Fix import sorting issues to resolve CI linting failures

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

docs/concepts/tools.mdx

@@ -106,6 +106,7 @@ Here is a list of the available tools and their descriptions:
 
 | Tool                             | Description                                                                                    |
 | :------------------------------- | :--------------------------------------------------------------------------------------------- |
+| **ApifyActorsTool**              | A tool that integrates Apify Actors with your workflows for web scraping and automation tasks. |
 | **BrowserbaseLoadTool**          | A tool for interacting with and extracting data from web browsers.                             |
 | **CodeDocsSearchTool**           | A RAG tool optimized for searching through code documentation and related technical documents. |
 | **CodeInterpreterTool**          | A tool for interpreting python code.                                                           |

docs/mint.json

@@ -154,6 +154,7 @@
       "group": "Tools",
       "pages": [
         "tools/aimindtool",
+        "tools/apifyactorstool",
         "tools/bravesearchtool",
         "tools/browserbaseloadtool",
         "tools/codedocssearchtool",
@@ -220,4 +221,4 @@
     "linkedin": "https://www.linkedin.com/company/crewai-inc",
     "youtube": "https://youtube.com/@crewAIInc"
   }
-}
+}

docs/tools/apifyactorstool.mdx

@@ -0,0 +1,99 @@
+---
+title: Apify Actors
+description: "`ApifyActorsTool` lets you call Apify Actors to provide your CrewAI workflows with web scraping, crawling, data extraction, and web automation capabilities."
+# hack to use custom Apify icon
+icon: "); -webkit-mask-image: url('https://upload.wikimedia.org/wikipedia/commons/a/ae/Apify.svg');/*"
+---
+
+# `ApifyActorsTool`
+
+Integrate [Apify Actors](https://apify.com/actors) into your CrewAI workflows.
+
+## Description
+
+The `ApifyActorsTool` connects [Apify Actors](https://apify.com/actors), cloud-based programs for web scraping and automation, to your CrewAI workflows.
+Use any of the 4,000+ Actors on [Apify Store](https://apify.com/store) for use cases such as extracting data from social media, search engines, online maps, e-commerce sites, travel portals, or general websites.
+
+For details, see the [Apify CrewAI integration](https://docs.apify.com/platform/integrations/crewai) in Apify documentation.
+
+## Steps to get started
+
+<Steps>
+    <Step title="Install dependencies">
+        Install `crewai[tools]` and `langchain-apify` using pip: `pip install 'crewai[tools]' langchain-apify`.
+    </Step>
+    <Step title="Obtain an Apify API token">
+        Sign up to [Apify Console](https://console.apify.com/) and get your [Apify API token](https://console.apify.com/settings/integrations)..
+    </Step>
+    <Step title="Configure environment">
+        Set your Apify API token as the `APIFY_API_TOKEN` environment variable to enable the tool's functionality.
+    </Step>
+</Steps>
+
+## Usage example
+
+Use the `ApifyActorsTool` manually to run the [RAG Web Browser Actor](https://apify.com/apify/rag-web-browser) to perform a web search:
+
+```python
+from crewai_tools import ApifyActorsTool
+
+# Initialize the tool with an Apify Actor
+tool = ApifyActorsTool(actor_name="apify/rag-web-browser")
+
+# Run the tool with input parameters
+results = tool.run(run_input={"query": "What is CrewAI?", "maxResults": 5})
+
+# Process the results
+for result in results:
+    print(f"URL: {result['metadata']['url']}")
+    print(f"Content: {result.get('markdown', 'N/A')[:100]}...")
+```
+
+### Expected output
+
+Here is the output from running the code above:
+
+```text
+URL: https://www.example.com/crewai-intro
+Content: CrewAI is a framework for building AI-powered workflows...
+URL: https://docs.crewai.com/
+Content: Official documentation for CrewAI...
+```
+
+The `ApifyActorsTool` automatically fetches the Actor definition and input schema from Apify using the provided `actor_name` and then constructs the tool description and argument schema. This means you need to specify only a valid `actor_name`, and the tool handles the rest when used with agents—no need to specify the `run_input`. Here's how it works:
+
+```python
+from crewai import Agent
+from crewai_tools import ApifyActorsTool
+
+rag_browser = ApifyActorsTool(actor_name="apify/rag-web-browser")
+
+agent = Agent(
+    role="Research Analyst",
+    goal="Find and summarize information about specific topics",
+    backstory="You are an experienced researcher with attention to detail",
+    tools=[rag_browser],
+)
+```
+
+You can run other Actors from [Apify Store](https://apify.com/store) simply by changing the `actor_name` and, when using it manually, adjusting the `run_input` based on the Actor input schema.
+
+For an example of usage with agents, see the [CrewAI Actor template](https://apify.com/templates/python-crewai).
+
+## Configuration
+
+The `ApifyActorsTool` requires these inputs to work:
+
+- **`actor_name`**
+  The ID of the Apify Actor to run, e.g., `"apify/rag-web-browser"`. Browse all Actors on [Apify Store](https://apify.com/store).
+- **`run_input`**
+  A dictionary of input parameters for the Actor when running the tool manually.
+  - For example, for the `apify/rag-web-browser` Actor: `{"query": "search term", "maxResults": 5}`
+  - See the Actor's [input schema](https://apify.com/apify/rag-web-browser/input-schema) for the list of input parameters.
+
+## Resources
+
+- **[Apify](https://apify.com/)**: Explore the Apify platform.
+- **[How to build an AI agent on Apify](https://blog.apify.com/how-to-build-an-ai-agent/)** - A complete step-by-step guide to creating, publishing, and monetizing AI agents on the Apify platform.
+- **[RAG Web Browser Actor](https://apify.com/apify/rag-web-browser)**: A popular Actor for web search for LLMs.
+- **[CrewAI Integration Guide](https://docs.apify.com/platform/integrations/crewai)**: Follow the official guide for integrating Apify and CrewAI.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.105.0"
+version = "0.108.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.105.0"
+__version__ = "0.108.0"
 __all__ = [
     "Agent",
     "Crew",

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,<3.13"
 dependencies = [
-    "crewai[tools]>=0.105.0,<1.0.0"
+    "crewai[tools]>=0.108.0,<1.0.0"
 ]
 
 [project.scripts]

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,<3.13"
 dependencies = [
-    "crewai[tools]>=0.105.0,<1.0.0",
+    "crewai[tools]>=0.108.0,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.105.0"
+    "crewai[tools]>=0.108.0"
 ]
 
 [tool.crewai]

src/crewai/task.py

@@ -9,6 +9,7 @@ from copy import copy
 from hashlib import md5
 from pathlib import Path
 from typing import (
+    TYPE_CHECKING,
     Any,
     Callable,
     ClassVar,
@@ -33,6 +34,9 @@ from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.security import Fingerprint, SecurityConfig
+
+if TYPE_CHECKING:
+    from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.guardrail_result import GuardrailResult
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
@@ -617,8 +621,17 @@ class Task(BaseModel):
 
     def copy(
         self, agents: List["BaseAgent"], task_mapping: Dict[str, "Task"]
-    ) -> "Task":
-        """Create a deep copy of the Task."""
+    ) -> Union["Task", "ConditionalTask"]:
+        """
+        Creates a deep copy of the task while preserving its specific type (Task or ConditionalTask).
+        
+        Args:
+            agents: List of agents to search for the agent by role
+            task_mapping: Dictionary mapping task keys to tasks
+            
+        Returns:
+            Union[Task, ConditionalTask]: A copy of the task maintaining its original type.
+        """
         exclude = {
             "id",
             "agent",
@@ -641,7 +654,9 @@ class Task(BaseModel):
         cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None
         cloned_tools = copy(self.tools) if self.tools else []
 
-        copied_task = Task(
+        # Use the actual class of the instance being copied, not just Task
+        task_class = self.__class__
+        copied_task = task_class(
             **copied_data,
             context=cloned_context,
             agent=cloned_agent,

src/crewai/tasks/conditional_task.py

@@ -1,8 +1,12 @@
-from typing import Any, Callable
+from typing import TYPE_CHECKING, Any, Callable
 
 from pydantic import Field
 
-from crewai.task import Task
+if TYPE_CHECKING:
+    from crewai.task import Task
+else:
+    # Import the base class at runtime
+    from crewai.task import Task
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 

tests/test_conditional_task_copy.py

@@ -0,0 +1,104 @@
+import pytest
+
+from crewai import Agent, Crew, Task
+from crewai.tasks.conditional_task import ConditionalTask
+from crewai.tasks.task_output import TaskOutput
+
+
+@pytest.fixture
+def test_agent():
+    """Fixture for creating a test agent."""
+    return Agent(
+        role="Researcher",
+        goal="Research topics",
+        backstory="You are a researcher."
+    )
+
+@pytest.fixture
+def test_task(test_agent):
+    """Fixture for creating a regular task."""
+    return Task(
+        description="Research topic A",
+        expected_output="Research results for topic A",
+        agent=test_agent
+    )
+
+@pytest.fixture
+def test_conditional_task(test_agent):
+    """Fixture for creating a conditional task."""
+    return ConditionalTask(
+        description="Research topic B if topic A was successful",
+        expected_output="Research results for topic B",
+        agent=test_agent,
+        condition=lambda output: "success" in output.raw.lower()
+    )
+
+@pytest.fixture
+def test_crew(test_agent, test_task, test_conditional_task):
+    """Fixture for creating a crew with both regular and conditional tasks."""
+    return Crew(
+        agents=[test_agent],
+        tasks=[test_task, test_conditional_task]
+    )
+
+
+def test_conditional_task_preserved_in_copy(test_crew):
+    """Test that ConditionalTask objects are preserved when copying a Crew."""
+    # Create a copy of the crew
+    crew_copy = test_crew.copy()
+    
+    # Check that the conditional task is still a ConditionalTask in the copied crew
+    assert isinstance(crew_copy.tasks[1], ConditionalTask)
+    assert hasattr(crew_copy.tasks[1], "should_execute")
+    
+def test_conditional_task_preserved_in_kickoff_for_each(test_crew, test_agent):
+    """Test that ConditionalTask objects are preserved when using kickoff_for_each."""
+    from unittest.mock import patch
+    
+    # Mock the kickoff method to avoid actual execution
+    with patch.object(Crew, "kickoff") as mock_kickoff:
+        # Set up the mock to return a TaskOutput
+        mock_output = TaskOutput(
+            description="Mock task output",
+            raw="Success with topic",
+            agent=test_agent.role
+        )
+        mock_kickoff.return_value = mock_output
+        
+        # Call kickoff_for_each with test inputs
+        inputs = [{"topic": "test1"}, {"topic": "test2"}]
+        test_crew.kickoff_for_each(inputs=inputs)
+        
+        # Verify the mock was called with the expected inputs
+        assert mock_kickoff.call_count == len(inputs)
+        
+        # Create a copy of the crew to verify the type preservation
+        # (since we can't directly access the crews created inside kickoff_for_each)
+        crew_copy = test_crew.copy()
+        assert isinstance(crew_copy.tasks[1], ConditionalTask)
+
+
+def test_conditional_task_copy_with_none_values(test_agent, test_task):
+    """Test that ConditionalTask objects are preserved when copying with optional fields."""
+    # Create a conditional task with optional fields
+    conditional_task = ConditionalTask(
+        description="Research topic B if topic A was successful",
+        expected_output="Research results for topic B",  # Required field
+        agent=test_agent,
+        condition=lambda output: "success" in output.raw.lower(),
+        context=None  # Optional field that can be None
+    )
+    
+    # Create a crew with both a regular task and the conditional task
+    crew = Crew(
+        agents=[test_agent],
+        tasks=[test_task, conditional_task]
+    )
+    
+    # Create a copy of the crew
+    crew_copy = crew.copy()
+    
+    # Check that the conditional task is still a ConditionalTask in the copied crew
+    assert isinstance(crew_copy.tasks[1], ConditionalTask)
+    assert hasattr(crew_copy.tasks[1], "should_execute")
+    assert crew_copy.tasks[1].context is None  # Verify None value is preserved

uv.lock

@@ -619,7 +619,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.105.0"
+version = "0.108.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },