "ko/api-reference/kickoff",
+                  "ko/api-reference/resume",
                   "ko/api-reference/status"
                 ]
               }

docs/en/api-reference/introduction.mdx

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

docs/en/api-reference/resume.mdx

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

docs/en/concepts/agents.mdx

@@ -20,7 +20,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 </Tip>
 
 <Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
-CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
+CrewAI AMP includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
 
 ![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
 

docs/en/concepts/cli.mdx

@@ -5,7 +5,7 @@ icon: terminal
 mode: "wide"
 ---
 
-<Warning>Since release 0.140.0, CrewAI Enterprise started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
+<Warning>Since release 0.140.0, CrewAI AMP started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
 
 ## Overview
 
@@ -186,9 +186,9 @@ def crew(self) -> Crew:
 
 ### 10. Deploy
 
-Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
+Deploy the crew or flow to [CrewAI AMP](https://app.crewai.com).
 
-- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
+- **Authentication**: You need to be authenticated to deploy to CrewAI AMP.
     You can login or create an account with:
     ```shell Terminal
     crewai login
@@ -203,7 +203,7 @@ Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
 
 ### 11. Organization Management
 
-Manage your CrewAI Enterprise organizations.
+Manage your CrewAI AMP organizations.
 
 ```shell Terminal
 crewai org [COMMAND] [OPTIONS]
@@ -227,17 +227,17 @@ crewai org switch <organization_id>
 ```
 
 <Note>
-You must be authenticated to CrewAI Enterprise to use these organization management commands.
+You must be authenticated to CrewAI AMP to use these organization management commands.
 </Note>
 
 - **Create a deployment** (continued):
     - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
 
-- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
+- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI AMP.
     ```shell Terminal
     crewai deploy push
     ```
-    - Initiates the deployment process on the CrewAI Enterprise platform.
+    - Initiates the deployment process on the CrewAI AMP platform.
     - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
 
 - **Deployment Status**: You can check the status of your deployment with:
@@ -262,7 +262,7 @@ You must be authenticated to CrewAI Enterprise to use these organization managem
     ```shell Terminal
     crewai deploy remove
     ```
-    This deletes the deployment from the CrewAI Enterprise platform.
+    This deletes the deployment from the CrewAI AMP platform.
 
 - **Help Command**: You can get help with the CLI with:
     ```shell Terminal
@@ -270,22 +270,20 @@ You must be authenticated to CrewAI Enterprise to use these organization managem
     ```
     This shows the help message for the CrewAI Deploy CLI.
 
-Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
+Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AMP](http://app.crewai.com) using the CLI.
 
 <iframe
-  width="100%"
-  height="400"
+  className="w-full aspect-video rounded-xl"
   src="https://www.youtube.com/embed/3EqSV-CYDZA"
   title="CrewAI Deployment Guide"
-  frameborder="0"
-  style={{ borderRadius: '10px' }}
+  frameBorder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowfullscreen
+  allowFullScreen
 ></iframe>
 
 ### 11. Login
 
-Authenticate with CrewAI Enterprise using a secure device code flow (no email entry required).
+Authenticate with CrewAI AMP using a secure device code flow (no email entry required).
 
 ```shell Terminal
 crewai login
@@ -356,7 +354,7 @@ crewai config reset
 
 #### Available Configuration Parameters
 
-- `enterprise_base_url`: Base URL of the CrewAI Enterprise instance
+- `enterprise_base_url`: Base URL of the CrewAI AMP instance
 - `oauth2_provider`: OAuth2 provider used for authentication (e.g., workos, okta, auth0)
 - `oauth2_audience`: OAuth2 audience value, typically used to identify the target API or resource
 - `oauth2_client_id`: OAuth2 client ID issued by the provider, used during authentication requests
@@ -372,7 +370,7 @@ crewai config list
 Example output:
 | Setting             | Value                    | Description                                                 |
 | :------------------ | :----------------------- | :---------------------------------------------------------- |
-| enterprise_base_url | https://app.crewai.com   | Base URL of the CrewAI Enterprise instance                  |
+| enterprise_base_url | https://app.crewai.com   | Base URL of the CrewAI AMP instance                  |
 | org_name            | Not set                  | Name of the currently active organization                   |
 | org_uuid            | Not set                  | UUID of the currently active organization                   |
 | oauth2_provider     | workos                   | OAuth2 provider (e.g., workos, okta, auth0)                 |
@@ -404,6 +402,10 @@ crewai config reset
 After resetting configuration, re-run `crewai login` to authenticate again.
 </Tip>
 
+<Tip>
+CrewAI CLI handles authentication to the Tool Repository automatically when adding packages to your project. Just append `crewai` before any `uv` command to use it. E.g. `crewai uv add requests`. For more information, see [Tool Repository](https://docs.crewai.com/enterprise/features/tool-repository) docs.
+</Tip>
+
 <Note>
 Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
 </Note>

docs/en/concepts/event-listener.mdx

@@ -20,7 +20,7 @@ CrewAI uses an event bus architecture to emit events throughout the execution li
 When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
 
 <Note type="info" title="Enterprise Enhancement: Prompt Tracing">
-CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
+CrewAI AMP provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
 
 ![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
 

docs/en/concepts/flows.mdx

@@ -875,14 +875,13 @@ By exploring these examples, you can gain insights into how to leverage CrewAI F
 Also, check out our YouTube video on how to use flows in CrewAI below!
 
 <iframe
-  width="560"
-  height="315"
+  className="w-full aspect-video rounded-xl"
   src="https://www.youtube.com/embed/MTb5my6VOT8"
-  title="YouTube video player"
-  frameborder="0"
+  title="CrewAI Flows overview"
+  frameBorder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowfullscreen
+  referrerPolicy="strict-origin-when-cross-origin"
+  allowFullScreen
 ></iframe>
 
 ## Running Flows

docs/en/concepts/llms.mdx

@@ -7,7 +7,7 @@ mode: "wide"
 
 ## Overview
 
-CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
+CrewAI integrates with multiple LLM providers through providers native sdks, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
 
 
 ## What are LLMs?
@@ -113,44 +113,104 @@ In this section, you'll find detailed examples that help you select, configure,
 
 <AccordionGroup>
   <Accordion title="OpenAI">
-    Set the following environment variables in your `.env` file:
+    CrewAI provides native integration with OpenAI through the OpenAI Python SDK.
 
     ```toml Code
     # Required
     OPENAI_API_KEY=sk-...
 
     # Optional
-    OPENAI_API_BASE=<custom-base-url>
-    OPENAI_ORGANIZATION=<your-org-id>
+    OPENAI_BASE_URL=<custom-base-url>
     ```
 
-    Example usage in your CrewAI project:
+    **Basic Usage:**
     ```python Code
     from crewai import LLM
 
     llm = LLM(
-        model="openai/gpt-4", # call model by provider/model_name
-        temperature=0.8,
-        max_tokens=150,
+        model="openai/gpt-4o",
+        api_key="your-api-key",  # Or set OPENAI_API_KEY
+        temperature=0.7,
+        max_tokens=4000
+    )
+    ```
+
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="openai/gpt-4o",
+        api_key="your-api-key",
+        base_url="https://api.openai.com/v1",  # Optional custom endpoint
+        organization="org-...",  # Optional organization ID
+        project="proj_...",  # Optional project ID
+        temperature=0.7,
+        max_tokens=4000,
+        max_completion_tokens=4000,  # For newer models
         top_p=0.9,
         frequency_penalty=0.1,
         presence_penalty=0.1,
         stop=["END"],
-        seed=42
+        seed=42,  # For reproducible outputs
+        stream=True,  # Enable streaming
+        timeout=60.0,  # Request timeout in seconds
+        max_retries=3,  # Maximum retry attempts
+        logprobs=True,  # Return log probabilities
+        top_logprobs=5,  # Number of most likely tokens
+        reasoning_effort="medium"  # For o1 models: low, medium, high
     )
     ```
 
-    OpenAI is one of the leading providers of LLMs with a wide range of models and features.
+    **Structured Outputs:**
+    ```python Code
+    from pydantic import BaseModel
+    from crewai import LLM
+
+    class ResponseFormat(BaseModel):
+        name: str
+        age: int
+        summary: str
+
+    llm = LLM(
+        model="openai/gpt-4o",
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `OPENAI_API_KEY`: Your OpenAI API key (required)
+    - `OPENAI_BASE_URL`: Custom base URL for OpenAI API (optional)
+
+    **Features:**
+    - Native function calling support (except o1 models)
+    - Structured outputs with JSON schema
+    - Streaming support for real-time responses
+    - Token usage tracking
+    - Stop sequences support (except o1 models)
+    - Log probabilities for token-level insights
+    - Reasoning effort control for o1 models
+
+    **Supported Models:**
 
     | Model               | Context Window   | Best For                                      |
     |---------------------|------------------|-----------------------------------------------|
-    | GPT-4               | 8,192 tokens     | High-accuracy tasks, complex reasoning        |
-    | GPT-4 Turbo         | 128,000 tokens   | Long-form content, document analysis          |
-    | GPT-4o & GPT-4o-mini  | 128,000 tokens  | Cost-effective large context processing       |
-    | o3-mini             | 200,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1-mini             | 128,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1-preview          | 128,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1                  | 200,000 tokens   | Fast reasoning, complex reasoning             |
+    | gpt-4.1             | 1M tokens        | Latest model with enhanced capabilities       |
+    | gpt-4.1-mini        | 1M tokens        | Efficient version with large context          |
+    | gpt-4.1-nano        | 1M tokens        | Ultra-efficient variant                       |
+    | gpt-4o              | 128,000 tokens   | Optimized for speed and intelligence          |
+    | gpt-4o-mini         | 200,000 tokens   | Cost-effective with large context             |
+    | gpt-4-turbo         | 128,000 tokens   | Long-form content, document analysis          |
+    | gpt-4               | 8,192 tokens     | High-accuracy tasks, complex reasoning        |
+    | o1                  | 200,000 tokens   | Advanced reasoning, complex problem-solving   |
+    | o1-preview          | 128,000 tokens   | Preview of reasoning capabilities             |
+    | o1-mini             | 128,000 tokens   | Efficient reasoning model                     |
+    | o3-mini             | 200,000 tokens   | Lightweight reasoning model                   |
+    | o4-mini             | 200,000 tokens   | Next-gen efficient reasoning                  |
+
+    **Note:** To use OpenAI, install the required dependencies:
+    ```bash
+    uv add "crewai[openai]"
+    ```
   </Accordion>
 
   <Accordion title="Meta-Llama">
@@ -187,69 +247,186 @@ In this section, you'll find detailed examples that help you select, configure,
   </Accordion>
 
   <Accordion title="Anthropic">
+    CrewAI provides native integration with Anthropic through the Anthropic Python SDK.
+
     ```toml Code
     # Required
     ANTHROPIC_API_KEY=sk-ant-...
-
-    # Optional
-    ANTHROPIC_API_BASE=<custom-base-url>
     ```
 
-    Example usage in your CrewAI project:
+    **Basic Usage:**
     ```python Code
+    from crewai import LLM
+
     llm = LLM(
-        model="anthropic/claude-3-sonnet-20240229-v1:0",
-        temperature=0.7
+        model="anthropic/claude-3-5-sonnet-20241022",
+        api_key="your-api-key",  # Or set ANTHROPIC_API_KEY
+        max_tokens=4096  # Required for Anthropic
     )
     ```
+
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="anthropic/claude-3-5-sonnet-20241022",
+        api_key="your-api-key",
+        base_url="https://api.anthropic.com",  # Optional custom endpoint
+        temperature=0.7,
+        max_tokens=4096,  # Required parameter
+        top_p=0.9,
+        stop_sequences=["END", "STOP"],  # Anthropic uses stop_sequences
+        stream=True,  # Enable streaming
+        timeout=60.0,  # Request timeout in seconds
+        max_retries=3  # Maximum retry attempts
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `ANTHROPIC_API_KEY`: Your Anthropic API key (required)
+
+    **Features:**
+    - Native tool use support for Claude 3+ models
+    - Streaming support for real-time responses
+    - Automatic system message handling
+    - Stop sequences for controlled output
+    - Token usage tracking
+    - Multi-turn tool use conversations
+
+    **Important Notes:**
+    - `max_tokens` is a **required** parameter for all Anthropic models
+    - Claude uses `stop_sequences` instead of `stop`
+    - System messages are handled separately from conversation messages
+    - First message must be from the user (automatically handled)
+    - Messages must alternate between user and assistant
+
+    **Supported Models:**
+
+    | Model                        | Context Window | Best For                                      |
+    |------------------------------|----------------|-----------------------------------------------|
+    | claude-3-7-sonnet            | 200,000 tokens | Advanced reasoning and agentic tasks          |
+    | claude-3-5-sonnet-20241022   | 200,000 tokens | Latest Sonnet with best performance           |
+    | claude-3-5-haiku             | 200,000 tokens | Fast, compact model for quick responses       |
+    | claude-3-opus                | 200,000 tokens | Most capable for complex tasks                |
+    | claude-3-sonnet              | 200,000 tokens | Balanced intelligence and speed               |
+    | claude-3-haiku               | 200,000 tokens | Fastest for simple tasks                      |
+    | claude-2.1                   | 200,000 tokens | Extended context, reduced hallucinations      |
+    | claude-2                     | 100,000 tokens | Versatile model for various tasks             |
+    | claude-instant               | 100,000 tokens | Fast, cost-effective for everyday tasks       |
+
+    **Note:** To use Anthropic, install the required dependencies:
+    ```bash
+    uv add "crewai[anthropic]"
+    ```
   </Accordion>
 
   <Accordion title="Google (Gemini API)">
-    Set your API key in your `.env` file. If you need a key, or need to find an
-    existing key, check [AI Studio](https://aistudio.google.com/apikey).
+    CrewAI provides native integration with Google Gemini through the Google Gen AI Python SDK.
+
+    Set your API key in your `.env` file. If you need a key, check [AI Studio](https://aistudio.google.com/apikey).
 
     ```toml .env
-    # https://ai.google.dev/gemini-api/docs/api-key
+    # Required (one of the following)
+    GOOGLE_API_KEY=<your-api-key>
     GEMINI_API_KEY=<your-api-key>
+
+    # Optional - for Vertex AI
+    GOOGLE_CLOUD_PROJECT=<your-project-id>
+    GOOGLE_CLOUD_LOCATION=<location>  # Defaults to us-central1
+    GOOGLE_GENAI_USE_VERTEXAI=true  # Set to use Vertex AI
     ```
 
-    Example usage in your CrewAI project:
+    **Basic Usage:**
     ```python Code
     from crewai import LLM
 
     llm = LLM(
         model="gemini/gemini-2.0-flash",
-        temperature=0.7,
+        api_key="your-api-key",  # Or set GOOGLE_API_KEY/GEMINI_API_KEY
+        temperature=0.7
     )
     ```
 
-    ### Gemini models
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-2.5-flash",
+        api_key="your-api-key",
+        temperature=0.7,
+        top_p=0.9,
+        top_k=40,  # Top-k sampling parameter
+        max_output_tokens=8192,
+        stop_sequences=["END", "STOP"],
+        stream=True,  # Enable streaming
+        safety_settings={
+            "HARM_CATEGORY_HARASSMENT": "BLOCK_NONE",
+            "HARM_CATEGORY_HATE_SPEECH": "BLOCK_NONE"
+        }
+    )
+    ```
+
+    **Vertex AI Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-1.5-pro",
+        project="your-gcp-project-id",
+        location="us-central1"  # GCP region
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `GOOGLE_API_KEY` or `GEMINI_API_KEY`: Your Google API key (required for Gemini API)
+    - `GOOGLE_CLOUD_PROJECT`: Google Cloud project ID (for Vertex AI)
+    - `GOOGLE_CLOUD_LOCATION`: GCP location (defaults to `us-central1`)
+    - `GOOGLE_GENAI_USE_VERTEXAI`: Set to `true` to use Vertex AI
+
+    **Features:**
+    - Native function calling support for Gemini 1.5+ and 2.x models
+    - Streaming support for real-time responses
+    - Multimodal capabilities (text, images, video)
+    - Safety settings configuration
+    - Support for both Gemini API and Vertex AI
+    - Automatic system instruction handling
+    - Token usage tracking
+
+    **Gemini Models:**
 
     Google offers a range of powerful models optimized for different use cases.
 
     | Model                          | Context Window | Best For                                                          |
     |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
+    | gemini-2.5-flash               | 1M tokens      | Adaptive thinking, cost efficiency                                |
+    | gemini-2.5-pro                 | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding         |
+    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking                         |
+    | gemini-2.0-flash-thinking      | 32,768 tokens  | Advanced reasoning with thinking process                          |
     | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
+    | gemini-1.5-pro                 | 2M tokens      | Best performing, logical reasoning, coding                        |
     | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+    | gemini-1.5-flash-8b            | 1M tokens      | Fastest, most cost-efficient                                      |
+    | gemini-1.0-pro                 | 32,768 tokens  | Earlier generation model                                          |
+
+    **Gemma Models:**
+
+    The Gemini API also supports [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
+
+    | Model          | Context Window | Best For                           |
+    |----------------|----------------|------------------------------------|
+    | gemma-3-1b     | 32,000 tokens  | Ultra-lightweight tasks            |
+    | gemma-3-4b     | 128,000 tokens | Efficient general-purpose tasks    |
+    | gemma-3-12b    | 128,000 tokens | Balanced performance and efficiency|
+    | gemma-3-27b    | 128,000 tokens | High-performance tasks             |
+
+    **Note:** To use Google Gemini, install the required dependencies:
+    ```bash
+    uv add "crewai[google-genai]"
+    ```
 
     The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
-
-    ### Gemma
-
-    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
-
-    | Model          | Context Window |
-    |----------------|----------------|
-    | gemma-3-1b-it  | 32k tokens     |
-    | gemma-3-4b-it  | 32k tokens     |
-    | gemma-3-12b-it | 32k tokens     |
-    | gemma-3-27b-it | 128k tokens    |
-
   </Accordion>
   <Accordion title="Google (Vertex AI)">
     Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
@@ -291,43 +468,146 @@ In this section, you'll find detailed examples that help you select, configure,
   </Accordion>
 
   <Accordion title="Azure">
+    CrewAI provides native integration with Azure AI Inference and Azure OpenAI through the Azure AI Inference Python SDK.
+
     ```toml Code
     # Required
     AZURE_API_KEY=<your-api-key>
-    AZURE_API_BASE=<your-resource-url>
-    AZURE_API_VERSION=<api-version>
+    AZURE_ENDPOINT=<your-endpoint-url>
 
     # Optional
-    AZURE_AD_TOKEN=<your-azure-ad-token>
-    AZURE_API_TYPE=<your-azure-api-type>
+    AZURE_API_VERSION=<api-version>  # Defaults to 2024-06-01
     ```
 
-    Example usage in your CrewAI project:
+    **Endpoint URL Formats:**
+
+    For Azure OpenAI deployments:
+    ```
+    https://<resource-name>.openai.azure.com/openai/deployments/<deployment-name>
+    ```
+
+    For Azure AI Inference endpoints:
+    ```
+    https://<resource-name>.inference.azure.com
+    ```
+
+    **Basic Usage:**
     ```python Code
     llm = LLM(
         model="azure/gpt-4",
-        api_version="2023-05-15"
+        api_key="<your-api-key>",  # Or set AZURE_API_KEY
+        endpoint="<your-endpoint-url>",
+        api_version="2024-06-01"
     )
     ```
+
+    **Advanced Configuration:**
+    ```python Code
+    llm = LLM(
+        model="azure/gpt-4o",
+        temperature=0.7,
+        max_tokens=4000,
+        top_p=0.9,
+        frequency_penalty=0.0,
+        presence_penalty=0.0,
+        stop=["END"],
+        stream=True,
+        timeout=60.0,
+        max_retries=3
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `AZURE_API_KEY`: Your Azure API key (required)
+    - `AZURE_ENDPOINT`: Your Azure endpoint URL (required, also checks `AZURE_OPENAI_ENDPOINT` and `AZURE_API_BASE`)
+    - `AZURE_API_VERSION`: API version (optional, defaults to `2024-06-01`)
+
+    **Features:**
+    - Native function calling support for Azure OpenAI models (gpt-4, gpt-4o, gpt-3.5-turbo, etc.)
+    - Streaming support for real-time responses
+    - Automatic endpoint URL validation and correction
+    - Comprehensive error handling with retry logic
+    - Token usage tracking
+
+    **Note:** To use Azure AI Inference, install the required dependencies:
+    ```bash
+    uv add "crewai[azure-ai-inference]"
+    ```
   </Accordion>
 
   <Accordion title="AWS Bedrock">
+    CrewAI provides native integration with AWS Bedrock through the boto3 SDK using the Converse API.
+
     ```toml Code
+    # Required
     AWS_ACCESS_KEY_ID=<your-access-key>
     AWS_SECRET_ACCESS_KEY=<your-secret-key>
-    AWS_DEFAULT_REGION=<your-region>
+
+    # Optional
+    AWS_SESSION_TOKEN=<your-session-token>  # For temporary credentials
+    AWS_DEFAULT_REGION=<your-region>  # Defaults to us-east-1
     ```
 
-    Example usage in your CrewAI project:
+    **Basic Usage:**
     ```python Code
+    from crewai import LLM
+
     llm = LLM(
-        model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
+        model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
+        region_name="us-east-1"
     )
     ```
 
-    Before using Amazon Bedrock, make sure you have boto3 installed in your environment
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
 
-    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API, enabling secure and responsible AI application development.
+    llm = LLM(
+        model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
+        aws_access_key_id="your-access-key",  # Or set AWS_ACCESS_KEY_ID
+        aws_secret_access_key="your-secret-key",  # Or set AWS_SECRET_ACCESS_KEY
+        aws_session_token="your-session-token",  # For temporary credentials
+        region_name="us-east-1",
+        temperature=0.7,
+        max_tokens=4096,
+        top_p=0.9,
+        top_k=250,  # For Claude models
+        stop_sequences=["END", "STOP"],
+        stream=True,  # Enable streaming
+        guardrail_config={  # Optional content filtering
+            "guardrailIdentifier": "your-guardrail-id",
+            "guardrailVersion": "1"
+        },
+        additional_model_request_fields={  # Model-specific parameters
+            "top_k": 250
+        }
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `AWS_ACCESS_KEY_ID`: AWS access key (required)
+    - `AWS_SECRET_ACCESS_KEY`: AWS secret key (required)
+    - `AWS_SESSION_TOKEN`: AWS session token for temporary credentials (optional)
+    - `AWS_DEFAULT_REGION`: AWS region (defaults to `us-east-1`)
+
+    **Features:**
+    - Native tool calling support via Converse API
+    - Streaming and non-streaming responses
+    - Comprehensive error handling with retry logic
+    - Guardrail configuration for content filtering
+    - Model-specific parameters via `additional_model_request_fields`
+    - Token usage tracking and stop reason logging
+    - Support for all Bedrock foundation models
+    - Automatic conversation format handling
+
+    **Important Notes:**
+    - Uses the modern Converse API for unified model access
+    - Automatic handling of model-specific conversation requirements
+    - System messages are handled separately from conversation
+    - First message must be from user (automatically handled)
+    - Some models (like Cohere) require conversation to end with user message
+
+    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API.
 
     | Model                   | Context Window       | Best For                                                          |
     |-------------------------|----------------------|-------------------------------------------------------------------|
@@ -357,7 +637,12 @@ In this section, you'll find detailed examples that help you select, configure,
     | Jamba-Instruct          | Up to 256k tokens    | Model with extended context window optimized for cost-effective text generation, summarization, and Q&A. |
     | Mistral 7B Instruct     | Up to 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
     | Mistral 8x7B Instruct   | Up to 32k tokens     | An MOE LLM that follows instructions, completes requests, and generates creative text. |
+    | DeepSeek R1             | 32,768 tokens        | Advanced reasoning model                                                       |
 
+    **Note:** To use AWS Bedrock, install the required dependencies:
+    ```bash
+    uv add "crewai[bedrock]"
+    ```
   </Accordion>
 
   <Accordion title="Amazon SageMaker">
@@ -899,7 +1184,7 @@ Learn how to get the most out of your LLM configuration:
   </Accordion>
 
   <Accordion title="Drop Additional Parameters">
-    CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
+    CrewAI internally uses native sdks for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
     For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:
 
     ```python
@@ -915,6 +1200,52 @@ Learn how to get the most out of your LLM configuration:
     )
     ```
   </Accordion>
+
+  <Accordion title="Transport Interceptors">
+    CrewAI provides message interceptors for several providers, allowing you to hook into request/response cycles at the transport layer.
+
+    **Supported Providers:**
+    - ✅ OpenAI
+    - ✅ Anthropic
+
+    **Basic Usage:**
+    ```python
+import httpx
+from crewai import LLM
+from crewai.llms.hooks import BaseInterceptor
+
+class CustomInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Custom interceptor to modify requests and responses."""
+
+    def on_outbound(self, request: httpx.Request) -> httpx.Request:
+        """Print request before sending to the LLM provider."""
+        print(request)
+        return request
+
+    def on_inbound(self, response: httpx.Response) -> httpx.Response:
+        """Process response after receiving from the LLM provider."""
+        print(f"Status: {response.status_code}")
+        print(f"Response time: {response.elapsed}")
+        return response
+
+# Use the interceptor with an LLM
+llm = LLM(
+    model="openai/gpt-4o",
+    interceptor=CustomInterceptor()
+)
+    ```
+
+    **Important Notes:**
+    - Both methods must return the received object or type of object.
+    - Modifying received objects may result in unexpected behavior or application crashes.
+    - Not all providers support interceptors - check the supported providers list above
+
+    <Info>
+      Interceptors operate at the transport layer. This is particularly useful for:
+      - Message transformation and filtering
+      - Debugging API interactions
+    </Info>
+  </Accordion>
 </AccordionGroup>
 
 ## Common Issues and Solutions

docs/en/concepts/tasks.mdx

@@ -14,7 +14,7 @@ Tasks provide all necessary details for execution, such as a description, the ag
 Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
 
 <Note type="info" title="Enterprise Enhancement: Visual Task Builder">
-CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
+CrewAI AMP includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
 
 ![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png)
 
@@ -897,14 +897,13 @@ except RuntimeError as e:
 Check out the video below to see how to use structured outputs in CrewAI:
 
 <iframe
-  width="560"
-  height="315"
+  className="w-full aspect-video rounded-xl"
   src="https://www.youtube.com/embed/dNpKQk5uxHw"
-  title="YouTube video player"
-  frameborder="0"
+  title="Structured outputs in CrewAI"
+  frameBorder="0"
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-  referrerpolicy="strict-origin-when-cross-origin"
-  allowfullscreen
+  referrerPolicy="strict-origin-when-cross-origin"
+  allowFullScreen
 ></iframe>
 
 ## Conclusion

docs/en/concepts/tools.mdx

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

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

@@ -1,12 +1,16 @@
 ---
 title: 'Agent Repositories'
 description: 'Learn how to use Agent Repositories to share and reuse your agents across teams and projects'
-icon: 'database'
+icon: 'people-group'
 mode: "wide"
 ---
 
 Agent Repositories allow enterprise users to store, share, and reuse agent definitions across teams and projects. This feature enables organizations to maintain a centralized library of standardized agents, promoting consistency and reducing duplication of effort.
 
+<Frame>
+  ![Agent Repositories](/images/enterprise/agent-repositories.png)
+</Frame>
+
 ## Benefits of Agent Repositories
 
 - **Standardization**: Maintain consistent agent definitions across your organization
@@ -14,25 +18,21 @@ Agent Repositories allow enterprise users to store, share, and reuse agent defin
 - **Governance**: Implement organization-wide policies for agent configurations
 - **Collaboration**: Enable teams to share and build upon each other's work
 
-## Using Agent Repositories
-
-### Prerequisites
+## Creating and Use Agent Repositories
 
 1. You must have an account at CrewAI, try the [free plan](https://app.crewai.com).
-2. You need to be authenticated using the CrewAI CLI.
-3. If you have more than one organization, make sure you are switched to the correct organization using the CLI command:
+2. Create agents with specific roles and goals for your workflows.
+3. Configure tools and capabilities for each specialized assistant.
+4. Deploy agents across projects via visual interface or API integration.
 
-```bash
-crewai org switch <org_id>
-```
+<Frame>
+  ![Agent Repositories](/images/enterprise/create-agent-repository.png)
+</Frame>
 
-### Creating and Managing Agents in Repositories
-
-To create and manage agents in repositories,Enterprise Dashboard.
 
 ### Loading Agents from Repositories
 
-You can load agents from repositories in your code using the `from_repository` parameter:
+You can load agents from repositories in your code using the `from_repository` parameter to run locally:
 
 ```python
 from crewai import Agent
@@ -42,7 +42,6 @@ from crewai import Agent
 researcher = Agent(
     from_repository="market-research-agent"
 )
-
 ```
 
 ### Overriding Repository Settings

docs/en/enterprise/features/automations.mdx

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

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

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

docs/en/enterprise/features/integrations.mdx

@@ -1,186 +0,0 @@
----
-title: Integrations
-description: "Connected applications for your agents to take actions."
-icon: "plug"
-mode: "wide"
----
-
-## Overview
-
-Enable your agents to authenticate with any OAuth enabled provider and take actions. From Salesforce and HubSpot to Google and GitHub, we've got you covered with 16+ integrated services.
-
-<Frame>
-  ![Integrations](/images/enterprise/crew_connectors.png)
-</Frame>
-
-## Supported Integrations
-
-### **Communication & Collaboration**
-- **Gmail** - Manage emails and drafts
-- **Slack** - Workspace notifications and alerts
-- **Microsoft** - Office 365 and Teams integration
-
-### **Project Management**
-- **Jira** - Issue tracking and project management
-- **ClickUp** - Task and productivity management
-- **Asana** - Team task and project coordination
-- **Notion** - Page and database management
-- **Linear** - Software project and bug tracking
-- **GitHub** - Repository and issue management
-
-### **Customer Relationship Management**
-- **Salesforce** - CRM account and opportunity management
-- **HubSpot** - Sales pipeline and contact management
-- **Zendesk** - Customer support ticket management
-
-### **Business & Finance**
-- **Stripe** - Payment processing and customer management
-- **Shopify** - E-commerce store and product management
-
-### **Productivity & Storage**
-- **Google Sheets** - Spreadsheet data synchronization
-- **Google Calendar** - Event and schedule management
-- **Box** - File storage and document management
-
-and more to come!
-
-## Prerequisites
-
-Before using Authentication Integrations, ensure you have:
-
-- A [CrewAI Enterprise](https://app.crewai.com) account. You can get started with a free trial.
-
-
-## Setting Up Integrations
-
-### 1. Connect Your Account
-
-1. Navigate to [CrewAI Enterprise](https://app.crewai.com)
-2. Go to **Integrations** tab - https://app.crewai.com/crewai_plus/connectors
-3. Click **Connect** on your desired service from the Authentication Integrations section
-4. Complete the OAuth authentication flow
-5. Grant necessary permissions for your use case
-6. All set! Get your Enterprise Token from your [CrewAI Enterprise](https://app.crewai.com) in **Integration** tab
-
-<Frame>
-  ![Integrations](/images/enterprise/enterprise_action_auth_token.png)
-</Frame>
-
-### 2. Install Integration Tools
-
-All you need is the latest version of `crewai-tools` package.
-
-```bash
-uv add crewai-tools
-```
-
-## Usage Examples
-
-### Basic Usage
-<Tip>
-  All the services you are authenticated into will be available as tools. So all you need to do is add the `CrewaiEnterpriseTools` to your agent and you are good to go.
-</Tip>
-
-```python
-from crewai import Agent, Task, Crew
-from crewai_tools import CrewaiEnterpriseTools
-
-# Get enterprise tools (Gmail tool will be included)
-enterprise_tools = CrewaiEnterpriseTools(
-    enterprise_token="your_enterprise_token"
-)
-# print the tools
-print(enterprise_tools)
-
-# Create an agent with Gmail capabilities
-email_agent = Agent(
-    role="Email Manager",
-    goal="Manage and organize email communications",
-    backstory="An AI assistant specialized in email management and communication.",
-    tools=enterprise_tools
-)
-
-# Task to send an email
-email_task = Task(
-    description="Draft and send a follow-up email to john@example.com about the project update",
-    agent=email_agent,
-    expected_output="Confirmation that email was sent successfully"
-)
-
-# Run the task
-crew = Crew(
-    agents=[email_agent],
-    tasks=[email_task]
-)
-
-# Run the crew
-crew.kickoff()
-```
-
-### Filtering Tools
-
-```python
-from crewai_tools import CrewaiEnterpriseTools
-
-enterprise_tools = CrewaiEnterpriseTools(
-    actions_list=["gmail_find_email"] # only gmail_find_email tool will be available
-)
-gmail_tool = enterprise_tools["gmail_find_email"]
-
-gmail_agent = Agent(
-    role="Gmail Manager",
-    goal="Manage gmail communications and notifications",
-    backstory="An AI assistant that helps coordinate gmail communications.",
-    tools=[gmail_tool]
-)
-
-notification_task = Task(
-    description="Find the email from john@example.com",
-    agent=gmail_agent,
-    expected_output="Email found from john@example.com"
-)
-
-# Run the task
-crew = Crew(
-    agents=[slack_agent],
-    tasks=[notification_task]
-)
-```
-
-## Best Practices
-
-### Security
-- **Principle of Least Privilege**: Only grant the minimum permissions required for your agents' tasks
-- **Regular Audits**: Periodically review connected integrations and their permissions
-- **Secure Credentials**: Never hardcode credentials; use CrewAI's secure authentication flow
-
-
-### Filtering Tools
-On a deployed crew, you can specify which actions are avialbel for each integration from the settings page of the service you connected to.
-
-<Frame>
-  ![Integrations](/images/enterprise/filtering_enterprise_action_tools.png)
-</Frame>
-
-
-### Scoped Deployments for multi user organizations
-You can deploy your crew and scope each integration to a specific user. For example, a crew that connects to google can use a specific user's gmail account.
-
-<Tip>
-  This is useful for multi user organizations where you want to scope the integration to a specific user.
-</Tip>
-
-
-Use the `user_bearer_token` to scope the integration to a specific user so that when the crew is kicked off, it will use the user's bearer token to authenticate with the integration. If user is not logged in, then the crew will not use any connected integrations. Use the default bearer token to authenticate with the integrations thats deployed with the crew.
-
-<Frame>
-  ![Integrations](/images/enterprise/user_bearer_token.png)
-</Frame>
-
-
-
-### Getting Help
-
-<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
-  Contact our support team for assistance with integration setup or troubleshooting.
-</Card>

docs/en/enterprise/features/marketplace.mdx

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

docs/en/enterprise/features/rbac.mdx

@@ -7,16 +7,16 @@ mode: "wide"
 
 ## Overview
 
-RBAC in CrewAI Enterprise enables secure, scalable access management through a combination of organization‑level roles and automation‑level visibility controls.
+RBAC in CrewAI AMP enables secure, scalable access management through a combination of organization‑level roles and automation‑level visibility controls.
 
 <Frame>
-  <img src="/images/enterprise/users_and_roles.png" alt="RBAC overview in CrewAI Enterprise" />
-  
+  <img src="/images/enterprise/users_and_roles.png" alt="RBAC overview in CrewAI AMP" />
+
 </Frame>
 
 ## Users and Roles
 
-Each member in your CrewAI workspace is assigned a role, which determines their access across various features. 
+Each member in your CrewAI workspace is assigned a role, which determines their access across various features.
 
 You can:
 
@@ -28,7 +28,7 @@ You can configure users and roles in Settings → Roles.
 
 <Steps>
   <Step title="Open Roles settings">
-    Go to <b>Settings → Roles</b> in CrewAI Enterprise.
+    Go to <b>Settings → Roles</b> in CrewAI AMP.
   </Step>
   <Step title="Choose a role type">
     Use a predefined role (<b>Owner</b>, <b>Member</b>) or click <b>Create role</b> to define a custom one.
@@ -93,12 +93,10 @@ The organization owner always has access. In private mode, only whitelisted user
 </Tip>
 
 <Frame>
-  <img src="/images/enterprise/visibility.png" alt="Automation Visibility settings in CrewAI Enterprise" />
-  
+  <img src="/images/enterprise/visibility.png" alt="Automation Visibility settings in CrewAI AMP" />
+
 </Frame>
 
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
   Contact our support team for assistance with RBAC questions.
 </Card>
-
-

docs/en/enterprise/features/tools-and-integrations.mdx

@@ -0,0 +1,250 @@
+---
+title: Tools & Integrations
+description: "Connect external apps and manage internal tools your agents can use."
+icon: "wrench"
+mode: "wide"
+---
+
+## Overview
+
+Tools & Integrations is the central hub for connecting third‑party apps and managing internal tools that your agents can use at runtime.
+
+<Frame>
+  ![Tools & Integrations Overview](/images/enterprise/crew_connectors.png)
+</Frame>
+
+## Explore
+
+<Tabs>
+  <Tab title="Integrations" icon="plug">
+
+  ## Agent Apps (Integrations)
+
+  Connect enterprise‑grade applications (e.g., Gmail, Google Drive, HubSpot, Slack) via OAuth to enable agent actions.
+
+  <Steps>
+    <Step title="Connect">
+      Click <b>Connect</b> on an app and complete OAuth.
+    </Step>
+    <Step title="Configure">
+      Optionally adjust scopes, triggers, and action availability.
+    </Step>
+    <Step title="Use in Agents">
+      Connected services become available as tools for your agents.
+    </Step>
+  </Steps>
+
+  <Frame>
+    ![Integrations Grid](/images/enterprise/agent-apps.png)
+  </Frame>
+
+  ### Connect your Account
+
+  1. Go to <Link href="https://app.crewai.com/crewai_plus/connectors">Integrations</Link>
+  2. Click <b>Connect</b> on the desired service
+  3. Complete the OAuth flow and grant scopes
+  4. Copy your Enterprise Token from <Link href="https://app.crewai.com/crewai_plus/settings/integrations">Integration Settings</Link>
+
+  <Frame>
+    ![Enterprise Token](/images/enterprise/enterprise_action_auth_token.png)
+  </Frame>
+
+  ### Install Integration Tools
+
+  To use the integrations locally, you need to install the latest `crewai-tools` package.
+
+  ```bash
+  uv add crewai-tools
+  ```
+
+  ### Environment Variable Setup
+
+  <Note>
+    To use integrations with `Agent(apps=[])`, you must set the `CREWAI_PLATFORM_INTEGRATION_TOKEN` environment variable with your Enterprise Token.
+  </Note>
+
+  ```bash
+  export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
+  ```
+
+  Or add it to your `.env` file:
+
+  ```
+  CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
+  ```
+
+  ### Usage Example
+
+  <Tip>
+    Use the new streamlined approach to integrate enterprise apps. Simply specify the app and its actions directly in the Agent configuration.
+  </Tip>
+
+  ```python
+  from crewai import Agent, Task, Crew
+
+  # Create an agent with Gmail capabilities
+  email_agent = Agent(
+      role="Email Manager",
+      goal="Manage and organize email communications",
+      backstory="An AI assistant specialized in email management and communication.",
+      apps=['gmail', 'gmail/send_email']  # Using canonical name 'gmail'
+  )
+
+  # Task to send an email
+  email_task = Task(
+      description="Draft and send a follow-up email to john@example.com about the project update",
+      agent=email_agent,
+      expected_output="Confirmation that email was sent successfully"
+  )
+
+  # Run the task
+  crew = Crew(
+      agents=[email_agent],
+      tasks=[email_task]
+  )
+
+  # Run the crew
+  crew.kickoff()
+  ```
+
+  ### Filtering Tools
+
+  ```python
+  from crewai import Agent, Task, Crew
+
+  # Create agent with specific Gmail actions only
+  gmail_agent = Agent(
+      role="Gmail Manager",
+      goal="Manage gmail communications and notifications",
+      backstory="An AI assistant that helps coordinate gmail communications.",
+      apps=['gmail/fetch_emails']  # Using canonical name with specific action
+  )
+
+  notification_task = Task(
+      description="Find the email from john@example.com",
+      agent=gmail_agent,
+      expected_output="Email found from john@example.com"
+  )
+
+  crew = Crew(
+      agents=[gmail_agent],
+      tasks=[notification_task]
+  )
+  ```
+
+  On a deployed crew, you can specify which actions are available for each integration from the service settings page.
+
+  <Frame>
+    ![Filter Actions](/images/enterprise/filtering_enterprise_action_tools.png)
+  </Frame>
+
+  ### Scoped Deployments (multi‑user orgs)
+
+  You can scope each integration to a specific user. For example, a crew that connects to Google can use a specific user’s Gmail account.
+
+  <Tip>
+    Useful when different teams/users must keep data access separated.
+  </Tip>
+
+  Use the `user_bearer_token` to scope authentication to the requesting user. If the user isn’t logged in, the crew won’t use connected integrations. Otherwise it falls back to the default bearer token configured for the deployment.
+
+  <Frame>
+    ![User Bearer Token](/images/enterprise/user_bearer_token.png)
+  </Frame>
+
+  <div id="catalog"></div>
+  ### Catalog
+
+  #### Communication & Collaboration
+  - Gmail — Manage emails and drafts
+  - Slack — Workspace notifications and alerts
+  - Microsoft — Office 365 and Teams integration
+
+  #### Project Management
+  - Jira — Issue tracking and project management
+  - ClickUp — Task and productivity management
+  - Asana — Team task and project coordination
+  - Notion — Page and database management
+  - Linear — Software project and bug tracking
+  - GitHub — Repository and issue management
+
+  #### Customer Relationship Management
+  - Salesforce — CRM account and opportunity management
+  - HubSpot — Sales pipeline and contact management
+  - Zendesk — Customer support ticket management
+
+  #### Business & Finance
+  - Stripe — Payment processing and customer management
+  - Shopify — E‑commerce store and product management
+
+  #### Productivity & Storage
+  - Google Sheets — Spreadsheet data synchronization
+  - Google Calendar — Event and schedule management
+  - Box — File storage and document management
+
+  …and more to come!
+
+  </Tab>
+  <Tab title="Internal Tools" icon="toolbox">
+
+  ## Internal Tools
+
+  Create custom tools locally, publish them on CrewAI AMP Tool Repository and use them in your agents.
+
+  <Tip>
+  Before running the commands below, make sure you log in to your CrewAI AMP account by running this command:
+    ```bash
+    crewai login
+    ```
+  </Tip>
+
+  <Frame>
+    ![Internal Tool Detail](/images/enterprise/tools-integrations-internal.png)
+  </Frame>
+
+  <Steps>
+    <Step title="Create">
+      Create a new tool locally.
+      ```bash
+      crewai tool create your-tool
+      ```
+    </Step>
+    <Step title="Publish">
+      Publish the tool to the CrewAI AMP Tool Repository.
+      ```bash
+      crewai tool publish
+      ```
+    </Step>
+    <Step title="Install">
+      Install the tool from the CrewAI AMP Tool Repository.
+      ```bash
+      crewai tool install your-tool
+      ```
+    </Step>
+  </Steps>
+
+  Manage:
+
+  - Name and description
+  - Visibility (Private / Public)
+  - Required environment variables
+  - Version history and downloads
+  - Team and role access
+
+  <Frame>
+    ![Internal Tool Detail](/images/enterprise/tool-configs.png)
+  </Frame>
+
+  </Tab>
+</Tabs>
+
+## Related
+
+<CardGroup cols={2}>
+  <Card title="Tool Repository" href="/en/enterprise/features/tool-repository" icon="toolbox">
+    Create, publish, and version custom tools for your organization.
+  </Card>
+  <Card title="Webhook Automation" href="/en/enterprise/guides/webhook-automation" icon="bolt">
+    Automate workflows and integrate with external platforms and services.
+  </Card>
+</CardGroup>

