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/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",
@@ -572,7 +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",
@@ -701,7 +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",
@@ -829,12 +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",
@@ -993,7 +983,6 @@
                 "group": "MCP 통합",
                 "pages": [
                   "ko/mcp/overview",
-                  "ko/mcp/dsl-integration",
                   "ko/mcp/stdio",
                   "ko/mcp/sse",
                   "ko/mcp/streamable-http",
@@ -1134,7 +1123,6 @@
                   "ko/observability/overview",
                   "ko/observability/arize-phoenix",
                   "ko/observability/braintrust",
-                  "ko/observability/datadog",
                   "ko/observability/langdb",
                   "ko/observability/langfuse",
                   "ko/observability/langtrace",
@@ -1262,12 +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/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">
@@ -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/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>

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/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/learn/a2a-agent-delegation.mdx

@@ -1,291 +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>
-
-## 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/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

@@ -8,39 +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:
-
-```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!
-```
-
-### 🔧 **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:
@@ -56,125 +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:
+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.
 
-```python
-from crewai import Agent, Task, Crew
-
-# Create agent with MCP tools
-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()
-```
-
-That's it! The MCP tools are automatically discovered and available to your agent.
-
-## MCP Reference Formats
-
-The `mcps` field supports various reference formats for maximum flexibility:
-
-### 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"
-]
-```
-
-### Mixed References
-
-```python
-mcps=[
-    "https://external-api.com/mcp",              # External server
-    "https://weather.service.com/mcp#forecast",  # Specific external tool
-    "crewai-amp:financial-insights",             # AMP service
-    "crewai-amp:data-analysis#sentiment_tool"    # Specific AMP tool
-]
-```
-
-## 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
-
-## Error Handling
-
-The MCP DSL integration is designed to be resilient:
-
-```python
-agent = Agent(
-    role="Resilient Agent",
-    goal="Continue working despite server issues",
-    backstory="Agent that handles failures gracefully",
-    mcps=[
-        "https://reliable-server.com/mcp",        # Will work
-        "https://unreachable-server.com/mcp",     # Will be skipped gracefully
-        "https://slow-server.com/mcp",            # Will timeout gracefully
-        "crewai-amp:working-service"              # Will work
-    ]
-)
-# Agent will use tools from working servers and log warnings for failing ones
-```
-
-## 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
 
@@ -374,19 +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>
@@ -394,7 +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>
@@ -402,7 +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>
@@ -410,7 +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>
@@ -418,7 +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/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/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>

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/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>

docs/ko/enterprise/integrations/microsoft_sharepoint.mdx

@@ -33,22 +33,6 @@ Microsoft SharePoint 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_teams.mdx

@@ -33,22 +33,6 @@ Microsoft Teams 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_word.mdx

@@ -33,22 +33,6 @@ Microsoft Word 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/notion.mdx

@@ -33,22 +33,6 @@ Notion 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/salesforce.mdx

@@ -17,38 +17,6 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요:
 - 적절한 권한이 있는 Salesforce 계정
 - [통합 페이지](https://app.crewai.com/integrations)를 통해 Salesforce 계정 연결
 
-## Salesforce 통합 설정
-
-### 1. Salesforce 계정 연결
-
-1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동합니다.
-2. 인증 통합 섹션에서 **Salesforce**를 찾습니다.
-3. **연결**을 클릭하고 OAuth 과정을 완료합니다.
-4. CRM 및 영업 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 도구
 
 ### **레코드 관리**

docs/ko/enterprise/integrations/shopify.mdx

@@ -17,38 +17,6 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요:
 - 적절한 관리자 권한이 있는 Shopify 스토어
 - [통합 페이지](https://app.crewai.com/integrations)를 통해 Shopify 스토어 연결
 
-## Shopify 통합 설정
-
-### 1. Shopify 스토어 연결
-
-1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동합니다.
-2. 인증 통합 섹션에서 **Shopify**를 찾습니다.
-3. **연결**을 클릭하고 OAuth 과정을 완료합니다.
-4. 스토어 및 제품 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 도구
 
