feat: bump versions to 1.0.0a4 (#3686)

Resolved merge conflicts:
- agent.py: kept main's docker validation, restored get_platform_tools method, fixed KnowledgeRetrievalStartedEvent position
- agent_utils.py: merged both LLM and LiteAgent imports
- test_cache_hitting.yaml: accepted main's test recordings
- Removed 4 deprecated test cassettes from old tests/ directory

Key changes from main:
- Enhanced knowledge event handling with from_agent/from_task parameters
- Updated LLMMessage typing in kickoff methods
- Added guardrail parameter to async kickoff
- Restored runtime validation in guardrail.py

.github/codeql/codeql-config.yml

@@ -2,27 +2,20 @@ name: "CodeQL Config"
 
 paths-ignore:
   # Ignore template files - these are boilerplate code that shouldn't be analyzed
-  - "lib/crewai/src/crewai/cli/templates/**"
+  - "src/crewai/cli/templates/**"
   # Ignore test cassettes - these are test fixtures/recordings
-  - "lib/crewai/tests/cassettes/**"
-  - "lib/crewai-tools/tests/cassettes/**"
+  - "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/**"
+  # Include all Python source code
+  - "src/**"
+  # Include tests (but exclude cassettes)
+  - "tests/**"
 
 # Configure specific queries or packs if needed
 # queries:
-#   - uses: security-and-quality
+#   - uses: security-and-quality

.github/dependabot.yml

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

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

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

.github/workflows/publish.yml

@@ -7,6 +7,7 @@ on:
 
 jobs:
   build:
+    if: github.event.release.prerelease == true
     name: Build packages
     runs-on: ubuntu-latest
     permissions:
@@ -24,7 +25,7 @@ jobs:
 
       - name: Build packages
         run: |
-          uv build --all-packages
+          uv build --prerelease="allow" --all-packages
           rm dist/.gitignore
 
       - name: Upload artifacts
@@ -34,6 +35,7 @@ jobs:
           path: dist/
 
   publish:
+    if: github.event.release.prerelease == true
     name: Publish to PyPI
     needs: build
     runs-on: ubuntu-latest

.pre-commit-config.yaml

@@ -3,25 +3,19 @@ repos:
     hooks:
       - id: ruff
         name: ruff
-        entry: bash -c 'source .venv/bin/activate && uv run ruff check --config pyproject.toml "$@"' --
+        entry: uv run ruff check
         language: system
-        pass_filenames: true
         types: [python]
+        exclude: ^lib/crewai/
       - id: ruff-format
         name: ruff-format
-        entry: bash -c 'source .venv/bin/activate && uv run ruff format --config pyproject.toml "$@"' --
+        entry: uv run ruff format
         language: system
-        pass_filenames: true
         types: [python]
+        exclude: ^lib/crewai/
       - id: mypy
         name: mypy
-        entry: bash -c 'source .venv/bin/activate && uv run mypy --config-file pyproject.toml "$@"' --
+        entry: uv run mypy
         language: system
-        pass_filenames: true
         types: [python]
-        exclude: ^(lib/crewai/src/crewai/cli/templates/|lib/crewai/tests/|lib/crewai-tools/tests/)
-  - repo: https://github.com/astral-sh/uv-pre-commit
-    rev: 0.9.3
-    hooks:
-      - id: uv-lock
-
+        exclude: ^lib/crewai/

docs/docs.json

@@ -134,7 +134,6 @@
                 "group": "MCP Integration",
                 "pages": [
                   "en/mcp/overview",
-                  "en/mcp/dsl-integration",
                   "en/mcp/stdio",
                   "en/mcp/sse",
                   "en/mcp/streamable-http",
@@ -276,7 +275,6 @@
                   "en/observability/overview",
                   "en/observability/arize-phoenix",
                   "en/observability/braintrust",
-                  "en/observability/datadog",
                   "en/observability/langdb",
                   "en/observability/langfuse",
                   "en/observability/langtrace",
@@ -313,10 +311,7 @@
                   "en/learn/multimodal-agents",
                   "en/learn/replay-tasks-from-latest-crew-kickoff",
                   "en/learn/sequential-process",
-                  "en/learn/using-annotations",
-                  "en/learn/execution-hooks",
-                  "en/learn/llm-hooks",
-                  "en/learn/tool-hooks"
+                  "en/learn/using-annotations"
                 ]
               },
               {
@@ -575,7 +570,6 @@
                 "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",
@@ -704,7 +698,6 @@
                   "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",
@@ -740,10 +733,7 @@
                   "pt-BR/learn/multimodal-agents",
                   "pt-BR/learn/replay-tasks-from-latest-crew-kickoff",
                   "pt-BR/learn/sequential-process",
-                  "pt-BR/learn/using-annotations",
-                  "pt-BR/learn/execution-hooks",
-                  "pt-BR/learn/llm-hooks",
-                  "pt-BR/learn/tool-hooks"
+                  "pt-BR/learn/using-annotations"
                 ]
               },
               {
@@ -835,12 +825,6 @@
                 "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",
@@ -999,7 +983,6 @@
                 "group": "MCP 통합",
                 "pages": [
                   "ko/mcp/overview",
-                  "ko/mcp/dsl-integration",
                   "ko/mcp/stdio",
                   "ko/mcp/sse",
                   "ko/mcp/streamable-http",
@@ -1140,7 +1123,6 @@
                   "ko/observability/overview",
                   "ko/observability/arize-phoenix",
                   "ko/observability/braintrust",
-                  "ko/observability/datadog",
                   "ko/observability/langdb",
                   "ko/observability/langfuse",
                   "ko/observability/langtrace",
@@ -1176,10 +1158,7 @@
                   "ko/learn/multimodal-agents",
                   "ko/learn/replay-tasks-from-latest-crew-kickoff",
                   "ko/learn/sequential-process",
-                  "ko/learn/using-annotations",
-                  "ko/learn/execution-hooks",
-                  "ko/learn/llm-hooks",
-                  "ko/learn/tool-hooks"
+                  "ko/learn/using-annotations"
                 ]
               },
               {
@@ -1271,12 +1250,6 @@
                 "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",

docs/en/concepts/cli.mdx

@@ -402,77 +402,6 @@ crewai config reset
 After resetting configuration, re-run `crewai login` to authenticate again.
 </Tip>
 
-### 14. Trace Management
-
-Manage trace collection preferences for your Crew and Flow executions.
-
-```shell Terminal
-crewai traces [COMMAND]
-```
-
-#### Commands:
-
-- `enable`: Enable trace collection for crew/flow executions
-```shell Terminal
-crewai traces enable
-```
-
-- `disable`: Disable trace collection for crew/flow executions
-```shell Terminal
-crewai traces disable
-```
-
-- `status`: Show current trace collection status
-```shell Terminal
-crewai traces status
-```
-
-#### How Tracing Works
-
-Trace collection is controlled by checking three settings in priority order:
-
-1. **Explicit flag in code** (highest priority - can enable OR disable):
-   ```python
-   crew = Crew(agents=[...], tasks=[...], tracing=True)   # Always enable
-   crew = Crew(agents=[...], tasks=[...], tracing=False)  # Always disable
-   crew = Crew(agents=[...], tasks=[...])                 # Check lower priorities (default)
-   ```
-   - `tracing=True` will **always enable** tracing (overrides everything)
-   - `tracing=False` will **always disable** tracing (overrides everything)
-   - `tracing=None` or omitted will check lower priority settings
-
-2. **Environment variable** (second priority):
-   ```env
-   CREWAI_TRACING_ENABLED=true
-   ```
-   - Checked only if `tracing` is not explicitly set to `True` or `False` in code
-   - Set to `true` or `1` to enable tracing
-
-3. **User preference** (lowest priority):
-   ```shell Terminal
-   crewai traces enable
-   ```
-   - Checked only if `tracing` is not set in code and `CREWAI_TRACING_ENABLED` is not set to `true`
-   - Running `crewai traces enable` is sufficient to enable tracing by itself
-
-<Note>
-**To enable tracing**, use any one of these methods:
-- Set `tracing=True` in your Crew/Flow code, OR
-- Add `CREWAI_TRACING_ENABLED=true` to your `.env` file, OR
-- Run `crewai traces enable`
-
-**To disable tracing**, use any ONE of these methods:
-- Set `tracing=False` in your Crew/Flow code (overrides everything), OR
-- Remove or set to `false` the `CREWAI_TRACING_ENABLED` env var, OR
-- Run `crewai traces disable`
-
-Higher priority settings override lower ones.
-</Note>
-
-<Tip>
-For more information about tracing, see the [Tracing documentation](/observability/tracing).
-</Tip>
-
 <Tip>
 CrewAI CLI handles authentication to the Tool Repository automatically when adding packages to your project. Just append `crewai` before any `uv` command to use it. E.g. `crewai uv add requests`. For more information, see [Tool Repository](https://docs.crewai.com/enterprise/features/tool-repository) docs.
 </Tip>

docs/en/concepts/knowledge.mdx

@@ -739,7 +739,7 @@ class KnowledgeMonitorListener(BaseEventListener):
 knowledge_monitor = KnowledgeMonitorListener()
 ```
 
-For more information on using events, see the [Event Listeners](/en/concepts/event-listener) documentation.
+For more information on using events, see the [Event Listeners](https://docs.crewai.com/concepts/event-listener) documentation.
 
 ### Custom Knowledge Sources
 

docs/en/concepts/llms.mdx

@@ -7,7 +7,7 @@ mode: "wide"
 
 ## Overview
 
-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.
+CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
 
 
 ## What are LLMs?
@@ -113,104 +113,44 @@ In this section, you'll find detailed examples that help you select, configure,
 
 <AccordionGroup>
   <Accordion title="OpenAI">
-    CrewAI provides native integration with OpenAI through the OpenAI Python SDK.
+    Set the following environment variables in your `.env` file:
 
     ```toml Code
     # Required
     OPENAI_API_KEY=sk-...
 
     # Optional
-    OPENAI_BASE_URL=<custom-base-url>
+    OPENAI_API_BASE=<custom-base-url>
+    OPENAI_ORGANIZATION=<your-org-id>
     ```
 
-    **Basic Usage:**
+    Example usage in your CrewAI project:
     ```python Code
     from crewai import LLM
 
     llm = LLM(
-        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
+        model="openai/gpt-4", # call model by provider/model_name
+        temperature=0.8,
+        max_tokens=150,
         top_p=0.9,
         frequency_penalty=0.1,
         presence_penalty=0.1,
         stop=["END"],
-        seed=42,  # 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
+        seed=42
     )
     ```
 
-    **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:**
+    OpenAI is one of the leading providers of LLMs with a wide range of models and features.
 
     | Model               | Context Window   | Best For                                      |
     |---------------------|------------------|-----------------------------------------------|
-    | 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]"
-    ```
+    | GPT-4               | 8,192 tokens     | High-accuracy tasks, complex reasoning        |
+    | GPT-4 Turbo         | 128,000 tokens   | Long-form content, document analysis          |
+    | GPT-4o & GPT-4o-mini  | 128,000 tokens  | Cost-effective large context processing       |
+    | o3-mini             | 200,000 tokens   | Fast reasoning, complex reasoning             |
+    | o1-mini             | 128,000 tokens   | Fast reasoning, complex reasoning             |
+    | o1-preview          | 128,000 tokens   | Fast reasoning, complex reasoning             |
+    | o1                  | 200,000 tokens   | Fast reasoning, complex reasoning             |
   </Accordion>
 
   <Accordion title="Meta-Llama">
@@ -247,186 +187,69 @@ 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>
     ```
 
-    **Basic Usage:**
+    Example usage in your CrewAI project:
     ```python Code
-    from crewai import LLM
-
     llm = LLM(
-        model="anthropic/claude-3-5-sonnet-20241022",
-        api_key="your-api-key",  # Or set ANTHROPIC_API_KEY
-        max_tokens=4096  # Required for Anthropic
+        model="anthropic/claude-3-sonnet-20240229-v1:0",
+        temperature=0.7
     )
     ```
-
-    **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)">
-    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).
+    Set your API key in your `.env` file. If you need a key, or need to find an
+    existing key, check [AI Studio](https://aistudio.google.com/apikey).
 
     ```toml .env
-    # Required (one of the following)
-    GOOGLE_API_KEY=<your-api-key>
+    # https://ai.google.dev/gemini-api/docs/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
     ```
 
-    **Basic Usage:**
+    Example usage in your CrewAI project:
     ```python Code
     from crewai import LLM
 
     llm = LLM(
         model="gemini/gemini-2.0-flash",
-        api_key="your-api-key",  # Or set GOOGLE_API_KEY/GEMINI_API_KEY
-        temperature=0.7
-    )
-    ```
-
-    **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:**
+    ### Gemini models
 
     Google offers a range of powerful models optimized for different use cases.
 
     | Model                          | Context Window | Best For                                                          |
     |--------------------------------|----------------|-------------------------------------------------------------------|
-    | 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-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
+    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
+    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
     | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
-    | gemini-1.5-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                                      |
-    | 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]"
-    ```
+    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
+    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
 
     The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
+
+    ### Gemma
+
+    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
+
+    | Model          | Context Window |
+    |----------------|----------------|
+    | gemma-3-1b-it  | 32k tokens     |
+    | gemma-3-4b-it  | 32k tokens     |
+    | gemma-3-12b-it | 32k tokens     |
+    | gemma-3-27b-it | 128k tokens    |
+
   </Accordion>
   <Accordion title="Google (Vertex AI)">
     Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
@@ -468,146 +291,43 @@ 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_ENDPOINT=<your-endpoint-url>
+    AZURE_API_BASE=<your-resource-url>
+    AZURE_API_VERSION=<api-version>
 
     # Optional
-    AZURE_API_VERSION=<api-version>  # Defaults to 2024-06-01
+    AZURE_AD_TOKEN=<your-azure-ad-token>
+    AZURE_API_TYPE=<your-azure-api-type>
     ```
 
-    **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:**
+    Example usage in your CrewAI project:
     ```python Code
     llm = LLM(
         model="azure/gpt-4",
-        api_key="<your-api-key>",  # Or set AZURE_API_KEY
-        endpoint="<your-endpoint-url>",
-        api_version="2024-06-01"
+        api_version="2023-05-15"
     )
     ```
-
-    **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>
-
-    # Optional
-    AWS_SESSION_TOKEN=<your-session-token>  # For temporary credentials
-    AWS_DEFAULT_REGION=<your-region>  # Defaults to us-east-1
+    AWS_DEFAULT_REGION=<your-region>
     ```
 
-    **Basic Usage:**
+    Example usage in your CrewAI project:
     ```python Code
-    from crewai import LLM
-
     llm = LLM(
-        model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
-        region_name="us-east-1"
+        model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
     )
     ```
 
-    **Advanced Configuration:**
-    ```python Code
-    from crewai import LLM
+    Before using Amazon Bedrock, make sure you have boto3 installed in your environment
 
-    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.
+    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API, enabling secure and responsible AI application development.
 
     | Model                   | Context Window       | Best For                                                          |
     |-------------------------|----------------------|-------------------------------------------------------------------|
@@ -637,12 +357,7 @@ 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">
@@ -1035,7 +750,7 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     ```
 
     <Tip>
-      [Click here](/en/concepts/event-listener#event-listeners) for more details
+      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details
     </Tip>
   </Tab>
 
@@ -1184,7 +899,7 @@ Learn how to get the most out of your LLM configuration:
   </Accordion>
 
   <Accordion title="Drop Additional Parameters">
-    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.
+    CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
     For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:
 
     ```python
@@ -1200,52 +915,6 @@ 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

@@ -60,7 +60,6 @@ crew = Crew(
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
 | **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
-| **Guardrails** _(optional)_       | `guardrails`       | `Optional[List[Callable] | List[str]]` | List of guardrails to validate task output before proceeding to next task.                                      |
 | **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]`     | Maximum number of retries when guardrail validation fails. Defaults to 3.                                         |
 
 <Note type="warning" title="Deprecated: max_retries">
@@ -224,7 +223,6 @@ By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput`
 | **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
 | **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
 | **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
-| **Messages**      | `messages`      | `list[LLMMessage]`         | The messages from the last task execution.                                                           |
 
 ### Task Methods and Properties
 
@@ -343,11 +341,7 @@ Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
 feedback to agents when their output doesn't meet specific criteria.
 
-CrewAI supports two types of guardrails:
-
-1. **Function-based guardrails**: Python functions with custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
-
-2. **LLM-based guardrails**: String descriptions that use the agent's LLM to validate outputs based on natural language criteria. These are ideal for complex or subjective validation requirements.
+Guardrails are implemented as Python functions that contain custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
 
 ### Function-Based Guardrails
 
@@ -361,12 +355,12 @@ def validate_blog_content(result: TaskOutput) -> Tuple[bool, Any]:
     """Validate blog content meets requirements."""
     try:
         # Check word count
-        word_count = len(result.raw.split())
+        word_count = len(result.split())
         if word_count > 200:
             return (False, "Blog content exceeds 200 words")
 
         # Additional validation logic here
-        return (True, result.raw.strip())
+        return (True, result.strip())
     except Exception as e:
         return (False, "Unexpected error during validation")
 
@@ -378,147 +372,6 @@ blog_task = Task(
 )
 ```
 
-### LLM-Based Guardrails (String Descriptions)
-
-Instead of writing custom validation functions, you can use string descriptions that leverage LLM-based validation. When you provide a string to the `guardrail` or `guardrails` parameter, CrewAI automatically creates an `LLMGuardrail` that uses the agent's LLM to validate the output based on your description.
-
-**Requirements**:
-- The task must have an `agent` assigned (the guardrail uses the agent's LLM)
-- Provide a clear, descriptive string explaining the validation criteria
-
-```python Code
-from crewai import Task
-
-# Single LLM-based guardrail
-blog_task = Task(
-    description="Write a blog post about AI",
-    expected_output="A blog post under 200 words",
-    agent=blog_agent,
-    guardrail="The blog post must be under 200 words and contain no technical jargon"
-)
-```
-
-LLM-based guardrails are particularly useful for:
-- **Complex validation logic** that's difficult to express programmatically
-- **Subjective criteria** like tone, style, or quality assessments
-- **Natural language requirements** that are easier to describe than code
-
-The LLM guardrail will:
-1. Analyze the task output against your description
-2. Return `(True, output)` if the output complies with the criteria
-3. Return `(False, feedback)` with specific feedback if validation fails
-
-**Example with detailed validation criteria**:
-
-```python Code
-research_task = Task(
-    description="Research the latest developments in quantum computing",
-    expected_output="A comprehensive research report",
-    agent=researcher_agent,
-    guardrail="""
-    The research report must:
-    - Be at least 1000 words long
-    - Include at least 5 credible sources
-    - Cover both technical and practical applications
-    - Be written in a professional, academic tone
-    - Avoid speculation or unverified claims
-    """
-)
-```
-
-### Multiple Guardrails
-
-You can apply multiple guardrails to a task using the `guardrails` parameter. Multiple guardrails are executed sequentially, with each guardrail receiving the output from the previous one. This allows you to chain validation and transformation steps.
-
-The `guardrails` parameter accepts:
-- A list of guardrail functions or string descriptions
-- A single guardrail function or string (same as `guardrail`)
-
-**Note**: If `guardrails` is provided, it takes precedence over `guardrail`. The `guardrail` parameter will be ignored when `guardrails` is set.
-
-```python Code
-from typing import Tuple, Any
-from crewai import TaskOutput, Task
-
-def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
-    """Validate word count is within limits."""
-    word_count = len(result.raw.split())
-    if word_count < 100:
-        return (False, f"Content too short: {word_count} words. Need at least 100 words.")
-    if word_count > 500:
-        return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
-    return (True, result.raw)
-
-def validate_no_profanity(result: TaskOutput) -> Tuple[bool, Any]:
-    """Check for inappropriate language."""
-    profanity_words = ["badword1", "badword2"]  # Example list
-    content_lower = result.raw.lower()
-    for word in profanity_words:
-        if word in content_lower:
-            return (False, f"Inappropriate language detected: {word}")
-    return (True, result.raw)
-
-def format_output(result: TaskOutput) -> Tuple[bool, Any]:
-    """Format and clean the output."""
-    formatted = result.raw.strip()
-    # Capitalize first letter
-    formatted = formatted[0].upper() + formatted[1:] if formatted else formatted
-    return (True, formatted)
-
-# Apply multiple guardrails sequentially
-blog_task = Task(
-    description="Write a blog post about AI",
-    expected_output="A well-formatted blog post between 100-500 words",
-    agent=blog_agent,
-    guardrails=[
-        validate_word_count,      # First: validate length
-        validate_no_profanity,    # Second: check content
-        format_output             # Third: format the result
-    ],
-    guardrail_max_retries=3
-)
-```
-
-In this example, the guardrails execute in order:
-1. `validate_word_count` checks the word count
-2. `validate_no_profanity` checks for inappropriate language (using the output from step 1)
-3. `format_output` formats the final result (using the output from step 2)
-
-If any guardrail fails, the error is sent back to the agent, and the task is retried up to `guardrail_max_retries` times.
-
-**Mixing function-based and LLM-based guardrails**:
-
-You can combine both function-based and string-based guardrails in the same list:
-
-```python Code
-from typing import Tuple, Any
-from crewai import TaskOutput, Task
-
-def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
-    """Validate word count is within limits."""
-    word_count = len(result.raw.split())
-    if word_count < 100:
-        return (False, f"Content too short: {word_count} words. Need at least 100 words.")
-    if word_count > 500:
-        return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
-    return (True, result.raw)
-
-# Mix function-based and LLM-based guardrails
-blog_task = Task(
-    description="Write a blog post about AI",
-    expected_output="A well-formatted blog post between 100-500 words",
-    agent=blog_agent,
-    guardrails=[
-        validate_word_count,  # Function-based: precise word count check
-        "The content must be engaging and suitable for a general audience",  # LLM-based: subjective quality check
-        "The writing style should be clear, concise, and free of technical jargon"  # LLM-based: style validation
-    ],
-    guardrail_max_retries=3
-)
-```
-
-This approach combines the precision of programmatic validation with the flexibility of LLM-based assessment for subjective criteria.
-
 ### Guardrail Function Requirements
 
 1. **Function Signature**:

docs/en/enterprise/features/marketplace.mdx

@@ -37,7 +37,7 @@ you can use them locally or refine them to your needs.
   <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/guides/tool-repository#tool-repository" icon="toolbox">
+  <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">

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

@@ -57,22 +57,6 @@ Tools & Integrations is the central hub for connecting third‑party apps and ma
   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>
@@ -241,7 +225,7 @@ Tools & Integrations is the central hub for connecting third‑party apps and ma
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Tool Repository" href="/en/enterprise/guides/tool-repository#tool-repository" icon="toolbox">
+  <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">

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

@@ -117,50 +117,27 @@ Before wiring a trigger into production, make sure you:
 - Decide whether to pass trigger context automatically using `allow_crewai_trigger_context`
 - Set up monitoring—webhook logs, CrewAI execution history, and optional external alerting
 
-### Testing Triggers Locally with CLI
+### Payload & Crew Examples Repository
 
-The CrewAI CLI provides powerful commands to help you develop and test trigger-driven automations without deploying to production.
+We maintain a comprehensive repository with end-to-end trigger examples to help you build and test your automations:
 
-#### List Available Triggers
+This repository contains:
 
-View all available triggers for your connected integrations:
+- **Realistic payload samples** for every supported trigger integration
+- **Ready-to-run crew implementations** that parse each payload and turn it into a business workflow
+- **Multiple scenarios per integration** (e.g., new events, updates, deletions) so you can match the shape of your data
 
-```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>
+| Integration | When it fires | Payload Samples | Crew Examples |
+| :-- | :-- | :-- | :-- |
+| Gmail | New messages, thread updates | [New alerts, thread updates](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | [`new-email-crew.py`, `gmail-alert-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) |
+| Google Calendar | Event created / updated / started / ended / cancelled | [Event lifecycle payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | [`calendar-event-crew.py`, `calendar-meeting-crew.py`, `calendar-working-location-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) |
+| Google Drive | File created / updated / deleted | [File lifecycle payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | [`drive-file-crew.py`, `drive-file-deletion-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) |
+| Outlook | New email, calendar event removed | [Outlook payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | [`outlook-message-crew.py`, `outlook-event-removal-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) |
+| OneDrive | File operations (create, update, share, delete) | [OneDrive payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | [`onedrive-file-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) |
+| HubSpot | Record created / updated (contacts, companies, deals) | [HubSpot payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | [`hubspot-company-crew.py`, `hubspot-contact-crew.py`, `hubspot-record-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) |
+| Microsoft Teams | Chat thread created | [Teams chat payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | [`teams-chat-created-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) |
 
+Use these samples to understand payload shape, copy the matching crew, and then replace the test payload with your live trigger data.
 
 ### Triggers with Crew
 
@@ -264,20 +241,15 @@ def delegate_to_crew(self, crewai_trigger_payload: dict = None):
 ## Troubleshooting
 
 **Trigger not firing:**
-- 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
+- Verify the trigger is enabled
+- Check integration connection status
 
 **Execution failures:**
 - Check the execution logs for error details
-- 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
+- If you are developing, make sure the inputs include the `crewai_trigger_payload` parameter with the correct payload
 
 Automation triggers transform your CrewAI deployments into responsive, event-driven systems that can seamlessly integrate with your existing business processes and tools.
+
+<Card title="CrewAI AMP Trigger Examples" href="https://github.com/crewAIInc/crewai-enterprise-trigger-examples" icon="github">
+    Check them out on GitHub!
+</Card>

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

@@ -51,25 +51,16 @@ class GmailProcessingCrew:
         )
 ```
 
-The Gmail payload will be available via the standard context mechanisms.
+The Gmail payload will be available via the standard context mechanisms. See the payload samples repository for structure and fields.
 
-### Testing Locally
+### Sample payloads & crews
 
-Test your Gmail trigger integration locally using the CrewAI CLI:
+The [CrewAI AMP Trigger Examples repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) includes:
 
-```bash
-# View all available triggers
-crewai triggers list
+- `new-email-payload-1.json` / `new-email-payload-2.json` — production-style new message alerts with matching crews in `new-email-crew.py`
+- `thread-updated-sample-1.json` — follow-up messages on an existing thread, processed by `gmail-alert-crew.py`
 
-# 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>
+Use these samples to validate your parsing logic locally before wiring the trigger to your live Gmail accounts.
 
 ## Monitoring Executions
 
@@ -79,10 +70,16 @@ Track history and performance of triggered runs:
   <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
 </Frame>
 
+## Payload Reference
+
+See the sample payloads and field descriptions:
+
+<Card title="Gmail samples in Trigger Examples Repo" href="https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail" icon="envelopes-bulk">
+    Gmail samples in Trigger Examples Repo
+</Card>
+
 ## 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

docs/en/enterprise/guides/google-calendar-trigger.mdx

@@ -39,23 +39,16 @@ print(result.raw)
 
 Use `crewai_trigger_payload` exactly as it is delivered by the trigger so the crew can extract the proper fields.
 
-## Testing Locally
+## Sample payloads & crews
 
-Test your Google Calendar trigger integration locally using the CrewAI CLI:
+The [Google Calendar examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) show how to handle multiple event types:
 
-```bash
-# View all available triggers
-crewai triggers list
+- `new-event.json` → standard event creation handled by `calendar-event-crew.py`
+- `event-updated.json` / `event-started.json` / `event-ended.json` → in-flight updates processed by `calendar-meeting-crew.py`
+- `event-canceled.json` → cancellation workflow that alerts attendees via `calendar-meeting-crew.py`
+- Working location events use `calendar-working-location-crew.py` to extract on-site schedules
 
-# Simulate a Google Calendar trigger with realistic payload
-crewai triggers run google_calendar/event_changed
-```
-
-The `crewai triggers run` command will execute your crew with a complete Calendar payload, allowing you to test your parsing logic before deployment.
-
-<Warning>
-  Use `crewai triggers run google_calendar/event_changed` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload.
-</Warning>
+Each crew transforms raw event metadata (attendees, rooms, working locations) into the summaries your teams need.
 
 ## Monitoring Executions
 
@@ -68,7 +61,5 @@ The **Executions** list in the deployment dashboard tracks every triggered run a
 ## Troubleshooting
 
 - Ensure the correct Google account is connected and the trigger is enabled
-- Test locally with `crewai triggers run google_calendar/event_changed` to see the exact payload structure
 - Confirm your workflow handles all-day events (payloads use `start.date` and `end.date` instead of timestamps)
 - Check execution logs if reminders or attendee arrays are missing—calendar permissions can limit fields in the payload
-- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution

docs/en/enterprise/guides/google-drive-trigger.mdx

@@ -36,23 +36,15 @@ crew.kickoff({
 })
 ```
 
-## Testing Locally
+## Sample payloads & crews
 
-Test your Google Drive trigger integration locally using the CrewAI CLI:
+Explore the [Google Drive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) to cover different operations:
 
-```bash
-# View all available triggers
-crewai triggers list
+- `new-file.json` → new uploads processed by `drive-file-crew.py`
+- `updated-file.json` → file edits and metadata changes handled by `drive-file-crew.py`
+- `deleted-file.json` → deletion events routed through `drive-file-deletion-crew.py`
 
-# Simulate a Google Drive trigger with realistic payload
-crewai triggers run google_drive/file_changed
-```
-
-The `crewai triggers run` command will execute your crew with a complete Drive payload, allowing you to test your parsing logic before deployment.
-
-<Warning>
-  Use `crewai triggers run google_drive/file_changed` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload.
-</Warning>
+Each crew highlights the file name, operation type, owner, permissions, and security considerations so downstream systems can respond appropriately.
 
 ## Monitoring Executions
 
@@ -65,7 +57,5 @@ Track history and performance of triggered runs with the **Executions** list in
 ## Troubleshooting
 
 - Verify Google Drive is connected and the trigger toggle is enabled
-- Test locally with `crewai triggers run google_drive/file_changed` to see the exact payload structure
 - If a payload is missing permission data, ensure the connected account has access to the file or folder
 - The trigger sends file IDs only; use the Drive API if you need to fetch binary content during the crew run
-- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution

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

@@ -49,4 +49,16 @@ This guide provides a step-by-step process to set up HubSpot triggers for CrewAI
     </Step>
 </Steps>
 
+## Additional Resources
+
+### Sample payloads & crews
+
+You can jump-start development with the [HubSpot examples in the trigger repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot):
+
+- `record-created-contact.json`, `record-updated-contact.json` → contact lifecycle events handled by `hubspot-contact-crew.py`
+- `record-created-company.json`, `record-updated-company.json` → company enrichment flows in `hubspot-company-crew.py`
+- `record-created-deals.json`, `record-updated-deals.json` → deal pipeline automation in `hubspot-record-crew.py`
+
+Each crew demonstrates how to parse HubSpot record fields, enrich context, and return structured insights.
+
 For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows).

docs/en/enterprise/guides/microsoft-teams-trigger.mdx

@@ -37,28 +37,16 @@ print(result.raw)
 
 The crew parses thread metadata (subject, created time, roster) and generates an action plan for the receiving team.
 
-## Testing Locally
+## Sample payloads & crews
 
-Test your Microsoft Teams trigger integration locally using the CrewAI CLI:
+The [Microsoft Teams examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) include:
 
-```bash
-# View all available triggers
-crewai triggers list
+- `chat-created.json` → chat creation payload processed by `teams-chat-created-crew.py`
 
-# Simulate a Microsoft Teams trigger with realistic payload
-crewai triggers run microsoft_teams/teams_message_created
-```
-
-The `crewai triggers run` command will execute your crew with a complete Teams payload, allowing you to test your parsing logic before deployment.
-
-<Warning>
-  Use `crewai triggers run microsoft_teams/teams_message_created` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload.
-</Warning>
+The crew demonstrates how to extract participants, initial messages, tenant information, and compliance metadata from the Microsoft Graph webhook payload.
 
 ## Troubleshooting
 
 - Ensure the Teams connection is active; it must be refreshed if the tenant revokes permissions
-- Test locally with `crewai triggers run microsoft_teams/teams_message_created` to see the exact payload structure
 - Confirm the webhook subscription in Microsoft 365 is still valid if payloads stop arriving
 - Review execution logs for payload shape mismatches—Graph notifications may omit fields when a chat is private or restricted
-- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution

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

@@ -36,28 +36,18 @@ crew.kickoff({
 
 The crew inspects file metadata, user activity, and permission changes to produce a compliance-friendly summary.
 
-## Testing Locally
+## Sample payloads & crews
 
-Test your OneDrive trigger integration locally using the CrewAI CLI:
+The [OneDrive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) showcase how to:
 
-```bash
-# View all available triggers
-crewai triggers list
+- Parse file metadata, size, and folder paths
+- Track who created and last modified the file
+- Highlight permission and external sharing changes
 
-# Simulate a OneDrive trigger with realistic payload
-crewai triggers run microsoft_onedrive/file_changed
-```
-
-The `crewai triggers run` command will execute your crew with a complete OneDrive payload, allowing you to test your parsing logic before deployment.
-
-<Warning>
-  Use `crewai triggers run microsoft_onedrive/file_changed` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload.
-</Warning>
+`onedrive-file-crew.py` bundles the analysis and summarization tasks so you can add remediation steps as needed.
 
 ## Troubleshooting
 
 - Ensure the connected account has permission to read the file metadata included in the webhook
-- Test locally with `crewai triggers run microsoft_onedrive/file_changed` to see the exact payload structure
 - If the trigger fires but the payload is missing `permissions`, confirm the site-level sharing settings allow Graph to return this field
 - For large tenants, filter notifications upstream so the crew only runs on relevant directories
-- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution

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

@@ -36,28 +36,17 @@ crew.kickoff({
 
 The crew extracts sender details, subject, body preview, and attachments before generating a structured response.
 
-## Testing Locally
+## Sample payloads & crews
 
-Test your Outlook trigger integration locally using the CrewAI CLI:
+Review the [Outlook examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) for two common scenarios:
 
-```bash
-# View all available triggers
-crewai triggers list
+- `new-message.json` → new mail notifications parsed by `outlook-message-crew.py`
+- `event-removed.json` → calendar cleanup handled by `outlook-event-removal-crew.py`
 
-# Simulate an Outlook trigger with realistic payload
-crewai triggers run microsoft_outlook/email_received
-```
-
-The `crewai triggers run` command will execute your crew with a complete Outlook payload, allowing you to test your parsing logic before deployment.
-
-<Warning>
-  Use `crewai triggers run microsoft_outlook/email_received` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload.
-</Warning>
+Each crew demonstrates how to handle Microsoft Graph payloads, normalize headers, and keep humans in-the-loop with concise summaries.
 
 ## Troubleshooting
 
 - Verify the Outlook connector is still authorized; the subscription must be renewed periodically
-- Test locally with `crewai triggers run microsoft_outlook/email_received` to see the exact payload structure
 - If attachments are missing, confirm the webhook subscription includes the `includeResourceData` flag
 - Review execution logs when events fail to match—cancellation payloads lack attendee lists by design and the crew should account for that
-- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution

docs/en/enterprise/guides/tool-repository.mdx

@@ -21,7 +21,7 @@ The repository is not a version control system. Use Git to track code changes an
 Before using the Tool Repository, ensure you have:
 
 - A [CrewAI AMP](https://app.crewai.com) account
-- [CrewAI CLI](/en/concepts/cli#cli) installed
+- [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) installed
 - uv>=0.5.0 installed. Check out [how to upgrade](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv)
 - [Git](https://git-scm.com) installed and configured
 - Access permissions to publish or install tools in your CrewAI AMP organization
@@ -112,7 +112,7 @@ By default, tools are published as private. To make a tool public:
 crewai tool publish --public
 ```
 
-For more details on how to build tools, see [Creating your own tools](/en/concepts/tools#creating-your-own-tools).
+For more details on how to build tools, see [Creating your own tools](https://docs.crewai.com/concepts/tools#creating-your-own-tools).
 
 ## Updating Tools
 

docs/en/enterprise/integrations/asana.mdx

@@ -33,22 +33,6 @@ Before using the Asana integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/box.mdx

@@ -33,22 +33,6 @@ Before using the Box integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/clickup.mdx

@@ -33,22 +33,6 @@ Before using the ClickUp integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/github.mdx

@@ -33,22 +33,6 @@ Before using the GitHub integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/gmail.mdx

@@ -33,22 +33,6 @@ Before using the Gmail integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/google_calendar.mdx

@@ -33,22 +33,6 @@ Before using the Google Calendar integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/google_contacts.mdx

@@ -33,22 +33,6 @@ Before using the Google Contacts integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/google_docs.mdx

@@ -33,22 +33,6 @@ Before using the Google Docs integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/google_drive.mdx

@@ -33,22 +33,6 @@ Before using the Google Drive integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/google_sheets.mdx

@@ -34,22 +34,6 @@ Before using the Google Sheets integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/google_slides.mdx

@@ -33,22 +33,6 @@ Before using the Google Slides integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/hubspot.mdx

@@ -33,22 +33,6 @@ Before using the HubSpot integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/jira.mdx

@@ -33,22 +33,6 @@ Before using the Jira integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/linear.mdx

@@ -33,22 +33,6 @@ Before using the Linear integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/microsoft_excel.mdx

@@ -33,22 +33,6 @@ Before using the Microsoft Excel integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/microsoft_onedrive.mdx

@@ -33,22 +33,6 @@ Before using the Microsoft OneDrive integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/microsoft_outlook.mdx

@@ -33,22 +33,6 @@ Before using the Microsoft Outlook integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/microsoft_sharepoint.mdx

@@ -33,22 +33,6 @@ Before using the Microsoft SharePoint integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/microsoft_teams.mdx

@@ -33,22 +33,6 @@ Before using the Microsoft Teams integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/microsoft_word.mdx

@@ -33,22 +33,6 @@ Before using the Microsoft Word integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/notion.mdx

@@ -33,22 +33,6 @@ Before using the Notion integration, ensure you have:
 uv add crewai-tools
 ```
 
-### 3. 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
-```
-
 ## Available Actions
 
 <AccordionGroup>

docs/en/enterprise/integrations/salesforce.mdx

@@ -17,38 +17,6 @@ Before using the Salesforce integration, ensure you have:
 - A Salesforce account with appropriate permissions
 - Connected your Salesforce account through the [Integrations page](https://app.crewai.com/integrations)
 
-## Setting Up Salesforce Integration
-
-### 1. Connect Your Salesforce Account
-
-1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
-2. Find **Salesforce** in the Authentication Integrations section
-3. Click **Connect** and complete the OAuth flow
-4. Grant the necessary permissions for CRM and sales management
-5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations)
-
-### 2. Install Required Package
-
-```bash
-uv add crewai-tools
-```
-
-### 3. 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
-```
-
 ## Available Tools
 
 ### **Record Management**

docs/en/enterprise/integrations/shopify.mdx

@@ -17,38 +17,6 @@ Before using the Shopify integration, ensure you have:
 - A Shopify store with appropriate admin permissions
 - Connected your Shopify store through the [Integrations page](https://app.crewai.com/integrations)
 
-## Setting Up Shopify Integration
-
-### 1. Connect Your Shopify Store
-
-1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
-2. Find **Shopify** in the Authentication Integrations section
-3. Click **Connect** and complete the OAuth flow
-4. Grant the necessary permissions for store and product management
-5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations)
-
-### 2. Install Required Package
-
-```bash
-uv add crewai-tools
-```
-
-### 3. 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
-```
-
 ## Available Tools
 
 ### **Customer Management**

docs/en/enterprise/integrations/slack.mdx

@@ -17,38 +17,6 @@ Before using the Slack integration, ensure you have:
 - A Slack workspace with appropriate permissions
 - Connected your Slack workspace through the [Integrations page](https://app.crewai.com/integrations)
 
-## Setting Up Slack Integration
-
-### 1. Connect Your Slack Workspace
-
-1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
-2. Find **Slack** in the Authentication Integrations section
-3. Click **Connect** and complete the OAuth flow
-4. Grant the necessary permissions for team communication
-5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations)
-
-### 2. Install Required Package
-
-```bash
-uv add crewai-tools
-```
-
-### 3. 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
-```
-
 ## Available Tools
 
 ### **User Management**

docs/en/enterprise/integrations/stripe.mdx

@@ -17,38 +17,6 @@ Before using the Stripe integration, ensure you have:
 - A Stripe account with appropriate API permissions
 - Connected your Stripe account through the [Integrations page](https://app.crewai.com/integrations)
 
-## Setting Up Stripe Integration
-
-### 1. Connect Your Stripe Account
-
-1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
-2. Find **Stripe** in the Authentication Integrations section
-3. Click **Connect** and complete the OAuth flow
-4. Grant the necessary permissions for payment processing
-5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations)
-
-### 2. Install Required Package
-
-```bash
-uv add crewai-tools
-```
-
-### 3. 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
-```
-
 ## Available Tools
 
 ### **Customer Management**

docs/en/enterprise/integrations/zendesk.mdx

@@ -17,38 +17,6 @@ Before using the Zendesk integration, ensure you have:
 - A Zendesk account with appropriate API permissions
 - Connected your Zendesk account through the [Integrations page](https://app.crewai.com/integrations)
 
-## Setting Up Zendesk Integration
-
-### 1. Connect Your Zendesk Account
-
-1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
-2. Find **Zendesk** in the Authentication Integrations section
-3. Click **Connect** and complete the OAuth flow
-4. Grant the necessary permissions for ticket and user management
-5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations)
-
-### 2. Install Required Package
-
-```bash
-uv add crewai-tools
-```
-
-### 3. 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
-```
-
 ## Available Tools
 
 ### **Ticket Management**

docs/en/enterprise/resources/frequently-asked-questions.mdx

@@ -49,7 +49,7 @@ mode: "wide"
 
         To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. This input can provide extra context, clarify ambiguities, or validate the agent's output.
 
-        For detailed implementation guidance, see our [Human-in-the-Loop guide](/en/enterprise/guides/human-in-the-loop).
+        For detailed implementation guidance, see our [Human-in-the-Loop guide](/en/how-to/human-in-the-loop).
     </Accordion>
 
     <Accordion title="What advanced customization options are available for tailoring and enhancing agent behavior and capabilities in CrewAI?">
@@ -142,7 +142,7 @@ mode: "wide"
     <Accordion title="How can I create custom tools for my CrewAI agents?">
         You can create custom tools by subclassing the `BaseTool` class provided by CrewAI or by using the tool decorator. Subclassing involves defining a new class that inherits from `BaseTool`, specifying the name, description, and the `_run` method for operational logic. The tool decorator allows you to create a `Tool` object directly with the required attributes and a functional logic.
 
-        <Card href="/en/learn/create-custom-tools" icon="code">CrewAI Tools Guide</Card>
+        <Card href="https://docs.crewai.com/how-to/create-custom-tools" icon="code">CrewAI Tools Guide</Card>
     </Accordion>
 
     <Accordion title="How can you control the maximum number of requests per minute that the entire crew can perform?">

docs/en/learn/a2a-agent-delegation.mdx

@@ -1,295 +0,0 @@
----
-title: Agent-to-Agent (A2A) Protocol
-description: Enable CrewAI agents to delegate tasks to remote A2A-compliant agents for specialized handling
-icon: network-wired
-mode: "wide"
----
-
-## A2A Agent Delegation
-
-CrewAI supports the Agent-to-Agent (A2A) protocol, allowing agents to delegate tasks to remote specialized agents. The agent's LLM automatically decides whether to handle a task directly or delegate to an A2A agent based on the task requirements.
-
-<Note>
-  A2A delegation requires the `a2a-sdk` package. Install with: `uv add 'crewai[a2a]'` or `pip install 'crewai[a2a]'`
-</Note>
-
-## How It Works
-
-When an agent is configured with A2A capabilities:
-
-1. The LLM analyzes each task
-2. It decides to either:
-   - Handle the task directly using its own capabilities
-   - Delegate to a remote A2A agent for specialized handling
-3. If delegating, the agent communicates with the remote A2A agent through the protocol
-4. Results are returned to the CrewAI workflow
-
-## Basic Configuration
-
-Configure an agent for A2A delegation by setting the `a2a` parameter:
-
-```python Code
-from crewai import Agent, Crew, Task
-from crewai.a2a import A2AConfig
-
-agent = Agent(
-    role="Research Coordinator",
-    goal="Coordinate research tasks efficiently",
-    backstory="Expert at delegating to specialized research agents",
-    llm="gpt-4o",
-    a2a=A2AConfig(
-        endpoint="https://example.com/.well-known/agent-card.json",
-        timeout=120,
-        max_turns=10
-    )
-)
-
-task = Task(
-    description="Research the latest developments in quantum computing",
-    expected_output="A comprehensive research report",
-    agent=agent
-)
-
-crew = Crew(agents=[agent], tasks=[task], verbose=True)
-result = crew.kickoff()
-```
-
-## Configuration Options
-
-The `A2AConfig` class accepts the following parameters:
-
-<ParamField path="endpoint" type="str" required>
-  The A2A agent endpoint URL (typically points to `.well-known/agent-card.json`)
-</ParamField>
-
-<ParamField path="auth" type="AuthScheme" default="None">
-  Authentication scheme for the A2A agent. Supports Bearer tokens, OAuth2, API keys, and HTTP authentication.
-</ParamField>
-
-<ParamField path="timeout" type="int" default="120">
-  Request timeout in seconds
-</ParamField>
-
-<ParamField path="max_turns" type="int" default="10">
-  Maximum number of conversation turns with the A2A agent
-</ParamField>
-
-<ParamField path="response_model" type="type[BaseModel]" default="None">
-  Optional Pydantic model for requesting structured output from an A2A agent. A2A protocol does not
-  enforce this, so an A2A agent does not need to honor this request.
-</ParamField>
-
-<ParamField path="fail_fast" type="bool" default="True">
-  Whether to raise an error immediately if agent connection fails. When `False`, the agent continues with available agents and informs the LLM about unavailable ones.
-</ParamField>
-
-<ParamField path="trust_remote_completion_status" type="bool" default="False">
-  When `True`, returns the A2A agent's result directly when it signals completion. When `False`, allows the server agent to review the result and potentially continue the conversation.
-</ParamField>
-
-## Authentication
-
-For A2A agents that require authentication, use one of the provided auth schemes:
-
-<Tabs>
-  <Tab title="Bearer Token">
-    ```python Code
-from crewai.a2a import A2AConfig
-from crewai.a2a.auth import BearerTokenAuth
-
-agent = Agent(
-    role="Secure Coordinator",
-    goal="Coordinate tasks with secured agents",
-    backstory="Manages secure agent communications",
-    llm="gpt-4o",
-    a2a=A2AConfig(
-        endpoint="https://secure-agent.example.com/.well-known/agent-card.json",
-        auth=BearerTokenAuth(token="your-bearer-token"),
-        timeout=120
-    )
-)
-    ```
-  </Tab>
-
-  <Tab title="API Key">
-    ```python Code
-from crewai.a2a import A2AConfig
-from crewai.a2a.auth import APIKeyAuth
-
-agent = Agent(
-    role="API Coordinator",
-    goal="Coordinate with API-based agents",
-    backstory="Manages API-authenticated communications",
-    llm="gpt-4o",
-    a2a=A2AConfig(
-        endpoint="https://api-agent.example.com/.well-known/agent-card.json",
-        auth=APIKeyAuth(
-            api_key="your-api-key",
-            location="header",  # or "query" or "cookie"
-            name="X-API-Key"
-        ),
-        timeout=120
-    )
-)
-    ```
-  </Tab>
-
-  <Tab title="OAuth2">
-    ```python Code
-from crewai.a2a import A2AConfig
-from crewai.a2a.auth import OAuth2ClientCredentials
-
-agent = Agent(
-    role="OAuth Coordinator",
-    goal="Coordinate with OAuth-secured agents",
-    backstory="Manages OAuth-authenticated communications",
-    llm="gpt-4o",
-    a2a=A2AConfig(
-        endpoint="https://oauth-agent.example.com/.well-known/agent-card.json",
-        auth=OAuth2ClientCredentials(
-            token_url="https://auth.example.com/oauth/token",
-            client_id="your-client-id",
-            client_secret="your-client-secret",
-            scopes=["read", "write"]
-        ),
-        timeout=120
-    )
-)
-    ```
-  </Tab>
-
-  <Tab title="HTTP Basic">
-    ```python Code
-from crewai.a2a import A2AConfig
-from crewai.a2a.auth import HTTPBasicAuth
-
-agent = Agent(
-    role="Basic Auth Coordinator",
-    goal="Coordinate with basic auth agents",
-    backstory="Manages basic authentication communications",
-    llm="gpt-4o",
-    a2a=A2AConfig(
-        endpoint="https://basic-agent.example.com/.well-known/agent-card.json",
-        auth=HTTPBasicAuth(
-            username="your-username",
-            password="your-password"
-        ),
-        timeout=120
-    )
-)
-    ```
-  </Tab>
-</Tabs>
-
-## Multiple A2A Agents
-
-Configure multiple A2A agents for delegation by passing a list:
-
-```python Code
-from crewai.a2a import A2AConfig
-from crewai.a2a.auth import BearerTokenAuth
-
-agent = Agent(
-    role="Multi-Agent Coordinator",
-    goal="Coordinate with multiple specialized agents",
-    backstory="Expert at delegating to the right specialist",
-    llm="gpt-4o",
-    a2a=[
-        A2AConfig(
-            endpoint="https://research.example.com/.well-known/agent-card.json",
-            timeout=120
-        ),
-        A2AConfig(
-            endpoint="https://data.example.com/.well-known/agent-card.json",
-            auth=BearerTokenAuth(token="data-token"),
-            timeout=90
-        )
-    ]
-)
-```
-
-The LLM will automatically choose which A2A agent to delegate to based on the task requirements.
-
-## Error Handling
-
-Control how agent connection failures are handled using the `fail_fast` parameter:
-
-```python Code
-from crewai.a2a import A2AConfig
-
-# Fail immediately on connection errors (default)
-agent = Agent(
-    role="Research Coordinator",
-    goal="Coordinate research tasks",
-    backstory="Expert at delegation",
-    llm="gpt-4o",
-    a2a=A2AConfig(
-        endpoint="https://research.example.com/.well-known/agent-card.json",
-        fail_fast=True
-    )
-)
-
-# Continue with available agents
-agent = Agent(
-    role="Multi-Agent Coordinator",
-    goal="Coordinate with multiple agents",
-    backstory="Expert at working with available resources",
-    llm="gpt-4o",
-    a2a=[
-        A2AConfig(
-            endpoint="https://primary.example.com/.well-known/agent-card.json",
-            fail_fast=False
-        ),
-        A2AConfig(
-            endpoint="https://backup.example.com/.well-known/agent-card.json",
-            fail_fast=False
-        )
-    ]
-)
-```
-
-When `fail_fast=False`:
-- If some agents fail, the LLM is informed which agents are unavailable and can delegate to working agents
-- If all agents fail, the LLM receives a notice about unavailable agents and handles the task directly
-- Connection errors are captured and included in the context for better decision-making
-
-## Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Set Appropriate Timeouts" icon="clock">
-    Configure timeouts based on expected A2A agent response times. Longer-running tasks may need higher timeout values.
-  </Card>
-
-  <Card title="Limit Conversation Turns" icon="comments">
-    Use `max_turns` to prevent excessive back-and-forth. The agent will automatically conclude conversations before hitting the limit.
-  </Card>
-
-  <Card title="Use Resilient Error Handling" icon="shield-check">
-    Set `fail_fast=False` for production environments with multiple agents to gracefully handle connection failures and maintain workflow continuity.
-  </Card>
-
-  <Card title="Secure Your Credentials" icon="lock">
-    Store authentication tokens and credentials as environment variables, not in code.
-  </Card>
-
-  <Card title="Monitor Delegation Decisions" icon="eye">
-    Use verbose mode to observe when the LLM chooses to delegate versus handle tasks directly.
-  </Card>
-</CardGroup>
-
-## Supported Authentication Methods
-
-- **Bearer Token** - Simple token-based authentication
-- **OAuth2 Client Credentials** - OAuth2 flow for machine-to-machine communication
-- **OAuth2 Authorization Code** - OAuth2 flow requiring user authorization
-- **API Key** - Key-based authentication (header, query param, or cookie)
-- **HTTP Basic** - Username/password authentication
-- **HTTP Digest** - Digest authentication (requires `httpx-auth` package)
-
-## Learn More
-
-For more information about the A2A protocol and reference implementations:
-
-- [A2A Protocol Documentation](https://a2a-protocol.org)
-- [A2A Sample Implementations](https://github.com/a2aproject/a2a-samples)
-- [A2A Python SDK](https://github.com/a2aproject/a2a-python)

docs/en/learn/execution-hooks.mdx

@@ -1,522 +0,0 @@
----
-title: Execution Hooks Overview
-description: Understanding and using execution hooks in CrewAI for fine-grained control over agent operations
-mode: "wide"
----
-
-Execution Hooks provide fine-grained control over the runtime behavior of your CrewAI agents. Unlike kickoff hooks that run before and after crew execution, execution hooks intercept specific operations during agent execution, allowing you to modify behavior, implement safety checks, and add comprehensive monitoring.
-
-## Types of Execution Hooks
-
-CrewAI provides two main categories of execution hooks:
-
-### 1. [LLM Call Hooks](/learn/llm-hooks)
-
-Control and monitor language model interactions:
-- **Before LLM Call**: Modify prompts, validate inputs, implement approval gates
-- **After LLM Call**: Transform responses, sanitize outputs, update conversation history
-
-**Use Cases:**
-- Iteration limiting
-- Cost tracking and token usage monitoring
-- Response sanitization and content filtering
-- Human-in-the-loop approval for LLM calls
-- Adding safety guidelines or context
-- Debug logging and request/response inspection
-
-[View LLM Hooks Documentation →](/learn/llm-hooks)
-
-### 2. [Tool Call Hooks](/learn/tool-hooks)
-
-Control and monitor tool execution:
-- **Before Tool Call**: Modify inputs, validate parameters, block dangerous operations
-- **After Tool Call**: Transform results, sanitize outputs, log execution details
-
-**Use Cases:**
-- Safety guardrails for destructive operations
-- Human approval for sensitive actions
-- Input validation and sanitization
-- Result caching and rate limiting
-- Tool usage analytics
-- Debug logging and monitoring
-
-[View Tool Hooks Documentation →](/learn/tool-hooks)
-
-## Hook Registration Methods
-
-### 1. Decorator-Based Hooks (Recommended)
-
-The cleanest and most Pythonic way to register hooks:
-
-```python
-from crewai.hooks import before_llm_call, after_llm_call, before_tool_call, after_tool_call
-
-@before_llm_call
-def limit_iterations(context):
-    """Prevent infinite loops by limiting iterations."""
-    if context.iterations > 10:
-        return False  # Block execution
-    return None
-
-@after_llm_call
-def sanitize_response(context):
-    """Remove sensitive data from LLM responses."""
-    if "API_KEY" in context.response:
-        return context.response.replace("API_KEY", "[REDACTED]")
-    return None
-
-@before_tool_call
-def block_dangerous_tools(context):
-    """Block destructive operations."""
-    if context.tool_name == "delete_database":
-        return False  # Block execution
-    return None
-
-@after_tool_call
-def log_tool_result(context):
-    """Log tool execution."""
-    print(f"Tool {context.tool_name} completed")
-    return None
-```
-
-### 2. Crew-Scoped Hooks
-
-Apply hooks only to specific crew instances:
-
-```python
-from crewai import CrewBase
-from crewai.project import crew
-from crewai.hooks import before_llm_call_crew, after_tool_call_crew
-
-@CrewBase
-class MyProjCrew:
-    @before_llm_call_crew
-    def validate_inputs(self, context):
-        # Only applies to this crew
-        print(f"LLM call in {self.__class__.__name__}")
-        return None
-
-    @after_tool_call_crew
-    def log_results(self, context):
-        # Crew-specific logging
-        print(f"Tool result: {context.tool_result[:50]}...")
-        return None
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential
-        )
-```
-
-## Hook Execution Flow
-
-### LLM Call Flow
-
-```
-Agent needs to call LLM
-    ↓
-[Before LLM Call Hooks Execute]
-    ├→ Hook 1: Validate iteration count
-    ├→ Hook 2: Add safety context
-    └→ Hook 3: Log request
-    ↓
-If any hook returns False:
-    ├→ Block LLM call
-    └→ Raise ValueError
-    ↓
-If all hooks return True/None:
-    ├→ LLM call proceeds
-    └→ Response generated
-    ↓
-[After LLM Call Hooks Execute]
-    ├→ Hook 1: Sanitize response
-    ├→ Hook 2: Log response
-    └→ Hook 3: Update metrics
-    ↓
-Final response returned
-```
-
-### Tool Call Flow
-
-```
-Agent needs to execute tool
-    ↓
-[Before Tool Call Hooks Execute]
-    ├→ Hook 1: Check if tool is allowed
-    ├→ Hook 2: Validate inputs
-    └→ Hook 3: Request approval if needed
-    ↓
-If any hook returns False:
-    ├→ Block tool execution
-    └→ Return error message
-    ↓
-If all hooks return True/None:
-    ├→ Tool execution proceeds
-    └→ Result generated
-    ↓
-[After Tool Call Hooks Execute]
-    ├→ Hook 1: Sanitize result
-    ├→ Hook 2: Cache result
-    └→ Hook 3: Log metrics
-    ↓
-Final result returned
-```
-
-## Hook Context Objects
-
-### LLMCallHookContext
-
-Provides access to LLM execution state:
-
-```python
-class LLMCallHookContext:
-    executor: CrewAgentExecutor  # Full executor access
-    messages: list               # Mutable message list
-    agent: Agent                 # Current agent
-    task: Task                   # Current task
-    crew: Crew                   # Crew instance
-    llm: BaseLLM                 # LLM instance
-    iterations: int              # Current iteration
-    response: str | None         # LLM response (after hooks)
-```
-
-### ToolCallHookContext
-
-Provides access to tool execution state:
-
-```python
-class ToolCallHookContext:
-    tool_name: str               # Tool being called
-    tool_input: dict             # Mutable input parameters
-    tool: CrewStructuredTool     # Tool instance
-    agent: Agent | None          # Agent executing
-    task: Task | None            # Current task
-    crew: Crew | None            # Crew instance
-    tool_result: str | None      # Tool result (after hooks)
-```
-
-## Common Patterns
-
-### Safety and Validation
-
-```python
-@before_tool_call
-def safety_check(context):
-    """Block destructive operations."""
-    dangerous = ['delete_file', 'drop_table', 'system_shutdown']
-    if context.tool_name in dangerous:
-        print(f"🛑 Blocked: {context.tool_name}")
-        return False
-    return None
-
-@before_llm_call
-def iteration_limit(context):
-    """Prevent infinite loops."""
-    if context.iterations > 15:
-        print("⛔ Maximum iterations exceeded")
-        return False
-    return None
-```
-
-### Human-in-the-Loop
-
-```python
-@before_tool_call
-def require_approval(context):
-    """Require approval for sensitive operations."""
-    sensitive = ['send_email', 'make_payment', 'post_message']
-
-    if context.tool_name in sensitive:
-        response = context.request_human_input(
-            prompt=f"Approve {context.tool_name}?",
-            default_message="Type 'yes' to approve:"
-        )
-
-        if response.lower() != 'yes':
-            return False
-
-    return None
-```
-
-### Monitoring and Analytics
-
-```python
-from collections import defaultdict
-import time
-
-metrics = defaultdict(lambda: {'count': 0, 'total_time': 0})
-
-@before_tool_call
-def start_timer(context):
-    context.tool_input['_start'] = time.time()
-    return None
-
-@after_tool_call
-def track_metrics(context):
-    start = context.tool_input.get('_start', time.time())
-    duration = time.time() - start
-
-    metrics[context.tool_name]['count'] += 1
-    metrics[context.tool_name]['total_time'] += duration
-
-    return None
-
-# View metrics
-def print_metrics():
-    for tool, data in metrics.items():
-        avg = data['total_time'] / data['count']
-        print(f"{tool}: {data['count']} calls, {avg:.2f}s avg")
-```
-
-### Response Sanitization
-
-```python
-import re
-
-@after_llm_call
-def sanitize_llm_response(context):
-    """Remove sensitive data from LLM responses."""
-    if not context.response:
-        return None
-
-    result = context.response
-    result = re.sub(r'(api[_-]?key)["\']?\s*[:=]\s*["\']?[\w-]+',
-                   r'\1: [REDACTED]', result, flags=re.IGNORECASE)
-    return result
-
-@after_tool_call
-def sanitize_tool_result(context):
-    """Remove sensitive data from tool results."""
-    if not context.tool_result:
-        return None
-
-    result = context.tool_result
-    result = re.sub(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
-                   '[EMAIL-REDACTED]', result)
-    return result
-```
-
-## Hook Management
-
-### Clearing All Hooks
-
-```python
-from crewai.hooks import clear_all_global_hooks
-
-# Clear all hooks at once
-result = clear_all_global_hooks()
-print(f"Cleared {result['total']} hooks")
-# Output: {'llm_hooks': (2, 1), 'tool_hooks': (1, 2), 'total': (3, 3)}
-```
-
-### Clearing Specific Hook Types
-
-```python
-from crewai.hooks import (
-    clear_before_llm_call_hooks,
-    clear_after_llm_call_hooks,
-    clear_before_tool_call_hooks,
-    clear_after_tool_call_hooks
-)
-
-# Clear specific types
-llm_before_count = clear_before_llm_call_hooks()
-tool_after_count = clear_after_tool_call_hooks()
-```
-
-### Unregistering Individual Hooks
-
-```python
-from crewai.hooks import (
-    unregister_before_llm_call_hook,
-    unregister_after_tool_call_hook
-)
-
-def my_hook(context):
-    ...
-
-# Register
-register_before_llm_call_hook(my_hook)
-
-# Later, unregister
-success = unregister_before_llm_call_hook(my_hook)
-print(f"Unregistered: {success}")
-```
-
-## Best Practices
-
-### 1. Keep Hooks Focused
-Each hook should have a single, clear responsibility:
-
-```python
-# ✅ Good - focused responsibility
-@before_tool_call
-def validate_file_path(context):
-    if context.tool_name == 'read_file':
-        if '..' in context.tool_input.get('path', ''):
-            return False
-    return None
-
-# ❌ Bad - too many responsibilities
-@before_tool_call
-def do_everything(context):
-    # Validation + logging + metrics + approval...
-    ...
-```
-
-### 2. Handle Errors Gracefully
-
-```python
-@before_llm_call
-def safe_hook(context):
-    try:
-        # Your logic
-        if some_condition:
-            return False
-    except Exception as e:
-        print(f"Hook error: {e}")
-        return None  # Allow execution despite error
-```
-
-### 3. Modify Context In-Place
-
-```python
-# ✅ Correct - modify in-place
-@before_llm_call
-def add_context(context):
-    context.messages.append({"role": "system", "content": "Be concise"})
-
-# ❌ Wrong - replaces reference
-@before_llm_call
-def wrong_approach(context):
-    context.messages = [{"role": "system", "content": "Be concise"}]
-```
-
-### 4. Use Type Hints
-
-```python
-from crewai.hooks import LLMCallHookContext, ToolCallHookContext
-
-def my_llm_hook(context: LLMCallHookContext) -> bool | None:
-    # IDE autocomplete and type checking
-    return None
-
-def my_tool_hook(context: ToolCallHookContext) -> str | None:
-    return None
-```
-
-### 5. Clean Up in Tests
-
-```python
-import pytest
-from crewai.hooks import clear_all_global_hooks
-
-@pytest.fixture(autouse=True)
-def clean_hooks():
-    """Reset hooks before each test."""
-    yield
-    clear_all_global_hooks()
-```
-
-## When to Use Which Hook
-
-### Use LLM Hooks When:
-- Implementing iteration limits
-- Adding context or safety guidelines to prompts
-- Tracking token usage and costs
-- Sanitizing or transforming responses
-- Implementing approval gates for LLM calls
-- Debugging prompt/response interactions
-
-### Use Tool Hooks When:
-- Blocking dangerous or destructive operations
-- Validating tool inputs before execution
-- Implementing approval gates for sensitive actions
-- Caching tool results
-- Tracking tool usage and performance
-- Sanitizing tool outputs
-- Rate limiting tool calls
-
-### Use Both When:
-Building comprehensive observability, safety, or approval systems that need to monitor all agent operations.
-
-## Alternative Registration Methods
-
-### Programmatic Registration (Advanced)
-
-For dynamic hook registration or when you need to register hooks programmatically:
-
-```python
-from crewai.hooks import (
-    register_before_llm_call_hook,
-    register_after_tool_call_hook
-)
-
-def my_hook(context):
-    return None
-
-# Register programmatically
-register_before_llm_call_hook(my_hook)
-
-# Useful for:
-# - Loading hooks from configuration
-# - Conditional hook registration
-# - Plugin systems
-```
-
-**Note:** For most use cases, decorators are cleaner and more maintainable.
-
-## Performance Considerations
-
-1. **Keep Hooks Fast**: Hooks execute on every call - avoid heavy computation
-2. **Cache When Possible**: Store expensive validations or lookups
-3. **Be Selective**: Use crew-scoped hooks when global hooks aren't needed
-4. **Monitor Hook Overhead**: Profile hook execution time in production
-5. **Lazy Import**: Import heavy dependencies only when needed
-
-## Debugging Hooks
-
-### Enable Debug Logging
-
-```python
-import logging
-
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger(__name__)
-
-@before_llm_call
-def debug_hook(context):
-    logger.debug(f"LLM call: {context.agent.role}, iteration {context.iterations}")
-    return None
-```
-
-### Hook Execution Order
-
-Hooks execute in registration order. If a before hook returns `False`, subsequent hooks don't execute:
-
-```python
-# Register order matters!
-register_before_tool_call_hook(hook1)  # Executes first
-register_before_tool_call_hook(hook2)  # Executes second
-register_before_tool_call_hook(hook3)  # Executes third
-
-# If hook2 returns False:
-# - hook1 executed
-# - hook2 executed and returned False
-# - hook3 NOT executed
-# - Tool call blocked
-```
-
-## Related Documentation
-
-- [LLM Call Hooks →](/learn/llm-hooks) - Detailed LLM hook documentation
-- [Tool Call Hooks →](/learn/tool-hooks) - Detailed tool hook documentation
-- [Before and After Kickoff Hooks →](/learn/before-and-after-kickoff-hooks) - Crew lifecycle hooks
-- [Human-in-the-Loop →](/learn/human-in-the-loop) - Human input patterns
-
-## Conclusion
-
-Execution hooks provide powerful control over agent runtime behavior. Use them to implement safety guardrails, approval workflows, comprehensive monitoring, and custom business logic. Combined with proper error handling, type safety, and performance considerations, hooks enable production-ready, secure, and observable agent systems.