docs/en/enterprise/features/traces.mdx

@@ -11,7 +11,7 @@ Traces provide comprehensive visibility into your crew executions, helping you m
 
 ## What are Traces?
 
-Traces in CrewAI Enterprise are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record:
+Traces in CrewAI AMP are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record:
 
 - Agent thoughts and reasoning
 - Task execution details
@@ -28,9 +28,9 @@ Traces in CrewAI Enterprise are detailed execution records that capture every as
 
 <Steps>
   <Step title="Navigate to the Traces Tab">
-    Once in your CrewAI Enterprise dashboard, click on the **Traces** to view all execution records.
+    Once in your CrewAI AMP dashboard, click on the **Traces** to view all execution records.
   </Step>
-  
+
   <Step title="Select an Execution">
     You'll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace.
   </Step>
@@ -112,7 +112,7 @@ Traces are invaluable for troubleshooting issues with your crews:
 <Steps>
   <Step title="Identify Failure Points">
     When a crew execution doesn't produce the expected results, examine the trace to find where things went wrong. Look for:
-    
+
     - Failed tasks
     - Unexpected agent decisions
     - Tool usage errors
@@ -122,19 +122,19 @@ Traces are invaluable for troubleshooting issues with your crews:
       ![Failure Points](/images/enterprise/failure.png)
     </Frame>
   </Step>
-  
+
   <Step title="Optimize Performance">
     Use execution metrics to identify performance bottlenecks:
-    
+
     - Tasks that took longer than expected
     - Excessive token usage
     - Redundant tool operations
     - Unnecessary API calls
   </Step>
-  
+
   <Step title="Improve Cost Efficiency">
     Analyze token usage and cost estimates to optimize your crew's efficiency:
-    
+
     - Consider using smaller models for simpler tasks
     - Refine prompts to be more concise
     - Cache frequently accessed information
@@ -153,5 +153,5 @@ CrewAI batches trace uploads to reduce overhead on high-volume runs:
 This yields more stable tracing under load while preserving detailed task/agent telemetry.
 
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
-  Contact our support team for assistance with trace analysis or any other CrewAI Enterprise features.
-</Card>
+  Contact our support team for assistance with trace analysis or any other CrewAI AMP features.
+</Card>

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

@@ -7,8 +7,8 @@ mode: "wide"
 
 ## Overview
 
-Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to 
-CrewAI Enterprise, such as model calls, tool usage, and flow steps.
+Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to
+CrewAI AMP, such as model calls, tool usage, and flow steps.
 
 ## Usage
 
@@ -65,99 +65,104 @@ CrewAI supports both system events and custom events in Enterprise Event Streami
 
 ### Flow Events:
 
-  - flow_created
-  - flow_started
-  - flow_finished
-  - flow_plot
-  - method_execution_started
-  - method_execution_finished
-  - method_execution_failed
+  - `flow_created`
+  - `flow_started`
+  - `flow_finished`
+  - `flow_plot`
+  - `method_execution_started`
+  - `method_execution_finished`
+  - `method_execution_failed`
 
 ### Agent Events:
 
-  - agent_execution_started
-  - agent_execution_completed
-  - agent_execution_error
-  - lite_agent_execution_started
-  - lite_agent_execution_completed
-  - lite_agent_execution_error
-  - agent_logs_started
-  - agent_logs_execution
-  - agent_evaluation_started
-  - agent_evaluation_completed
-  - agent_evaluation_failed
+  - `agent_execution_started`
+  - `agent_execution_completed`
+  - `agent_execution_error`
+  - `lite_agent_execution_started`
+  - `lite_agent_execution_completed`
+  - `lite_agent_execution_error`
+  - `agent_logs_started`
+  - `agent_logs_execution`
+  - `agent_evaluation_started`
+  - `agent_evaluation_completed`
+  - `agent_evaluation_failed`
 
 ### Crew Events:
 
-  - crew_kickoff_started
-  - crew_kickoff_completed
-  - crew_kickoff_failed
-  - crew_train_started
-  - crew_train_completed
-  - crew_train_failed
-  - crew_test_started
-  - crew_test_completed
-  - crew_test_failed
-  - crew_test_result
+  - `crew_kickoff_started`
+  - `crew_kickoff_completed`
+  - `crew_kickoff_failed`
+  - `crew_train_started`
+  - `crew_train_completed`
+  - `crew_train_failed`
+  - `crew_test_started`
+  - `crew_test_completed`
+  - `crew_test_failed`
+  - `crew_test_result`
 
 ### Task Events:
 
-  - task_started
-  - task_completed
-  - task_failed
-  - task_evaluation
+  - `task_started`
+  - `task_completed`
+  - `task_failed`
+  - `task_evaluation`
 
 ### Tool Usage Events:
 
-  - tool_usage_started
-  - tool_usage_finished
-  - tool_usage_error
-  - tool_validate_input_error
-  - tool_selection_error
-  - tool_execution_error
+  - `tool_usage_started`
+  - `tool_usage_finished`
+  - `tool_usage_error`
+  - `tool_validate_input_error`
+  - `tool_selection_error`
+  - `tool_execution_error`
 
 ### LLM Events:
 
-  - llm_call_started
-  - llm_call_completed
-  - llm_call_failed
-  - llm_stream_chunk
+  - `llm_call_started`
+  - `llm_call_completed`
+  - `llm_call_failed`
+  - `llm_stream_chunk`
 
 ### LLM Guardrail Events:
 
-  - llm_guardrail_started
-  - llm_guardrail_completed
+  - `llm_guardrail_started`
+  - `llm_guardrail_completed`
 
 ### Memory Events:
 
-  - memory_query_started
-  - memory_query_completed
-  - memory_query_failed
-  - memory_save_started
-  - memory_save_completed
-  - memory_save_failed
-  - memory_retrieval_started
-  - memory_retrieval_completed
+  - `memory_query_started`
+  - `memory_query_completed`
+  - `memory_query_failed`
+  - `memory_save_started`
+  - `memory_save_completed`
+  - `memory_save_failed`
+  - `memory_retrieval_started`
+  - `memory_retrieval_completed`
 
 ### Knowledge Events:
 
-  - knowledge_search_query_started
-  - knowledge_search_query_completed
-  - knowledge_search_query_failed
-  - knowledge_query_started
-  - knowledge_query_completed
-  - knowledge_query_failed
+  - `knowledge_search_query_started`
+  - `knowledge_search_query_completed`
+  - `knowledge_search_query_failed`
+  - `knowledge_query_started`
+  - `knowledge_query_completed`
+  - `knowledge_query_failed`
 
 ### Reasoning Events:
 
-  - agent_reasoning_started
-  - agent_reasoning_completed
-  - agent_reasoning_failed
+  - `agent_reasoning_started`
+  - `agent_reasoning_completed`
+  - `agent_reasoning_failed`
 
-Event names match the internal event bus. See [GitHub source](https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events) for the full list.
+Event names match the internal event bus. See GitHub for the full list of events.
 
