Fix hierarchical delegation bug where manager couldn't delegate to all crew agents

Fixes #3887

In hierarchical process, when a task was assigned to a specific agent,
the manager's delegation tools were only given access to that task's
assigned agent instead of all crew agents. This caused delegation
errors when trying to delegate to other agents in the crew.

Changes:
- Updated _update_manager_tools in crew.py to pass self.agents instead
  of [task.agent] to _inject_delegation_tools
- Added comprehensive tests reproducing the bug scenario
- Updated existing test to reflect correct behavior where manager can
  delegate to all crew agents

The fix ensures that in hierarchical mode, the manager agent can
delegate to any agent in the crew's agents list, not just the task's
assigned agent.

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

.github/dependabot.yml

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

.pre-commit-config.yaml

@@ -19,6 +19,7 @@ repos:
         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:

docs/docs.json

@@ -276,6 +276,7 @@
                   "en/observability/overview",
                   "en/observability/arize-phoenix",
                   "en/observability/braintrust",
+                  "en/observability/datadog",
                   "en/observability/langdb",
                   "en/observability/langfuse",
                   "en/observability/langtrace",
@@ -700,6 +701,7 @@
                   "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",
@@ -1132,6 +1134,7 @@
                   "ko/observability/overview",
                   "ko/observability/arize-phoenix",
                   "ko/observability/braintrust",
+                  "ko/observability/datadog",
                   "ko/observability/langdb",
                   "ko/observability/langfuse",
                   "ko/observability/langtrace",

docs/en/concepts/llms.mdx

@@ -1200,6 +1200,52 @@ Learn how to get the most out of your LLM configuration:
     )
     ```
   </Accordion>
+
+  <Accordion title="Transport Interceptors">
+    CrewAI provides message interceptors for several providers, allowing you to hook into request/response cycles at the transport layer.
+
+    **Supported Providers:**
+    - ✅ OpenAI
+    - ✅ Anthropic
+
+    **Basic Usage:**
+    ```python
+import httpx
+from crewai import LLM
+from crewai.llms.hooks import BaseInterceptor
+
+class CustomInterceptor(BaseInterceptor[httpx.Request, httpx.Response]):
+    """Custom interceptor to modify requests and responses."""
+
+    def on_outbound(self, request: httpx.Request) -> httpx.Request:
+        """Print request before sending to the LLM provider."""
+        print(request)
+        return request
+
+    def on_inbound(self, response: httpx.Response) -> httpx.Response:
+        """Process response after receiving from the LLM provider."""
+        print(f"Status: {response.status_code}")
+        print(f"Response time: {response.elapsed}")
+        return response
+
+# Use the interceptor with an LLM
+llm = LLM(
+    model="openai/gpt-4o",
+    interceptor=CustomInterceptor()
+)
+    ```
+
+    **Important Notes:**
+    - Both methods must return the received object or type of object.
+    - Modifying received objects may result in unexpected behavior or application crashes.
+    - Not all providers support interceptors - check the supported providers list above
+
+    <Info>
+      Interceptors operate at the transport layer. This is particularly useful for:
+      - Message transformation and filtering
+      - Debugging API interactions
+    </Info>
+  </Accordion>
 </AccordionGroup>
 
 ## Common Issues and Solutions

docs/en/concepts/tasks.mdx

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

docs/en/enterprise/integrations/asana.mdx

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -34,6 +34,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -17,6 +17,38 @@ 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

@@ -17,6 +17,38 @@ 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

@@ -17,6 +17,38 @@ 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

@@ -17,6 +17,38 @@ 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

@@ -17,6 +17,38 @@ 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

@@ -0,0 +1,291 @@
+---
+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/overview.mdx

@@ -11,9 +11,13 @@ The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP)
 
 CrewAI offers **two approaches** for MCP integration:
 
-### Simple DSL Integration** (Recommended)
+### 🚀 **Simple DSL Integration** (Recommended)
 
-Use the `mcps` field directly on agents for seamless MCP tool integration:
+Use the `mcps` field directly on agents for seamless MCP tool integration. The DSL supports both **string references** (for quick setup) and **structured configurations** (for full control).
+
+#### String-Based References (Quick Setup)
+
+Perfect for remote HTTPS servers and CrewAI AMP marketplace:
 
 ```python
 from crewai import Agent
@@ -32,6 +36,46 @@ agent = Agent(
 # MCP tools are now automatically available to your agent!
 ```
 
+#### Structured Configurations (Full Control)
+
+For complete control over connection settings, tool filtering, and all transport types:
+
+```python
+from crewai import Agent
+from crewai.mcp import MCPServerStdio, MCPServerHTTP, MCPServerSSE
+from crewai.mcp.filters import create_static_tool_filter
+
+agent = Agent(
+    role="Advanced Research Analyst",
+    goal="Research with full control over MCP connections",
+    backstory="Expert researcher with advanced tool access",
+    mcps=[
+        # Stdio transport for local servers
+        MCPServerStdio(
+            command="npx",
+            args=["-y", "@modelcontextprotocol/server-filesystem"],
+            env={"API_KEY": "your_key"},
+            tool_filter=create_static_tool_filter(
+                allowed_tool_names=["read_file", "list_directory"]
+            ),
+            cache_tools_list=True,
+        ),
+        # HTTP/Streamable HTTP transport for remote servers
+        MCPServerHTTP(
+            url="https://api.example.com/mcp",
+            headers={"Authorization": "Bearer your_token"},
+            streamable=True,
+            cache_tools_list=True,
+        ),
+        # SSE transport for real-time streaming
+        MCPServerSSE(
+            url="https://stream.example.com/mcp/sse",
+            headers={"Authorization": "Bearer your_token"},
+        ),
+    ]
+)
+```
+
 ### 🔧 **Advanced: MCPServerAdapter** (For Complex Scenarios)
 
 For advanced use cases requiring manual connection management, the `crewai-tools` library provides the `MCPServerAdapter` class.
@@ -68,12 +112,14 @@ uv pip install 'crewai-tools[mcp]'
 
 ## Quick Start: Simple DSL Integration
 
-The easiest way to integrate MCP servers is using the `mcps` field on your agents:
+The easiest way to integrate MCP servers is using the `mcps` field on your agents. You can use either string references or structured configurations.
+
+### Quick Start with String References
 
 ```python
 from crewai import Agent, Task, Crew
 
-# Create agent with MCP tools
+# Create agent with MCP tools using string references
 research_agent = Agent(
     role="Research Analyst",
     goal="Find and analyze information using advanced search tools",
@@ -96,13 +142,53 @@ crew = Crew(agents=[research_agent], tasks=[research_task])
 result = crew.kickoff()
 ```
 
+### Quick Start with Structured Configurations
+
+```python
+from crewai import Agent, Task, Crew
+from crewai.mcp import MCPServerStdio, MCPServerHTTP, MCPServerSSE
+
+# Create agent with structured MCP configurations
+research_agent = Agent(
+    role="Research Analyst",
+    goal="Find and analyze information using advanced search tools",
+    backstory="Expert researcher with access to multiple data sources",
+    mcps=[
+        # Local stdio server
+        MCPServerStdio(
+            command="python",
+            args=["local_server.py"],
+            env={"API_KEY": "your_key"},
+        ),
+        # Remote HTTP server
+        MCPServerHTTP(
+            url="https://api.research.com/mcp",
+            headers={"Authorization": "Bearer your_token"},
+        ),
+    ]
+)
+
+# Create task
+research_task = Task(
+    description="Research the latest developments in AI agent frameworks",
+    expected_output="Comprehensive research report with citations",
+    agent=research_agent
+)
+
+# Create and run crew
+crew = Crew(agents=[research_agent], tasks=[research_task])
+result = crew.kickoff()
+```
+
 That's it! The MCP tools are automatically discovered and available to your agent.
 
 ## MCP Reference Formats
 
-The `mcps` field supports various reference formats for maximum flexibility:
+The `mcps` field supports both **string references** (for quick setup) and **structured configurations** (for full control). You can mix both formats in the same list.
 
-### External MCP Servers
+### String-Based References
+
+#### External MCP Servers
 
 ```python
 mcps=[
@@ -117,7 +203,7 @@ mcps=[
 ]
 ```
 
-### CrewAI AMP Marketplace
+#### CrewAI AMP Marketplace
 
 ```python
 mcps=[
@@ -133,17 +219,166 @@ mcps=[
 ]
 ```
 
-### Mixed References
+### Structured Configurations
+
+#### Stdio Transport (Local Servers)
+
+Perfect for local MCP servers that run as processes:
 
 ```python
+from crewai.mcp import MCPServerStdio
+from crewai.mcp.filters import create_static_tool_filter
+
 mcps=[
-    "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
+    MCPServerStdio(
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-filesystem"],
+        env={"API_KEY": "your_key"},
+        tool_filter=create_static_tool_filter(
+            allowed_tool_names=["read_file", "write_file"]
+        ),
+        cache_tools_list=True,
+    ),
+    # Python-based server
+    MCPServerStdio(
+        command="python",
+        args=["path/to/server.py"],
+        env={"UV_PYTHON": "3.12", "API_KEY": "your_key"},
+    ),
 ]
 ```
 
+#### HTTP/Streamable HTTP Transport (Remote Servers)
+
+For remote MCP servers over HTTP/HTTPS:
+
+```python
+from crewai.mcp import MCPServerHTTP
+
+mcps=[
+    # Streamable HTTP (default)
+    MCPServerHTTP(
+        url="https://api.example.com/mcp",
+        headers={"Authorization": "Bearer your_token"},
+        streamable=True,
+        cache_tools_list=True,
+    ),
+    # Standard HTTP
+    MCPServerHTTP(
+        url="https://api.example.com/mcp",
+        headers={"Authorization": "Bearer your_token"},
+        streamable=False,
+    ),
+]
+```
+
+#### SSE Transport (Real-Time Streaming)
+
+For remote servers using Server-Sent Events:
+
+```python
+from crewai.mcp import MCPServerSSE
+
+mcps=[
+    MCPServerSSE(
+        url="https://stream.example.com/mcp/sse",
+        headers={"Authorization": "Bearer your_token"},
+        cache_tools_list=True,
+    ),
+]
+```
+
+### Mixed References
+
+You can combine string references and structured configurations:
+
+```python
+from crewai.mcp import MCPServerStdio, MCPServerHTTP
+
+mcps=[
+    # String references
+    "https://external-api.com/mcp",              # External server
+    "crewai-amp:financial-insights",             # AMP service
+
+    # Structured configurations
+    MCPServerStdio(
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-filesystem"],
+    ),
+    MCPServerHTTP(
+        url="https://api.example.com/mcp",
+        headers={"Authorization": "Bearer token"},
+    ),
+]
+```
+
+### Tool Filtering
+
+Structured configurations support advanced tool filtering:
+
+```python
+from crewai.mcp import MCPServerStdio
+from crewai.mcp.filters import create_static_tool_filter, create_dynamic_tool_filter, ToolFilterContext
+
+# Static filtering (allow/block lists)
+static_filter = create_static_tool_filter(
+    allowed_tool_names=["read_file", "write_file"],
+    blocked_tool_names=["delete_file"],
+)
+
+# Dynamic filtering (context-aware)
+def dynamic_filter(context: ToolFilterContext, tool: dict) -> bool:
+    # Block dangerous tools for certain agent roles
+    if context.agent.role == "Code Reviewer":
+        if "delete" in tool.get("name", "").lower():
+            return False
+    return True
+
+mcps=[
+    MCPServerStdio(
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-filesystem"],
+        tool_filter=static_filter,  # or dynamic_filter
+    ),
+]
+```
+
+## Configuration Parameters
+
+Each transport type supports specific configuration options:
+
+### MCPServerStdio Parameters
+
+- **`command`** (required): Command to execute (e.g., `"python"`, `"node"`, `"npx"`, `"uvx"`)
+- **`args`** (optional): List of command arguments (e.g., `["server.py"]` or `["-y", "@mcp/server"]`)
+- **`env`** (optional): Dictionary of environment variables to pass to the process
+- **`tool_filter`** (optional): Tool filter function for filtering available tools
+- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
+
+### MCPServerHTTP Parameters
+
+- **`url`** (required): Server URL (e.g., `"https://api.example.com/mcp"`)
+- **`headers`** (optional): Dictionary of HTTP headers for authentication or other purposes
+- **`streamable`** (optional): Whether to use streamable HTTP transport (default: `True`)
+- **`tool_filter`** (optional): Tool filter function for filtering available tools
+- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
+
+### MCPServerSSE Parameters
+
+- **`url`** (required): Server URL (e.g., `"https://api.example.com/mcp/sse"`)
+- **`headers`** (optional): Dictionary of HTTP headers for authentication or other purposes
+- **`tool_filter`** (optional): Tool filter function for filtering available tools
+- **`cache_tools_list`** (optional): Whether to cache the tool list for faster subsequent access (default: `False`)
+
+### Common Parameters
+
+All transport types support:
+- **`tool_filter`**: Filter function to control which tools are available. Can be:
+  - `None` (default): All tools are available
+  - Static filter: Created with `create_static_tool_filter()` for allow/block lists
+  - Dynamic filter: Created with `create_dynamic_tool_filter()` for context-aware filtering
+- **`cache_tools_list`**: When `True`, caches the tool list after first discovery to improve performance on subsequent connections
+
 ## Key Features
 
 - 🔄 **Automatic Tool Discovery**: Tools are automatically discovered and integrated
@@ -152,26 +387,47 @@ mcps=[
 - 🛡️ **Error Resilience**: Graceful handling of unavailable servers
 - ⏱️ **Timeout Protection**: Built-in timeouts prevent hanging connections
 - 📊 **Transparent Integration**: Works seamlessly with existing CrewAI features
+- 🔧 **Full Transport Support**: Stdio, HTTP/Streamable HTTP, and SSE transports
+- 🎯 **Advanced Filtering**: Static and dynamic tool filtering capabilities
+- 🔐 **Flexible Authentication**: Support for headers, environment variables, and query parameters
 
 ## Error Handling
 
-The MCP DSL integration is designed to be resilient:
+The MCP DSL integration is designed to be resilient and handles failures gracefully:
 
 ```python
+from crewai import Agent
+from crewai.mcp import MCPServerStdio, MCPServerHTTP
+
 agent = Agent(
     role="Resilient Agent",
     goal="Continue working despite server issues",
     backstory="Agent that handles failures gracefully",
     mcps=[
+        # String references
         "https://reliable-server.com/mcp",        # Will work
         "https://unreachable-server.com/mcp",     # Will be skipped gracefully
-        "https://slow-server.com/mcp",            # Will timeout gracefully
-        "crewai-amp:working-service"              # Will work
+        "crewai-amp:working-service",             # Will work
+
+        # Structured configs
+        MCPServerStdio(
+            command="python",
+            args=["reliable_server.py"],          # Will work
+        ),
+        MCPServerHTTP(
+            url="https://slow-server.com/mcp",     # Will timeout gracefully
+        ),
     ]
 )
 # Agent will use tools from working servers and log warnings for failing ones
 ```
 
+All connection errors are handled gracefully:
+- **Connection failures**: Logged as warnings, agent continues with available tools
+- **Timeout errors**: Connections timeout after 30 seconds (configurable)
+- **Authentication errors**: Logged clearly for debugging
+- **Invalid configurations**: Validation errors are raised at agent creation time
+
 ## Advanced: MCPServerAdapter
 
 For complex scenarios requiring manual connection management, use the `MCPServerAdapter` class from `crewai-tools`. Using a Python context manager (`with` statement) is the recommended approach as it automatically handles starting and stopping the connection to the MCP server.

docs/en/observability/datadog.mdx

@@ -0,0 +1,109 @@
+---
+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

@@ -23,14 +23,16 @@ Here's a minimal example of how to use the tool:
 
 ```python
 from crewai import Agent
-from crewai_tools import QdrantVectorSearchTool
+from crewai_tools import QdrantVectorSearchTool, QdrantConfig
 
-# Initialize the tool
+# Initialize the tool with QdrantConfig
 qdrant_tool = QdrantVectorSearchTool(
+    qdrant_config=QdrantConfig(
         qdrant_url="your_qdrant_url",
         qdrant_api_key="your_qdrant_api_key",
         collection_name="your_collection"
     )
+)
 
 # Create an agent that uses the tool
 agent = Agent(
@@ -82,7 +84,7 @@ def extract_text_from_pdf(pdf_path):
 def get_openai_embedding(text):
     response = client.embeddings.create(
         input=text,
-        model="text-embedding-3-small"
+        model="text-embedding-3-large"
     )
     return response.data[0].embedding
 
@@ -96,7 +98,7 @@ def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
         qdrant.delete_collection(collection_name)
     qdrant.create_collection(
         collection_name=collection_name,
-        vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
+        vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
     )
 
     # Store embeddings
@@ -120,13 +122,17 @@ 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
     )
+)
 
 # Create CrewAI agents
 search_agent = Agent(
@@ -180,21 +186,82 @@ print(result)
 ## Tool Parameters
 
 ### Required Parameters
-- `qdrant_url` (str): The URL of your Qdrant server
-- `qdrant_api_key` (str): API key for authentication with Qdrant
-- `collection_name` (str): Name of the Qdrant collection to search
+- `qdrant_config` (QdrantConfig): Configuration object containing all Qdrant settings
 
-### Optional Parameters
+### QdrantConfig Parameters
+- `qdrant_url` (str): The URL of your Qdrant server
+- `qdrant_api_key` (str, optional): API key for authentication with Qdrant
+- `collection_name` (str): Name of the Qdrant collection to search
 - `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` (str, optional): Value to filter by
+- `filter_value` (Any, optional): Value to filter by
 
 ## Return Format
 
@@ -214,7 +281,7 @@ The tool returns results in JSON format:
 
 ## Default Embedding
 
-By default, the tool uses OpenAI's `text-embedding-3-small` model for vectorization. This requires:
+By default, the tool uses OpenAI's `text-embedding-3-large` model for vectorization. This requires:
 - OpenAI API key set in environment: `OPENAI_API_KEY`
 
 ## Custom Embeddings
@@ -248,10 +315,14 @@ def custom_embeddings(text: str) -> list[float]:
     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",
+        collection_name="your_collection"
+    ),
     custom_embedding_fn=custom_embeddings  # Pass your custom function
 )
 ```

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=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",
-            ),
-        ),
-    )
+    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),
+            }
+        },
+    }
 )
 ```

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

@@ -46,23 +46,25 @@ 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=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",
-            ),
-        ),
-    )
+    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),
+            }
+        },
+    }
 )
 ```

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=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",
-            ),
-        ),
-    )
+    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),
+            }
+        },
+    }
 )
 ```

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

@@ -48,27 +48,25 @@ 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=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",
-            ),
-        ),
-    )
+    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),
+            }
+        },
+    }
 )
 ```

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

@@ -45,28 +45,64 @@ 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:
+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.
 
 ```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=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",
-            ),
-        ),
-    )
+    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.
+                    }
+        },
+    }
 )
 ```

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

@@ -57,25 +57,41 @@ 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=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",
-            ),
-        ),
-    )
+    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").
+            }
+        },
+    }
 )
 ```

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=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",
-            ),
-        ),
-    )
+    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),
+            }
+        },
+    }
 )
 ```

docs/ko/enterprise/integrations/asana.mdx

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -17,6 +17,38 @@ 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

@@ -34,6 +34,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -33,6 +33,22 @@ 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

@@ -17,6 +17,38 @@ 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

@@ -17,6 +17,38 @@ 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

@@ -17,6 +17,38 @@ 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

@@ -17,6 +17,38 @@ 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

@@ -17,6 +17,38 @@ 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/observability/datadog.mdx

@@ -0,0 +1,109 @@
+---
+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

@@ -23,14 +23,16 @@ uv add qdrant-client
 
 ```python
 from crewai import Agent
-from crewai_tools import QdrantVectorSearchTool
+from crewai_tools import QdrantVectorSearchTool, QdrantConfig
 
-# Initialize the tool
+# QdrantConfig로 도구 초기화
 qdrant_tool = QdrantVectorSearchTool(
+    qdrant_config=QdrantConfig(
         qdrant_url="your_qdrant_url",
         qdrant_api_key="your_qdrant_api_key",
         collection_name="your_collection"
     )
+)
 
 # Create an agent that uses the tool
 agent = Agent(
@@ -82,7 +84,7 @@ def extract_text_from_pdf(pdf_path):
 def get_openai_embedding(text):
     response = client.embeddings.create(
         input=text,
-        model="text-embedding-3-small"
+        model="text-embedding-3-large"
     )
     return response.data[0].embedding
 
@@ -96,7 +98,7 @@ def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
         qdrant.delete_collection(collection_name)
     qdrant.create_collection(
         collection_name=collection_name,
-        vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
+        vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
     )
 
     # Store embeddings
@@ -120,13 +122,17 @@ 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
     )
+)
 
 # Create CrewAI agents
 search_agent = Agent(
@@ -180,21 +186,82 @@ print(result)
 ## 도구 매개변수
 
 ### 필수 파라미터
-- `qdrant_url` (str): Qdrant 서버의 URL
-- `qdrant_api_key` (str): Qdrant 인증을 위한 API 키
-- `collection_name` (str): 검색할 Qdrant 컬렉션의 이름
+- `qdrant_config` (QdrantConfig): 모든 Qdrant 설정을 포함하는 구성 객체
 
-### 선택적 매개변수
+### QdrantConfig 매개변수
+- `qdrant_url` (str): Qdrant 서버의 URL
+- `qdrant_api_key` (str, 선택 사항): Qdrant 인증을 위한 API 키
+- `collection_name` (str): 검색할 Qdrant 컬렉션의 이름
 - `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` (str, 선택 사항): 필터 기준 값
+- `filter_value` (Any, 선택 사항): 필터 기준 값
 
 ## 반환 형식
 
@@ -214,7 +281,7 @@ print(result)
 
 ## 기본 임베딩
 
-기본적으로, 이 도구는 벡터화를 위해 OpenAI의 `text-embedding-3-small` 모델을 사용합니다. 이를 위해서는 다음이 필요합니다:
+기본적으로, 이 도구는 벡터화를 위해 OpenAI의 `text-embedding-3-large` 모델을 사용합니다. 이를 위해서는 다음이 필요합니다:
 - 환경변수에 설정된 OpenAI API 키: `OPENAI_API_KEY`
 
 ## 커스텀 임베딩
@@ -248,10 +315,14 @@ def custom_embeddings(text: str) -> list[float]:
     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",
+        collection_name="your_collection"
+    ),
     custom_embedding_fn=custom_embeddings  # Pass your custom function
 )
 ```

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=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",
-            ),
-        ),
-    )
+    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),
+            }
+        },
+    }
 )
 ```

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

@@ -46,23 +46,25 @@ 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=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",
-            ),
-        ),
-    )
+    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),
+            }
+        },
+    }
 )
 ```

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=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",
-            ),
-        ),
-    )
+    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),
+            }
+        },
+    }
 )
 ```

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

@@ -48,27 +48,25 @@ 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=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",
-            ),
-        ),
-    )
+    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),
+            }
+        },
+    }
 )
 ```

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

@@ -45,28 +45,60 @@ 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:
+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.
 
 ```python Code
+from crewai_tools import PDFSearchTool
+from chromadb.config import Settings  # Chroma 영속성 설정
+
 tool = PDFSearchTool(
-    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",
-            ),
-        ),
-    )
+    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").
+            }
+        },
+    }
 )
 ```

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

@@ -57,25 +57,34 @@ 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=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",
-            ),
-        ),
-    )
+    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").
+            }
+        },
+    }
 )
 ```

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

@@ -54,25 +54,25 @@ tool = XMLSearchTool(xml='path/to/your/xmlfile.xml')
 기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이징하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다.
 
 ```python Code
+from chromadb.config import Settings
+
 tool = XMLSearchTool(
-    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",
-            ),
-        ),
-    )
+    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),
+            }
+        },
+    }
 )
 ```

docs/pt-BR/enterprise/integrations/asana.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração com o Asana, assegure-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/clickup.mdx

@@ -33,6 +33,22 @@ Antes de utilizar a integração com o ClickUp, certifique-se de que você possu
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/gmail.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração com o Gmail, certifique-se de que você possui:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/google_calendar.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração com o Google Calendar, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/google_contacts.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração Google Contacts, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/google_docs.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração Google Docs, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/google_drive.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração Google Drive, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 Para informações detalhadas sobre parâmetros e uso, consulte a [documentação em inglês](../../../en/enterprise/integrations/google_drive).

docs/pt-BR/enterprise/integrations/google_sheets.mdx

@@ -34,6 +34,22 @@ Antes de utilizar a integração com o Google Sheets, certifique-se de que você
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/google_slides.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração Google Slides, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/hubspot.mdx

@@ -33,6 +33,22 @@ Antes de utilizar a integração com o HubSpot, certifique-se de que você possu
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/linear.mdx

@@ -33,6 +33,22 @@ Antes de utilizar a integração com o Linear, certifique-se de que você possui
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/microsoft_excel.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração Microsoft Excel, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/microsoft_onedrive.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração Microsoft OneDrive, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/microsoft_outlook.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração Microsoft Outlook, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/microsoft_sharepoint.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração Microsoft SharePoint, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/microsoft_teams.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração Microsoft Teams, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/microsoft_word.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração Microsoft Word, certifique-se de ter:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/notion.mdx

@@ -33,6 +33,22 @@ Antes de usar a integração com o Notion, certifique-se de que você tem:
 uv add crewai-tools
 ```
 
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ações Disponíveis
 
 <AccordionGroup>

docs/pt-BR/enterprise/integrations/salesforce.mdx

@@ -17,6 +17,38 @@ Antes de usar a integração Salesforce, certifique-se de que você possui:
 - Uma conta Salesforce com permissões apropriadas
 - Sua conta Salesforce conectada via a [página de Integrações](https://app.crewai.com/integrations)
 
+## Configurando a Integração Salesforce
+
+### 1. Conecte sua Conta Salesforce
+
+1. Acesse [CrewAI AMP Integrações](https://app.crewai.com/crewai_plus/connectors)
+2. Encontre **Salesforce** na seção Integrações de Autenticação
+3. Clique em **Conectar** e complete o fluxo OAuth
+4. Conceda as permissões necessárias para gerenciamento de CRM e vendas
+5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations)
+
+### 2. Instale o Pacote Necessário
+
+```bash
+uv add crewai-tools
+```
+
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ferramentas Disponíveis
 
 ### **Gerenciamento de Registros**

docs/pt-BR/enterprise/integrations/shopify.mdx

@@ -17,6 +17,38 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu
 - Uma loja Shopify com permissões administrativas adequadas
 - Sua loja Shopify conectada através da [página de Integrações](https://app.crewai.com/integrations)
 
+## Configurando a Integração Shopify
+
+### 1. Conecte sua Loja Shopify
+
+1. Acesse [CrewAI AMP Integrações](https://app.crewai.com/crewai_plus/connectors)
+2. Encontre **Shopify** na seção Integrações de Autenticação
+3. Clique em **Conectar** e complete o fluxo OAuth
+4. Conceda as permissões necessárias para gerenciamento de loja e produtos
+5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations)
+
+### 2. Instale o Pacote Necessário
+
+```bash
+uv add crewai-tools
+```
+
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ferramentas Disponíveis
 
 ### **Gerenciamento de Clientes**

docs/pt-BR/enterprise/integrations/slack.mdx

@@ -17,6 +17,38 @@ Antes de usar a integração com o Slack, certifique-se de que você tenha:
 - Um workspace do Slack com permissões apropriadas
 - Seu workspace do Slack conectado por meio da [página de Integrações](https://app.crewai.com/integrations)
 
+## Configurando a Integração Slack
+
+### 1. Conecte seu Workspace do Slack
+
+1. Acesse [CrewAI AMP Integrações](https://app.crewai.com/crewai_plus/connectors)
+2. Encontre **Slack** na seção Integrações de Autenticação
+3. Clique em **Conectar** e complete o fluxo OAuth
+4. Conceda as permissões necessárias para comunicação em equipe
+5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations)
+
+### 2. Instale o Pacote Necessário
+
+```bash
+uv add crewai-tools
+```
+
+### 3. Configuração de variável de ambiente
+
+<Note>
+  Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token.
+</Note>
+
+```bash
+export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token"
+```
+
+Ou adicione ao seu arquivo `.env`:
+
+```
+CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token
+```
+
 ## Ferramentas Disponíveis
 
 ### **Gerenciamento de Usuários**