feat: bump versions to 1.2.1 (#3800 )

* feat: bump versions to 1.2.1 * updated templates too
docs: describe mandatory env-var to call Platform tools for each integration (#3803 )
2025-12-29 18:58:30 +00:00 · 2025-10-27 09:12:04 -07:00 · 2025-10-27 10:01:41 -04:00 · 2025-10-27 09:48:38 -04:00 · 2025-10-24 18:42:08 -07:00 · 2025-10-23 18:04:34 -07:00
126 changed files with 9007 additions and 761 deletions
--- a/.github/codeql/codeql-config.yml
+++ b/.github/codeql/codeql-config.yml
@@ -2,20 +2,27 @@ name: "CodeQL Config"

 paths-ignore:
  # Ignore template files - these are boilerplate code that shouldn't be analyzed
-  - "src/crewai/cli/templates/**"
+  - "lib/crewai/src/crewai/cli/templates/**"
  # Ignore test cassettes - these are test fixtures/recordings
-  - "tests/cassettes/**"
+  - "lib/crewai/tests/cassettes/**"
+  - "lib/crewai-tools/tests/cassettes/**"
  # Ignore cache and build artifacts
  - ".cache/**"
  # Ignore documentation build artifacts
  - "docs/.cache/**"
-  
+  # Ignore experimental code
+  - "lib/crewai/src/crewai/experimental/a2a/**"
+
 paths:
-  # Include all Python source code
-  - "src/**"
-  # Include tests (but exclude cassettes)
-  - "tests/**"
+  # Include all Python source code from workspace packages
+  - "lib/crewai/src/**"
+  - "lib/crewai-tools/src/**"
+  - "lib/devtools/src/**"
+  # Include tests (but exclude cassettes via paths-ignore)
+  - "lib/crewai/tests/**"
+  - "lib/crewai-tools/tests/**"
+  - "lib/devtools/tests/**"

 # Configure specific queries or packs if needed
 # queries:
-#   - uses: security-and-quality
+#   - uses: security-and-quality
--- a/docs/docs.json
+++ b/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",
--- a/docs/en/concepts/llms.mdx
+++ b/docs/en/concepts/llms.mdx
@@ -7,7 +7,7 @@ mode: "wide"

 ## Overview

-CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
+CrewAI integrates with multiple LLM providers through providers native sdks, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.


 ## What are LLMs?
@@ -113,44 +113,104 @@ In this section, you'll find detailed examples that help you select, configure,

 <AccordionGroup>
  <Accordion title="OpenAI">
-    Set the following environment variables in your `.env` file:
+    CrewAI provides native integration with OpenAI through the OpenAI Python SDK.

    ```toml Code
    # Required
    OPENAI_API_KEY=sk-...

    # Optional
-    OPENAI_API_BASE=<custom-base-url>
-    OPENAI_ORGANIZATION=<your-org-id>
+    OPENAI_BASE_URL=<custom-base-url>
    ```

-    Example usage in your CrewAI project:
+    **Basic Usage:**
    ```python Code
    from crewai import LLM

    llm = LLM(
-        model="openai/gpt-4", # call model by provider/model_name
-        temperature=0.8,
-        max_tokens=150,
+        model="openai/gpt-4o",
+        api_key="your-api-key",  # Or set OPENAI_API_KEY
+        temperature=0.7,
+        max_tokens=4000
+    )
+    ```
+
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="openai/gpt-4o",
+        api_key="your-api-key",
+        base_url="https://api.openai.com/v1",  # Optional custom endpoint
+        organization="org-...",  # Optional organization ID
+        project="proj_...",  # Optional project ID
+        temperature=0.7,
+        max_tokens=4000,
+        max_completion_tokens=4000,  # For newer models
        top_p=0.9,
        frequency_penalty=0.1,
        presence_penalty=0.1,
        stop=["END"],
-        seed=42
+        seed=42,  # For reproducible outputs
+        stream=True,  # Enable streaming
+        timeout=60.0,  # Request timeout in seconds
+        max_retries=3,  # Maximum retry attempts
+        logprobs=True,  # Return log probabilities
+        top_logprobs=5,  # Number of most likely tokens
+        reasoning_effort="medium"  # For o1 models: low, medium, high
    )
    ```

-    OpenAI is one of the leading providers of LLMs with a wide range of models and features.
+    **Structured Outputs:**
+    ```python Code
+    from pydantic import BaseModel
+    from crewai import LLM
+
+    class ResponseFormat(BaseModel):
+        name: str
+        age: int
+        summary: str
+
+    llm = LLM(
+        model="openai/gpt-4o",
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `OPENAI_API_KEY`: Your OpenAI API key (required)
+    - `OPENAI_BASE_URL`: Custom base URL for OpenAI API (optional)
+
+    **Features:**
+    - Native function calling support (except o1 models)
+    - Structured outputs with JSON schema
+    - Streaming support for real-time responses
+    - Token usage tracking
+    - Stop sequences support (except o1 models)
+    - Log probabilities for token-level insights
+    - Reasoning effort control for o1 models
+
+    **Supported Models:**

    | Model               | Context Window   | Best For                                      |
    |---------------------|------------------|-----------------------------------------------|
-    | GPT-4               | 8,192 tokens     | High-accuracy tasks, complex reasoning        |
-    | GPT-4 Turbo         | 128,000 tokens   | Long-form content, document analysis          |
-    | GPT-4o & GPT-4o-mini  | 128,000 tokens  | Cost-effective large context processing       |
-    | o3-mini             | 200,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1-mini             | 128,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1-preview          | 128,000 tokens   | Fast reasoning, complex reasoning             |
-    | o1                  | 200,000 tokens   | Fast reasoning, complex reasoning             |
+    | gpt-4.1             | 1M tokens        | Latest model with enhanced capabilities       |
+    | gpt-4.1-mini        | 1M tokens        | Efficient version with large context          |
+    | gpt-4.1-nano        | 1M tokens        | Ultra-efficient variant                       |
+    | gpt-4o              | 128,000 tokens   | Optimized for speed and intelligence          |
+    | gpt-4o-mini         | 200,000 tokens   | Cost-effective with large context             |
+    | gpt-4-turbo         | 128,000 tokens   | Long-form content, document analysis          |
+    | gpt-4               | 8,192 tokens     | High-accuracy tasks, complex reasoning        |
+    | o1                  | 200,000 tokens   | Advanced reasoning, complex problem-solving   |
+    | o1-preview          | 128,000 tokens   | Preview of reasoning capabilities             |
+    | o1-mini             | 128,000 tokens   | Efficient reasoning model                     |
+    | o3-mini             | 200,000 tokens   | Lightweight reasoning model                   |
+    | o4-mini             | 200,000 tokens   | Next-gen efficient reasoning                  |
+
+    **Note:** To use OpenAI, install the required dependencies:
+    ```bash
+    uv add "crewai[openai]"
+    ```
  </Accordion>

  <Accordion title="Meta-Llama">
@@ -187,69 +247,186 @@ In this section, you'll find detailed examples that help you select, configure,
  </Accordion>

  <Accordion title="Anthropic">
+    CrewAI provides native integration with Anthropic through the Anthropic Python SDK.
+
    ```toml Code
    # Required
    ANTHROPIC_API_KEY=sk-ant-...
-
-    # Optional
-    ANTHROPIC_API_BASE=<custom-base-url>
    ```

-    Example usage in your CrewAI project:
+    **Basic Usage:**
    ```python Code
+    from crewai import LLM
+
    llm = LLM(
-        model="anthropic/claude-3-sonnet-20240229-v1:0",
-        temperature=0.7
+        model="anthropic/claude-3-5-sonnet-20241022",
+        api_key="your-api-key",  # Or set ANTHROPIC_API_KEY
+        max_tokens=4096  # Required for Anthropic
    )
    ```
+
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="anthropic/claude-3-5-sonnet-20241022",
+        api_key="your-api-key",
+        base_url="https://api.anthropic.com",  # Optional custom endpoint
+        temperature=0.7,
+        max_tokens=4096,  # Required parameter
+        top_p=0.9,
+        stop_sequences=["END", "STOP"],  # Anthropic uses stop_sequences
+        stream=True,  # Enable streaming
+        timeout=60.0,  # Request timeout in seconds
+        max_retries=3  # Maximum retry attempts
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `ANTHROPIC_API_KEY`: Your Anthropic API key (required)
+
+    **Features:**
+    - Native tool use support for Claude 3+ models
+    - Streaming support for real-time responses
+    - Automatic system message handling
+    - Stop sequences for controlled output
+    - Token usage tracking
+    - Multi-turn tool use conversations
+
+    **Important Notes:**
+    - `max_tokens` is a **required** parameter for all Anthropic models
+    - Claude uses `stop_sequences` instead of `stop`
+    - System messages are handled separately from conversation messages
+    - First message must be from the user (automatically handled)
+    - Messages must alternate between user and assistant
+
+    **Supported Models:**
+
+    | Model                        | Context Window | Best For                                      |
+    |------------------------------|----------------|-----------------------------------------------|
+    | claude-3-7-sonnet            | 200,000 tokens | Advanced reasoning and agentic tasks          |
+    | claude-3-5-sonnet-20241022   | 200,000 tokens | Latest Sonnet with best performance           |
+    | claude-3-5-haiku             | 200,000 tokens | Fast, compact model for quick responses       |
+    | claude-3-opus                | 200,000 tokens | Most capable for complex tasks                |
+    | claude-3-sonnet              | 200,000 tokens | Balanced intelligence and speed               |
+    | claude-3-haiku               | 200,000 tokens | Fastest for simple tasks                      |
+    | claude-2.1                   | 200,000 tokens | Extended context, reduced hallucinations      |
+    | claude-2                     | 100,000 tokens | Versatile model for various tasks             |
+    | claude-instant               | 100,000 tokens | Fast, cost-effective for everyday tasks       |
+
+    **Note:** To use Anthropic, install the required dependencies:
+    ```bash
+    uv add "crewai[anthropic]"
+    ```
  </Accordion>

  <Accordion title="Google (Gemini API)">
-    Set your API key in your `.env` file. If you need a key, or need to find an
-    existing key, check [AI Studio](https://aistudio.google.com/apikey).
+    CrewAI provides native integration with Google Gemini through the Google Gen AI Python SDK.
+
+    Set your API key in your `.env` file. If you need a key, check [AI Studio](https://aistudio.google.com/apikey).

    ```toml .env
-    # https://ai.google.dev/gemini-api/docs/api-key
+    # Required (one of the following)
+    GOOGLE_API_KEY=<your-api-key>
    GEMINI_API_KEY=<your-api-key>
+
+    # Optional - for Vertex AI
+    GOOGLE_CLOUD_PROJECT=<your-project-id>
+    GOOGLE_CLOUD_LOCATION=<location>  # Defaults to us-central1
+    GOOGLE_GENAI_USE_VERTEXAI=true  # Set to use Vertex AI
    ```

-    Example usage in your CrewAI project:
+    **Basic Usage:**
    ```python Code
    from crewai import LLM

    llm = LLM(
        model="gemini/gemini-2.0-flash",
-        temperature=0.7,
+        api_key="your-api-key",  # Or set GOOGLE_API_KEY/GEMINI_API_KEY
+        temperature=0.7
    )
    ```

-    ### Gemini models
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-2.5-flash",
+        api_key="your-api-key",
+        temperature=0.7,
+        top_p=0.9,
+        top_k=40,  # Top-k sampling parameter
+        max_output_tokens=8192,
+        stop_sequences=["END", "STOP"],
+        stream=True,  # Enable streaming
+        safety_settings={
+            "HARM_CATEGORY_HARASSMENT": "BLOCK_NONE",
+            "HARM_CATEGORY_HATE_SPEECH": "BLOCK_NONE"
+        }
+    )
+    ```
+
+    **Vertex AI Configuration:**
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-1.5-pro",
+        project="your-gcp-project-id",
+        location="us-central1"  # GCP region
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `GOOGLE_API_KEY` or `GEMINI_API_KEY`: Your Google API key (required for Gemini API)
+    - `GOOGLE_CLOUD_PROJECT`: Google Cloud project ID (for Vertex AI)
+    - `GOOGLE_CLOUD_LOCATION`: GCP location (defaults to `us-central1`)
+    - `GOOGLE_GENAI_USE_VERTEXAI`: Set to `true` to use Vertex AI
+
+    **Features:**
+    - Native function calling support for Gemini 1.5+ and 2.x models
+    - Streaming support for real-time responses
+    - Multimodal capabilities (text, images, video)
+    - Safety settings configuration
+    - Support for both Gemini API and Vertex AI
+    - Automatic system instruction handling
+    - Token usage tracking
+
+    **Gemini Models:**

    Google offers a range of powerful models optimized for different use cases.

    | Model                          | Context Window | Best For                                                          |
    |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
+    | gemini-2.5-flash               | 1M tokens      | Adaptive thinking, cost efficiency                                |
+    | gemini-2.5-pro                 | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding         |
+    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking                         |
+    | gemini-2.0-flash-thinking      | 32,768 tokens  | Advanced reasoning with thinking process                          |
    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
+    | gemini-1.5-pro                 | 2M tokens      | Best performing, logical reasoning, coding                        |
    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+    | gemini-1.5-flash-8b            | 1M tokens      | Fastest, most cost-efficient                                      |
+    | gemini-1.0-pro                 | 32,768 tokens  | Earlier generation model                                          |
+
+    **Gemma Models:**
+
+    The Gemini API also supports [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
+
+    | Model          | Context Window | Best For                           |
+    |----------------|----------------|------------------------------------|
+    | gemma-3-1b     | 32,000 tokens  | Ultra-lightweight tasks            |
+    | gemma-3-4b     | 128,000 tokens | Efficient general-purpose tasks    |
+    | gemma-3-12b    | 128,000 tokens | Balanced performance and efficiency|
+    | gemma-3-27b    | 128,000 tokens | High-performance tasks             |
+
+    **Note:** To use Google Gemini, install the required dependencies:
+    ```bash
+    uv add "crewai[google-genai]"
+    ```

    The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
-
-    ### Gemma
-
-    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
-
-    | Model          | Context Window |
-    |----------------|----------------|
-    | gemma-3-1b-it  | 32k tokens     |
-    | gemma-3-4b-it  | 32k tokens     |
-    | gemma-3-12b-it | 32k tokens     |
-    | gemma-3-27b-it | 128k tokens    |
-
  </Accordion>
  <Accordion title="Google (Vertex AI)">
    Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
@@ -291,43 +468,146 @@ In this section, you'll find detailed examples that help you select, configure,
  </Accordion>

  <Accordion title="Azure">
+    CrewAI provides native integration with Azure AI Inference and Azure OpenAI through the Azure AI Inference Python SDK.
+
    ```toml Code
    # Required
    AZURE_API_KEY=<your-api-key>
-    AZURE_API_BASE=<your-resource-url>
-    AZURE_API_VERSION=<api-version>
+    AZURE_ENDPOINT=<your-endpoint-url>

    # Optional
-    AZURE_AD_TOKEN=<your-azure-ad-token>
-    AZURE_API_TYPE=<your-azure-api-type>
+    AZURE_API_VERSION=<api-version>  # Defaults to 2024-06-01
    ```

-    Example usage in your CrewAI project:
+    **Endpoint URL Formats:**
+
+    For Azure OpenAI deployments:
+    ```
+    https://<resource-name>.openai.azure.com/openai/deployments/<deployment-name>
+    ```
+
+    For Azure AI Inference endpoints:
+    ```
+    https://<resource-name>.inference.azure.com
+    ```
+
+    **Basic Usage:**
    ```python Code
    llm = LLM(
        model="azure/gpt-4",
-        api_version="2023-05-15"
+        api_key="<your-api-key>",  # Or set AZURE_API_KEY
+        endpoint="<your-endpoint-url>",
+        api_version="2024-06-01"
    )
    ```
+
+    **Advanced Configuration:**
+    ```python Code
+    llm = LLM(
+        model="azure/gpt-4o",
+        temperature=0.7,
+        max_tokens=4000,
+        top_p=0.9,
+        frequency_penalty=0.0,
+        presence_penalty=0.0,
+        stop=["END"],
+        stream=True,
+        timeout=60.0,
+        max_retries=3
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `AZURE_API_KEY`: Your Azure API key (required)
+    - `AZURE_ENDPOINT`: Your Azure endpoint URL (required, also checks `AZURE_OPENAI_ENDPOINT` and `AZURE_API_BASE`)
+    - `AZURE_API_VERSION`: API version (optional, defaults to `2024-06-01`)
+
+    **Features:**
+    - Native function calling support for Azure OpenAI models (gpt-4, gpt-4o, gpt-3.5-turbo, etc.)
+    - Streaming support for real-time responses
+    - Automatic endpoint URL validation and correction
+    - Comprehensive error handling with retry logic
+    - Token usage tracking
+
+    **Note:** To use Azure AI Inference, install the required dependencies:
+    ```bash
+    uv add "crewai[azure-ai-inference]"
+    ```
  </Accordion>