 You can emit your own custom events, and they will be delivered through the webhook stream alongside system events.
 
+<CardGroup>
+ <Card title="GitHub" icon="github" href="https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events">
+  Full list of events
+ </Card>
 <Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
   Contact our support team for assistance with webhook integration or troubleshooting.
-</Card>
+</Card>
+</CardGroup>

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

@@ -1,22 +1,72 @@
 ---
-title: "Automation Triggers"
-description: "Automatically execute your CrewAI workflows when specific events occur in connected integrations"
-icon: "bolt"
+title: "Triggers Overview"
+description: "Understand how CrewAI AMP triggers work, how to manage them, and where to find integration-specific playbooks"
+icon: "face-smile"
 mode: "wide"
 ---
 
-Automation triggers enable you to automatically run your CrewAI deployments when specific events occur in your connected integrations, creating powerful event-driven workflows that respond to real-time changes in your business systems.
+CrewAI AMP triggers connect your automations to real-time events across the tools your teams already use. Instead of polling systems or relying on manual kickoffs, triggers listen for changes—new emails, calendar updates, CRM status changes—and immediately launch the crew or flow you specify.
 
-## Overview
+<Frame>
+  ![Automation Triggers Overview](/images/enterprise/crew_connectors.png)
+</Frame>
 
-With automation triggers, you can:
+### Integration Playbooks
+
+Deep-dive guides walk through setup and sample workflows for each integration:
+
+<CardGroup cols={2}>
+  <Card title="Gmail Trigger" icon="envelope">
+    <a href="/en/enterprise/guides/gmail-trigger">Enable crews when emails arrive or threads update.</a>
+  </Card>
+
+  <Card title="Google Calendar Trigger" icon="calendar-days">
+    <a href="/en/enterprise/guides/google-calendar-trigger">React to calendar events as they are created, updated, or cancelled.</a>
+  </Card>
+
+  <Card title="Google Drive Trigger" icon="folder-open">
+    <a href="/en/enterprise/guides/google-drive-trigger">Handle Drive file uploads, edits, and deletions.</a>
+  </Card>
+
+  <Card title="Outlook Trigger" icon="envelope-open">
+    <a href="/en/enterprise/guides/outlook-trigger">Automate responses to new Outlook messages and calendar updates.</a>
+  </Card>
+
+  <Card title="OneDrive Trigger" icon="cloud">
+    <a href="/en/enterprise/guides/onedrive-trigger">Audit file activity and sharing changes in OneDrive.</a>
+  </Card>
+
+  <Card title="Microsoft Teams Trigger" icon="comments">
+    <a href="/en/enterprise/guides/microsoft-teams-trigger">Kick off workflows when new Teams chats start.</a>
+  </Card>
+
+  <Card title="HubSpot Trigger" icon="hubspot">
+    <a href="/en/enterprise/guides/hubspot-trigger">Launch automations from HubSpot workflows and lifecycle events.</a>
+  </Card>
+
+  <Card title="Salesforce Trigger" icon="salesforce">
+    <a href="/en/enterprise/guides/salesforce-trigger">Connect Salesforce processes to CrewAI for CRM automation.</a>
+  </Card>
+
+  <Card title="Slack Trigger" icon="slack">
+    <a href="/en/enterprise/guides/slack-trigger">Start crews directly from Slack slash commands.</a>
+  </Card>
+
+  <Card title="Zapier Trigger" icon="bolt">
+    <a href="/en/enterprise/guides/zapier-trigger">Bridge CrewAI with thousands of Zapier-supported apps.</a>
+  </Card>
+</CardGroup>
+
+## Trigger Capabilities
+
+With triggers, you can:
 
 - **Respond to real-time events** - Automatically execute workflows when specific conditions are met
 - **Integrate with external systems** - Connect with platforms like Gmail, Outlook, OneDrive, JIRA, Slack, Stripe and more
 - **Scale your automation** - Handle high-volume events without manual intervention
 - **Maintain context** - Access trigger data within your crews and flows
 
-## Managing Automation Triggers
+## Managing Triggers
 
 ### Viewing Available Triggers
 
@@ -25,7 +75,7 @@ To access and manage your automation triggers:
 1. Navigate to your deployment in the CrewAI dashboard
 2. Click on the **Triggers** tab to view all available trigger integrations
 
-<Frame>
+<Frame caption="Example of available automation triggers for a Gmail deployment">
   <img src="/images/enterprise/list-available-triggers.png" alt="List of available automation triggers" />
 </Frame>
 
@@ -35,7 +85,7 @@ This view shows all the trigger integrations available for your deployment, alon
 
 Each trigger can be easily enabled or disabled using the toggle switch:
 
-<Frame>
+<Frame caption="Enable or disable triggers with toggle">
   <img src="/images/enterprise/trigger-selected.png" alt="Enable or disable triggers with toggle" />
 </Frame>
 
@@ -48,24 +98,69 @@ Simply click the toggle to change the trigger state. Changes take effect immedia
 
 Track the performance and history of your triggered executions:
 
-<Frame>
+<Frame caption="List of executions triggered by automation">
   <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
 </Frame>
 
-## Building Automation
+## Building Trigger-Driven Automations
 
 Before building your automation, it's helpful to understand the structure of trigger payloads that your crews and flows will receive.
 
-### Payload Samples Repository
+### Trigger Setup Checklist
 
-We maintain a comprehensive repository with sample payloads from various trigger sources to help you build and test your automations:
+Before wiring a trigger into production, make sure you:
 
-**🔗 [CrewAI Enterprise Trigger Payload Samples](https://github.com/crewAIInc/crewai-enterprise-trigger-payload-samples)**
+- Connect the integration under **Tools & Integrations** and complete any OAuth or API key steps
+- Enable the trigger toggle on the deployment that should respond to events
+- Provide any required environment variables (API tokens, tenant IDs, shared secrets)
+- Create or update tasks that can parse the incoming payload within the first crew task or flow step
+- Decide whether to pass trigger context automatically using `allow_crewai_trigger_context`
+- Set up monitoring—webhook logs, CrewAI execution history, and optional external alerting
 
-This repository contains:
+### Testing Triggers Locally with CLI
+
+The CrewAI CLI provides powerful commands to help you develop and test trigger-driven automations without deploying to production.
+
+#### List Available Triggers
+
+View all available triggers for your connected integrations:
+
+```bash
+crewai triggers list
+```
+
+This command displays all triggers available based on your connected integrations, showing:
+- Integration name and connection status
+- Available trigger types
+- Trigger names and descriptions
+
+#### Simulate Trigger Execution
+
+Test your crew with realistic trigger payloads before deployment:
+
+```bash
+crewai triggers run <trigger_name>
+```
+
+For example:
+
+```bash
+crewai triggers run microsoft_onedrive/file_changed
+```
+
+This command:
+- Executes your crew locally
+- Passes a complete, realistic trigger payload
+- Simulates exactly how your crew will be called in production
+
+<Warning>
+  **Important Development Notes:**
+  - Use `crewai triggers run <trigger>` to simulate trigger execution during development
+  - Using `crewai run` will NOT simulate trigger calls and won't pass the trigger payload
+  - After deployment, your crew will be executed with the actual trigger payload
+  - If your crew expects parameters that aren't in the trigger payload, execution may fail
+</Warning>
 
-- **Real payload examples** from different trigger sources (Gmail, Google Drive, etc.)
-- **Payload structure documentation** showing the format and available fields
 
 ### Triggers with Crew
 
@@ -169,11 +264,20 @@ def delegate_to_crew(self, crewai_trigger_payload: dict = None):
 ## Troubleshooting
 
 **Trigger not firing:**
-- Verify the trigger is enabled
-- Check integration connection status
+- Verify the trigger is enabled in your deployment's Triggers tab
+- Check integration connection status under Tools & Integrations
+- Ensure all required environment variables are properly configured
 
 **Execution failures:**
 - Check the execution logs for error details
-- If you are developing, make sure the inputs include the `crewai_trigger_payload` parameter with the correct payload
+- Use `crewai triggers run <trigger_name>` to test locally and see the exact payload structure
+- Verify your crew can handle the `crewai_trigger_payload` parameter
+- Ensure your crew doesn't expect parameters that aren't included in the trigger payload
+
+**Development issues:**
+- Always test with `crewai triggers run <trigger>` before deploying to see the complete payload
+- Remember that `crewai run` does NOT simulate trigger calls—use `crewai triggers run` instead
+- Use `crewai triggers list` to verify which triggers are available for your connected integrations
+- After deployment, your crew will receive the actual trigger payload, so test thoroughly locally first
 
 Automation triggers transform your CrewAI deployments into responsive, event-driven systems that can seamlessly integrate with your existing business processes and tools.