 ### **고객 관리**

docs/ko/enterprise/integrations/slack.mdx

@@ -17,38 +17,6 @@ Slack 통합을 사용하기 전에 다음을 확인하십시오:
 - 적절한 권한이 있는 Slack 워크스페이스
 - [통합 페이지](https://app.crewai.com/integrations)를 통해 Slack 워크스페이스를 연결함
 
-## Slack 통합 설정
-
-### 1. Slack 워크스페이스 연결
-
-1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동합니다.
-2. 인증 통합 섹션에서 **Slack**을 찾습니다.
-3. **연결**을 클릭하고 OAuth 과정을 완료합니다.
-4. 팀 커뮤니케이션에 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 도구
 
 ### **사용자 관리**

docs/ko/enterprise/integrations/stripe.mdx

@@ -17,38 +17,6 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요:
 - 적절한 API 권한이 있는 Stripe 계정
 - [통합 페이지](https://app.crewai.com/integrations)를 통해 Stripe 계정 연결
 
-## Stripe 통합 설정
-
-### 1. Stripe 계정 연결
-
-1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동합니다.
-2. 인증 통합 섹션에서 **Stripe**를 찾습니다.
-3. **연결**을 클릭하고 OAuth 과정을 완료합니다.
-4. 결제 처리에 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 도구
 
 ### **고객 관리**

docs/ko/enterprise/integrations/zendesk.mdx

@@ -17,38 +17,6 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요.
 - 적절한 API 권한이 있는 Zendesk 계정
 - [통합 페이지](https://app.crewai.com/integrations)를 통해 Zendesk 계정 연결
 
-## Zendesk 통합 설정
-
-### 1. Zendesk 계정 연결
-
-1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동합니다.
-2. 인증 통합 섹션에서 **Zendesk**를 찾습니다.
-3. **연결**을 클릭하고 OAuth 과정을 완료합니다.
-4. 티켓 및 사용자 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 도구
 
 ### **티켓 관리**

docs/ko/mcp/dsl-integration.mdx

@@ -1,232 +0,0 @@
----
-title: MCP DSL 통합
-description: CrewAI의 간단한 DSL 구문을 사용하여 mcps 필드로 MCP 서버를 에이전트와 직접 통합하는 방법을 알아보세요.
-icon: code
-mode: "wide"
----
-
-## 개요
-
-CrewAI의 MCP DSL(Domain Specific Language) 통합은 에이전트를 MCP(Model Context Protocol) 서버에 연결하는 **가장 간단한 방법**을 제공합니다. 에이전트에 `mcps` 필드만 추가하면 CrewAI가 모든 복잡성을 자동으로 처리합니다.
-
-<Info>
-이는 대부분의 MCP 사용 사례에 **권장되는 접근 방식**입니다. 수동 연결 관리가 필요한 고급 시나리오의 경우 [MCPServerAdapter](/ko/mcp/overview#advanced-mcpserveradapter)를 참조하세요.
-</Info>
-
-## 기본 사용법
-
-`mcps` 필드를 사용하여 에이전트에 MCP 서버를 추가하세요:
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="연구 보조원",
-    goal="연구 및 분석 업무 지원",
-    backstory="고급 연구 도구에 접근할 수 있는 전문가 보조원",
-    mcps=[
-        "https://mcp.exa.ai/mcp?api_key=your_key&profile=research"
-    ]
-)
-
-# MCP 도구들이 이제 자동으로 사용 가능합니다!
-# 수동 연결 관리나 도구 구성이 필요 없습니다
-```
-
-## 지원되는 참조 형식
-
-### 외부 MCP 원격 서버
-
-```python
-# 기본 HTTPS 서버
-"https://api.example.com/mcp"
-
-# 인증이 포함된 서버
-"https://mcp.exa.ai/mcp?api_key=your_key&profile=your_profile"
-
-# 사용자 정의 경로가 있는 서버
-"https://services.company.com/api/v1/mcp"
-```
-
-### 특정 도구 선택
-
-`#` 구문을 사용하여 서버에서 특정 도구를 선택하세요:
-
-```python
-# 날씨 서버에서 예보 도구만 가져오기
-"https://weather.api.com/mcp#get_forecast"
-
-# Exa에서 검색 도구만 가져오기
-"https://mcp.exa.ai/mcp?api_key=your_key#web_search_exa"
-```
-
-### CrewAI AMP 마켓플레이스
-
-CrewAI AMP 마켓플레이스의 도구에 액세스하세요:
-
-```python
-# 모든 도구가 포함된 전체 서비스
-"crewai-amp:financial-data"
-
-# AMP 서비스의 특정 도구
-"crewai-amp:research-tools#pubmed_search"
-
-# 다중 AMP 서비스
-mcps=[
-    "crewai-amp:weather-insights",
-    "crewai-amp:market-analysis",
-    "crewai-amp:social-media-monitoring"
-]
-```
-
-## 완전한 예제
-
-다음은 여러 MCP 서버를 사용하는 완전한 예제입니다:
-
-```python
-from crewai import Agent, Task, Crew, Process
-
-# 다중 MCP 소스를 가진 에이전트 생성
-multi_source_agent = Agent(
-    role="다중 소스 연구 분석가",
-    goal="다중 데이터 소스를 사용한 종합적인 연구 수행",
-    backstory="""웹 검색, 날씨 데이터, 금융 정보,
-    학술 연구 도구에 접근할 수 있는 전문가 연구원""",
-    mcps=[
-        # 외부 MCP 서버
-        "https://mcp.exa.ai/mcp?api_key=your_exa_key&profile=research",
-        "https://weather.api.com/mcp#get_current_conditions",
-
-        # CrewAI AMP 마켓플레이스
-        "crewai-amp:financial-insights",
-        "crewai-amp:academic-research#pubmed_search",
-        "crewai-amp:market-intelligence#competitor_analysis"
-    ]
-)
-
-# 종합적인 연구 작업 생성
-research_task = Task(
-    description="""AI 에이전트가 비즈니스 생산성에 미치는 영향을 연구하세요.
-    원격 근무에 대한 현재 날씨 영향, 금융 시장 트렌드,
-    AI 에이전트 프레임워크에 대한 최근 학술 발표를 포함하세요.""",
-    expected_output="""다음을 다루는 종합 보고서:
-    1. AI 에이전트 비즈니스 영향 분석
-    2. 원격 근무를 위한 날씨 고려사항
-    3. AI 관련 금융 시장 트렌드
-    4. 학술 연구 인용 및 통찰
-    5. 경쟁 환경 분석""",
-    agent=multi_source_agent
-)
-
-# crew 생성 및 실행
-research_crew = Crew(
-    agents=[multi_source_agent],
-    tasks=[research_task],
-    process=Process.sequential,
-    verbose=True
-)
-
-result = research_crew.kickoff()
-print(f"{len(multi_source_agent.mcps)}개의 MCP 데이터 소스로 연구 완료")
-```
-
-## 주요 기능
-
-- 🔄 **자동 도구 발견**: 도구들이 자동으로 발견되고 통합됩니다
-- 🏷️ **이름 충돌 방지**: 서버 이름이 도구 이름에 접두사로 붙습니다
-- ⚡ **성능 최적화**: 스키마 캐싱과 온디맨드 연결
-- 🛡️ **오류 복원력**: 사용할 수 없는 서버의 우아한 처리
-- ⏱️ **타임아웃 보호**: 내장 타임아웃으로 연결 중단 방지
-- 📊 **투명한 통합**: 기존 CrewAI 기능과 완벽한 연동
-
-## 오류 처리
-
-MCP DSL 통합은 복원력 있게 설계되었습니다:
-
-```python
-agent = Agent(
-    role="복원력 있는 에이전트",
-    goal="서버 문제에도 불구하고 작업 계속",
-    backstory="장애를 우아하게 처리하는 에이전트",
-    mcps=[
-        "https://reliable-server.com/mcp",        # 작동할 것
-        "https://unreachable-server.com/mcp",     # 우아하게 건너뛸 것
-        "https://slow-server.com/mcp",            # 우아하게 타임아웃될 것
-        "crewai-amp:working-service"              # 작동할 것
-    ]
-)
-# 에이전트는 작동하는 서버의 도구를 사용하고 실패한 서버에 대한 경고를 로그에 남깁니다
-```
-
-## 성능 기능
-
-### 자동 캐싱
-
-도구 스키마는 성능 향상을 위해 5분간 캐시됩니다:
-
-```python
-# 첫 번째 에이전트 생성 - 서버에서 도구 발견
-agent1 = Agent(role="첫 번째", goal="테스트", backstory="테스트",
-               mcps=["https://api.example.com/mcp"])
-
-# 두 번째 에이전트 생성 (5분 이내) - 캐시된 도구 스키마 사용
-agent2 = Agent(role="두 번째", goal="테스트", backstory="테스트",
-               mcps=["https://api.example.com/mcp"])  # 훨씬 빠릅니다!
-```
-
-### 온디맨드 연결
-
-도구 연결은 실제로 사용될 때만 설정됩니다:
-
-```python
-# 에이전트 생성은 빠름 - 아직 MCP 연결을 만들지 않음
-agent = Agent(
-    role="온디맨드 에이전트",
-    goal="도구를 효율적으로 사용",
-    backstory="필요할 때만 연결하는 효율적인 에이전트",
-    mcps=["https://api.example.com/mcp"]
-)
-
-# MCP 연결은 도구가 실제로 실행될 때만 만들어집니다
-# 이는 연결 오버헤드를 최소화하고 시작 성능을 개선합니다
-```
-
-## 모범 사례
-
-### 1. 가능하면 특정 도구 사용
-
-```python
-# 좋음 - 필요한 도구만 가져오기
-mcps=["https://weather.api.com/mcp#get_forecast"]
-
-# 덜 효율적 - 서버의 모든 도구 가져오기
-mcps=["https://weather.api.com/mcp"]
-```
-
-### 2. 인증을 안전하게 처리
-
-```python
-import os
-
-# 환경 변수에 API 키 저장
-exa_key = os.getenv("EXA_API_KEY")
-exa_profile = os.getenv("EXA_PROFILE")
-
-agent = Agent(
-    role="안전한 에이전트",
-    goal="MCP 도구를 안전하게 사용",
-    backstory="보안을 고려하는 에이전트",
-    mcps=[f"https://mcp.exa.ai/mcp?api_key={exa_key}&profile={exa_profile}"]
-)
-```
-
-### 3. 서버 장애 계획
-
-```python
-# 항상 백업 옵션 포함
-mcps=[
-    "https://primary-api.com/mcp",       # 주요 선택
-    "https://backup-api.com/mcp",        # 백업 옵션
-    "crewai-amp:reliable-service"        # AMP 폴백
-]
-```

docs/ko/mcp/overview.mdx

@@ -8,37 +8,12 @@ mode: "wide"
 ## 개요
 
 [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP)는 AI 에이전트가 MCP 서버로 알려진 외부 서비스와 통신함으로써 LLM에 컨텍스트를 제공할 수 있도록 표준화된 방식을 제공합니다.
-
-CrewAI는 MCP 통합을 위한 **두 가지 접근 방식**을 제공합니다:
-
-### 🚀 **새로운 기능: 간단한 DSL 통합** (권장)
-
-에이전트에 `mcps` 필드를 직접 사용하여 완벽한 MCP 도구 통합을 구현하세요:
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="연구 분석가",
-    goal="정보를 연구하고 분석",
-    backstory="외부 도구에 접근할 수 있는 전문가 연구원",
-    mcps=[
-        "https://mcp.exa.ai/mcp?api_key=your_key",           # 외부 MCP 서버
-        "https://api.weather.com/mcp#get_forecast",          # 서버의 특정 도구
-        "crewai-amp:financial-data",                         # CrewAI AMP 마켓플레이스
-        "crewai-amp:research-tools#pubmed_search"            # 특정 AMP 도구
-    ]
-)
-# MCP 도구들이 이제 자동으로 에이전트에서 사용 가능합니다!
-```
-
-### 🔧 **고급: MCPServerAdapter** (복잡한 시나리오용)
-
-수동 연결 관리가 필요한 고급 사용 사례의 경우 `crewai-tools` 라이브러리는 `MCPServerAdapter` 클래스를 제공합니다.
+`crewai-tools` 라이브러리는 CrewAI의 기능을 확장하여, 이러한 MCP 서버에서 제공하는 툴을 에이전트에 원활하게 통합할 수 있도록 해줍니다.
+이를 통해 여러분의 crew는 방대한 기능 에코시스템에 접근할 수 있습니다.
 
 현재 다음과 같은 전송 메커니즘을 지원합니다:
 
-- **HTTPS**: 원격 서버용 (HTTPS를 통한 보안 통신)
+- **Stdio**: 로컬 서버용 (동일 머신 내 프로세스 간 표준 입력/출력을 통한 통신)
 - **Server-Sent Events (SSE)**: 원격 서버용 (서버에서 클라이언트로의 일방향, 실시간 데이터 스트리밍, HTTP 기반)
 - **Streamable HTTP**: 원격 서버용 (유연하며 잠재적으로 양방향 통신이 가능, 주로 SSE를 활용한 서버-클라이언트 스트림 제공, HTTP 기반)
 

docs/ko/observability/datadog.mdx

@@ -1,109 +0,0 @@
----
-title: Datadog 통합
-description: Datadog을 CrewAI와 통합하여 LLM Observability 트레이스들을 Datadog에 제출하는 방법을 알아보세요.
-icon: dog
-mode: "wide"
----
-
-# Datadog을 CrewAI와 통합하기
-
-이 가이드에서는 Datadog 자동 계측을 사용하여 **Datadog**을 **CrewAI**와 통합하는 방법을 보여드립니다. 이 가이드가 끝나면 LLM Observability 트레이스를 Datadog에 제출하고 CrewAI 에이전트 실행을 Datadog LLM Observability의 에이전트 실행 보기에서 볼 수 있게 됩니다.
-
-## Datadog LLM Observability란 무엇인가요?
-
-[Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/)는 AI 엔지니어, 데이터 과학자, 애플리케이션 개발자가 LLM 애플리케이션을 신속하게 개발, 평가, 모니터링할 수 있도록 도와줍니다. 구조화된 실험, AI 에이전트 전반의 엔드투엔드 추적, 평가를 통해 결과물 품질, 성능, 비용, 전반적인 위험을 확실하게 개선할 수 있습니다.
-
-## 시작하기
-
-### 설치 종속성
-
-```shell
-pip install ddtrace crewai crewai-tools
-```
-
-### 환경 변수 설정하기
-
-Datadog API 키가 없는 경우, [계정 만들기](https://www.datadoghq.com/) 및 [API 키 받기](https://docs.datadoghq.com/account_management/api-app-keys/#api-keys)를 할 수 있습니다.
-
-또한 다음 환경 변수에 ML 애플리케이션 이름을 지정해야 합니다. ML 애플리케이션은 특정 LLM 기반 애플리케이션과 관련된 LLM Observability 트레이스의 그룹입니다. ML 애플리케이션 이름 제한에 대한 자세한 내용은 [ML 애플리케이션 이름 지정 가이드라인](https://docs.datadoghq.com/llm_observability/instrumentation/sdk?tab=python#application-naming-guidelines)을 참조하세요.
-
-```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
-```
-
-또한 LLM 공급자 API 키를 설정합니다.
-
-```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>
-...
-```
-
-### 크루AI 에이전트 애플리케이션 생성하기
-
-```python
-# crewai_agent.py
-from crewai import Agent, Task, Crew
-
-from crewai_tools import (
-    WebsiteSearchTool
-)
-
-web_rag_tool = WebsiteSearchTool()
-
-writer = Agent(
-    role="작가",
-    goal="시를 통해 어린이들이 수학을 흥미롭고 이해하기 쉽게 설명합니다",
-    backstory="당신은 하이쿠를 쓰는 전문가이지만 수학은 전혀 모릅니다.",
-    tools=[web_rag_tool],
-)
-
-task = Task(
-    description=("{곱셈}이란 무엇인가요?"),
-    expected_output=("답을 포함하는 하이쿠를 작성하세요."),
-    agent=writer
-)
-
-crew = Crew(
-    agents=[writer],
-    tasks=[task],
-    share_crew=False
-)
-
-output = crew.kickoff(dict(곱셈="2 * 2"))
-```
-
-### Datadog 자동 계측을 사용하여 애플리케이션 실행하기
-
-[환경 변수](#환경-변수-설정하기)를 설정하면 이제 Datadog 자동 계측을 통해 애플리케이션을 실행할 수 있습니다.
-
-```shell
-ddtrace-run python crewai_agent.py
-```
-
-### Datadog에서 트레이스 추적하기
-
-애플리케이션을 실행한 후 왼쪽 상단 드롭다운에서 선택한 ML 애플리케이션 이름을 선택하면 [Datadog LLM Observability의 트레이스 보기](https://app.datadoghq.com/llm/traces)에서 트레이스들을 확인할 수 있습니다.
-
-트레이스를 클릭하면 사용된 총 토큰, LLM 호출 수, 사용된 모델, 예상 비용 등 트레이스에 대한 세부 정보가 표시됩니다. 특정 스팬(span)을 클릭하면 이러한 세부 정보의 범위가 좁혀지고 관련 입력, 출력 및 메타데이터가 표시됩니다.
-
-<Frame>
-<img src="/images/datadog-llm-observability-1.png" alt="Datadog LLM 옵저버빌리티 추적 보기" />
-</Frame>
-
-또한, 트레이스의 제어 및 데이터 흐름을 보여주는 트레이스의 실행 그래프 보기를 볼 수 있으며, 이는 더 큰 에이전트로 확장하여 LLM 호출, 도구 호출 및 에이전트 상호 작용 간의 핸드오프와 관계를 보여줍니다.
-
-<Frame>
-<img src="/images/datadog-llm-observability-2.png" alt="Datadog LLM Observability 에이전트 실행 흐름 보기" />
-</Frame>
-
-## 참조
-
-- [Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/)
-- [Datadog LLM 옵저버빌리티 크루AI 자동 계측](https://docs.datadoghq.com/llm_observability/instrumentation/auto_instrumentation?tab=python#crew-ai)

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

@@ -23,15 +23,13 @@ uv add qdrant-client
 
 ```python
 from crewai import Agent
-from crewai_tools import QdrantVectorSearchTool, QdrantConfig
+from crewai_tools import QdrantVectorSearchTool
 
-# QdrantConfig로 도구 초기화
+# 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)
 ## 도구 매개변수
 
 ### 필수 파라미터
-- `qdrant_config` (QdrantConfig): 모든 Qdrant 설정을 포함하는 구성 객체
-
-### QdrantConfig 매개변수
 - `qdrant_url` (str): Qdrant 서버의 URL
-- `qdrant_api_key` (str, 선택 사항): Qdrant 인증을 위한 API 키
+- `qdrant_api_key` (str): Qdrant 인증을 위한 API 키
 - `collection_name` (str): 검색할 Qdrant 컬렉션의 이름
+
+### 선택적 매개변수
 - `limit` (int): 반환할 최대 결과 수 (기본값: 3)
 - `score_threshold` (float): 최소 유사도 점수 임계값 (기본값: 0.35)
-- `filter` (Any, 선택 사항): 고급 필터링을 위한 Qdrant Filter 인스턴스 (기본값: None)
-
-### 선택적 도구 매개변수
 - `custom_embedding_fn` (Callable[[str], list[float]]): 텍스트 벡터화를 위한 사용자 지정 함수
-- `qdrant_package` (str): Qdrant의 기본 패키지 경로 (기본값: "qdrant_client")
-- `client` (Any): 사전 초기화된 Qdrant 클라이언트 (선택 사항)
-
-## 고급 필터링
-
-QdrantVectorSearchTool은 검색 결과를 세밀하게 조정할 수 있는 강력한 필터링 기능을 지원합니다:
-
-### 동적 필터링
-검색 시 `filter_by` 및 `filter_value` 매개변수를 사용하여 즉석에서 결과를 필터링할 수 있습니다:
-
-```python
-# 에이전트는 도구를 호출할 때 이러한 매개변수를 사용합니다
-# 도구 스키마는 filter_by 및 filter_value를 허용합니다
-# 예시: 카테고리 필터를 사용한 검색
-# 결과는 category == "기술"인 항목으로 필터링됩니다
-```
-
-### QdrantConfig를 사용한 사전 설정 필터
-복잡한 필터링의 경우 구성에서 Qdrant Filter 인스턴스를 사용하세요:
-
-```python
-from qdrant_client.http import models as qmodels
-from crewai_tools import QdrantVectorSearchTool, QdrantConfig
-
-# 특정 조건에 대한 필터 생성
-preset_filter = qmodels.Filter(
-    must=[
-        qmodels.FieldCondition(
-            key="category",
-            match=qmodels.MatchValue(value="research")
-        ),
-        qmodels.FieldCondition(
-            key="year",
-            match=qmodels.MatchValue(value=2024)
-        )
-    ]
-)
-
-# 사전 설정 필터로 도구 초기화
-qdrant_tool = QdrantVectorSearchTool(
-    qdrant_config=QdrantConfig(
-        qdrant_url="your_url",
-        qdrant_api_key="your_key",
-        collection_name="your_collection",
-        filter=preset_filter  # 모든 검색에 적용되는 사전 설정 필터
-    )
-)
-```
-
-### 필터 결합
-도구는 `QdrantConfig`의 사전 설정 필터와 `filter_by` 및 `filter_value`의 동적 필터를 자동으로 결합합니다:
-
-```python
-# QdrantConfig에 category="research"에 대한 사전 설정 필터가 있고
-# 검색에서 filter_by="year", filter_value=2024를 사용하는 경우
-# 두 필터가 모두 결합됩니다 (AND 논리)
-```
 
 ## 검색 매개변수
 
 이 도구는 스키마에서 다음과 같은 매개변수를 허용합니다:
 - `query` (str): 유사한 문서를 찾기 위한 검색 쿼리
 - `filter_by` (str, 선택 사항): 필터링할 메타데이터 필드
-- `filter_value` (Any, 선택 사항): 필터 기준 값
+- `filter_value` (str, 선택 사항): 필터 기준 값
 
 ## 반환 형식
 
@@ -281,7 +214,7 @@ qdrant_tool = QdrantVectorSearchTool(
 
 ## 기본 임베딩
 
-기본적으로, 이 도구는 벡터화를 위해 OpenAI의 `text-embedding-3-large` 모델을 사용합니다. 이를 위해서는 다음이 필요합니다:
+기본적으로, 이 도구는 벡터화를 위해 OpenAI의 `text-embedding-3-small` 모델을 사용합니다. 이를 위해서는 다음이 필요합니다:
 - 환경변수에 설정된 OpenAI API 키: `OPENAI_API_KEY`
 
 ## 커스텀 임베딩
@@ -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
 )
 ```
@@ -341,4 +270,4 @@ tool = QdrantVectorSearchTool(
 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
-```
+```

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

@@ -54,25 +54,25 @@ tool = CSVSearchTool()
 기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 사용자 지정하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
 
 ```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",  # 또는 "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/ko/tools/file-document/directorysearchtool.mdx

@@ -46,25 +46,23 @@ tool = DirectorySearchTool(directory='/path/to/directory')
 DirectorySearchTool은 기본적으로 OpenAI를 사용하여 임베딩 및 요약을 수행합니다. 이 설정의 커스터마이즈 옵션에는 모델 공급자 및 구성을 변경하는 것이 포함되어 있어, 고급 사용자를 위한 유연성을 향상시킵니다.
 
 ```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",  # 또는 "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/ko/tools/file-document/docxsearchtool.mdx

@@ -56,25 +56,25 @@ tool = DOCXSearchTool(docx='path/to/your/document.docx')
 기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이즈하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
 
 ```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",  # 또는 "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/ko/tools/file-document/mdxsearchtool.mdx

@@ -48,25 +48,27 @@ tool = MDXSearchTool(mdx='path/to/your/document.mdx')
 이 도구는 기본적으로 임베딩과 요약을 위해 OpenAI를 사용합니다. 커스터마이징을 위해 아래와 같이 설정 딕셔너리를 사용할 수 있습니다.
 
 ```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",  # 또는 "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", # 옵션에는 google, openai, anthropic, llama2 등이 있습니다.
+            config=dict(
+                model="llama2",
+                # 선택적 파라미터를 여기에 포함할 수 있습니다.
+                # temperature=0.5,
+                # top_p=1,
+                # stream=true,
+            ),
+        ),
+        embedder=dict(
+            provider="google", # 또는 openai, ollama, ...
+            config=dict(
+                model="models/embedding-001",
+                task_type="retrieval_document",
+                # 임베딩에 대한 선택적 제목을 여기에 추가할 수 있습니다.
+                # title="Embeddings",
+            ),
+        ),
+    )
 )
 ```

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

@@ -45,60 +45,28 @@ tool = PDFSearchTool(pdf='path/to/your/document.pdf')
 
 ## 커스텀 모델 및 임베딩
 
-기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이즈하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다. 참고: 임베딩은 벡터DB에 저장되어야 하므로 vectordb 설정이 필요합니다.
+기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이즈하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
 
 ```python Code
-from crewai_tools import PDFSearchTool
-from chromadb.config import Settings  # Chroma 영속성 설정
-
 tool = PDFSearchTool(
-    config={
-        # 필수: 임베딩 제공자와 설정
-        "embedding_model": {
-            # 사용 가능 공급자: "openai", "azure", "google-generativeai", "google-vertex",
-            # "voyageai", "cohere", "huggingface", "jina", "sentence-transformer",
-            # "text2vec", "ollama", "openclip", "instructor", "onnx", "roboflow", "watsonx", "custom"
-            "provider": "openai",
-            "config": {
-                # "model" 키는 내부적으로 "model_name"으로 매핑됩니다.
-                "model": "text-embedding-3-small",
-                # 선택: API 키 (미설정 시 환경변수 사용)
-                # "api_key": "sk-...",
-
-                # 공급자별 예시
-                # --- Google ---
-                # (provider를 "google-generativeai"로 설정)
-                # "model": "models/embedding-001",
-                # "task_type": "retrieval_document",
-
-                # --- Cohere ---
-                # (provider를 "cohere"로 설정)
-                # "model": "embed-english-v3.0",
-
-                # --- Ollama(로컬) ---
-                # (provider를 "ollama"로 설정)
-                # "model": "nomic-embed-text",
-            },
-        },
-
-        # 필수: 벡터DB 설정
-        "vectordb": {
-            "provider": "chromadb",  # 또는 "qdrant"
-            "config": {
-                # Chroma 설정 예시
-                # "settings": Settings(
-                #     persist_directory="/content/chroma",
-                #     allow_reset=True,
-                #     is_persistent=True,
-                # ),
-
-                # Qdrant 설정 예시
-                # from qdrant_client.models import VectorParams, Distance
-                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
-
-                # 참고: 컬렉션 이름은 도구에서 관리합니다(기본값: "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/ko/tools/file-document/txtsearchtool.mdx

@@ -57,34 +57,25 @@ tool = TXTSearchTool(txt='path/to/text/file.txt')
 모델을 커스터마이징하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
 
 ```python Code
-from chromadb.config import Settings
-
 tool = TXTSearchTool(
-    config={
-        # 필수: 임베딩 제공자 + 설정
-        "embedding_model": {
-            "provider": "openai",  # 또는 google-generativeai, cohere, ollama 등
-            "config": {
-                "model": "text-embedding-3-small",
-                # "api_key": "sk-...",  # 환경변수 사용 시 생략 가능
-                # 공급자별 예시: Google → model: "models/embedding-001", task_type: "retrieval_document"
-            },
-        },
-
-        # 필수: 벡터DB 설정
-        "vectordb": {
-            "provider": "chromadb",  # 또는 "qdrant"
-            "config": {
-                # Chroma 설정(영속성 예시)
-                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
-
-                # Qdrant 벡터 파라미터 예시:
-                # from qdrant_client.models import VectorParams, Distance
-                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
-
-                # 참고: 컬렉션 이름은 도구에서 관리합니다(기본값: "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",
+            ),
+        ),
+    )
 )
 ```