docs/en/learn/hierarchical-process.mdx

@@ -97,7 +97,7 @@ project_crew = Crew(
 ```
 
 <Tip>
-    For more details on creating and customizing a manager agent, check out the [Custom Manager Agent documentation](/en/learn/custom-manager-agent).
+    For more details on creating and customizing a manager agent, check out the [Custom Manager Agent documentation](https://docs.crewai.com/how-to/custom-manager-agent#custom-manager-agent).
 </Tip>
 
 

docs/en/learn/llm-hooks.mdx

@@ -1,427 +0,0 @@
----
-title: LLM Call Hooks
-description: Learn how to use LLM call hooks to intercept, modify, and control language model interactions in CrewAI
-mode: "wide"
----
-
-LLM Call Hooks provide fine-grained control over language model interactions during agent execution. These hooks allow you to intercept LLM calls, modify prompts, transform responses, implement approval gates, and add custom logging or monitoring.
-
-## Overview
-
-LLM hooks are executed at two critical points:
-- **Before LLM Call**: Modify messages, validate inputs, or block execution
-- **After LLM Call**: Transform responses, sanitize outputs, or modify conversation history
-
-## Hook Types
-
-### Before LLM Call Hooks
-
-Executed before every LLM call, these hooks can:
-- Inspect and modify messages sent to the LLM
-- Block LLM execution based on conditions
-- Implement rate limiting or approval gates
-- Add context or system messages
-- Log request details
-
-**Signature:**
-```python
-def before_hook(context: LLMCallHookContext) -> bool | None:
-    # Return False to block execution
-    # Return True or None to allow execution
-    ...
-```
-
-### After LLM Call Hooks
-
-Executed after every LLM call, these hooks can:
-- Modify or sanitize LLM responses
-- Add metadata or formatting
-- Log response details
-- Update conversation history
-- Implement content filtering
-
-**Signature:**
-```python
-def after_hook(context: LLMCallHookContext) -> str | None:
-    # Return modified response string
-    # Return None to keep original response
-    ...
-```
-
-## LLM Hook Context
-
-The `LLMCallHookContext` object provides comprehensive access to execution state:
-
-```python
-class LLMCallHookContext:
-    executor: CrewAgentExecutor  # Full executor reference
-    messages: list               # Mutable message list
-    agent: Agent                 # Current agent
-    task: Task                   # Current task
-    crew: Crew                   # Crew instance
-    llm: BaseLLM                 # LLM instance
-    iterations: int              # Current iteration count
-    response: str | None         # LLM response (after hooks only)
-```
-
-### Modifying Messages
-
-**Important:** Always modify messages in-place:
-
-```python
-# ✅ Correct - modify in-place
-def add_context(context: LLMCallHookContext) -> None:
-    context.messages.append({"role": "system", "content": "Be concise"})
-
-# ❌ Wrong - replaces list reference
-def wrong_approach(context: LLMCallHookContext) -> None:
-    context.messages = [{"role": "system", "content": "Be concise"}]
-```
-
-## Registration Methods
-
-### 1. Global Hook Registration
-
-Register hooks that apply to all LLM calls across all crews:
-
-```python
-from crewai.hooks import register_before_llm_call_hook, register_after_llm_call_hook
-
-def log_llm_call(context):
-    print(f"LLM call by {context.agent.role} at iteration {context.iterations}")
-    return None  # Allow execution
-
-register_before_llm_call_hook(log_llm_call)
-```
-
-### 2. Decorator-Based Registration
-
-Use decorators for cleaner syntax:
-
-```python
-from crewai.hooks import before_llm_call, after_llm_call
-
-@before_llm_call
-def validate_iteration_count(context):
-    if context.iterations > 10:
-        print("⚠️ Exceeded maximum iterations")
-        return False  # Block execution
-    return None
-
-@after_llm_call
-def sanitize_response(context):
-    if context.response and "API_KEY" in context.response:
-        return context.response.replace("API_KEY", "[REDACTED]")
-    return None
-```
-
-### 3. Crew-Scoped Hooks
-
-Register hooks for a specific crew instance:
-
-```python
-@CrewBase
-class MyProjCrew:
-    @before_llm_call_crew
-    def validate_inputs(self, context):
-        # Only applies to this crew
-        if context.iterations == 0:
-            print(f"Starting task: {context.task.description}")
-        return None
-
-    @after_llm_call_crew
-    def log_responses(self, context):
-        # Crew-specific response logging
-        print(f"Response length: {len(context.response)}")
-        return None
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-## Common Use Cases
-
-### 1. Iteration Limiting
-
-```python
-@before_llm_call
-def limit_iterations(context: LLMCallHookContext) -> bool | None:
-    max_iterations = 15
-    if context.iterations > max_iterations:
-        print(f"⛔ Blocked: Exceeded {max_iterations} iterations")
-        return False  # Block execution
-    return None
-```
-
-### 2. Human Approval Gate
-
-```python
-@before_llm_call
-def require_approval(context: LLMCallHookContext) -> bool | None:
-    if context.iterations > 5:
-        response = context.request_human_input(
-            prompt=f"Iteration {context.iterations}: Approve LLM call?",
-            default_message="Press Enter to approve, or type 'no' to block:"
-        )
-        if response.lower() == "no":
-            print("🚫 LLM call blocked by user")
-            return False
-    return None
-```
-
-### 3. Adding System Context
-
-```python
-@before_llm_call
-def add_guardrails(context: LLMCallHookContext) -> None:
-    # Add safety guidelines to every LLM call
-    context.messages.append({
-        "role": "system",
-        "content": "Ensure responses are factual and cite sources when possible."
-    })
-    return None
-```
-
-### 4. Response Sanitization
-
-```python
-@after_llm_call
-def sanitize_sensitive_data(context: LLMCallHookContext) -> str | None:
-    if not context.response:
-        return None
-
-    # Remove sensitive patterns
-    import re
-    sanitized = context.response
-    sanitized = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[SSN-REDACTED]', sanitized)
-    sanitized = re.sub(r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b', '[CARD-REDACTED]', sanitized)
-
-    return sanitized
-```
-
-### 5. Cost Tracking
-
-```python
-import tiktoken
-
-@before_llm_call
-def track_token_usage(context: LLMCallHookContext) -> None:
-    encoding = tiktoken.get_encoding("cl100k_base")
-    total_tokens = sum(
-        len(encoding.encode(msg.get("content", "")))
-        for msg in context.messages
-    )
-    print(f"📊 Input tokens: ~{total_tokens}")
-    return None
-
-@after_llm_call
-def track_response_tokens(context: LLMCallHookContext) -> None:
-    if context.response:
-        encoding = tiktoken.get_encoding("cl100k_base")
-        tokens = len(encoding.encode(context.response))
-        print(f"📊 Response tokens: ~{tokens}")
-    return None
-```
-
-### 6. Debug Logging
-
-```python
-@before_llm_call
-def debug_request(context: LLMCallHookContext) -> None:
-    print(f"""
-    🔍 LLM Call Debug:
-    - Agent: {context.agent.role}
-    - Task: {context.task.description[:50]}...
-    - Iteration: {context.iterations}
-    - Message Count: {len(context.messages)}
-    - Last Message: {context.messages[-1] if context.messages else 'None'}
-    """)
-    return None
-
-@after_llm_call
-def debug_response(context: LLMCallHookContext) -> None:
-    if context.response:
-        print(f"✅ Response Preview: {context.response[:100]}...")
-    return None
-```
-
-## Hook Management
-
-### Unregistering Hooks
-
-```python
-from crewai.hooks import (
-    unregister_before_llm_call_hook,
-    unregister_after_llm_call_hook
-)
-
-# Unregister specific hook
-def my_hook(context):
-    ...
-
-register_before_llm_call_hook(my_hook)
-# Later...
-unregister_before_llm_call_hook(my_hook)  # Returns True if found
-```
-
-### Clearing Hooks
-
-```python
-from crewai.hooks import (
-    clear_before_llm_call_hooks,
-    clear_after_llm_call_hooks,
-    clear_all_llm_call_hooks
-)
-
-# Clear specific hook type
-count = clear_before_llm_call_hooks()
-print(f"Cleared {count} before hooks")
-
-# Clear all LLM hooks
-before_count, after_count = clear_all_llm_call_hooks()
-print(f"Cleared {before_count} before and {after_count} after hooks")
-```
-
-### Listing Registered Hooks
-
-```python
-from crewai.hooks import (
-    get_before_llm_call_hooks,
-    get_after_llm_call_hooks
-)
-
-# Get current hooks
-before_hooks = get_before_llm_call_hooks()
-after_hooks = get_after_llm_call_hooks()
-
-print(f"Registered: {len(before_hooks)} before, {len(after_hooks)} after")
-```
-
-## Advanced Patterns
-
-### Conditional Hook Execution
-
-```python
-@before_llm_call
-def conditional_blocking(context: LLMCallHookContext) -> bool | None:
-    # Only block for specific agents
-    if context.agent.role == "researcher" and context.iterations > 10:
-        return False
-
-    # Only block for specific tasks
-    if "sensitive" in context.task.description.lower() and context.iterations > 5:
-        return False
-
-    return None
-```
-
-### Context-Aware Modifications
-
-```python
-@before_llm_call
-def adaptive_prompting(context: LLMCallHookContext) -> None:
-    # Add different context based on iteration
-    if context.iterations == 0:
-        context.messages.append({
-            "role": "system",
-            "content": "Start with a high-level overview."
-        })
-    elif context.iterations > 3:
-        context.messages.append({
-            "role": "system",
-            "content": "Focus on specific details and provide examples."
-        })
-    return None
-```
-
-### Chaining Hooks
-
-```python
-# Multiple hooks execute in registration order
-
-@before_llm_call
-def first_hook(context):
-    print("1. First hook executed")
-    return None
-
-@before_llm_call
-def second_hook(context):
-    print("2. Second hook executed")
-    return None
-
-@before_llm_call
-def blocking_hook(context):
-    if context.iterations > 10:
-        print("3. Blocking hook - execution stopped")
-        return False  # Subsequent hooks won't execute
-    print("3. Blocking hook - execution allowed")
-    return None
-```
-
-## Best Practices
-
-1. **Keep Hooks Focused**: Each hook should have a single responsibility
-2. **Avoid Heavy Computation**: Hooks execute on every LLM call
-3. **Handle Errors Gracefully**: Use try-except to prevent hook failures from breaking execution
-4. **Use Type Hints**: Leverage `LLMCallHookContext` for better IDE support
-5. **Document Hook Behavior**: Especially for blocking conditions
-6. **Test Hooks Independently**: Unit test hooks before using in production
-7. **Clear Hooks in Tests**: Use `clear_all_llm_call_hooks()` between test runs
-8. **Modify In-Place**: Always modify `context.messages` in-place, never replace
-
-## Error Handling
-
-```python
-@before_llm_call
-def safe_hook(context: LLMCallHookContext) -> bool | None:
-    try:
-        # Your hook logic
-        if some_condition:
-            return False
-    except Exception as e:
-        print(f"⚠️ Hook error: {e}")
-        # Decide: allow or block on error
-        return None  # Allow execution despite error
-```
-
-## Type Safety
-
-```python
-from crewai.hooks import LLMCallHookContext, BeforeLLMCallHookType, AfterLLMCallHookType
-
-# Explicit type annotations
-def my_before_hook(context: LLMCallHookContext) -> bool | None:
-    return None
-
-def my_after_hook(context: LLMCallHookContext) -> str | None:
-    return None
-
-# Type-safe registration
-register_before_llm_call_hook(my_before_hook)
-register_after_llm_call_hook(my_after_hook)
-```
-
-## Troubleshooting
-
-### Hook Not Executing
-- Verify hook is registered before crew execution
-- Check if previous hook returned `False` (blocks subsequent hooks)
-- Ensure hook signature matches expected type
-
-### Message Modifications Not Persisting
-- Use in-place modifications: `context.messages.append()`
-- Don't replace the list: `context.messages = []`
-
-### Response Modifications Not Working
-- Return the modified string from after hooks
-- Returning `None` keeps the original response
-
-## Conclusion
-
-LLM Call Hooks provide powerful capabilities for controlling and monitoring language model interactions in CrewAI. Use them to implement safety guardrails, approval gates, logging, cost tracking, and response sanitization. Combined with proper error handling and type safety, hooks enable robust and production-ready agent systems.

docs/en/learn/tool-hooks.mdx

@@ -1,600 +0,0 @@
----
-title: Tool Call Hooks
-description: Learn how to use tool call hooks to intercept, modify, and control tool execution in CrewAI
-mode: "wide"
----
-
-Tool Call Hooks provide fine-grained control over tool execution during agent operations. These hooks allow you to intercept tool calls, modify inputs, transform outputs, implement safety checks, and add comprehensive logging or monitoring.
-
-## Overview
-
-Tool hooks are executed at two critical points:
-- **Before Tool Call**: Modify inputs, validate parameters, or block execution
-- **After Tool Call**: Transform results, sanitize outputs, or log execution details
-
-## Hook Types
-
-### Before Tool Call Hooks
-
-Executed before every tool execution, these hooks can:
-- Inspect and modify tool inputs
-- Block tool execution based on conditions
-- Implement approval gates for dangerous operations
-- Validate parameters
-- Log tool invocations
-
-**Signature:**
-```python
-def before_hook(context: ToolCallHookContext) -> bool | None:
-    # Return False to block execution
-    # Return True or None to allow execution
-    ...
-```
-
-### After Tool Call Hooks
-
-Executed after every tool execution, these hooks can:
-- Modify or sanitize tool results
-- Add metadata or formatting
-- Log execution results
-- Implement result validation
-- Transform output formats
-
-**Signature:**
-```python
-def after_hook(context: ToolCallHookContext) -> str | None:
-    # Return modified result string
-    # Return None to keep original result
-    ...
-```
-
-## Tool Hook Context
-
-The `ToolCallHookContext` object provides comprehensive access to tool execution state:
-
-```python
-class ToolCallHookContext:
-    tool_name: str                    # Name of the tool being called
-    tool_input: dict[str, Any]        # Mutable tool input parameters
-    tool: CrewStructuredTool          # Tool instance reference
-    agent: Agent | BaseAgent | None   # Agent executing the tool
-    task: Task | None                 # Current task
-    crew: Crew | None                 # Crew instance
-    tool_result: str | None           # Tool result (after hooks only)
-```
-
-### Modifying Tool Inputs
-
-**Important:** Always modify tool inputs in-place:
-
-```python
-# ✅ Correct - modify in-place
-def sanitize_input(context: ToolCallHookContext) -> None:
-    context.tool_input['query'] = context.tool_input['query'].lower()
-
-# ❌ Wrong - replaces dict reference
-def wrong_approach(context: ToolCallHookContext) -> None:
-    context.tool_input = {'query': 'new query'}
-```
-
-## Registration Methods
-
-### 1. Global Hook Registration
-
-Register hooks that apply to all tool calls across all crews:
-
-```python
-from crewai.hooks import register_before_tool_call_hook, register_after_tool_call_hook
-
-def log_tool_call(context):
-    print(f"Tool: {context.tool_name}")
-    print(f"Input: {context.tool_input}")
-    return None  # Allow execution
-
-register_before_tool_call_hook(log_tool_call)
-```
-
-### 2. Decorator-Based Registration
-
-Use decorators for cleaner syntax:
-
-```python
-from crewai.hooks import before_tool_call, after_tool_call
-
-@before_tool_call
-def block_dangerous_tools(context):
-    dangerous_tools = ['delete_database', 'drop_table', 'rm_rf']
-    if context.tool_name in dangerous_tools:
-        print(f"⛔ Blocked dangerous tool: {context.tool_name}")
-        return False  # Block execution
-    return None
-
-@after_tool_call
-def sanitize_results(context):
-    if context.tool_result and "password" in context.tool_result.lower():
-        return context.tool_result.replace("password", "[REDACTED]")
-    return None
-```
-
-### 3. Crew-Scoped Hooks
-
-Register hooks for a specific crew instance:
-
-```python
-@CrewBase
-class MyProjCrew:
-    @before_tool_call_crew
-    def validate_tool_inputs(self, context):
-        # Only applies to this crew
-        if context.tool_name == "web_search":
-            if not context.tool_input.get('query'):
-                print("❌ Invalid search query")
-                return False
-        return None
-
-    @after_tool_call_crew
-    def log_tool_results(self, context):
-        # Crew-specific tool logging
-        print(f"✅ {context.tool_name} completed")
-        return None
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-## Common Use Cases
-
-### 1. Safety Guardrails
-
-```python
-@before_tool_call
-def safety_check(context: ToolCallHookContext) -> bool | None:
-    # Block tools that could cause harm
-    destructive_tools = [
-        'delete_file',
-        'drop_table',
-        'remove_user',
-        'system_shutdown'
-    ]
-
-    if context.tool_name in destructive_tools:
-        print(f"🛑 Blocked destructive tool: {context.tool_name}")
-        return False
-
-    # Warn on sensitive operations
-    sensitive_tools = ['send_email', 'post_to_social_media', 'charge_payment']
-    if context.tool_name in sensitive_tools:
-        print(f"⚠️  Executing sensitive tool: {context.tool_name}")
-
-    return None
-```
-
-### 2. Human Approval Gate
-
-```python
-@before_tool_call
-def require_approval_for_actions(context: ToolCallHookContext) -> bool | None:
-    approval_required = [
-        'send_email',
-        'make_purchase',
-        'delete_file',
-        'post_message'
-    ]
-
-    if context.tool_name in approval_required:
-        response = context.request_human_input(
-            prompt=f"Approve {context.tool_name}?",
-            default_message=f"Input: {context.tool_input}\nType 'yes' to approve:"
-        )
-
-        if response.lower() != 'yes':
-            print(f"❌ Tool execution denied: {context.tool_name}")
-            return False
-
-    return None
-```
-
-### 3. Input Validation and Sanitization
-
-```python
-@before_tool_call
-def validate_and_sanitize_inputs(context: ToolCallHookContext) -> bool | None:
-    # Validate search queries
-    if context.tool_name == 'web_search':
-        query = context.tool_input.get('query', '')
-        if len(query) < 3:
-            print("❌ Search query too short")
-            return False
-
-        # Sanitize query
-        context.tool_input['query'] = query.strip().lower()
-
-    # Validate file paths
-    if context.tool_name == 'read_file':
-        path = context.tool_input.get('path', '')
-        if '..' in path or path.startswith('/'):
-            print("❌ Invalid file path")
-            return False
-
-    return None
-```
-
-### 4. Result Sanitization
-
-```python
-@after_tool_call
-def sanitize_sensitive_data(context: ToolCallHookContext) -> str | None:
-    if not context.tool_result:
-        return None
-
-    import re
-    result = context.tool_result
-
-    # Remove API keys
-    result = re.sub(
-        r'(api[_-]?key|token)["\']?\s*[:=]\s*["\']?[\w-]+',
-        r'\1: [REDACTED]',
-        result,
-        flags=re.IGNORECASE
-    )
-
-    # Remove email addresses
-    result = re.sub(
-        r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
-        '[EMAIL-REDACTED]',
-        result
-    )
-
-    # Remove credit card numbers
-    result = re.sub(
-        r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b',
-        '[CARD-REDACTED]',
-        result
-    )
-
-    return result
-```
-
-### 5. Tool Usage Analytics
-
-```python
-import time
-from collections import defaultdict
-
-tool_stats = defaultdict(lambda: {'count': 0, 'total_time': 0, 'failures': 0})
-
-@before_tool_call
-def start_timer(context: ToolCallHookContext) -> None:
-    context.tool_input['_start_time'] = time.time()
-    return None
-
-@after_tool_call
-def track_tool_usage(context: ToolCallHookContext) -> None:
-    start_time = context.tool_input.get('_start_time', time.time())
-    duration = time.time() - start_time
-
-    tool_stats[context.tool_name]['count'] += 1
-    tool_stats[context.tool_name]['total_time'] += duration
-
-    if not context.tool_result or 'error' in context.tool_result.lower():
-        tool_stats[context.tool_name]['failures'] += 1
-
-    print(f"""
-    📊 Tool Stats for {context.tool_name}:
-    - Executions: {tool_stats[context.tool_name]['count']}
-    - Avg Time: {tool_stats[context.tool_name]['total_time'] / tool_stats[context.tool_name]['count']:.2f}s
-    - Failures: {tool_stats[context.tool_name]['failures']}
-    """)
-
-    return None
-```
-
-### 6. Rate Limiting
-
-```python
-from collections import defaultdict
-from datetime import datetime, timedelta
-
-tool_call_history = defaultdict(list)
-
-@before_tool_call
-def rate_limit_tools(context: ToolCallHookContext) -> bool | None:
-    tool_name = context.tool_name
-    now = datetime.now()
-
-    # Clean old entries (older than 1 minute)
-    tool_call_history[tool_name] = [
-        call_time for call_time in tool_call_history[tool_name]
-        if now - call_time < timedelta(minutes=1)
-    ]
-
-    # Check rate limit (max 10 calls per minute)
-    if len(tool_call_history[tool_name]) >= 10:
-        print(f"🚫 Rate limit exceeded for {tool_name}")
-        return False
-
-    # Record this call
-    tool_call_history[tool_name].append(now)
-    return None
-```
-
-### 7. Caching Tool Results
-
-```python
-import hashlib
-import json
-
-tool_cache = {}
-
-def cache_key(tool_name: str, tool_input: dict) -> str:
-    """Generate cache key from tool name and input."""
-    input_str = json.dumps(tool_input, sort_keys=True)
-    return hashlib.md5(f"{tool_name}:{input_str}".encode()).hexdigest()
-
-@before_tool_call
-def check_cache(context: ToolCallHookContext) -> bool | None:
-    key = cache_key(context.tool_name, context.tool_input)
-    if key in tool_cache:
-        print(f"💾 Cache hit for {context.tool_name}")
-        # Note: Can't return cached result from before hook
-        # Would need to implement this differently
-    return None
-
-@after_tool_call
-def cache_result(context: ToolCallHookContext) -> None:
-    if context.tool_result:
-        key = cache_key(context.tool_name, context.tool_input)
-        tool_cache[key] = context.tool_result
-        print(f"💾 Cached result for {context.tool_name}")
-    return None
-```
-
-### 8. Debug Logging
-
-```python
-@before_tool_call
-def debug_tool_call(context: ToolCallHookContext) -> None:
-    print(f"""
-    🔍 Tool Call Debug:
-    - Tool: {context.tool_name}
-    - Agent: {context.agent.role if context.agent else 'Unknown'}
-    - Task: {context.task.description[:50] if context.task else 'Unknown'}...
-    - Input: {context.tool_input}
-    """)
-    return None
-
-@after_tool_call
-def debug_tool_result(context: ToolCallHookContext) -> None:
-    if context.tool_result:
-        result_preview = context.tool_result[:200]
-        print(f"✅ Result Preview: {result_preview}...")
-    else:
-        print("⚠️  No result returned")
-    return None
-```
-
-## Hook Management
-
-### Unregistering Hooks
-
-```python
-from crewai.hooks import (
-    unregister_before_tool_call_hook,
-    unregister_after_tool_call_hook
-)
-
-# Unregister specific hook
-def my_hook(context):
-    ...
-
-register_before_tool_call_hook(my_hook)
-# Later...
-success = unregister_before_tool_call_hook(my_hook)
-print(f"Unregistered: {success}")
-```
-
-### Clearing Hooks
-
-```python
-from crewai.hooks import (
-    clear_before_tool_call_hooks,
-    clear_after_tool_call_hooks,
-    clear_all_tool_call_hooks
-)
-
-# Clear specific hook type
-count = clear_before_tool_call_hooks()
-print(f"Cleared {count} before hooks")
-
-# Clear all tool hooks
-before_count, after_count = clear_all_tool_call_hooks()
-print(f"Cleared {before_count} before and {after_count} after hooks")
-```
-
-### Listing Registered Hooks
-
-```python
-from crewai.hooks import (
-    get_before_tool_call_hooks,
-    get_after_tool_call_hooks
-)
-
-# Get current hooks
-before_hooks = get_before_tool_call_hooks()
-after_hooks = get_after_tool_call_hooks()
-
-print(f"Registered: {len(before_hooks)} before, {len(after_hooks)} after")
-```
-
-## Advanced Patterns
-
-### Conditional Hook Execution
-
-```python
-@before_tool_call
-def conditional_blocking(context: ToolCallHookContext) -> bool | None:
-    # Only block for specific agents
-    if context.agent and context.agent.role == "junior_agent":
-        if context.tool_name in ['delete_file', 'send_email']:
-            print(f"❌ Junior agents cannot use {context.tool_name}")
-            return False
-
-    # Only block during specific tasks
-    if context.task and "sensitive" in context.task.description.lower():
-        if context.tool_name == 'web_search':
-            print("❌ Web search blocked for sensitive tasks")
-            return False
-
-    return None
-```
-
-### Context-Aware Input Modification
-
-```python
-@before_tool_call
-def enhance_tool_inputs(context: ToolCallHookContext) -> None:
-    # Add context based on agent role
-    if context.agent and context.agent.role == "researcher":
-        if context.tool_name == 'web_search':
-            # Add domain restrictions for researchers
-            context.tool_input['domains'] = ['edu', 'gov', 'org']
-
-    # Add context based on task
-    if context.task and "urgent" in context.task.description.lower():
-        if context.tool_name == 'send_email':
-            context.tool_input['priority'] = 'high'
-
-    return None
-```
-
-### Tool Chain Monitoring
-
-```python
-tool_call_chain = []
-
-@before_tool_call
-def track_tool_chain(context: ToolCallHookContext) -> None:
-    tool_call_chain.append({
-        'tool': context.tool_name,
-        'timestamp': time.time(),
-        'agent': context.agent.role if context.agent else 'Unknown'
-    })
-
-    # Detect potential infinite loops
-    recent_calls = tool_call_chain[-5:]
-    if len(recent_calls) == 5 and all(c['tool'] == context.tool_name for c in recent_calls):
-        print(f"⚠️  Warning: {context.tool_name} called 5 times in a row")
-
-    return None
-```
-
-## Best Practices
-
-1. **Keep Hooks Focused**: Each hook should have a single responsibility
-2. **Avoid Heavy Computation**: Hooks execute on every tool call
-3. **Handle Errors Gracefully**: Use try-except to prevent hook failures
-4. **Use Type Hints**: Leverage `ToolCallHookContext` for better IDE support
-5. **Document Blocking Conditions**: Make it clear when/why tools are blocked
-6. **Test Hooks Independently**: Unit test hooks before using in production
-7. **Clear Hooks in Tests**: Use `clear_all_tool_call_hooks()` between test runs
-8. **Modify In-Place**: Always modify `context.tool_input` in-place, never replace
-9. **Log Important Decisions**: Especially when blocking tool execution
-10. **Consider Performance**: Cache expensive validations when possible
-
-## Error Handling
-
-```python
-@before_tool_call
-def safe_validation(context: ToolCallHookContext) -> bool | None:
-    try:
-        # Your validation logic
-        if not validate_input(context.tool_input):
-            return False
-    except Exception as e:
-        print(f"⚠️ Hook error: {e}")
-        # Decide: allow or block on error
-        return None  # Allow execution despite error
-```
-
-## Type Safety
-
-```python
-from crewai.hooks import ToolCallHookContext, BeforeToolCallHookType, AfterToolCallHookType
-
-# Explicit type annotations
-def my_before_hook(context: ToolCallHookContext) -> bool | None:
-    return None
-
-def my_after_hook(context: ToolCallHookContext) -> str | None:
-    return None
-
-# Type-safe registration
-register_before_tool_call_hook(my_before_hook)
-register_after_tool_call_hook(my_after_hook)
-```
-
-## Integration with Existing Tools
-
-### Wrapping Existing Validation
-
-```python
-def existing_validator(tool_name: str, inputs: dict) -> bool:
-    """Your existing validation function."""
-    # Your validation logic
-    return True
-
-@before_tool_call
-def integrate_validator(context: ToolCallHookContext) -> bool | None:
-    if not existing_validator(context.tool_name, context.tool_input):
-        print(f"❌ Validation failed for {context.tool_name}")
-        return False
-    return None
-```
-
-### Logging to External Systems
-
-```python
-import logging
-
-logger = logging.getLogger(__name__)
-
-@before_tool_call
-def log_to_external_system(context: ToolCallHookContext) -> None:
-    logger.info(f"Tool call: {context.tool_name}", extra={
-        'tool_name': context.tool_name,
-        'tool_input': context.tool_input,
-        'agent': context.agent.role if context.agent else None
-    })
-    return None
-```
-
-## Troubleshooting
-
-### Hook Not Executing
-- Verify hook is registered before crew execution
-- Check if previous hook returned `False` (blocks execution and subsequent hooks)
-- Ensure hook signature matches expected type
-
-### Input Modifications Not Working
-- Use in-place modifications: `context.tool_input['key'] = value`
-- Don't replace the dict: `context.tool_input = {}`
-
-### Result Modifications Not Working
-- Return the modified string from after hooks
-- Returning `None` keeps the original result
-- Ensure the tool actually returned a result
-
-### Tool Blocked Unexpectedly
-- Check all before hooks for blocking conditions
-- Verify hook execution order
-- Add debug logging to identify which hook is blocking
-
-## Conclusion
-
-Tool Call Hooks provide powerful capabilities for controlling and monitoring tool execution in CrewAI. Use them to implement safety guardrails, approval gates, input validation, result sanitization, logging, and analytics. Combined with proper error handling and type safety, hooks enable secure and production-ready agent systems with comprehensive observability.

docs/en/mcp/dsl-integration.mdx

@@ -1,344 +0,0 @@
----
-title: MCP DSL Integration
-description: Learn how to use CrewAI's simple DSL syntax to integrate MCP servers directly with your agents using the mcps field.
-icon: code
-mode: "wide"
----
-
-## Overview
-
-CrewAI's MCP DSL (Domain Specific Language) integration provides the **simplest way** to connect your agents to MCP (Model Context Protocol) servers. Just add an `mcps` field to your agent and CrewAI handles all the complexity automatically.
-
-<Info>
-This is the **recommended approach** for most MCP use cases. For advanced scenarios requiring manual connection management, see [MCPServerAdapter](/en/mcp/overview#advanced-mcpserveradapter).
-</Info>
-
-## Basic Usage
-
-Add MCP servers to your agent using the `mcps` field:
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="Research Assistant",
-    goal="Help with research and analysis tasks",
-    backstory="Expert assistant with access to advanced research tools",
-    mcps=[
-        "https://mcp.exa.ai/mcp?api_key=your_key&profile=research"
-    ]
-)
-
-# MCP tools are now automatically available!
-# No need for manual connection management or tool configuration
-```
-
-## Supported Reference Formats
-
-### External MCP Remote Servers
-
-```python
-# Basic HTTPS server
-"https://api.example.com/mcp"
-
-# Server with authentication
-"https://mcp.exa.ai/mcp?api_key=your_key&profile=your_profile"
-
-# Server with custom path
-"https://services.company.com/api/v1/mcp"
-```
-
-### Specific Tool Selection
-
-Use the `#` syntax to select specific tools from a server:
-
-```python
-# Get only the forecast tool from weather server
-"https://weather.api.com/mcp#get_forecast"
-
-# Get only the search tool from Exa
-"https://mcp.exa.ai/mcp?api_key=your_key#web_search_exa"
-```
-
-### CrewAI AMP Marketplace
-
-Access tools from the CrewAI AMP marketplace:
-
-```python
-# Full service with all tools
-"crewai-amp:financial-data"
-
-# Specific tool from AMP service
-"crewai-amp:research-tools#pubmed_search"
-
-# Multiple AMP services
-mcps=[
-    "crewai-amp:weather-insights",
-    "crewai-amp:market-analysis",
-    "crewai-amp:social-media-monitoring"
-]
-```
-
-## Complete Example
-
-Here's a complete example using multiple MCP servers:
-
-```python
-from crewai import Agent, Task, Crew, Process
-
-# Create agent with multiple MCP sources
-multi_source_agent = Agent(
-    role="Multi-Source Research Analyst",
-    goal="Conduct comprehensive research using multiple data sources",
-    backstory="""Expert researcher with access to web search, weather data,
-    financial information, and academic research tools""",
-    mcps=[
-        # External MCP servers
-        "https://mcp.exa.ai/mcp?api_key=your_exa_key&profile=research",
-        "https://weather.api.com/mcp#get_current_conditions",
-
-        # CrewAI AMP marketplace
-        "crewai-amp:financial-insights",
-        "crewai-amp:academic-research#pubmed_search",
-        "crewai-amp:market-intelligence#competitor_analysis"
-    ]
-)
-
-# Create comprehensive research task
-research_task = Task(
-    description="""Research the impact of AI agents on business productivity.
-    Include current weather impacts on remote work, financial market trends,
-    and recent academic publications on AI agent frameworks.""",
-    expected_output="""Comprehensive report covering:
-    1. AI agent business impact analysis
-    2. Weather considerations for remote work
-    3. Financial market trends related to AI
-    4. Academic research citations and insights
-    5. Competitive landscape analysis""",
-    agent=multi_source_agent
-)
-
-# Create and execute crew
-research_crew = Crew(
-    agents=[multi_source_agent],
-    tasks=[research_task],
-    process=Process.sequential,
-    verbose=True
-)
-
-result = research_crew.kickoff()
-print(f"Research completed with {len(multi_source_agent.mcps)} MCP data sources")
-```
-
-## Tool Naming and Organization
-
-CrewAI automatically handles tool naming to prevent conflicts:
-
-```python
-# Original MCP server has tools: "search", "analyze"
-# CrewAI creates tools: "mcp_exa_ai_search", "mcp_exa_ai_analyze"
-
-agent = Agent(
-    role="Tool Organization Demo",
-    goal="Show how tool naming works",
-    backstory="Demonstrates automatic tool organization",
-    mcps=[
-        "https://mcp.exa.ai/mcp?api_key=key",      # Tools: mcp_exa_ai_*
-        "https://weather.service.com/mcp",         # Tools: weather_service_com_*
-        "crewai-amp:financial-data"                # Tools: financial_data_*
-    ]
-)
-
-# Each server's tools get unique prefixes based on the server name
-# This prevents naming conflicts between different MCP servers
-```
-
-## Error Handling and Resilience
-
-The MCP DSL is designed to be robust and user-friendly:
-
-### Graceful Server Failures
-
-```python
-agent = Agent(
-    role="Resilient Researcher",
-    goal="Research despite server issues",
-    backstory="Experienced researcher who adapts to available tools",
-    mcps=[
-        "https://primary-server.com/mcp",         # Primary data source
-        "https://backup-server.com/mcp",          # Backup if primary fails
-        "https://unreachable-server.com/mcp",     # Will be skipped with warning
-        "crewai-amp:reliable-service"             # Reliable AMP service
-    ]
-)
-
-# Agent will:
-# 1. Successfully connect to working servers
-# 2. Log warnings for failing servers
-# 3. Continue with available tools
-# 4. Not crash or hang on server failures
-```
-
-### Timeout Protection
-
-All MCP operations have built-in timeouts:
-
-- **Connection timeout**: 10 seconds
-- **Tool execution timeout**: 30 seconds
-- **Discovery timeout**: 15 seconds
-
-```python
-# These servers will timeout gracefully if unresponsive
-mcps=[
-    "https://slow-server.com/mcp",        # Will timeout after 10s if unresponsive
-    "https://overloaded-api.com/mcp"      # Will timeout if discovery takes > 15s
-]
-```
-
-## Performance Features
-
-### Automatic Caching
-
-Tool schemas are cached for 5 minutes to improve performance:
-
-```python
-# First agent creation - discovers tools from server
-agent1 = Agent(role="First", goal="Test", backstory="Test",
-               mcps=["https://api.example.com/mcp"])
-
-# Second agent creation (within 5 minutes) - uses cached tool schemas
-agent2 = Agent(role="Second", goal="Test", backstory="Test",
-               mcps=["https://api.example.com/mcp"])  # Much faster!
-```
-
-### On-Demand Connections
-
-Tool connections are established only when tools are actually used:
-
-```python
-# Agent creation is fast - no MCP connections made yet
-agent = Agent(
-    role="On-Demand Agent",
-    goal="Use tools efficiently",
-    backstory="Efficient agent that connects only when needed",
-    mcps=["https://api.example.com/mcp"]
-)
-
-# MCP connection is made only when a tool is actually executed
-# This minimizes connection overhead and improves startup performance
-```
-
-## Integration with Existing Features
-
-MCP tools work seamlessly with other CrewAI features:
-
-```python
-from crewai.tools import BaseTool
-
-class CustomTool(BaseTool):
-    name: str = "custom_analysis"
-    description: str = "Custom analysis tool"
-
-    def _run(self, **kwargs):
-        return "Custom analysis result"
-
-agent = Agent(
-    role="Full-Featured Agent",
-    goal="Use all available tool types",
-    backstory="Agent with comprehensive tool access",
-
-    # All tool types work together
-    tools=[CustomTool()],                          # Custom tools
-    apps=["gmail", "slack"],                       # Platform integrations
-    mcps=[                                         # MCP servers
-        "https://mcp.exa.ai/mcp?api_key=key",
-        "crewai-amp:research-tools"
-    ],
-
-    verbose=True,
-    max_iter=15
-)
-```
-
-## Best Practices
-
-### 1. Use Specific Tools When Possible
-
-```python
-# Good - only get the tools you need
-mcps=["https://weather.api.com/mcp#get_forecast"]
-
-# Less efficient - gets all tools from server
-mcps=["https://weather.api.com/mcp"]
-```
-
-### 2. Handle Authentication Securely
-
-```python
-import os
-
-# Store API keys in environment variables
-exa_key = os.getenv("EXA_API_KEY")
-exa_profile = os.getenv("EXA_PROFILE")
-
-agent = Agent(
-    role="Secure Agent",
-    goal="Use MCP tools securely",
-    backstory="Security-conscious agent",
-    mcps=[f"https://mcp.exa.ai/mcp?api_key={exa_key}&profile={exa_profile}"]
-)
-```
-
-### 3. Plan for Server Failures
-
-```python
-# Always include backup options
-mcps=[
-    "https://primary-api.com/mcp",       # Primary choice
-    "https://backup-api.com/mcp",        # Backup option
-    "crewai-amp:reliable-service"        # AMP fallback
-]
-```
-
-### 4. Use Descriptive Agent Roles
-
-```python
-agent = Agent(
-    role="Weather-Enhanced Market Analyst",
-    goal="Analyze markets considering weather impacts",
-    backstory="Financial analyst with access to weather data for agricultural market insights",
-    mcps=[
-        "https://weather.service.com/mcp#get_forecast",
-        "crewai-amp:financial-data#stock_analysis"
-    ]
-)
-```
-
-## Troubleshooting
-
-### Common Issues
-
-**No tools discovered:**
-```python
-# Check your MCP server URL and authentication
-# Verify the server is running and accessible
-mcps=["https://mcp.example.com/mcp?api_key=valid_key"]
-```
-
-**Connection timeouts:**
-```python
-# Server may be slow or overloaded
-# CrewAI will log warnings and continue with other servers
-# Check server status or try backup servers
-```
-
-**Authentication failures:**
-```python
-# Verify API keys and credentials
-# Check server documentation for required parameters
-# Ensure query parameters are properly URL encoded
-```
-
-## Advanced: MCPServerAdapter
-
-For complex scenarios requiring manual connection management, use the `MCPServerAdapter` class from `crewai-tools`. Using a Python context manager (`with` statement) is the recommended approach as it automatically handles starting and stopping the connection to the MCP server.

docs/en/mcp/overview.mdx

@@ -8,83 +8,14 @@ mode: "wide"
 ## Overview
 
 The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) provides a standardized way for AI agents to provide context to LLMs by communicating with external services, known as MCP Servers.
-
-CrewAI offers **two approaches** for MCP integration:
-
-### 🚀 **Simple DSL Integration** (Recommended)
-
-Use the `mcps` field directly on agents for seamless MCP tool integration. The DSL supports both **string references** (for quick setup) and **structured configurations** (for full control).
-
-#### String-Based References (Quick Setup)
-
-Perfect for remote HTTPS servers and CrewAI AMP marketplace:
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="Research Analyst",
-    goal="Research and analyze information",
-    backstory="Expert researcher with access to external tools",
-    mcps=[
-        "https://mcp.exa.ai/mcp?api_key=your_key",           # External MCP server
-        "https://api.weather.com/mcp#get_forecast",          # Specific tool from server
-        "crewai-amp:financial-data",                         # CrewAI AMP marketplace
-        "crewai-amp:research-tools#pubmed_search"            # Specific AMP tool
-    ]
-)
-# MCP tools are now automatically available to your agent!
-```
-
-#### Structured Configurations (Full Control)
-
-For complete control over connection settings, tool filtering, and all transport types:
-
-```python
-from crewai import Agent
-from crewai.mcp import MCPServerStdio, MCPServerHTTP, MCPServerSSE
-from crewai.mcp.filters import create_static_tool_filter
-
-agent = Agent(
-    role="Advanced Research Analyst",
-    goal="Research with full control over MCP connections",
-    backstory="Expert researcher with advanced tool access",
-    mcps=[
-        # Stdio transport for local servers
-        MCPServerStdio(
-            command="npx",
-            args=["-y", "@modelcontextprotocol/server-filesystem"],
-            env={"API_KEY": "your_key"},
-            tool_filter=create_static_tool_filter(
-                allowed_tool_names=["read_file", "list_directory"]
-            ),
-            cache_tools_list=True,
-        ),
-        # HTTP/Streamable HTTP transport for remote servers
-        MCPServerHTTP(
-            url="https://api.example.com/mcp",
-            headers={"Authorization": "Bearer your_token"},
-            streamable=True,
-            cache_tools_list=True,
-        ),
-        # SSE transport for real-time streaming
-        MCPServerSSE(
-            url="https://stream.example.com/mcp/sse",
-            headers={"Authorization": "Bearer your_token"},
-        ),
-    ]
-)
-```
-
-### 🔧 **Advanced: MCPServerAdapter** (For Complex Scenarios)
-
-For advanced use cases requiring manual connection management, the `crewai-tools` library provides the `MCPServerAdapter` class.
+The `crewai-tools` library extends CrewAI's capabilities by allowing you to seamlessly integrate tools from these MCP servers into your agents.
+This gives your crews access to a vast ecosystem of functionalities.
 
 We currently support the following transport mechanisms:
 
 - **Stdio**: for local servers (communication via standard input/output between processes on the same machine)
 - **Server-Sent Events (SSE)**: for remote servers (unidirectional, real-time data streaming from server to client over HTTP)
-- **Streamable HTTPS**: for remote servers (flexible, potentially bi-directional communication over HTTPS, often utilizing SSE for server-to-client streams)
+- **Streamable HTTP**: for remote servers (flexible, potentially bi-directional communication over HTTP, often utilizing SSE for server-to-client streams)
 
 ## Video Tutorial
 Watch this video tutorial for a comprehensive guide on MCP integration with CrewAI:
@@ -100,337 +31,17 @@ Watch this video tutorial for a comprehensive guide on MCP integration with Crew
 
 ## Installation
 
-CrewAI MCP integration requires the `mcp` library:
+Before you start using MCP with `crewai-tools`, you need to install the `mcp` extra `crewai-tools` dependency with the following command:
 
 ```shell
-# For Simple DSL Integration (Recommended)
-uv add mcp
-
-# For Advanced MCPServerAdapter usage
 uv pip install 'crewai-tools[mcp]'
 ```
 
-## Quick Start: Simple DSL Integration
+## Key Concepts & Getting Started
 
-The easiest way to integrate MCP servers is using the `mcps` field on your agents. You can use either string references or structured configurations.
+The `MCPServerAdapter` class from `crewai-tools` is the primary way to connect to an MCP server and make its tools available to your CrewAI agents. It supports different transport mechanisms and simplifies connection management.
 
-### Quick Start with String References
-
-```python
-from crewai import Agent, Task, Crew
-
-# Create agent with MCP tools using string references
-research_agent = Agent(
-    role="Research Analyst",
-    goal="Find and analyze information using advanced search tools",
-    backstory="Expert researcher with access to multiple data sources",
-    mcps=[
-        "https://mcp.exa.ai/mcp?api_key=your_key&profile=your_profile",
-        "crewai-amp:weather-service#current_conditions"
-    ]
-)
-
-# Create task
-research_task = Task(
-    description="Research the latest developments in AI agent frameworks",
-    expected_output="Comprehensive research report with citations",
-    agent=research_agent
-)
-
-# Create and run crew
-crew = Crew(agents=[research_agent], tasks=[research_task])
-result = crew.kickoff()
-```
-
-### Quick Start with Structured Configurations
-
-```python
-from crewai import Agent, Task, Crew
-from crewai.mcp import MCPServerStdio, MCPServerHTTP, MCPServerSSE
-
-# Create agent with structured MCP configurations
-research_agent = Agent(
-    role="Research Analyst",
-    goal="Find and analyze information using advanced search tools",
-    backstory="Expert researcher with access to multiple data sources",
-    mcps=[
-        # Local stdio server
-        MCPServerStdio(
-            command="python",
-            args=["local_server.py"],
-            env={"API_KEY": "your_key"},
-        ),
-        # Remote HTTP server
-        MCPServerHTTP(
-            url="https://api.research.com/mcp",
-            headers={"Authorization": "Bearer your_token"},
-        ),
-    ]
-)
-
-# Create task
-research_task = Task(
-    description="Research the latest developments in AI agent frameworks",
-    expected_output="Comprehensive research report with citations",
-    agent=research_agent
-)
-
-# Create and run crew
-crew = Crew(agents=[research_agent], tasks=[research_task])
-result = crew.kickoff()
-```
-
-That's it! The MCP tools are automatically discovered and available to your agent.
-
-## MCP Reference Formats
-
-The `mcps` field supports both **string references** (for quick setup) and **structured configurations** (for full control). You can mix both formats in the same list.
-
-### String-Based References
-
-#### External MCP Servers
-
-```python
-mcps=[
-    # Full server - get all available tools
-    "https://mcp.example.com/api",
-
-    # Specific tool from server using # syntax
-    "https://api.weather.com/mcp#get_current_weather",
-
-    # Server with authentication parameters
-    "https://mcp.exa.ai/mcp?api_key=your_key&profile=your_profile"
-]
-```
-
-#### CrewAI AMP Marketplace
-
-```python
-mcps=[
-    # Full AMP MCP service - get all available tools
-    "crewai-amp:financial-data",
-
-    # Specific tool from AMP service using # syntax
-    "crewai-amp:research-tools#pubmed_search",
-
-    # Multiple AMP services
-    "crewai-amp:weather-service",
-    "crewai-amp:market-analysis"
-]
-```
-
-### Structured Configurations
-
-#### Stdio Transport (Local Servers)
-
-Perfect for local MCP servers that run as processes:
-
-```python
-from crewai.mcp import MCPServerStdio
-from crewai.mcp.filters import create_static_tool_filter
-
-mcps=[
-    MCPServerStdio(
-        command="npx",
-        args=["-y", "@modelcontextprotocol/server-filesystem"],
-        env={"API_KEY": "your_key"},
-        tool_filter=create_static_tool_filter(
-            allowed_tool_names=["read_file", "write_file"]
-        ),
-        cache_tools_list=True,
-    ),
-    # Python-based server
-    MCPServerStdio(
-        command="python",
-        args=["path/to/server.py"],
-        env={"UV_PYTHON": "3.12", "API_KEY": "your_key"},
-    ),
-]
-```
-
-#### HTTP/Streamable HTTP Transport (Remote Servers)
-
-For remote MCP servers over HTTP/HTTPS:
-
-```python
-from crewai.mcp import MCPServerHTTP
-
-mcps=[
-    # Streamable HTTP (default)
-    MCPServerHTTP(
-        url="https://api.example.com/mcp",
-        headers={"Authorization": "Bearer your_token"},
-        streamable=True,
-        cache_tools_list=True,
-    ),
-    # Standard HTTP
-    MCPServerHTTP(
-        url="https://api.example.com/mcp",
-        headers={"Authorization": "Bearer your_token"},
-        streamable=False,
-    ),
-]
-```
-
-#### SSE Transport (Real-Time Streaming)
-
-For remote servers using Server-Sent Events:
-
-```python
-from crewai.mcp import MCPServerSSE
-
-mcps=[
-    MCPServerSSE(
-        url="https://stream.example.com/mcp/sse",
-        headers={"Authorization": "Bearer your_token"},
-        cache_tools_list=True,
-    ),
-]
-```
-
-### Mixed References
-
-You can combine string references and structured configurations:
-
-```python
-from crewai.mcp import MCPServerStdio, MCPServerHTTP
-
-mcps=[
-    # String references
-    "https://external-api.com/mcp",              # External server
-    "crewai-amp:financial-insights",             # AMP service
-
-    # Structured configurations
-    MCPServerStdio(
-        command="npx",
-        args=["-y", "@modelcontextprotocol/server-filesystem"],
-    ),
-    MCPServerHTTP(
-        url="https://api.example.com/mcp",
-        headers={"Authorization": "Bearer token"},
-    ),
-]
-```
-
-### Tool Filtering
-
-Structured configurations support advanced tool filtering:
-
-```python
-from crewai.mcp import MCPServerStdio
-from crewai.mcp.filters import create_static_tool_filter, create_dynamic_tool_filter, ToolFilterContext
-
-# Static filtering (allow/block lists)
-static_filter = create_static_tool_filter(
-    allowed_tool_names=["read_file", "write_file"],
-    blocked_tool_names=["delete_file"],
-)
-
-# Dynamic filtering (context-aware)
-def dynamic_filter(context: ToolFilterContext, tool: dict) -> bool:
-    # Block dangerous tools for certain agent roles
-    if context.agent.role == "Code Reviewer":
-        if "delete" in tool.get("name", "").lower():
-            return False
-    return True
-
-mcps=[
-    MCPServerStdio(
-        command="npx",
-        args=["-y", "@modelcontextprotocol/server-filesystem"],
-        tool_filter=static_filter,  # or dynamic_filter
-    ),
-]
-```
-
-## Configuration Parameters
-
-Each transport type supports specific configuration options:
-
-### MCPServerStdio Parameters
-
-- **`command`** (required): Command to execute (e.g., `"python"`, `"node"`, `"npx"`, `"uvx"`)
-- **`args`** (optional): List of command arguments (e.g., `["server.py"]` or `["-y", "@mcp/server"]`)
-- **`env`** (optional): Dictionary of environment variables to pass to the process
-- **`tool_filter`** (optional): Tool filter function for filtering available tools
-- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
-
-### MCPServerHTTP Parameters
-
-- **`url`** (required): Server URL (e.g., `"https://api.example.com/mcp"`)
-- **`headers`** (optional): Dictionary of HTTP headers for authentication or other purposes
-- **`streamable`** (optional): Whether to use streamable HTTP transport (default: `True`)
-- **`tool_filter`** (optional): Tool filter function for filtering available tools
-- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
-
-### MCPServerSSE Parameters
-
-- **`url`** (required): Server URL (e.g., `"https://api.example.com/mcp/sse"`)
-- **`headers`** (optional): Dictionary of HTTP headers for authentication or other purposes
-- **`tool_filter`** (optional): Tool filter function for filtering available tools
-- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
-
-### Common Parameters
-
-All transport types support:
-- **`tool_filter`**: Filter function to control which tools are available. Can be:
-  - `None` (default): All tools are available
-  - Static filter: Created with `create_static_tool_filter()` for allow/block lists
-  - Dynamic filter: Created with `create_dynamic_tool_filter()` for context-aware filtering
-- **`cache_tools_list`**: When `True`, caches the tool list after first discovery to improve performance on subsequent connections
-
-## Key Features
-
-- 🔄 **Automatic Tool Discovery**: Tools are automatically discovered and integrated
-- 🏷️ **Name Collision Prevention**: Server names are prefixed to tool names
-- ⚡ **Performance Optimized**: On-demand connections with schema caching
-- 🛡️ **Error Resilience**: Graceful handling of unavailable servers
-- ⏱️ **Timeout Protection**: Built-in timeouts prevent hanging connections
-- 📊 **Transparent Integration**: Works seamlessly with existing CrewAI features
-- 🔧 **Full Transport Support**: Stdio, HTTP/Streamable HTTP, and SSE transports
-- 🎯 **Advanced Filtering**: Static and dynamic tool filtering capabilities
-- 🔐 **Flexible Authentication**: Support for headers, environment variables, and query parameters
-
-## Error Handling
-
-The MCP DSL integration is designed to be resilient and handles failures gracefully:
-
-```python
-from crewai import Agent
-from crewai.mcp import MCPServerStdio, MCPServerHTTP
-
-agent = Agent(
-    role="Resilient Agent",
-    goal="Continue working despite server issues",
-    backstory="Agent that handles failures gracefully",
-    mcps=[
-        # String references
-        "https://reliable-server.com/mcp",        # Will work
-        "https://unreachable-server.com/mcp",     # Will be skipped gracefully
-        "crewai-amp:working-service",             # Will work
-
-        # Structured configs
-        MCPServerStdio(
-            command="python",
-            args=["reliable_server.py"],          # Will work
-        ),
-        MCPServerHTTP(
-            url="https://slow-server.com/mcp",     # Will timeout gracefully
-        ),
-    ]
-)
-# Agent will use tools from working servers and log warnings for failing ones
-```
-
-All connection errors are handled gracefully:
-- **Connection failures**: Logged as warnings, agent continues with available tools
-- **Timeout errors**: Connections timeout after 30 seconds (configurable)
-- **Authentication errors**: Logged clearly for debugging
-- **Invalid configurations**: Validation errors are raised at agent creation time
-
-## Advanced: MCPServerAdapter
-
-For complex scenarios requiring manual connection management, use the `MCPServerAdapter` class from `crewai-tools`. Using a Python context manager (`with` statement) is the recommended approach as it automatically handles starting and stopping the connection to the MCP server.
+Using a Python context manager (`with` statement) is the **recommended approach** for `MCPServerAdapter`. It automatically handles starting and stopping the connection to the MCP server.
 
 ## Connection Configuration
 
@@ -630,19 +241,11 @@ class CrewWithCustomTimeout:
 ## Explore MCP Integrations
 
 <CardGroup cols={2}>
-  <Card
-    title="Simple DSL Integration"
-    icon="code"
-    href="/en/mcp/dsl-integration"
-    color="#3B82F6"
-  >
-    **Recommended**: Use the simple `mcps=[]` field syntax for effortless MCP integration.
-  </Card>
   <Card
     title="Stdio Transport"
     icon="server"
     href="/en/mcp/stdio"
-    color="#10B981"
+    color="#3B82F6"
   >
     Connect to local MCP servers via standard input/output. Ideal for scripts and local executables.
   </Card>
@@ -650,7 +253,7 @@ class CrewWithCustomTimeout:
     title="SSE Transport"
     icon="wifi"
     href="/en/mcp/sse"
-    color="#F59E0B"
+    color="#10B981"
   >
     Integrate with remote MCP servers using Server-Sent Events for real-time data streaming.
   </Card>
@@ -658,7 +261,7 @@ class CrewWithCustomTimeout:
     title="Streamable HTTP Transport"
     icon="globe"
     href="/en/mcp/streamable-http"
-    color="#8B5CF6"
+    color="#F59E0B"
   >
     Utilize flexible Streamable HTTP for robust communication with remote MCP servers.
   </Card>
@@ -666,7 +269,7 @@ class CrewWithCustomTimeout:
     title="Connecting to Multiple Servers"
     icon="layer-group"
     href="/en/mcp/multiple-servers"
-    color="#EF4444"
+    color="#8B5CF6"
   >
     Aggregate tools from several MCP servers simultaneously using a single adapter.
   </Card>
@@ -674,7 +277,7 @@ class CrewWithCustomTimeout:
     title="Security Considerations"
     icon="lock"
     href="/en/mcp/security"
-    color="#DC2626"
+    color="#EF4444"
   >
     Review important security best practices for MCP integration to keep your agents safe.
   </Card>

docs/en/observability/datadog.mdx

@@ -1,109 +0,0 @@
----
-title: Datadog Integration
-description: Learn how to integrate Datadog with CrewAI to submit LLM Observability traces to Datadog.
-icon: dog
-mode: "wide"
----
-
-# Integrate Datadog with CrewAI
-
-This guide will demonstrate how to integrate **[Datadog LLM Observability](https://docs.datadoghq.com/llm_observability/)** with **CrewAI** using [Datadog auto-instrumentation](https://docs.datadoghq.com/llm_observability/instrumentation/auto_instrumentation?tab=python). By the end of this guide, you will be able to submit LLM Observability traces to Datadog and view your CrewAI agent runs in Datadog LLM Observability's [Agentic Execution View](https://docs.datadoghq.com/llm_observability/monitoring/agent_monitoring).
-
-## What is Datadog LLM Observability?
-
-[Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/) helps AI engineers, data scientists, and application developers quickly develop, evaluate, and monitor LLM applications. Confidently improve output quality, performance, costs, and overall risk with structured experiments, end-to-end tracing across AI agents, and evaluations.
-
-## Getting Started
-
-### Install Dependencies
-
-```shell
-pip install ddtrace crewai crewai-tools
-```
-
-### Set Environment Variables
-
-If you do not have a Datadog API key, you can [create an account](https://www.datadoghq.com/) and [get your API key](https://docs.datadoghq.com/account_management/api-app-keys/#api-keys).
-
-You will also need to specify an ML Application name in the following environment variables. An ML Application is a grouping of LLM Observability traces associated with a specific LLM-based application. See [ML Application Naming Guidelines](https://docs.datadoghq.com/llm_observability/instrumentation/sdk?tab=python#application-naming-guidelines) for more information on limitations with ML Application names.
-
-```shell
-export DD_API_KEY=<YOUR_DD_API_KEY>
-export DD_SITE=<YOUR_DD_SITE>
-export DD_LLMOBS_ENABLED=true
-export DD_LLMOBS_ML_APP=<YOUR_ML_APP_NAME>
-export DD_LLMOBS_AGENTLESS_ENABLED=true
-export DD_APM_TRACING_ENABLED=false
-```
-
-Additionally, configure any LLM provider API keys
-
-```shell
-export OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
-export ANTHROPIC_API_KEY=<YOUR_ANTHROPIC_API_KEY>
-export GEMINI_API_KEY=<YOUR_GEMINI_API_KEY>
-...
-```
-
-### Create a CrewAI Agent Application
-
-```python
-# crewai_agent.py
-from crewai import Agent, Task, Crew
-
-from crewai_tools import (
-    WebsiteSearchTool
-)
-
-web_rag_tool = WebsiteSearchTool()
-
-writer = Agent(
-    role="Writer",
-    goal="You make math engaging and understandable for young children through poetry",
-    backstory="You're an expert in writing haikus but you know nothing of math.",
-    tools=[web_rag_tool],
-)
-
-task = Task(
-    description=("What is {multiplication}?"),
-    expected_output=("Compose a haiku that includes the answer."),
-    agent=writer
-)
-
-crew = Crew(
-    agents=[writer],
-    tasks=[task],
-    share_crew=False
-)
-
-output = crew.kickoff(dict(multiplication="2 * 2"))
-```
-
-### Run the Application with Datadog Auto-Instrumentation
-
-With the [environment variables](#set-environment-variables) set, you can now run the application with Datadog auto-instrumentation.
-
-```shell
-ddtrace-run python crewai_agent.py
-```
-
-### View the Traces in Datadog
-
-After running the application, you can view the traces in [Datadog LLM Observability's Traces View](https://app.datadoghq.com/llm/traces), selecting the ML Application name you chose from the top-left dropdown.
-
-Clicking on a trace will show you the details of the trace, including total tokens used, number of LLM calls, models used, and estimated cost. Clicking into a specific span will narrow down these details, and show related input, output, and metadata.
-
-<Frame>
-<img src="/images/datadog-llm-observability-1.png" alt="Datadog LLM Observability Trace View" />
-</Frame>
-
-Additionally, you can view the execution graph view of the trace, which shows the control and data flow of the trace, which will scale with larger agents to show handoffs and relationships between LLM calls, tool calls, and agent interactions.
-
-<Frame>
-<img src="/images/datadog-llm-observability-2.png" alt="Datadog LLM Observability Agent Execution Flow View" />
-</Frame>
-
-## References
-
-- [Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/)
-- [Datadog LLM Observability CrewAI Auto-Instrumentation](https://docs.datadoghq.com/llm_observability/instrumentation/auto_instrumentation?tab=python#crew-ai)

docs/en/observability/portkey.mdx

@@ -733,7 +733,9 @@ Here's a basic configuration to route requests to OpenAI, specifically using GPT
     - Collect relevant metadata to filter logs
     - Enforce access permissions
 
-    Create API keys through the [Portkey App](https://app.portkey.ai/)
+    Create API keys through:
+    - [Portkey App](https://app.portkey.ai/)
+    - [API Key Management API](/en/api-reference/admin-api/control-plane/api-keys/create-api-key)
 
     Example using Python SDK:
     ```python
@@ -756,7 +758,7 @@ Here's a basic configuration to route requests to OpenAI, specifically using GPT
     )
     ```
 
-    For detailed key management instructions, see the [Portkey documentation](https://portkey.ai/docs).
+    For detailed key management instructions, see our [API Keys documentation](/en/api-reference/admin-api/control-plane/api-keys/create-api-key).
   </Accordion>
 
   <Accordion title="Step 4: Deploy & Monitor">

docs/en/tools/cloud-storage/overview.mdx

@@ -18,7 +18,7 @@ These tools enable your agents to interact with cloud services, access cloud sto
     Write and upload files to Amazon S3 storage.
   </Card>
 
-  <Card title="Bedrock Invoke Agent" icon="aws" href="/en/tools/integration/bedrockinvokeagenttool">
+  <Card title="Bedrock Invoke Agent" icon="aws" href="/en/tools/cloud-storage/bedrockinvokeagenttool">
     Invoke Amazon Bedrock agents for AI-powered tasks.
   </Card>
 

docs/en/tools/database-data/qdrantvectorsearchtool.mdx

@@ -23,15 +23,13 @@ Here's a minimal example of how to use the tool:
 
 ```python
 from crewai import Agent
-from crewai_tools import QdrantVectorSearchTool, QdrantConfig
+from crewai_tools import QdrantVectorSearchTool
 
-# Initialize the tool with QdrantConfig
+# Initialize the tool
 qdrant_tool = QdrantVectorSearchTool(
-    qdrant_config=QdrantConfig(
-        qdrant_url="your_qdrant_url",
-        qdrant_api_key="your_qdrant_api_key",
-        collection_name="your_collection"
-    )
+    qdrant_url="your_qdrant_url",
+    qdrant_api_key="your_qdrant_api_key",
+    collection_name="your_collection"
 )
 
 # Create an agent that uses the tool
@@ -84,7 +82,7 @@ def extract_text_from_pdf(pdf_path):
 def get_openai_embedding(text):
     response = client.embeddings.create(
         input=text,
-        model="text-embedding-3-large"
+        model="text-embedding-3-small"
     )
     return response.data[0].embedding
 
@@ -92,13 +90,13 @@ def get_openai_embedding(text):
 def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
     # Extract text from PDF
     text_chunks = extract_text_from_pdf(pdf_path)
-
+    
     # Create Qdrant collection
     if qdrant.collection_exists(collection_name):
         qdrant.delete_collection(collection_name)
     qdrant.create_collection(
         collection_name=collection_name,
-        vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
+        vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
     )
 
     # Store embeddings
@@ -122,23 +120,19 @@ pdf_path = "path/to/your/document.pdf"
 load_pdf_to_qdrant(pdf_path, qdrant, collection_name)
 
 # Initialize Qdrant search tool
-from crewai_tools import QdrantConfig
-
 qdrant_tool = QdrantVectorSearchTool(
-    qdrant_config=QdrantConfig(
-        qdrant_url=os.getenv("QDRANT_URL"),
-        qdrant_api_key=os.getenv("QDRANT_API_KEY"),
-        collection_name=collection_name,
-        limit=3,
-        score_threshold=0.35
-    )
+    qdrant_url=os.getenv("QDRANT_URL"),
+    qdrant_api_key=os.getenv("QDRANT_API_KEY"),
+    collection_name=collection_name,
+    limit=3,
+    score_threshold=0.35
 )
 
 # Create CrewAI agents
 search_agent = Agent(
     role="Senior Semantic Search Agent",
     goal="Find and analyze documents based on semantic search",
-    backstory="""You are an expert research assistant who can find relevant
+    backstory="""You are an expert research assistant who can find relevant 
     information using semantic search in a Qdrant database.""",
     tools=[qdrant_tool],
     verbose=True
@@ -147,7 +141,7 @@ search_agent = Agent(
 answer_agent = Agent(
     role="Senior Answer Assistant",
     goal="Generate answers to questions based on the context provided",
-    backstory="""You are an expert answer assistant who can generate
+    backstory="""You are an expert answer assistant who can generate 
     answers to questions based on the context provided.""",
     tools=[qdrant_tool],
     verbose=True
@@ -186,82 +180,21 @@ print(result)
 ## Tool Parameters
 
 ### Required Parameters
-- `qdrant_config` (QdrantConfig): Configuration object containing all Qdrant settings
-
-### QdrantConfig Parameters
 - `qdrant_url` (str): The URL of your Qdrant server
-- `qdrant_api_key` (str, optional): API key for authentication with Qdrant
+- `qdrant_api_key` (str): API key for authentication with Qdrant
 - `collection_name` (str): Name of the Qdrant collection to search
+
+### Optional Parameters
 - `limit` (int): Maximum number of results to return (default: 3)
 - `score_threshold` (float): Minimum similarity score threshold (default: 0.35)
-- `filter` (Any, optional): Qdrant Filter instance for advanced filtering (default: None)
-
-### Optional Tool Parameters
 - `custom_embedding_fn` (Callable[[str], list[float]]): Custom function for text vectorization
-- `qdrant_package` (str): Base package path for Qdrant (default: "qdrant_client")
-- `client` (Any): Pre-initialized Qdrant client (optional)
-
-## Advanced Filtering
-
-The QdrantVectorSearchTool supports powerful filtering capabilities to refine your search results:
-
-### Dynamic Filtering
-Use `filter_by` and `filter_value` parameters in your search to filter results on-the-fly:
-
-```python
-# Agent will use these parameters when calling the tool
-# The tool schema accepts filter_by and filter_value
-# Example: search with category filter
-# Results will be filtered where category == "technology"
-```
-
-### Preset Filters with QdrantConfig
-For complex filtering, use Qdrant Filter instances in your configuration:
-
-```python
-from qdrant_client.http import models as qmodels
-from crewai_tools import QdrantVectorSearchTool, QdrantConfig
-
-# Create a filter for specific conditions
-preset_filter = qmodels.Filter(
-    must=[
-        qmodels.FieldCondition(
-            key="category",
-            match=qmodels.MatchValue(value="research")
-        ),
-        qmodels.FieldCondition(
-            key="year",
-            match=qmodels.MatchValue(value=2024)
-        )
-    ]
-)
-
-# Initialize tool with preset filter
-qdrant_tool = QdrantVectorSearchTool(
-    qdrant_config=QdrantConfig(
-        qdrant_url="your_url",
-        qdrant_api_key="your_key",
-        collection_name="your_collection",
-        filter=preset_filter  # Preset filter applied to all searches
-    )
-)
-```
-
-### Combining Filters
-The tool automatically combines preset filters from `QdrantConfig` with dynamic filters from `filter_by` and `filter_value`:
-
-```python
-# If QdrantConfig has a preset filter for category="research"
-# And the search uses filter_by="year", filter_value=2024
-# Both filters will be combined (AND logic)
-```
 
 ## Search Parameters
 
 The tool accepts these parameters in its schema:
 - `query` (str): The search query to find similar documents
 - `filter_by` (str, optional): Metadata field to filter on
-- `filter_value` (Any, optional): Value to filter by
+- `filter_value` (str, optional): Value to filter by
 
 ## Return Format
 
@@ -281,7 +214,7 @@ The tool returns results in JSON format:
 
 ## Default Embedding
 
-By default, the tool uses OpenAI's `text-embedding-3-large` model for vectorization. This requires:
+By default, the tool uses OpenAI's `text-embedding-3-small` model for vectorization. This requires:
 - OpenAI API key set in environment: `OPENAI_API_KEY`
 
 ## Custom Embeddings
@@ -307,22 +240,18 @@ def custom_embeddings(text: str) -> list[float]:
     # Tokenize and get model outputs
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
     outputs = model(**inputs)
-
+    
     # Use mean pooling to get text embedding
     embeddings = outputs.last_hidden_state.mean(dim=1)
-
+    
     # Convert to list of floats and return
     return embeddings[0].tolist()
 
 # Use custom embeddings with the tool
-from crewai_tools import QdrantConfig
-
 tool = QdrantVectorSearchTool(
-    qdrant_config=QdrantConfig(
-        qdrant_url="your_url",
-        qdrant_api_key="your_key",
-        collection_name="your_collection"
-    ),
+    qdrant_url="your_url",
+    qdrant_api_key="your_key",
+    collection_name="your_collection",
     custom_embedding_fn=custom_embeddings  # Pass your custom function
 )
 ```
@@ -340,4 +269,4 @@ Required environment variables:
 ```bash
 export QDRANT_URL="your_qdrant_url"  # If not provided in constructor
 export QDRANT_API_KEY="your_api_key"  # If not provided in constructor
-export OPENAI_API_KEY="your_openai_key"  # If using default embeddings
+export OPENAI_API_KEY="your_openai_key"  # If using default embeddings

docs/en/tools/file-document/csvsearchtool.mdx

@@ -54,25 +54,25 @@ The following parameters can be used to customize the `CSVSearchTool`'s behavior
 By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
 
 ```python Code
-from chromadb.config import Settings
-
 tool = CSVSearchTool(
-    config={
-        "embedding_model": {
-            "provider": "openai",
-            "config": {
-                "model": "text-embedding-3-small",
-                # "api_key": "sk-...",
-            },
-        },
-        "vectordb": {
-            "provider": "chromadb",  # or "qdrant"
-            "config": {
-                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
-                # from qdrant_client.models import VectorParams, Distance
-                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
-            }
-        },
-    }
+    config=dict(
+        llm=dict(
+            provider="ollama", # or google, openai, anthropic, llama2, ...
+            config=dict(
+                model="llama2",
+                # temperature=0.5,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="google", # or openai, ollama, ...
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
 )
 ```

docs/en/tools/file-document/directorysearchtool.mdx

@@ -46,25 +46,23 @@ tool = DirectorySearchTool(directory='/path/to/directory')
 The DirectorySearchTool uses OpenAI for embeddings and summarization by default. Customization options for these settings include changing the model provider and configuration, enhancing flexibility for advanced users.
 
 ```python Code
-from chromadb.config import Settings
-
 tool = DirectorySearchTool(
-    config={
-        "embedding_model": {
-            "provider": "openai",
-            "config": {
-                "model": "text-embedding-3-small",
-                # "api_key": "sk-...",
-            },
-        },
-        "vectordb": {
-            "provider": "chromadb",  # or "qdrant"
-            "config": {
-                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
-                # from qdrant_client.models import VectorParams, Distance
-                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
-            }
-        },
-    }
+    config=dict(
+        llm=dict(
+            provider="ollama", # Options include ollama, google, anthropic, llama2, and more
+            config=dict(
+                model="llama2",
+                # Additional configurations here
+            ),
+        ),
+        embedder=dict(
+            provider="google", # or openai, ollama, ...
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
 )
 ```

docs/en/tools/file-document/docxsearchtool.mdx

@@ -56,25 +56,25 @@ The following parameters can be used to customize the `DOCXSearchTool`'s behavio
 By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
 
 ```python Code
-from chromadb.config import Settings
-
 tool = DOCXSearchTool(
-    config={
-        "embedding_model": {
-            "provider": "openai",
-            "config": {
-                "model": "text-embedding-3-small",
-                # "api_key": "sk-...",
-            },
-        },
-        "vectordb": {
-            "provider": "chromadb",  # or "qdrant"
-            "config": {
-                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
-                # from qdrant_client.models import VectorParams, Distance
-                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
-            }
-        },
-    }
+    config=dict(
+        llm=dict(
+            provider="ollama", # or google, openai, anthropic, llama2, ...
+            config=dict(
+                model="llama2",
+                # temperature=0.5,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="google", # or openai, ollama, ...
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
 )
 ```

docs/en/tools/file-document/mdxsearchtool.mdx

@@ -48,25 +48,27 @@ tool = MDXSearchTool(mdx='path/to/your/document.mdx')
 The tool defaults to using OpenAI for embeddings and summarization. For customization, utilize a configuration dictionary as shown below:
 
 ```python Code
-from chromadb.config import Settings
-
 tool = MDXSearchTool(
-    config={
-        "embedding_model": {
-            "provider": "openai",
-            "config": {
-                "model": "text-embedding-3-small",
-                # "api_key": "sk-...",
-            },
-        },
-        "vectordb": {
-            "provider": "chromadb",  # or "qdrant"
-            "config": {
-                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
-                # from qdrant_client.models import VectorParams, Distance
-                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
-            }
-        },
-    }
+    config=dict(
+        llm=dict(
+            provider="ollama", # Options include google, openai, anthropic, llama2, etc.
+            config=dict(
+                model="llama2",
+                # Optional parameters can be included here.
+                # temperature=0.5,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="google", # or openai, ollama, ...
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # Optional title for the embeddings can be added here.
+                # title="Embeddings",
+            ),
+        ),
+    )
 )
 ```

docs/en/tools/file-document/pdfsearchtool.mdx

@@ -45,64 +45,28 @@ tool = PDFSearchTool(pdf='path/to/your/document.pdf')
 
 ## Custom model and embeddings
 
-By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows. Note: a vector database is required because generated embeddings must be stored and queried from a vectordb.
+By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
 
 ```python Code
-from crewai_tools import PDFSearchTool
-
-# - embedding_model (required): choose provider + provider-specific config
-# - vectordb (required): choose vector DB and pass its config
-
 tool = PDFSearchTool(
-    config={
-        "embedding_model": {
-            # Supported providers: "openai", "azure", "google-generativeai", "google-vertex",
-            # "voyageai", "cohere", "huggingface", "jina", "sentence-transformer",
-            # "text2vec", "ollama", "openclip", "instructor", "onnx", "roboflow", "watsonx", "custom"
-            "provider": "openai",  # or: "google-generativeai", "cohere", "ollama", ...
-            "config": {
-                # Model identifier for the chosen provider. "model" will be auto-mapped to "model_name" internally.
-                "model": "text-embedding-3-small",
-                # Optional: API key. If omitted, the tool will use provider-specific env vars when available
-                # (e.g., OPENAI_API_KEY for provider="openai").
-                # "api_key": "sk-...",
-
-                # Provider-specific examples:
-                # --- Google Generative AI ---
-                # (Set provider="google-generativeai" above)
-                # "model": "models/embedding-001",
-                # "task_type": "retrieval_document",
-                # "title": "Embeddings",
-
-                # --- Cohere ---
-                # (Set provider="cohere" above)
-                # "model": "embed-english-v3.0",
-
-                # --- Ollama (local) ---
-                # (Set provider="ollama" above)
-                # "model": "nomic-embed-text",
-            },
-        },
-        "vectordb": {
-                    "provider": "chromadb",  # or "qdrant"
-                    "config": {
-                        # For ChromaDB: pass "settings" (chromadb.config.Settings) or rely on defaults.
-                        # Example (uncomment and import):
-                        # from chromadb.config import Settings
-                        # "settings": Settings(
-                        #     persist_directory="/content/chroma",
-                        #     allow_reset=True,
-                        #     is_persistent=True,
-                        # ),
-
-                        # For Qdrant: pass "vectors_config" (qdrant_client.models.VectorParams).
-                        # Example (uncomment and import):
-                        # from qdrant_client.models import VectorParams, Distance
-                        # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
-
-                        # Note: collection name is controlled by the tool (default: "rag_tool_collection"), not set here.
-                    }
-        },
-    }
+    config=dict(
+        llm=dict(
+            provider="ollama", # or google, openai, anthropic, llama2, ...
+            config=dict(
+                model="llama2",
+                # temperature=0.5,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="google", # or openai, ollama, ...
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
 )
 ```

docs/en/tools/file-document/txtsearchtool.mdx

@@ -57,41 +57,25 @@ By default, the tool uses OpenAI for both embeddings and summarization.
 To customize the model, you can use a config dictionary as follows:
 
 ```python Code
-from chromadb.config import Settings
-
 tool = TXTSearchTool(
-    config={
-        # Required: embeddings provider + config
-        "embedding_model": {
-            "provider": "openai",  # or google-generativeai, cohere, ollama, ...
-            "config": {
-                "model": "text-embedding-3-small",
-                # "api_key": "sk-...",  # optional if env var is set
-                # Provider examples:
-                # Google → model: "models/embedding-001", task_type: "retrieval_document"
-                # Cohere → model: "embed-english-v3.0"
-                # Ollama → model: "nomic-embed-text"
-            },
-        },
-
-        # Required: vector database config
-        "vectordb": {
-            "provider": "chromadb",  # or "qdrant"
-            "config": {
-                # Chroma settings (optional persistence)
-                # "settings": Settings(
-                #     persist_directory="/content/chroma",
-                #     allow_reset=True,
-                #     is_persistent=True,
-                # ),
-
-                # Qdrant vector params example:
-                # from qdrant_client.models import VectorParams, Distance
-                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
-
-                # Note: collection name is controlled by the tool (default: "rag_tool_collection").
-            }
-        },
-    }
+    config=dict(
+        llm=dict(
+            provider="ollama", # or google, openai, anthropic, llama2, ...
+            config=dict(
+                model="llama2",
+                # temperature=0.5,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="google", # or openai, ollama, ...
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
 )
 ```

docs/en/tools/file-document/xmlsearchtool.mdx

@@ -54,25 +54,25 @@ It is an optional parameter during the tool's initialization but must be provide
 By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
 
 ```python Code  
-from chromadb.config import Settings
-
 tool = XMLSearchTool(
-    config={
-        "embedding_model": {
-            "provider": "openai",
-            "config": {
-                "model": "text-embedding-3-small",
-                # "api_key": "sk-...",
-            },
-        },
-        "vectordb": {
-            "provider": "chromadb",  # or "qdrant"
-            "config": {
-                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
-                # from qdrant_client.models import VectorParams, Distance
-                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
-            }
-        },
-    }
+    config=dict(
+        llm=dict(
+            provider="ollama", # or google, openai, anthropic, llama2, ...
+            config=dict(
+                model="llama2",
+                # temperature=0.5,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="google", # or openai, ollama, ...
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # title="Embeddings",
+            ),
+        ),
+    )
 )
 ```

docs/en/tools/tool-integrations/overview.mdx

@@ -11,7 +11,7 @@ mode: "wide"
   <Card
     title="Bedrock Invoke Agent Tool"
     icon="cloud"
-    href="/en/tools/integration/bedrockinvokeagenttool"
+    href="/en/tools/tool-integrations/bedrockinvokeagenttool"
     color="#0891B2"
   >
     Invoke Amazon Bedrock Agents from CrewAI to orchestrate actions across AWS services.
@@ -20,7 +20,7 @@ mode: "wide"
   <Card
     title="CrewAI Automation Tool"
     icon="bolt"
-    href="/en/tools/integration/crewaiautomationtool"
+    href="/en/tools/tool-integrations/crewaiautomationtool"
     color="#7C3AED"
   >
     Automate deployment and operations by integrating CrewAI with external platforms and workflows.

docs/ko/changelog.mdx

@@ -632,11 +632,11 @@ mode: "wide"
 
   ## 기여
 
-  기여를 원하시면, [기여 가이드](https://github.com/crewAIInc/crewAI/blob/main/CONTRIBUTING.md)를 참조하세요.
+  기여를 원하시면, [기여 가이드](CONTRIBUTING.md)를 참조하세요.
 
   ## 라이센스
 
-  이 프로젝트는 MIT 라이센스 하에 배포됩니다. 자세한 내용은 [LICENSE](https://github.com/crewAIInc/crewAI/blob/main/LICENSE) 파일을 확인하세요.
+  이 프로젝트는 MIT 라이센스 하에 배포됩니다. 자세한 내용은 [LICENSE](LICENSE) 파일을 확인하세요.
 </Update>
 
 <Update label="2025년 5월 22일">

docs/ko/concepts/knowledge.mdx

@@ -706,7 +706,7 @@ class KnowledgeMonitorListener(BaseEventListener):
 knowledge_monitor = KnowledgeMonitorListener()
 ```
 
-이벤트 사용에 대한 자세한 내용은 [이벤트 리스너](/ko/concepts/event-listener) 문서를 참고하세요.
+이벤트 사용에 대한 자세한 내용은 [이벤트 리스너](https://docs.crewai.com/concepts/event-listener) 문서를 참고하세요.
 
 ### 맞춤형 지식 소스
 

docs/ko/concepts/llms.mdx

@@ -748,7 +748,7 @@ CrewAI는 LLM의 스트리밍 응답을 지원하여, 애플리케이션이 출
     ```
 
     <Tip>
-      [자세한 내용은 여기를 클릭하세요](/ko/concepts/event-listener#event-listeners)
+      [자세한 내용은 여기를 클릭하세요](https://docs.crewai.com/concepts/event-listener#event-listeners)
     </Tip>
   </Tab>
 

docs/ko/enterprise/features/marketplace.mdx

@@ -36,7 +36,7 @@ mode: "wide"
   <Card title="도구 & 통합" href="/ko/enterprise/features/tools-and-integrations" icon="wrench">
     에이전트가 사용할 외부 앱 연결 및 내부 도구 관리.
   </Card>
-  <Card title="도구 저장소" href="/ko/enterprise/guides/tool-repository" icon="toolbox">
+  <Card title="도구 저장소" href="/ko/enterprise/features/tool-repository" icon="toolbox">
     크루 기능을 확장할 수 있도록 도구를 게시하고 설치.
   </Card>
   <Card title="에이전트 저장소" href="/ko/enterprise/features/agent-repositories" icon="people-group">

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

@@ -57,22 +57,6 @@ mode: "wide"
   uv add crewai-tools
   ```
 
-  ### 환경 변수 설정
-
-  <Note>
-    `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-  </Note>
-
-  ```bash
-  export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-  ```
-
-  또는 `.env` 파일에 추가하세요:
-
-  ```
-  CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-  ```
-
   ### 사용 예시
 
   <Tip>
@@ -231,7 +215,7 @@ mode: "wide"
 ## 관련 문서
 
 <CardGroup cols={2}>
-  <Card title="도구 저장소" href="/ko/enterprise/guides/tool-repository" icon="toolbox">
+  <Card title="도구 저장소" href="/ko/enterprise/features/tool-repository" icon="toolbox">
     크루 기능을 확장할 수 있도록 도구를 게시하고 설치하세요.
   </Card>
   <Card title="Webhook 자동화" href="/ko/enterprise/guides/webhook-automation" icon="bolt">

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

@@ -110,49 +110,19 @@ CrewAI AMP 트리거는 팀이 이미 사용하고 있는 도구의 실시간
 - `allow_crewai_trigger_context` 옵션으로 컨텍스트 자동 주입 여부를 결정했나요?
 - 웹훅 로그, CrewAI 실행 기록, 외부 알림 등 모니터링을 준비했나요?
 
-### CLI로 로컬에서 트리거 테스트
+### Payload & Crew 예제 저장소
 
-CrewAI CLI는 프로덕션에 배포하기 전에 트리거 기반 자동화를 개발하고 테스트할 수 있는 강력한 명령을 제공합니다.
+| 통합 | 동작 시점 | Payload 예제 | Crew 예제 |
+| :-- | :-- | :-- | :-- |
+| Gmail | 신규 메일, 스레드 업데이트 | [Gmail payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | [`new-email-crew.py`, `gmail-alert-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) |
+| Google Calendar | 이벤트 생성/수정/시작/종료/취소 | [Calendar payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | [`calendar-event-crew.py`, `calendar-meeting-crew.py`, `calendar-working-location-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) |
+| Google Drive | 파일 생성/수정/삭제 | [Drive payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | [`drive-file-crew.py`, `drive-file-deletion-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) |
+| Outlook | 새 이메일, 이벤트 제거 | [Outlook payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | [`outlook-message-crew.py`, `outlook-event-removal-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) |
+| OneDrive | 파일 작업(생성, 수정, 공유, 삭제) | [OneDrive payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | [`onedrive-file-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) |
+| HubSpot | 레코드 생성/업데이트(연락처, 회사, 딜) | [HubSpot payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | [`hubspot-company-crew.py`, `hubspot-contact-crew.py`, `hubspot-record-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) |
+| Microsoft Teams | 채팅 생성 | [Teams payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | [`teams-chat-created-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) |
 
-#### 사용 가능한 트리거 목록 보기
-
-연결된 통합에 사용 가능한 모든 트리거를 확인하세요:
-
-```bash
-crewai triggers list
-```
-
-이 명령은 연결된 통합을 기반으로 사용 가능한 모든 트리거를 표시합니다:
-- 통합 이름 및 연결 상태
-- 사용 가능한 트리거 유형
-- 트리거 이름 및 설명
-
-#### 트리거 실행 시뮬레이션
-
-배포 전에 실제 트리거 payload로 크루를 테스트하세요:
-
-```bash
-crewai triggers run <트리거_이름>
-```
-
-예시:
-
-```bash
-crewai triggers run microsoft_onedrive/file_changed
-```
-
-이 명령은:
-- 로컬에서 크루를 실행합니다
-- 완전하고 실제적인 트리거 payload를 전달합니다
-- 프로덕션에서 크루가 호출되는 방식을 정확히 시뮬레이션합니다
-
-<Warning>
-  **중요한 개발 노트:**
-  - 개발 중 트리거 실행을 시뮬레이션하려면 `crewai triggers run <trigger>`를 사용하세요
-  - `crewai run`을 사용하면 트리거 호출을 시뮬레이션하지 않으며 트리거 payload를 전달하지 않습니다
-  - 배포 후에는 실제 트리거 payload로 크루가 실행됩니다
-  - 크루가 트리거 payload에 없는 매개변수를 기대하면 실행이 실패할 수 있습니다
-</Warning>
+예제 payload를 참고해 파싱 로직을 검증하고, 제공되는 crew를 복사해 실제 데이터로 교체하세요.
 
 ### 트리거와 Crew 연동
 
@@ -221,20 +191,17 @@ def delegate_to_crew(self, crewai_trigger_payload: dict = None):
 ## 문제 해결
 
 **트리거가 실행되지 않나요?**
-- 배포의 Triggers 탭에서 트리거가 활성화되어 있는지 확인하세요
-- Tools & Integrations에서 통합 연결 상태를 확인하세요
-- 필요한 모든 환경 변수가 올바르게 구성되어 있는지 확인하세요
+- 트리거가 활성 상태인지 확인하세요.
+- 통합 연결 상태를 확인하세요.
 
 **실행 중 오류가 발생하나요?**
-- 실행 로그에서 오류 세부 정보를 확인하세요
-- `crewai triggers run <트리거_이름>`을 사용하여 로컬에서 테스트하고 정확한 payload 구조를 확인하세요
-- 크루가 `crewai_trigger_payload` 매개변수를 처리할 수 있는지 확인하세요
-- 크루가 트리거 payload에 포함되지 않은 매개변수를 기대하지 않는지 확인하세요
-
-**개발 문제:**
-- 배포하기 전에 항상 `crewai triggers run <trigger>`로 테스트하여 전체 payload를 확인하세요
-- `crewai run`은 트리거 호출을 시뮬레이션하지 않으므로 `crewai triggers run`을 대신 사용하세요
-- `crewai triggers list`를 사용하여 연결된 통합에 사용 가능한 트리거를 확인하세요
-- 배포 후 크루는 실제 트리거 payload를 받으므로 먼저 로컬에서 철저히 테스트하세요
+- 실행 로그에서 오류 메시지를 확인하세요.
+- 개발 중이라면 `crewai_trigger_payload`가 올바른 데이터로 전달되고 있는지 확인하세요.
 
 트리거를 활용하면 CrewAI 자동화를 이벤트 기반 시스템으로 전환하여 기존 비즈니스 프로세스와 도구에 자연스럽게 녹여낼 수 있습니다.
+
+<Callout icon="github" title="예제 저장소">
+  <a href="https://github.com/crewAIInc/crewai-enterprise-trigger-examples">
+    CrewAI AMP Trigger Examples
+  </a>
+</Callout>

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

@@ -51,25 +51,16 @@ class GmailProcessingCrew:
         )
 ```
 
-The Gmail payload will be available via the standard context mechanisms.
+The Gmail payload will be available via the standard context mechanisms. See the payload samples repository for structure and fields.
 
-### 로컬에서 테스트
+### Sample payloads & crews
 
-CrewAI CLI를 사용하여 Gmail 트리거 통합을 로컬에서 테스트하세요:
+The [CrewAI AMP Trigger Examples repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) includes:
 
-```bash
-# 사용 가능한 모든 트리거 보기
-crewai triggers list
+- `new-email-payload-1.json` / `new-email-payload-2.json` — production-style new message alerts with matching crews in `new-email-crew.py`
+- `thread-updated-sample-1.json` — follow-up messages on an existing thread, processed by `gmail-alert-crew.py`
 
-# 실제 payload로 Gmail 트리거 시뮬레이션
-crewai triggers run gmail/new_email
-```
-
-`crewai triggers run` 명령은 완전한 Gmail payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
-
-<Warning>
-  개발 중에는 `crewai triggers run gmail/new_email`을 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
-</Warning>
+Use these samples to validate your parsing logic locally before wiring the trigger to your live Gmail accounts.
 
 ## Monitoring Executions
 
@@ -79,10 +70,16 @@ Track history and performance of triggered runs:
   <img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
 </Frame>
 
+## Payload Reference
+
+See the sample payloads and field descriptions:
+
+<Card title="Gmail samples in Trigger Examples Repo" href="https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail" icon="envelopes-bulk">
+    Gmail samples in Trigger Examples Repo
+</Card>
+
 ## Troubleshooting
 
 - Ensure Gmail is connected in Tools & Integrations
 - Verify the Gmail Trigger is enabled on the Triggers tab
-- `crewai triggers run gmail/new_email`로 로컬 테스트하여 정확한 payload 구조를 확인하세요
 - Check the execution logs and confirm the payload is passed as `crewai_trigger_payload`
-- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님)

docs/ko/enterprise/guides/google-calendar-trigger.mdx

@@ -39,23 +39,16 @@ print(result.raw)
 
 Use `crewai_trigger_payload` exactly as it is delivered by the trigger so the crew can extract the proper fields.
 
-## 로컬에서 테스트
+## Sample payloads & crews
 
-CrewAI CLI를 사용하여 Google Calendar 트리거 통합을 로컬에서 테스트하세요:
+The [Google Calendar examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) show how to handle multiple event types:
 
-```bash
-# 사용 가능한 모든 트리거 보기
-crewai triggers list
+- `new-event.json` → standard event creation handled by `calendar-event-crew.py`
+- `event-updated.json` / `event-started.json` / `event-ended.json` → in-flight updates processed by `calendar-meeting-crew.py`
+- `event-canceled.json` → cancellation workflow that alerts attendees via `calendar-meeting-crew.py`
+- Working location events use `calendar-working-location-crew.py` to extract on-site schedules
 
-# 실제 payload로 Google Calendar 트리거 시뮬레이션
-crewai triggers run google_calendar/event_changed
-```
-
-`crewai triggers run` 명령은 완전한 Calendar payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
-
-<Warning>
-  개발 중에는 `crewai triggers run google_calendar/event_changed`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
-</Warning>
+Each crew transforms raw event metadata (attendees, rooms, working locations) into the summaries your teams need.
 
 ## Monitoring Executions
 
@@ -68,7 +61,5 @@ The **Executions** list in the deployment dashboard tracks every triggered run a
 ## Troubleshooting
 
 - Ensure the correct Google account is connected and the trigger is enabled
-- `crewai triggers run google_calendar/event_changed`로 로컬 테스트하여 정확한 payload 구조를 확인하세요
 - Confirm your workflow handles all-day events (payloads use `start.date` and `end.date` instead of timestamps)
 - Check execution logs if reminders or attendee arrays are missing—calendar permissions can limit fields in the payload
-- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님)

docs/ko/enterprise/guides/google-drive-trigger.mdx

@@ -36,23 +36,15 @@ crew.kickoff({
 })
 ```
 
-## 로컬에서 테스트
+## Sample payloads & crews
 
-CrewAI CLI를 사용하여 Google Drive 트리거 통합을 로컬에서 테스트하세요:
+Explore the [Google Drive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) to cover different operations:
 
-```bash
-# 사용 가능한 모든 트리거 보기
-crewai triggers list
+- `new-file.json` → new uploads processed by `drive-file-crew.py`
+- `updated-file.json` → file edits and metadata changes handled by `drive-file-crew.py`
+- `deleted-file.json` → deletion events routed through `drive-file-deletion-crew.py`
 
-# 실제 payload로 Google Drive 트리거 시뮬레이션
-crewai triggers run google_drive/file_changed
-```
-
-`crewai triggers run` 명령은 완전한 Drive payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
-
-<Warning>
-  개발 중에는 `crewai triggers run google_drive/file_changed`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
-</Warning>
+Each crew highlights the file name, operation type, owner, permissions, and security considerations so downstream systems can respond appropriately.
 
 ## Monitoring Executions
 
@@ -65,7 +57,5 @@ Track history and performance of triggered runs with the **Executions** list in
 ## Troubleshooting
 
 - Verify Google Drive is connected and the trigger toggle is enabled
-- `crewai triggers run google_drive/file_changed`로 로컬 테스트하여 정확한 payload 구조를 확인하세요
 - If a payload is missing permission data, ensure the connected account has access to the file or folder
 - The trigger sends file IDs only; use the Drive API if you need to fetch binary content during the crew run
-- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님)

docs/ko/enterprise/guides/hubspot-trigger.mdx

@@ -49,4 +49,6 @@ mode: "wide"
     </Step>
 </Steps>
 
+## 추가 자료
+
 사용 가능한 작업과 사용자 지정 옵션에 대한 자세한 정보는 [HubSpot 워크플로우 문서](https://knowledge.hubspot.com/workflows/create-workflows)를 참고하세요.

docs/ko/enterprise/guides/microsoft-teams-trigger.mdx

@@ -37,28 +37,16 @@ print(result.raw)
 
 The crew parses thread metadata (subject, created time, roster) and generates an action plan for the receiving team.
 
-## 로컬에서 테스트
+## Sample payloads & crews
 
-CrewAI CLI를 사용하여 Microsoft Teams 트리거 통합을 로컬에서 테스트하세요:
+The [Microsoft Teams examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) include:
 
-```bash
-# 사용 가능한 모든 트리거 보기
-crewai triggers list
+- `chat-created.json` → chat creation payload processed by `teams-chat-created-crew.py`
 
-# 실제 payload로 Microsoft Teams 트리거 시뮬레이션
-crewai triggers run microsoft_teams/teams_message_created
-```
-
-`crewai triggers run` 명령은 완전한 Teams payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
-
-<Warning>
-  개발 중에는 `crewai triggers run microsoft_teams/teams_message_created`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
-</Warning>
+The crew demonstrates how to extract participants, initial messages, tenant information, and compliance metadata from the Microsoft Graph webhook payload.
 
 ## Troubleshooting
 
 - Ensure the Teams connection is active; it must be refreshed if the tenant revokes permissions
-- `crewai triggers run microsoft_teams/teams_message_created`로 로컬 테스트하여 정확한 payload 구조를 확인하세요
 - Confirm the webhook subscription in Microsoft 365 is still valid if payloads stop arriving
 - Review execution logs for payload shape mismatches—Graph notifications may omit fields when a chat is private or restricted
-- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님)

docs/ko/enterprise/guides/onedrive-trigger.mdx

@@ -36,28 +36,18 @@ crew.kickoff({
 
 The crew inspects file metadata, user activity, and permission changes to produce a compliance-friendly summary.
 
-## 로컬에서 테스트
+## Sample payloads & crews
 
-CrewAI CLI를 사용하여 OneDrive 트리거 통합을 로컬에서 테스트하세요:
+The [OneDrive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) showcase how to:
 
-```bash
-# 사용 가능한 모든 트리거 보기
-crewai triggers list
+- Parse file metadata, size, and folder paths
+- Track who created and last modified the file
+- Highlight permission and external sharing changes
 
-# 실제 payload로 OneDrive 트리거 시뮬레이션
-crewai triggers run microsoft_onedrive/file_changed
-```
-
-`crewai triggers run` 명령은 완전한 OneDrive payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
-
-<Warning>
-  개발 중에는 `crewai triggers run microsoft_onedrive/file_changed`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
-</Warning>
+`onedrive-file-crew.py` bundles the analysis and summarization tasks so you can add remediation steps as needed.
 
 ## Troubleshooting
 
 - Ensure the connected account has permission to read the file metadata included in the webhook
-- `crewai triggers run microsoft_onedrive/file_changed`로 로컬 테스트하여 정확한 payload 구조를 확인하세요
 - If the trigger fires but the payload is missing `permissions`, confirm the site-level sharing settings allow Graph to return this field
 - For large tenants, filter notifications upstream so the crew only runs on relevant directories
-- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님)

docs/ko/enterprise/guides/outlook-trigger.mdx

@@ -36,28 +36,17 @@ crew.kickoff({
 
 The crew extracts sender details, subject, body preview, and attachments before generating a structured response.
 
-## 로컬에서 테스트
+## Sample payloads & crews
 
-CrewAI CLI를 사용하여 Outlook 트리거 통합을 로컬에서 테스트하세요:
+Review the [Outlook examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) for two common scenarios:
 
-```bash
-# 사용 가능한 모든 트리거 보기
-crewai triggers list
+- `new-message.json` → new mail notifications parsed by `outlook-message-crew.py`
+- `event-removed.json` → calendar cleanup handled by `outlook-event-removal-crew.py`
 
-# 실제 payload로 Outlook 트리거 시뮬레이션
-crewai triggers run microsoft_outlook/email_received
-```
-
-`crewai triggers run` 명령은 완전한 Outlook payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다.
-
-<Warning>
-  개발 중에는 `crewai triggers run microsoft_outlook/email_received`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
-</Warning>
+Each crew demonstrates how to handle Microsoft Graph payloads, normalize headers, and keep humans in-the-loop with concise summaries.
 
 ## Troubleshooting
 
 - Verify the Outlook connector is still authorized; the subscription must be renewed periodically
-- `crewai triggers run microsoft_outlook/email_received`로 로컬 테스트하여 정확한 payload 구조를 확인하세요
 - If attachments are missing, confirm the webhook subscription includes the `includeResourceData` flag
 - Review execution logs when events fail to match—cancellation payloads lack attendee lists by design and the crew should account for that
-- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님)

docs/ko/enterprise/guides/tool-repository.mdx

@@ -21,7 +21,7 @@ Tool Repository는 CrewAI 도구를 위한 패키지 관리자입니다. 사용
 Tool Repository를 사용하기 전에 다음이 준비되어 있어야 합니다:
 
 - [CrewAI AMP](https://app.crewai.com) 계정
-- [CrewAI CLI](/ko/concepts/cli#cli) 설치됨
+- [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) 설치됨
 - uv>=0.5.0 이 설치되어 있어야 합니다. [업그레이드 방법](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv)을 참고하세요.
 - [Git](https://git-scm.com) 설치 및 구성 완료
 - CrewAI AMP 조직에서 도구를 게시하거나 설치할 수 있는 액세스 권한
@@ -66,7 +66,7 @@ crewai tool publish
 crewai tool publish --public
 ```
 
-도구 빌드에 대한 자세한 내용은 [나만의 도구 만들기](/ko/concepts/tools#creating-your-own-tools)를 참고하세요.
+도구 빌드에 대한 자세한 내용은 [나만의 도구 만들기](https://docs.crewai.com/concepts/tools#creating-your-own-tools)를 참고하세요.
 
 ## 도구 업데이트
 

docs/ko/enterprise/integrations/asana.mdx

@@ -33,22 +33,6 @@ Asana 연동을 사용하기 전에 다음을 확인하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/box.mdx

@@ -33,22 +33,6 @@ Box 통합을 사용하기 전에 다음을 확인하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 액션
 
 <AccordionGroup>

docs/ko/enterprise/integrations/clickup.mdx

@@ -33,22 +33,6 @@ ClickUp 통합을 사용하기 전에 다음을 준비해야 합니다:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 동작
 
 <AccordionGroup>

docs/ko/enterprise/integrations/github.mdx

@@ -33,22 +33,6 @@ GitHub 통합을 사용하기 전에 다음을 확인하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/gmail.mdx

@@ -33,22 +33,6 @@ Gmail 통합을 사용하기 전에 다음을 확인하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/google_calendar.mdx

@@ -33,22 +33,6 @@ Google Calendar 통합을 사용하기 전에 다음을 준비해야 합니다:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/google_contacts.mdx

@@ -33,22 +33,6 @@ Google Contacts 통합을 사용하기 전에 다음 사항을 확인하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/google_docs.mdx

@@ -33,22 +33,6 @@ Google Docs 통합을 사용하기 전에 다음 사항을 확인하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/google_drive.mdx

@@ -17,38 +17,6 @@ Google Drive 통합을 사용하기 전에 다음 사항을 확인하세요:
 - Google Drive 액세스 권한이 있는 Google 계정
 - [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Google 계정 연결
 
-## Google Drive 통합 설정
-
-### 1. Google 계정 연결
-
-1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동합니다.
-2. 인증 통합 섹션에서 **Google Drive**를 찾습니다.
-3. **연결**을 클릭하고 OAuth 과정을 완료합니다.
-4. 파일 및 폴더 관리에 필요한 권한을 부여합니다.
-5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다.
-
-### 2. 필수 패키지 설치
-
-```bash
-uv add crewai-tools
-```
-
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 자세한 매개변수 및 사용법은 [영어 문서](../../../en/enterprise/integrations/google_drive)를 참조하세요.

docs/ko/enterprise/integrations/google_sheets.mdx

@@ -34,22 +34,6 @@ Google Sheets 통합을 사용하기 전에 다음을 확인하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/google_slides.mdx

@@ -33,22 +33,6 @@ Google Slides 통합을 사용하기 전에 다음 사항을 확인하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/hubspot.mdx

@@ -33,22 +33,6 @@ HubSpot 통합을 사용하기 전에 다음을 확인하세요.
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 액션
 
 <AccordionGroup>

docs/ko/enterprise/integrations/jira.mdx

@@ -33,22 +33,6 @@ Jira 통합을 사용하기 전에 다음을 준비하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/linear.mdx

@@ -33,22 +33,6 @@ Linear 통합을 사용하기 전에 다음을 확인하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/microsoft_excel.mdx

@@ -33,22 +33,6 @@ Microsoft Excel 통합을 사용하기 전에 다음 사항을 확인하세요:
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/microsoft_onedrive.mdx

@@ -33,22 +33,6 @@ Microsoft OneDrive 통합을 사용하기 전에 다음 사항을 확인하세
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>

docs/ko/enterprise/integrations/microsoft_outlook.mdx

@@ -33,22 +33,6 @@ Microsoft Outlook 통합을 사용하기 전에 다음 사항을 확인하세요
 uv add crewai-tools
 ```
 
-### 3. 환경 변수 설정
-
-<Note>
-  `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다.
-</Note>
-
-```bash
-export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token"
-```
-
-또는 `.env` 파일에 추가하세요:
-
-```
-CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
-```
-
 ## 사용 가능한 작업
 
 <AccordionGroup>