  <Accordion title="AWS Bedrock">
+    CrewAI provides native integration with AWS Bedrock through the boto3 SDK using the Converse API.
+
    ```toml Code
+    # Required
    AWS_ACCESS_KEY_ID=<your-access-key>
    AWS_SECRET_ACCESS_KEY=<your-secret-key>
-    AWS_DEFAULT_REGION=<your-region>
+
+    # Optional
+    AWS_SESSION_TOKEN=<your-session-token>  # For temporary credentials
+    AWS_DEFAULT_REGION=<your-region>  # Defaults to us-east-1
    ```

-    Example usage in your CrewAI project:
+    **Basic Usage:**
    ```python Code
+    from crewai import LLM
+
    llm = LLM(
-        model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
+        model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
+        region_name="us-east-1"
    )
    ```

-    Before using Amazon Bedrock, make sure you have boto3 installed in your environment
+    **Advanced Configuration:**
+    ```python Code
+    from crewai import LLM

-    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API, enabling secure and responsible AI application development.
+    llm = LLM(
+        model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
+        aws_access_key_id="your-access-key",  # Or set AWS_ACCESS_KEY_ID
+        aws_secret_access_key="your-secret-key",  # Or set AWS_SECRET_ACCESS_KEY
+        aws_session_token="your-session-token",  # For temporary credentials
+        region_name="us-east-1",
+        temperature=0.7,
+        max_tokens=4096,
+        top_p=0.9,
+        top_k=250,  # For Claude models
+        stop_sequences=["END", "STOP"],
+        stream=True,  # Enable streaming
+        guardrail_config={  # Optional content filtering
+            "guardrailIdentifier": "your-guardrail-id",
+            "guardrailVersion": "1"
+        },
+        additional_model_request_fields={  # Model-specific parameters
+            "top_k": 250
+        }
+    )
+    ```
+
+    **Supported Environment Variables:**
+    - `AWS_ACCESS_KEY_ID`: AWS access key (required)
+    - `AWS_SECRET_ACCESS_KEY`: AWS secret key (required)
+    - `AWS_SESSION_TOKEN`: AWS session token for temporary credentials (optional)
+    - `AWS_DEFAULT_REGION`: AWS region (defaults to `us-east-1`)
+
+    **Features:**
+    - Native tool calling support via Converse API
+    - Streaming and non-streaming responses
+    - Comprehensive error handling with retry logic
+    - Guardrail configuration for content filtering
+    - Model-specific parameters via `additional_model_request_fields`
+    - Token usage tracking and stop reason logging
+    - Support for all Bedrock foundation models
+    - Automatic conversation format handling
+
+    **Important Notes:**
+    - Uses the modern Converse API for unified model access
+    - Automatic handling of model-specific conversation requirements
+    - System messages are handled separately from conversation
+    - First message must be from user (automatically handled)
+    - Some models (like Cohere) require conversation to end with user message
+
+    [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html) is a managed service that provides access to multiple foundation models from top AI companies through a unified API.

    | Model                   | Context Window       | Best For                                                          |
    |-------------------------|----------------------|-------------------------------------------------------------------|
@@ -357,7 +637,12 @@ In this section, you'll find detailed examples that help you select, configure,
    | Jamba-Instruct          | Up to 256k tokens    | Model with extended context window optimized for cost-effective text generation, summarization, and Q&A. |
    | Mistral 7B Instruct     | Up to 32k tokens     | This LLM follows instructions, completes requests, and generates creative text. |
    | Mistral 8x7B Instruct   | Up to 32k tokens     | An MOE LLM that follows instructions, completes requests, and generates creative text. |
+    | DeepSeek R1             | 32,768 tokens        | Advanced reasoning model                                                       |

+    **Note:** To use AWS Bedrock, install the required dependencies:
+    ```bash
+    uv add "crewai[bedrock]"
+    ```
  </Accordion>

  <Accordion title="Amazon SageMaker">
@@ -899,7 +1184,7 @@ Learn how to get the most out of your LLM configuration:
  </Accordion>

  <Accordion title="Drop Additional Parameters">
-    CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
+    CrewAI internally uses native sdks for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
    For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:

    ```python
--- a/docs/en/enterprise/integrations/asana.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/box.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/clickup.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/github.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/gmail.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/google_calendar.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/google_contacts.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/google_docs.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/google_drive.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/google_sheets.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/google_slides.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/hubspot.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/jira.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/linear.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/microsoft_excel.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/microsoft_onedrive.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/microsoft_outlook.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/microsoft_sharepoint.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/microsoft_teams.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/microsoft_word.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/notion.mdx
+++ b/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>
--- a/docs/en/enterprise/integrations/salesforce.mdx
+++ b/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**
--- a/docs/en/enterprise/integrations/shopify.mdx
+++ b/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**
--- a/docs/en/enterprise/integrations/slack.mdx
+++ b/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**
--- a/docs/en/enterprise/integrations/stripe.mdx
+++ b/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**
--- a/docs/en/enterprise/integrations/zendesk.mdx
+++ b/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**
--- a/docs/en/observability/datadog.mdx
+++ b/docs/en/observability/datadog.mdx
@@ -0,0 +1,105 @@
+---
+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.
+
+![Datadog LLM Observability Trace View](/images/datadog-llm-observability-1.png)
+
+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.
+
+![Datadog LLM Observability Agent Execution Flow View](/images/datadog-llm-observability-2.png)
+
+## 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)
--- a/docs/en/tools/tool-integrations/overview.mdx
+++ b/docs/en/tools/tool-integrations/overview.mdx
@@ -11,7 +11,7 @@ mode: "wide"
  <Card
    title="Bedrock Invoke Agent Tool"
    icon="cloud"
-    href="/en/tools/tool-integrations/bedrockinvokeagenttool"
+    href="/en/tools/integration/bedrockinvokeagenttool"
    color="#0891B2"
  >
    Invoke Amazon Bedrock Agents from CrewAI to orchestrate actions across AWS services.
@@ -20,7 +20,7 @@ mode: "wide"
  <Card
    title="CrewAI Automation Tool"
    icon="bolt"
-    href="/en/tools/tool-integrations/crewaiautomationtool"
+    href="/en/tools/integration/crewaiautomationtool"
    color="#7C3AED"
  >
    Automate deployment and operations by integrating CrewAI with external platforms and workflows.
--- a/docs/images/datadog-llm-observability-1.png
+++ b/docs/images/datadog-llm-observability-1.png
--- a/docs/images/datadog-llm-observability-2.png
+++ b/docs/images/datadog-llm-observability-2.png
--- a/docs/ko/enterprise/integrations/asana.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/box.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/clickup.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/github.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/gmail.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/google_calendar.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/google_contacts.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/google_docs.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/google_drive.mdx
+++ b/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.
--- a/docs/ko/enterprise/integrations/google_sheets.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/google_slides.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/hubspot.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/jira.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/linear.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/microsoft_excel.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/microsoft_onedrive.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/microsoft_outlook.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/microsoft_sharepoint.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/microsoft_teams.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/microsoft_word.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/notion.mdx
+++ b/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>
--- a/docs/ko/enterprise/integrations/salesforce.mdx
+++ b/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**
--- a/docs/ko/enterprise/integrations/shopify.mdx
+++ b/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**
--- a/docs/ko/enterprise/integrations/slack.mdx
+++ b/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**
--- a/docs/ko/enterprise/integrations/stripe.mdx
+++ b/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**
--- a/docs/ko/enterprise/integrations/zendesk.mdx
+++ b/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**
--- a/docs/ko/observability/datadog.mdx
+++ b/docs/ko/observability/datadog.mdx
@@ -0,0 +1,105 @@
+---
+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.
+
+![Datadog LLM 옵저버빌리티 추적 보기](/images/datadog-llm-observability-1.png)
+
+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.
+
+![Datadog LLM Observability 에이전트 실행 흐름 보기](/images/datadog-llm-observability-2.png)
+
+## 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)
--- a/docs/pt-BR/enterprise/integrations/asana.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/clickup.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/gmail.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/google_calendar.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/google_contacts.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/google_docs.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/google_drive.mdx
+++ b/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).
--- a/docs/pt-BR/enterprise/integrations/google_sheets.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/google_slides.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/hubspot.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/linear.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/microsoft_excel.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/microsoft_onedrive.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/microsoft_outlook.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/microsoft_sharepoint.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/microsoft_teams.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/microsoft_word.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/notion.mdx
+++ b/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>
--- a/docs/pt-BR/enterprise/integrations/salesforce.mdx
+++ b/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**
--- a/docs/pt-BR/enterprise/integrations/shopify.mdx
+++ b/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**
--- a/docs/pt-BR/enterprise/integrations/slack.mdx
+++ b/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**
--- a/docs/pt-BR/enterprise/integrations/stripe.mdx
+++ b/docs/pt-BR/enterprise/integrations/stripe.mdx
@@ -17,6 +17,38 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem:
 - Uma conta Stripe com permissões apropriadas de API
 - Sua conta Stripe conectada através da [página de Integrações](https://app.crewai.com/integrations)

+## Configurando a Integração Stripe
+
+### 1. Conecte sua Conta Stripe
+
+1. Acesse [CrewAI AMP Integrações](https://app.crewai.com/crewai_plus/connectors)
+2. Encontre **Stripe** na seção Integrações de Autenticação
+3. Clique em **Conectar** e complete o fluxo OAuth
+4. Conceda as permissões necessárias para processamento de pagamentos
+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**
--- a/docs/pt-BR/enterprise/integrations/zendesk.mdx
+++ b/docs/pt-BR/enterprise/integrations/zendesk.mdx
@@ -17,6 +17,38 @@ Antes de usar a integração com o Zendesk, certifique-se de que você possui:
 - Uma conta Zendesk com permissões apropriadas de API
 - Sua conta Zendesk conectada através da [página de Integrações](https://app.crewai.com/integrations)

+## Configurando a Integração Zendesk
+
+### 1. Conecte sua Conta Zendesk
+
+1. Acesse [CrewAI AMP Integrações](https://app.crewai.com/crewai_plus/connectors)
+2. Encontre **Zendesk** na seção Integrações de Autenticação
+3. Clique em **Conectar** e complete o fluxo OAuth
+4. Conceda as permissões necessárias para gerenciamento de tickets e usuários
+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 Tickets**
--- a/docs/pt-BR/observability/datadog.mdx
+++ b/docs/pt-BR/observability/datadog.mdx
@@ -0,0 +1,105 @@
+---
+title: Integração Datadog
+description: Saiba como integrar o Datadog com o CrewAI para enviar os rastros de observabilidade do LLM para o Datadog.
+icon: dog
+mode: "wide"
+---
+
+# Integrar o Datadog com a CrewAI
+
+Este guia demonstrará como integrar o **Datadog** com o **CrewAI** usando a instrumentação automática do Datadog. Ao final deste guia, você poderá enviar rastreamentos do LLM Observability para o Datadog e visualizar as execuções do agente CrewAI no Agentic Execution View do Datadog LLM Observability.
+
+## O que é o Datadog LLM Observability?
+
+O [Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/) ajuda os engenheiros de IA, cientistas de dados e desenvolvedores de aplicativos a desenvolver, avaliar e monitorar rapidamente os aplicativos LLM. Melhore com confiança a qualidade dos resultados, o desempenho, os custos e o risco geral com experimentos estruturados, rastreamento de ponta a ponta em agentes de IA e avaliações.
+
+## Primeiros passos
+
+### Instalar dependências
+
+```shell
+pip install ddtrace crewai crewai-tools
+```
+
+### Definir variáveis de ambiente
+
+Se você não tiver uma chave de API da Datadog, poderá [criar uma conta](https://www.datadoghq.com/) e [obter sua chave de API](https://docs.datadoghq.com/account_management/api-app-keys/#api-keys).
+
+Você também precisará especificar um nome de aplicativo de ML nas seguintes variáveis de ambiente. Um aplicativo de ML é um agrupamento de rastros de observabilidade do LLM associados a um aplicativo específico baseado em LLM. Consulte [ML Application Naming Guidelines](https://docs.datadoghq.com/llm_observability/instrumentation/sdk?tab=python#application-naming-guidelines) para obter mais informações sobre as limitações dos nomes de aplicativos do ML.
+
+```shell
+export DD_API_KEY=<SUA_API_KEY>
+export DD_SITE=<Seu_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
+```
+
+Além disso, configure todas as chaves de API do provedor LLM
+
+```shell
+export OPENAI_API_KEY=<SUA_OPENAI_API_KEY>
+export ANTHROPIC_API_KEY=<YOUR_ANTHROPIC_API_KEY>
+export GEMINI_API_KEY=<YOUR_GEMINI_API_KEY>
+...
+```
+
+### Criar um aplicativo agente CrewAI
+
+```python
+# crewai_agent.py
+from crewai import Agent, Task, Crew
+
+from crewai_tools import (
+    WebsiteSearchTool
+)
+
+web_rag_tool = WebsiteSearchTool()
+
+writer = Agent(
+    role="Writer",
+    goal="Você torna a matemática envolvente e compreensível para crianças pequenas por meio da poesia",
+    backstory="Você é um especialista em escrever haikus, mas não sabe nada de matemática",
+    tools=[web_rag_tool],
+)
+
+task = Task(
+    description=("O que é {multiplicação}?"),
+    expected_output=("Componha um haicai que inclua a resposta."),
+    agent=writer
+)
+
+crew = Crew(
+    agents=[writer],
+    tasks=[task],
+    share_crew=False
+)
+
+output = crew.kickoff(dict(multiplicação="2 * 2"))
+```
+
+### Executar o aplicativo com a instrumentação automática do Datadog
+
+Com as [variáveis de ambiente](#definir-variáveis-de-ambiente) definidas, agora você pode executar o aplicativo com a instrumentação automática do Datadog.
+
+```shell
+ddtrace-run python crewai_agent.py
+```
+
+### Visualizar os rastros no Datadog
+
+Depois de executar o aplicativo, você pode visualizar os traços na [Datadog LLM Observability's Traces View](https://app.datadoghq.com/llm/traces), selecionando o nome do aplicativo de ML que você escolheu no menu suspenso superior esquerdo.
+
+Ao clicar em um rastreamento, você verá os detalhes do rastreamento, incluindo o total de tokens usados, o número de chamadas LLM, os modelos usados e o custo estimado. Clicar em um intervalo específico reduzirá esses detalhes e mostrará a entrada, a saída e os metadados relacionados.
+
+![Visualização do rastreamento de observabilidade do Datadog LLM](/images/datadog-llm-observability-1.png)
+
+Além disso, você pode visualizar a visualização do gráfico de execução do rastreamento, que mostra o controle e o fluxo de dados do rastreamento, que será dimensionado com agentes maiores para mostrar transferências e relacionamentos entre chamadas LLM, chamadas de ferramentas e interações de agentes.
+
+![Visualização do fluxo de execução do agente de observabilidade do Datadog LLM](/images/datadog-llm-observability-2.png)
+
+## Referências
+
+- [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)
--- a/lib/crewai-tools/pyproject.toml
+++ b/lib/crewai-tools/pyproject.toml
@@ -12,7 +12,7 @@ dependencies = [
    "pytube>=15.0.0",
    "requests>=2.32.5",
    "docker>=7.1.0",
-    "crewai==1.0.0",
+    "crewai==1.2.1",
    "lancedb>=0.5.4",
    "tiktoken>=0.8.0",
    "beautifulsoup4>=4.13.4",
--- a/lib/crewai-tools/src/crewai_tools/init.py
+++ b/lib/crewai-tools/src/crewai_tools/init.py
@@ -287,4 +287,4 @@ __all__ = [
    "ZapierActionTools",
 ]

-__version__ = "1.0.0"
+__version__ = "1.2.1"
--- a/lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/qdrant_search_tool.py
+++ b/lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/qdrant_search_tool.py
@@ -1,80 +1,42 @@
-from collections.abc import Callable
+from __future__ import annotations
+
+import importlib
 import json
 import os
+from collections.abc import Callable
 from typing import Any

-
-try:
-    from qdrant_client import QdrantClient
-    from qdrant_client.http.models import FieldCondition, Filter, MatchValue
-
-    QDRANT_AVAILABLE = True
-except ImportError:
-    QDRANT_AVAILABLE = False
-    QdrantClient = Any  # type: ignore[assignment,misc]  # type placeholder
-    Filter = Any  # type: ignore[assignment,misc]
-    FieldCondition = Any  # type: ignore[assignment,misc]
-    MatchValue = Any  # type: ignore[assignment,misc]
-
 from crewai.tools import BaseTool, EnvVar
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+from pydantic.types import ImportString


 class QdrantToolSchema(BaseModel):
-    """Input for QdrantTool."""
+    query: str = Field(..., description="Query to search in Qdrant DB.")
+    filter_by: str | None = None
+    filter_value: str | None = None

-    query: str = Field(
-        ...,
-        description="The query to search retrieve relevant information from the Qdrant database. Pass only the query, not the question.",
-    )
-    filter_by: str | None = Field(
-        default=None,
-        description="Filter by properties. Pass only the properties, not the question.",
-    )
-    filter_value: str | None = Field(
-        default=None,
-        description="Filter by value. Pass only the value, not the question.",
-    )
+
+class QdrantConfig(BaseModel):
+    """All Qdrant connection and search settings."""
+
+    qdrant_url: str
+    qdrant_api_key: str | None = None
+    collection_name: str
+    limit: int = 3
+    score_threshold: float = 0.35
+    filter_conditions: list[tuple[str, Any]] = Field(default_factory=list)


 class QdrantVectorSearchTool(BaseTool):
-    """Tool to query and filter results from a Qdrant database.
-
-    This tool enables vector similarity search on internal documents stored in Qdrant,
-    with optional filtering capabilities.
-
-    Attributes:
-        client: Configured QdrantClient instance
-        collection_name: Name of the Qdrant collection to search
-        limit: Maximum number of results to return
-        score_threshold: Minimum similarity score threshold
-        qdrant_url: Qdrant server URL
-        qdrant_api_key: Authentication key for Qdrant
-    """
+    """Vector search tool for Qdrant."""

    model_config = ConfigDict(arbitrary_types_allowed=True)
-    client: QdrantClient = None  # type: ignore[assignment]
+
+    # --- Metadata ---
    name: str = "QdrantVectorSearchTool"
-    description: str = "A tool to search the Qdrant database for relevant information on internal documents."
+    description: str = "Search Qdrant vector DB for relevant documents."
    args_schema: type[BaseModel] = QdrantToolSchema
-    query: str | None = None
-    filter_by: str | None = None
-    filter_value: str | None = None
-    collection_name: str | None = None
-    limit: int | None = Field(default=3)
-    score_threshold: float = Field(default=0.35)
-    qdrant_url: str = Field(
-        ...,
-        description="The URL of the Qdrant server",
-    )
-    qdrant_api_key: str | None = Field(
-        default=None,
-        description="The API key for the Qdrant server",
-    )
-    custom_embedding_fn: Callable | None = Field(
-        default=None,
-        description="A custom embedding function to use for vectorization. If not provided, the default model will be used.",
-    )
    package_dependencies: list[str] = Field(default_factory=lambda: ["qdrant-client"])
    env_vars: list[EnvVar] = Field(
        default_factory=lambda: [
@@ -83,107 +45,81 @@ class QdrantVectorSearchTool(BaseTool):
            )
        ]
    )
+    qdrant_config: QdrantConfig
+    qdrant_package: ImportString[Any] = Field(
+        default="qdrant_client",
+        description="Base package path for Qdrant. Will dynamically import client and models.",
+    )
+    custom_embedding_fn: ImportString[Callable[[str], list[float]]] | None = Field(
+        default=None,
+        description="Optional embedding function or import path.",
+    )
+    client: Any | None = None

-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        if QDRANT_AVAILABLE:
-            self.client = QdrantClient(
-                url=self.qdrant_url,
-                api_key=self.qdrant_api_key if self.qdrant_api_key else None,
+    @model_validator(mode="after")
+    def _setup_qdrant(self) -> QdrantVectorSearchTool:
+        # Import the qdrant_package if it's a string
+        if isinstance(self.qdrant_package, str):
+            self.qdrant_package = importlib.import_module(self.qdrant_package)
+
+        if not self.client:
+            self.client = self.qdrant_package.QdrantClient(
+                url=self.qdrant_config.qdrant_url,
+                api_key=self.qdrant_config.qdrant_api_key or None,
            )
-        else:
-            import click
-
-            if click.confirm(
-                "The 'qdrant-client' package is required to use the QdrantVectorSearchTool. "
-                "Would you like to install it?"
-            ):
-                import subprocess
-
-                subprocess.run(["uv", "add", "qdrant-client"], check=True)  # noqa: S607
-            else:
-                raise ImportError(
-                    "The 'qdrant-client' package is required to use the QdrantVectorSearchTool. "
-                    "Please install it with: uv add qdrant-client"
-                )
+        return self

    def _run(
        self,
        query: str,
        filter_by: str | None = None,
-        filter_value: str | None = None,
+        filter_value: Any | None = None,
    ) -> str:
-        """Execute vector similarity search on Qdrant.
+        """Perform vector similarity search."""
+        filter_ = self.qdrant_package.http.models.Filter
+        field_condition = self.qdrant_package.http.models.FieldCondition
+        match_value = self.qdrant_package.http.models.MatchValue
+        conditions = self.qdrant_config.filter_conditions.copy()
+        if filter_by and filter_value is not None:
+            conditions.append((filter_by, filter_value))

-        Args:
-            query: Search query to vectorize and match
-            filter_by: Optional metadata field to filter on
-            filter_value: Optional value to filter by
-
-        Returns:
-            JSON string containing search results with metadata and scores
-
-        Raises:
-            ImportError: If qdrant-client is not installed
-            ValueError: If Qdrant credentials are missing
-        """
-        if not self.qdrant_url:
-            raise ValueError("QDRANT_URL is not set")
-
-        # Create filter if filter parameters are provided
-        search_filter = None
-        if filter_by and filter_value:
-            search_filter = Filter(
+        search_filter = (
+            filter_(
                must=[
-                    FieldCondition(key=filter_by, match=MatchValue(value=filter_value))
+                    field_condition(key=k, match=match_value(value=v))
+                    for k, v in conditions
                ]
            )
-
-        # Search in Qdrant using the built-in query method
-        query_vector = (
-            self._vectorize_query(query, embedding_model="text-embedding-3-large")
-            if not self.custom_embedding_fn
-            else self.custom_embedding_fn(query)
+            if conditions
+            else None
        )
-        search_results = self.client.query_points(
-            collection_name=self.collection_name,  # type: ignore[arg-type]
+        query_vector = (
+            self.custom_embedding_fn(query)
+            if self.custom_embedding_fn
+            else (
+                lambda: __import__("openai")
+                .Client(api_key=os.getenv("OPENAI_API_KEY"))
+                .embeddings.create(input=[query], model="text-embedding-3-large")
+                .data[0]
+                .embedding
+            )()
+        )
+        results = self.client.query_points(
+            collection_name=self.qdrant_config.collection_name,
            query=query_vector,
            query_filter=search_filter,
-            limit=self.limit,  # type: ignore[arg-type]
-            score_threshold=self.score_threshold,
+            limit=self.qdrant_config.limit,
+            score_threshold=self.qdrant_config.score_threshold,
        )

-        # Format results similar to storage implementation
-        results = []
-        # Extract the list of ScoredPoint objects from the tuple
-        for point in search_results:
-            result = {
-                "metadata": point[1][0].payload.get("metadata", {}),
-                "context": point[1][0].payload.get("text", ""),
-                "distance": point[1][0].score,
-            }
-            results.append(result)
-
-        return json.dumps(results, indent=2)
-
-    def _vectorize_query(self, query: str, embedding_model: str) -> list[float]:
-        """Default vectorization function with openai.
-
-        Args:
-            query (str): The query to vectorize
-            embedding_model (str): The embedding model to use
-
-        Returns:
-            list[float]: The vectorized query
-        """
-        import openai
-
-        client = openai.Client(api_key=os.getenv("OPENAI_API_KEY"))
-        return (
-            client.embeddings.create(
-                input=[query],
-                model=embedding_model,
-            )
-            .data[0]
-            .embedding
+        return json.dumps(
+            [
+                {
+                    "distance": p.score,
+                    "metadata": p.payload.get("metadata", {}) if p.payload else {},
+                    "context": p.payload.get("text", "") if p.payload else {},
+                }
+                for p in results.points
+            ],
+            indent=2,
        )
--- a/lib/crewai-tools/tests/test_optional_dependencies.py
+++ b/lib/crewai-tools/tests/test_optional_dependencies.py
@@ -31,6 +31,7 @@ def run_command(cmd, cwd):
    return subprocess.run(cmd, cwd=cwd, capture_output=True, text=True)


+@pytest.mark.skip(reason="Test takes too long in GitHub Actions (>30s timeout) due to dependency installation")
 def test_no_optional_dependencies_in_init(temp_project):
    """
    Test that crewai-tools can be imported without optional dependencies.
--- a/lib/crewai/pyproject.toml
+++ b/lib/crewai/pyproject.toml
@@ -49,7 +49,7 @@ Repository = "https://github.com/crewAIInc/crewAI"

 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.0.0",
+    "crewai-tools==1.2.1",
 ]
 embeddings = [
    "tiktoken~=0.8.0"
@@ -66,11 +66,6 @@ openpyxl = [
 mem0 = ["mem0ai>=0.1.94"]
 docling = [
    "docling>=2.12.0",
-]
-aisuite = [
-    "aisuite>=0.1.11",
-
-
 ]
 qdrant = [
    "qdrant-client[fastembed]>=1.14.3",
@@ -137,13 +132,3 @@ build-backend = "hatchling.build"

 [tool.hatch.version]
 path = "src/crewai/__init__.py"
-
-# Declare mutually exclusive extras due to conflicting httpx requirements
-# a2a requires httpx>=0.28.1, while aisuite requires httpx>=0.27.0,<0.28.0
-# [tool.uv]
-# conflicts = [
-#   [
-#     { extra = "a2a" },
-#     { extra = "aisuite" },
-#   ],
-# ]
--- a/lib/crewai/src/crewai/init.py
+++ b/lib/crewai/src/crewai/init.py
@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:

 _suppress_pydantic_deprecation_warnings()

-__version__ = "1.0.0"
+__version__ = "1.2.1"
 _telemetry_submitted = False


--- a/lib/crewai/src/crewai/agent.py
+++ b/lib/crewai/src/crewai/agent.py
@@ -1186,6 +1186,15 @@ class Agent(BaseAgent):
        Returns:
            LiteAgentOutput: The result of the agent execution.
        """
+        if self.apps:
+            platform_tools = self.get_platform_tools(self.apps)
+            if platform_tools:
+                self.tools.extend(platform_tools)
+        if self.mcps:
+            mcps = self.get_mcp_tools(self.mcps)
+            if mcps:
+                self.tools.extend(mcps)
+
        lite_agent = LiteAgent(
            id=self.id,
            role=self.role,
--- a/lib/crewai/src/crewai/cli/constants.py
+++ b/lib/crewai/src/crewai/cli/constants.py
@@ -322,7 +322,7 @@ MODELS = {
    ],
 }

-DEFAULT_LLM_MODEL = "gpt-4o-mini"
+DEFAULT_LLM_MODEL = "gpt-4.1-mini"

 JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"

--- a/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml
+++ b/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.203.1,<1.0.0"
+    "crewai[tools]==1.2.1"
 ]

 [project.scripts]
--- a/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml
+++ b/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.203.1,<1.0.0",
+    "crewai[tools]==1.2.1"
 ]

 [project.scripts]
--- a/lib/crewai/src/crewai/flow/flow_visualizer.py
+++ b/lib/crewai/src/crewai/flow/flow_visualizer.py
@@ -2,7 +2,7 @@
 from __future__ import annotations

 import os
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any

 from pyvis.network import Network  # type: ignore[import-untyped]

@@ -29,7 +29,7 @@ _printer = Printer()
 class FlowPlot:
    """Handles the creation and rendering of flow visualization diagrams."""

-    def __init__(self, flow: Flow) -> None:
+    def __init__(self, flow: Flow[Any]) -> None:
        """
        Initialize FlowPlot with a flow object.

@@ -136,7 +136,7 @@ class FlowPlot:
                f"Unexpected error during flow visualization: {e!s}"
            ) from e
        finally:
-            self._cleanup_pyvis_lib()
+            self._cleanup_pyvis_lib(filename)

    def _generate_final_html(self, network_html: str) -> str:
        """
@@ -186,26 +186,33 @@ class FlowPlot:
            raise IOError(f"Failed to generate visualization HTML: {e!s}") from e

    @staticmethod
-    def _cleanup_pyvis_lib() -> None:
+    def _cleanup_pyvis_lib(filename: str) -> None:
        """
        Clean up the generated lib folder from pyvis.

        This method safely removes the temporary lib directory created by pyvis
-        during network visualization generation.
+        during network visualization generation. The lib folder is created in the
+        same directory as the output HTML file.
+
+        Parameters
+        ----------
+        filename : str
+            The output filename (without .html extension) used for the visualization.
        """
        try:
-            lib_folder = safe_path_join("lib", root=os.getcwd())
-            if os.path.exists(lib_folder) and os.path.isdir(lib_folder):
-                import shutil
+            import shutil

-                shutil.rmtree(lib_folder)
-        except ValueError as e:
-            _printer.print(f"Error validating lib folder path: {e}", color="red")
+            output_dir = os.path.dirname(os.path.abspath(filename)) or os.getcwd()
+            lib_folder = os.path.join(output_dir, "lib")
+            if os.path.exists(lib_folder) and os.path.isdir(lib_folder):
+                vis_js = os.path.join(lib_folder, "vis-network.min.js")
+                if os.path.exists(vis_js):
+                    shutil.rmtree(lib_folder)
        except Exception as e:
            _printer.print(f"Error cleaning up lib folder: {e}", color="red")


-def plot_flow(flow: Flow, filename: str = "flow_plot") -> None:
+def plot_flow(flow: Flow[Any], filename: str = "flow_plot") -> None:
    """
    Convenience function to create and save a flow visualization.

--- a/lib/crewai/src/crewai/flow/html_template_handler.py
+++ b/lib/crewai/src/crewai/flow/html_template_handler.py
@@ -1,5 +1,8 @@
+"""HTML template processing and generation for flow visualization diagrams."""
+
 import base64
 import re
+from typing import Any

 from crewai.flow.path_utils import validate_path_exists

@@ -7,7 +10,7 @@ from crewai.flow.path_utils import validate_path_exists
 class HTMLTemplateHandler:
    """Handles HTML template processing and generation for flow visualization diagrams."""

-    def __init__(self, template_path, logo_path):
+    def __init__(self, template_path: str, logo_path: str) -> None:
        """
        Initialize HTMLTemplateHandler with validated template and logo paths.

@@ -29,23 +32,23 @@ class HTMLTemplateHandler:
        except ValueError as e:
            raise ValueError(f"Invalid template or logo path: {e}") from e

-    def read_template(self):
+    def read_template(self) -> str:
        """Read and return the HTML template file contents."""
        with open(self.template_path, "r", encoding="utf-8") as f:
            return f.read()

-    def encode_logo(self):
+    def encode_logo(self) -> str:
        """Convert the logo SVG file to base64 encoded string."""
        with open(self.logo_path, "rb") as logo_file:
            logo_svg_data = logo_file.read()
            return base64.b64encode(logo_svg_data).decode("utf-8")

-    def extract_body_content(self, html):
+    def extract_body_content(self, html: str) -> str:
        """Extract and return content between body tags from HTML string."""
        match = re.search("<body.*?>(.*?)</body>", html, re.DOTALL)
        return match.group(1) if match else ""

-    def generate_legend_items_html(self, legend_items):
+    def generate_legend_items_html(self, legend_items: list[dict[str, Any]]) -> str:
        """Generate HTML markup for the legend items."""
        legend_items_html = ""
        for item in legend_items:
@@ -73,7 +76,9 @@ class HTMLTemplateHandler:
                """
        return legend_items_html

-    def generate_final_html(self, network_body, legend_items_html, title="Flow Plot"):
+    def generate_final_html(
+        self, network_body: str, legend_items_html: str, title: str = "Flow Plot"
+    ) -> str:
        """Combine all components into final HTML document with network visualization."""
        html_template = self.read_template()
        logo_svg_base64 = self.encode_logo()
--- a/lib/crewai/src/crewai/flow/legend_generator.py
+++ b/lib/crewai/src/crewai/flow/legend_generator.py
@@ -1,4 +1,23 @@
-def get_legend_items(colors):
+"""Legend generation for flow visualization diagrams."""
+
+from typing import Any
+
+from crewai.flow.config import FlowColors
+
+
+def get_legend_items(colors: FlowColors) -> list[dict[str, Any]]:
+    """Generate legend items based on flow colors.
+
+    Parameters
+    ----------
+    colors : FlowColors
+        Dictionary containing color definitions for flow elements.
+
+    Returns
+    -------
+    list[dict[str, Any]]
+        List of legend item dictionaries with labels and styling.
+    """
    return [
        {"label": "Start Method", "color": colors["start"]},
        {"label": "Method", "color": colors["method"]},
@@ -24,7 +43,19 @@ def get_legend_items(colors):
    ]


-def generate_legend_items_html(legend_items):
+def generate_legend_items_html(legend_items: list[dict[str, Any]]) -> str:
+    """Generate HTML markup for legend items.
+
+    Parameters
+    ----------
+    legend_items : list[dict[str, Any]]
+        List of legend item dictionaries containing labels and styling.
+
+    Returns
+    -------
+    str
+        HTML string containing formatted legend items.
+    """
    legend_items_html = ""
    for item in legend_items:
        if "border" in item:
--- a/lib/crewai/src/crewai/flow/visualization_utils.py
+++ b/lib/crewai/src/crewai/flow/visualization_utils.py
@@ -36,28 +36,29 @@ from crewai.flow.utils import (
 from crewai.utilities.printer import Printer


-
 _printer = Printer()


 def method_calls_crew(method: Any) -> bool:
    """
-    Check if the method contains a call to `.crew()`.
+    Check if the method contains a call to `.crew()`, `.kickoff()`, or `.kickoff_async()`.

    Parameters
    ----------
    method : Any
-        The method to analyze for crew() calls.
+        The method to analyze for crew or agent execution calls.

    Returns
    -------
    bool
-        True if the method calls .crew(), False otherwise.
+        True if the method calls .crew(), .kickoff(), or .kickoff_async(), False otherwise.

    Notes
    -----
    Uses AST analysis to detect method calls, specifically looking for
-    attribute access of 'crew'.
+    attribute access of 'crew', 'kickoff', or 'kickoff_async'.
+    This includes both traditional Crew execution (.crew()) and Agent/LiteAgent
+    execution (.kickoff() or .kickoff_async()).
    """
    try:
        source = inspect.getsource(method)
@@ -68,14 +69,14 @@ def method_calls_crew(method: Any) -> bool:
        return False

    class CrewCallVisitor(ast.NodeVisitor):
-        """AST visitor to detect .crew() method calls."""
+        """AST visitor to detect .crew(), .kickoff(), or .kickoff_async() method calls."""

-        def __init__(self):
+        def __init__(self) -> None:
            self.found = False

-        def visit_Call(self, node):
+        def visit_Call(self, node: ast.Call) -> None:
            if isinstance(node.func, ast.Attribute):
-                if node.func.attr == "crew":
+                if node.func.attr in ("crew", "kickoff", "kickoff_async"):
                    self.found = True
            self.generic_visit(node)

@@ -113,7 +114,7 @@ def add_nodes_to_network(
    - Regular methods
    """

-    def human_friendly_label(method_name):
+    def human_friendly_label(method_name: str) -> str:
        return method_name.replace("_", " ").title()

    node_style: (
--- a/lib/crewai/src/crewai/llms/third_party/ai_suite.py
+++ b/lib/crewai/src/crewai/llms/third_party/ai_suite.py
@@ -1,99 +0,0 @@
-"""AI Suite LLM integration for CrewAI.
-
-This module provides integration with AI Suite for LLM capabilities.
-"""
-
-from typing import Any
-
-import aisuite as ai  # type: ignore
-
-from crewai.llms.base_llm import BaseLLM
-
-
-class AISuiteLLM(BaseLLM):
-    """AI Suite LLM implementation.
-
-    This class provides integration with AI Suite models through the BaseLLM interface.
-    """
-
-    def __init__(
-        self,
-        model: str,
-        temperature: float | None = None,
-        stop: list[str] | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Initialize the AI Suite LLM.
-
-        Args:
-            model: The model identifier for AI Suite.
-            temperature: Optional temperature setting for response generation.
-            stop: Optional list of stop sequences for generation.
-            **kwargs: Additional keyword arguments passed to the AI Suite client.
-        """
-        super().__init__(model=model, temperature=temperature, stop=stop)
-        self.client = ai.Client()
-        self.kwargs = kwargs
-
-    def call(  # type: ignore[override]
-        self,
-        messages: str | list[dict[str, str]],
-        tools: list[dict] | None = None,
-        callbacks: list[Any] | None = None,
-        available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
-    ) -> str | Any:
-        """Call the AI Suite LLM with the given messages.
-
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas for function calling.
-            callbacks: Optional list of callback functions.
-            available_functions: Optional dict mapping function names to callables.
-            from_task: Optional task caller.
-            from_agent: Optional agent caller.
-
-        Returns:
-            The text response from the LLM.
-        """
-        completion_params = self._prepare_completion_params(messages, tools)
-        response = self.client.chat.completions.create(**completion_params)
-
-        return response.choices[0].message.content
-
-    def _prepare_completion_params(
-        self,
-        messages: str | list[dict[str, str]],
-        tools: list[dict] | None = None,
-    ) -> dict[str, Any]:
-        """Prepare parameters for the AI Suite completion call.
-
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas.
-
-        Returns:
-            Dictionary of parameters for the completion API.
-        """
-        params: dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-            "temperature": self.temperature,
-            "tools": tools,
-            **self.kwargs,
-        }
-
-        if self.stop:
-            params["stop"] = self.stop
-
-        return params
-
-    @staticmethod
-    def supports_function_calling() -> bool:
-        """Check if the LLM supports function calling.
-
-        Returns:
-            False, as AI Suite does not currently support function calling.
-        """
-        return False
--- a/lib/crewai/src/crewai/mypy.py
+++ b/lib/crewai/src/crewai/mypy.py
@@ -0,0 +1,60 @@
+"""Mypy plugin for CrewAI decorator type checking.
+
+This plugin informs mypy about attributes injected by the @CrewBase decorator.
+"""
+
+from collections.abc import Callable
+
+from mypy.nodes import MDEF, SymbolTableNode, Var
+from mypy.plugin import ClassDefContext, Plugin
+from mypy.types import AnyType, TypeOfAny
+
+
+class CrewAIPlugin(Plugin):
+    """Mypy plugin that handles @CrewBase decorator attribute injection."""
+
+    def get_class_decorator_hook(
+        self, fullname: str
+    ) -> Callable[[ClassDefContext], None] | None:
+        """Return hook for class decorators.
+
+        Args:
+            fullname: Fully qualified name of the decorator.
+
+        Returns:
+            Hook function if this is a CrewBase decorator, None otherwise.
+        """
+        if fullname in ("crewai.project.CrewBase", "crewai.project.crew_base.CrewBase"):
+            return self._crew_base_hook
+        return None
+
+    @staticmethod
+    def _crew_base_hook(ctx: ClassDefContext) -> None:
+        """Add injected attributes to @CrewBase decorated classes.
+
+        Args:
+            ctx: Context for the class being decorated.
+        """
+        any_type = AnyType(TypeOfAny.explicit)
+        str_type = ctx.api.named_type("builtins.str")
+        dict_type = ctx.api.named_type("builtins.dict", [str_type, any_type])
+        agents_config_var = Var("agents_config", dict_type)
+        agents_config_var.info = ctx.cls.info
+        agents_config_var._fullname = f"{ctx.cls.info.fullname}.agents_config"
+        ctx.cls.info.names["agents_config"] = SymbolTableNode(MDEF, agents_config_var)
+        tasks_config_var = Var("tasks_config", dict_type)
+        tasks_config_var.info = ctx.cls.info
+        tasks_config_var._fullname = f"{ctx.cls.info.fullname}.tasks_config"
+        ctx.cls.info.names["tasks_config"] = SymbolTableNode(MDEF, tasks_config_var)
+
+
+def plugin(_: str) -> type[Plugin]:
+    """Entry point for mypy plugin.
+
+    Args:
+        _: Mypy version string.
+
+    Returns:
+        Plugin class.
+    """
+    return CrewAIPlugin
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Lorenze Jay	5d6b4c922b	feat: bump versions to 1.2.1 (#3800 ) * feat: bump versions to 1.2.1 * updated templates too	2025-10-27 09:12:04 -07:00
Lucas Gomide	b07c0fc45c	docs: describe mandatory env-var to call Platform tools for each integration (#3803 )	2025-10-27 10:01:41 -04:00
Sam Brenner	97853199c7	Add Datadog Integration Documentation (#3642 ) * add datadog llm observability integration guide * spacing fix * wording changes * alphabetize docs listing * Update docs/en/observability/datadog.mdx Co-authored-by: Barry Eom <31739208+barieom@users.noreply.github.com> * add translations * fix korean code block --------- Co-authored-by: Barry Eom <31739208+barieom@users.noreply.github.com> Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>	2025-10-27 09:48:38 -04:00
Lorenze Jay	494ed7e671	liteagent supports apps and mcps (#3794 ) Some checks failed CodeQL Advanced / Analyze (actions) (push) Has been cancelled Details CodeQL Advanced / Analyze (python) (push) Has been cancelled Details Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details Build uv cache / build-cache (3.10) (push) Has been cancelled Details Build uv cache / build-cache (3.11) (push) Has been cancelled Details Build uv cache / build-cache (3.12) (push) Has been cancelled Details Build uv cache / build-cache (3.13) (push) Has been cancelled Details * liteagent supports apps and mcps * generated cassettes for these	2025-10-24 18:42:08 -07:00
Lorenze Jay	a83c57a2f2	feat: bump versions to 1.2.0 (#3787 ) Some checks failed CodeQL Advanced / Analyze (actions) (push) Has been cancelled Details CodeQL Advanced / Analyze (python) (push) Has been cancelled Details Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details * feat: bump versions to 1.2.0 * also include projects	2025-10-23 18:04:34 -07:00
Lorenze Jay	08e15ab267	fix: update default LLM model and improve error logging in LLM utilities (#3785 ) * fix: update default LLM model and improve error logging in LLM utilities * Updated the default LLM model from "gpt-4o-mini" to "gpt-4.1-mini" for better performance. * Enhanced error logging in the LLM utilities to use logger.error instead of logger.debug, ensuring that errors are properly reported and raised. * Added tests to verify behavior when OpenAI API key is missing and when Anthropic dependency is not available, improving robustness and error handling in LLM creation. * fix: update test for default LLM model usage * Refactored the test_create_llm_with_none_uses_default_model to use the imported DEFAULT_LLM_MODEL constant instead of a hardcoded string. * Ensured that the test correctly asserts the model used is the current default, improving maintainability and consistency across tests. * change default model to gpt-4.1-mini * change default model use defualt	2025-10-23 17:54:11 -07:00
Greyson LaLonde	9728388ea7	fix: change flow viz del dir; method inspection Some checks failed CodeQL Advanced / Analyze (actions) (push) Has been cancelled Details CodeQL Advanced / Analyze (python) (push) Has been cancelled Details Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details * chore: update flow viz deletion dir, add typing * tests: add flow viz tests to ensure lib dir is not deleted	2025-10-22 19:32:38 -04:00
Greyson LaLonde	4371cf5690	chore: remove aisuite Some checks failed CodeQL Advanced / Analyze (actions) (push) Has been cancelled Details CodeQL Advanced / Analyze (python) (push) Has been cancelled Details Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details Build uv cache / build-cache (3.10) (push) Has been cancelled Details Build uv cache / build-cache (3.11) (push) Has been cancelled Details Build uv cache / build-cache (3.12) (push) Has been cancelled Details Build uv cache / build-cache (3.13) (push) Has been cancelled Details Little usage + blocking some features	2025-10-21 23:18:06 -04:00
Lorenze Jay	d28daa26cd	feat: bump versions to 1.1.0 (#3770 ) Some checks failed Build uv cache / build-cache (3.10) (push) Has been cancelled Details Build uv cache / build-cache (3.11) (push) Has been cancelled Details Build uv cache / build-cache (3.12) (push) Has been cancelled Details Build uv cache / build-cache (3.13) (push) Has been cancelled Details CodeQL Advanced / Analyze (actions) (push) Has been cancelled Details CodeQL Advanced / Analyze (python) (push) Has been cancelled Details Notify Downstream / notify-downstream (push) Has been cancelled Details * feat: bump versions to 1.1.0 * chore: bump template versions --------- Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com>	2025-10-21 15:52:44 -07:00
Lorenze Jay	a850813f2b	feat: enhance InternalInstructor to support multiple LLM providers (#3767 ) * feat: enhance InternalInstructor to support multiple LLM providers - Updated InternalInstructor to conditionally create an instructor client based on the LLM provider. - Introduced a new method _create_instructor_client to handle client creation using the modern from_provider pattern. - Added functionality to extract the provider from the LLM model name. - Implemented tests for InternalInstructor with various LLM providers including OpenAI, Anthropic, Gemini, and Azure, ensuring robust integration and error handling. This update improves flexibility and extensibility for different LLM integrations. * fix test	2025-10-21 15:24:59 -07:00
Cameron Warren	5944a39629	fix: correct broken integration documentation links Fix navigation paths for two integration tool cards that were redirecting to the introduction page instead of their intended documentation pages. Fixes #3516 Co-authored-by: Cwarre33 <cwarre33@charlotte.edu> Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>	2025-10-21 18:12:08 -04:00
Greyson LaLonde	c594859ed0	feat: mypy plugin base * feat: base mypy plugin with CrewBase * fix: add crew method to protocol	2025-10-21 17:36:08 -04:00
Daniel Barreto	2ee27efca7	feat: improvements on QdrantVectorSearchTool * Implement improvements on QdrantVectorSearchTool - Allow search filters to be set at the constructor level - Fix issue that prevented multiple records from being returned * Implement improvements on QdrantVectorSearchTool - Allow search filters to be set at the constructor level - Fix issue that prevented multiple records from being returned --------- Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>	2025-10-21 16:50:08 -04:00
Greyson LaLonde	f6e13eb890	chore: update codeql config paths to new folders * chore: update codeql config paths to new folders * tests: use threading.Condition for event check --------- Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>	2025-10-21 14:43:25 -04:00
Lorenze Jay	e7b3ce27ca	docs: update LLM integration details and examples * docs: update LLM integration details and examples - Changed references from LiteLLM to native SDKs for LLM providers. - Enhanced OpenAI and AWS Bedrock sections with new usage examples and advanced configuration options. - Added structured output examples and supported environment variables for better clarity. - Improved documentation on additional parameters and features for LLM configurations. * drop this example - should use strucutred output from task instead --------- Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>	2025-10-21 14:39:50 -04:00
Greyson LaLonde	dba27cf8b5	fix: fix double trace call; add types * fix: fix double trace call; add types * tests: skip long running uv install test, refactor in future --------- Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>	2025-10-21 14:15:39 -04:00
Greyson LaLonde	6469f224f6	chore: improve CrewBase typing	2025-10-21 13:58:35 -04:00
Greyson LaLonde	f3a63be215	tests: cassettes, threading for flow tests	2025-10-21 13:48:21 -04:00
Greyson LaLonde	01d8c189f0	fix: pin template versions to latest Some checks failed Notify Downstream / notify-downstream (push) Has been cancelled Details	2025-10-21 10:56:41 -04:00