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

@@ -19,8 +19,8 @@ This guide walks you through connecting Azure OpenAI with Crew Studio for seamle
             </Frame>
     </Step>
 
-    <Step title="Configure CrewAI Enterprise Connection">
-        4. In another tab, open `CrewAI Enterprise > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure.
+    <Step title="Configure CrewAI AMP Connection">
+        4. In another tab, open `CrewAI AMP > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure.
         5. On the same page, add environment variables from step 3:
             - One named `AZURE_DEPLOYMENT_TARGET_URL` (using the Target URI). The URL should look like this: https://your-deployment.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview
             - Another named `AZURE_API_KEY` (using the Key).
@@ -28,7 +28,7 @@ This guide walks you through connecting Azure OpenAI with Crew Studio for seamle
     </Step>
 
     <Step title="Set Default Configuration">
-        7. In `CrewAI Enterprise > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults.
+        7. In `CrewAI AMP > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults.
     </Step>
 
     <Step title="Configure Network Access">
@@ -49,4 +49,4 @@ If you encounter issues:
 - Verify the Target URI format matches the expected pattern
 - Check that the API key is correct and has proper permissions
 - Ensure network access is configured to allow CrewAI connections
-- Confirm the deployment model matches what you've configured in CrewAI 
+- Confirm the deployment model matches what you've configured in CrewAI

docs/en/enterprise/guides/build-crew.mdx

@@ -7,19 +7,17 @@ mode: "wide"
 
 ## Overview
 
-[CrewAI Enterprise](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments.
+[CrewAI AMP](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments.
 
 ## Getting Started
 
 <iframe
-    width="100%"
-    height="400"
-    src="https://www.youtube.com/embed/-kSOTtYzgEw"
-    title="Building Crews with CrewAI CLI"
-    frameborder="0"
-    style={{ borderRadius: '10px' }}
-    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-    allowfullscreen
+  className="w-full aspect-video rounded-xl"
+  src="https://www.youtube.com/embed/-kSOTtYzgEw"
+  title="Building crews with the CrewAI CLI"
+  frameBorder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+  allowFullScreen
 ></iframe>
 
 ### Installation and Setup

docs/en/enterprise/guides/capture_telemetry_logs.mdx

@@ -0,0 +1,35 @@
+---
+title: "Open Telemetry Logs"
+description: "Understand how to capture telemetry logs from your CrewAI AMP deployments"
+icon: "magnifying-glass-chart"
+mode: "wide"
+---
+
+CrewAI AMP provides a powerful way to capture telemetry logs from your deployments. This allows you to monitor the performance of your agents and workflows, and to debug issues that may arise.
+
+## Prerequisites
+
+<CardGroup cols={2}>
+  <Card title="ENTERPRISE OTEL SETUP enabled" icon="users">
+    Your organization should have ENTERPRISE OTEL SETUP enabled
+  </Card>
+  <Card title="OTEL collector setup" icon="server">
+    Your organization should have an OTEL collector setup or a provider like Datadog log intake setup
+  </Card>
+</CardGroup>
+
+
+## How to capture telemetry logs
+
+1. Go to settings/organization tab
+2. Configure your OTEL collector setup
+3. Save
+
+
+
+Example to setup OTEL log collection capture to Datadog.
+
+
+<Frame>
+  ![Capture Telemetry Logs](/images/crewai-otel-export.png)
+</Frame>

docs/en/enterprise/guides/deploy-crew.mdx

@@ -1,12 +1,12 @@
 ---
 title: "Deploy Crew"
-description: "Deploying a Crew on CrewAI Enterprise"
+description: "Deploying a Crew on CrewAI AMP"
 icon: "rocket"
 mode: "wide"
 ---
 
 <Note>
-After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI Enterprise platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow.
+After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI AMP platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow.
 </Note>
 
 ## Prerequisites
@@ -39,10 +39,10 @@ The CLI provides the fastest way to deploy locally developed crews to the Enterp
   </Step>
 
   <Step title="Authenticate with the Enterprise Platform">
-    First, you need to authenticate your CLI with the CrewAI Enterprise platform:
+    First, you need to authenticate your CLI with the CrewAI AMP platform:
 
     ```bash
-    # If you already have a CrewAI Enterprise account, or want to create one:
+    # If you already have a CrewAI AMP account, or want to create one:
     crewai login
     ```
 
@@ -124,7 +124,7 @@ The CrewAI CLI offers several commands to manage your deployments:
 
 ## Option 2: Deploy Directly via Web Interface
 
-You can also deploy your crews directly through the CrewAI Enterprise web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine.
+You can also deploy your crews directly through the CrewAI AMP web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine.
 
 <Steps>
 
@@ -134,9 +134,9 @@ You can also deploy your crews directly through the CrewAI Enterprise web interf
 
   </Step>
 
-  <Step title="Connecting GitHub to CrewAI Enterprise">
+  <Step title="Connecting GitHub to CrewAI AMP">
 
-    1. Log in to [CrewAI Enterprise](https://app.crewai.com)
+    1. Log in to [CrewAI AMP](https://app.crewai.com)
     2. Click on the button "Connect GitHub"
 
     <Frame>
@@ -190,7 +190,7 @@ You can also deploy your crews directly through the CrewAI Enterprise web interf
 ## ⚠️ Environment Variable Security Requirements
 
 <Warning>
-**Important**: CrewAI Enterprise has security restrictions on environment variable names that can cause deployment failures if not followed.
+**Important**: CrewAI AMP has security restrictions on environment variable names that can cause deployment failures if not followed.
 </Warning>
 
 ### Blocked Environment Variable Patterns

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

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

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

@@ -0,0 +1,88 @@
+---
+title: "Gmail Trigger"
+description: "Trigger automations when Gmail events occur (e.g., new emails, labels)."
+icon: "envelope"
+mode: "wide"
+---
+
+## Overview
+
+Use the Gmail Trigger to kick off your deployed crews when Gmail events happen in connected accounts, such as receiving a new email or messages matching a label/filter.
+
+<Tip>
+  Make sure Gmail is connected in Tools & Integrations and the trigger is enabled for your deployment.
+</Tip>
+
+## Enabling the Gmail Trigger
+
+1. Open your deployment in CrewAI AMP
+2. Go to the **Triggers** tab
+3. Locate **Gmail** and switch the toggle to enable
+
+<Frame>
+  <img src="/images/enterprise/trigger-selected.png" alt="Enable or disable triggers with toggle" />
+</Frame>
+
+## Example: Process new emails
+
+When a new email arrives, the Gmail Trigger will send the payload to your Crew or Flow. Below is a Crew example that parses and processes the trigger payload.
+
+```python
+@CrewBase
+class GmailProcessingCrew:
+    @agent
+    def parser(self) -> Agent:
+        return Agent(
+            config=self.agents_config['parser'],
+        )
+
+    @task
+    def parse_gmail_payload(self) -> Task:
+        return Task(
+            config=self.tasks_config['parse_gmail_payload'],
+            agent=self.parser(),
+        )
+
+    @task
+    def act_on_email(self) -> Task:
+        return Task(
+            config=self.tasks_config['act_on_email'],
+            agent=self.parser(),
+        )
+```
+
+The Gmail payload will be available via the standard context mechanisms.
+
+### Testing Locally
+
+Test your Gmail trigger integration locally using the CrewAI CLI:
+
+```bash
+# View all available triggers
+crewai triggers list
+
+# Simulate a Gmail trigger with realistic payload
+crewai triggers run gmail/new_email
+```
+
+The `crewai triggers run` command will execute your crew with a complete Gmail payload, allowing you to test your parsing logic before deployment.
+
+<Warning>
+  Use `crewai triggers run gmail/new_email` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload.
+</Warning>
+
+## Monitoring Executions
+
+Track history and performance of triggered runs:
+
+<Frame>
+  <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
+</Frame>
+
+## Troubleshooting
+
+- Ensure Gmail is connected in Tools & Integrations
+- Verify the Gmail Trigger is enabled on the Triggers tab
+- Test locally with `crewai triggers run gmail/new_email` to see the exact payload structure
+- Check the execution logs and confirm the payload is passed as `crewai_trigger_payload`
+- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution